Design and Development of an Intelligent Solar-Powered Lamp Post with Adaptive Lighting Control

Abstract

The increasing demand for sustainable outdoor lighting has accelerated the development of solar-powered lighting systems. However, conventional solar lamps typically employ fixed illumination levels and simple day–night switching mechanisms, resulting in inefficient battery utilization and limited adaptability to changing environmental conditions. This study proposes a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, adaptive pulse-width modulation (PWM)-based illumination control, ultrasonic sensing, wireless communication, and embedded control into a unified standalone platform. The TRIZ contradiction matrix was employed during the conceptual design stage to systematically resolve key engineering contradictions involving illumination performance, energy efficiency, hardware complexity, battery lifetime, and user convenience. The proposed prototype was developed using an AT89S51 microcontroller to coordinate battery charging protection, environmental sensing, adaptive brightness regulation, and manual wireless operation. Experimental validation demonstrated stable photovoltaic charging with a regulated battery charging voltage of 14.4 V, reliable execution of embedded control functions, seamless transition between manual and autonomous operating modes, and adaptive LED brightness regulation according to real-time environmental conditions. The integrated PWM control strategy reduced unnecessary energy consumption by dynamically adjusting illumination intensity based on object detection rather than maintaining constant full-power operation. The experimental results further verified the feasibility of combining software-driven adaptive control with renewable energy harvesting to achieve intelligent energy management without increasing hardware complexity. Overall, the proposed system demonstrates that the integration of TRIZ-based systematic innovation with embedded intelligent control provides a practical, energy-efficient, and cost-effective solution for autonomous outdoor lighting. The proposed architecture offers valuable engineering insights for future smart lighting applications in off-grid infrastructure, sustainable communities, and smart city environments.

1. Introduction

The accelerating transition toward sustainable energy systems has intensified global efforts to replace fossil-fuel-dependent infrastructures with renewable energy technologies capable of reducing greenhouse gas emissions, enhancing energy security, and supporting long-term environmental sustainability. Among the various renewable energy resources, solar photovoltaic (PV) technology has emerged as one of the most mature, economically viable, and environmentally benign solutions because of its widespread availability, modularity, declining installation costs, and continuous technological advancement. Recent developments in photovoltaic materials, battery storage technologies, and intelligent energy management systems have further strengthened the feasibility of deploying standalone solar-powered systems for distributed outdoor applications, particularly in regions with abundant solar irradiation. Consequently, solar-powered lighting systems have become an attractive alternative to conventional grid-connected lighting for residential, commercial, and public infrastructures, especially in tropical countries where consistent solar availability enables reliable year-round energy harvesting [1,2,3,4,5].

Outdoor lighting represents a significant contributor to global electricity consumption, accounting for a considerable proportion of municipal energy expenditure in many developing and developed countries. Conventional street and landscape lighting systems typically operate using fixed scheduling mechanisms or manual switching, resulting in unnecessary electricity consumption during periods of low human activity or favourable environmental conditions. Such operating strategies not only increase operational costs but also accelerate battery degradation in off-grid systems while contributing indirectly to carbon emissions when grid electricity is utilized. Recent literature therefore emphasizes that future outdoor lighting infrastructures should evolve beyond simple illumination devices toward intelligent energy-aware systems capable of adapting lighting intensity according to environmental conditions, occupancy patterns, and available stored energy [6]. This transition aligns closely with the broader objectives of smart cities, sustainable infrastructure, and energy-efficient urban development [7,8].

Solar-powered lighting systems have experienced remarkable technological evolution over the past decade. Early systems primarily focused on replacing grid electricity with photovoltaic modules and rechargeable batteries, whereas contemporary research integrates Internet of Things (IoT) communication, wireless monitoring, adaptive brightness control, occupancy sensing, cloud-based management, and intelligent energy optimization [9,10,11]. The integration of LEDs with adaptive control algorithms has substantially reduced lighting power consumption while simultaneously improving illumination quality and battery lifetime. Wireless networking further enables centralized supervision, predictive maintenance, and remote parameter adjustment, making modern solar lighting systems an essential component of future smart-city infrastructure. Nevertheless, despite these technological advancements, several existing implementations remain constrained by relatively high implementation costs, communication complexity, excessive computational requirements, or dependence on cloud connectivity, thereby limiting their suitability for low-cost standalone consumer applications [12,13,14,15,16].

An equally important aspect influencing the effectiveness of standalone solar lighting systems is intelligent energy management. Since photovoltaic generation is inherently intermittent and highly dependent on meteorological conditions, efficient utilization of harvested energy becomes essential for maintaining continuous nighttime illumination. Various energy management approaches have been proposed, including maximum power point tracking (MPPT), pulse-width modulation (PWM)-based battery charging, adaptive load regulation, predictive scheduling, and IoT-assisted monitoring platforms [17,18,19]. Among these techniques, PWM-based control continues to represent an attractive solution for low-cost embedded applications because it offers a favourable compromise between implementation simplicity, charging efficiency, hardware cost, and reliability. Furthermore, intelligent PWM dimming allows dynamic adjustment of LED brightness according to battery state-of-charge, thereby extending operating duration without significantly compromising illumination performance. Recent reviews further highlight that integrating intelligent energy management with renewable-powered lighting systems remains an active research direction for improving energy utilization and operational sustainability [20,21,22,23,24].

Besides efficient power management, adaptive sensing has become another key enabling technology for smart outdoor lighting. Rather than maintaining constant illumination throughout the night, intelligent lighting systems increasingly employ environmental and occupancy sensors to provide illumination only when required [25]. Passive infrared (PIR), microwave radar, image-based vision sensors, LiDAR, and ultrasonic sensors have all been investigated for occupancy detection. Among these alternatives, ultrasonic sensing provides an attractive solution because it is inexpensive, computationally lightweight, unaffected by ambient lighting conditions, and capable of accurately detecting nearby moving objects without requiring image processing or compromising user privacy. Recent investigations have demonstrated the effectiveness of ultrasonic sensors for human detection, intelligent parking systems, industrial monitoring, robotic localization, and autonomous surveillance, illustrating their versatility across numerous embedded applications. The incorporation of ultrasonic-based movement detection into standalone solar lighting systems therefore offers significant potential for reducing unnecessary energy consumption while simultaneously improving user convenience and operational autonomy [26,27,28,29].

Recent advances in embedded systems and wireless communication also have further accelerated the evolution of autonomous solar lighting from standalone illumination devices into intelligent cyber-physical systems capable of adaptive decision-making and remote management. Internet of Things (IoT) technologies enable real-time monitoring of battery condition, solar energy harvesting, lighting status, and fault diagnosis, thereby improving system reliability and reducing maintenance costs. Furthermore, cloud-assisted monitoring platforms and wireless communication protocols facilitate predictive maintenance and centralized management of distributed lighting infrastructures deployed in residential communities, parks, campuses, and smart-city environments. Despite these technological advancements, many reported systems rely on sophisticated microprocessors, multiple communication modules, cloud computing platforms, or artificial intelligence algorithms that inevitably increase hardware complexity, energy consumption, and implementation cost. Consequently, there remains considerable demand for simplified embedded architectures capable of delivering intelligent energy management while maintaining affordability and ease of deployment for standalone consumer applications. Recent reviews on IoT-integrated solar energy systems consistently identify low-cost embedded intelligence and energy-aware control as important future research directions [30,31,32,33].

Power electronic converters also play a fundamental role in determining the efficiency and reliability of photovoltaic lighting systems. Among the various DC–DC converter topologies, the buck converter remains one of the most widely adopted solutions because it provides stable voltage regulation with high conversion efficiency, compact implementation, and relatively low component count. When combined with pulse-width modulation (PWM) control, the buck converter enables precise regulation of charging current and LED driving voltage, thereby preventing battery overcharging, excessive discharge, and unnecessary power dissipation. Compared with more computationally intensive maximum power point tracking (MPPT) techniques, PWM-based regulation offers a practical compromise for low-power solar lighting applications where system simplicity, implementation cost, and reliability are primary design considerations. Consequently, numerous recent embedded photovoltaic systems continue to employ PWM-controlled DC–DC converters for efficient battery management and adaptive illumination control, particularly in standalone renewable energy applications requiring long operating lifetimes and minimal maintenance [34,35,36].

From the embedded controller perspective, established 8-bit architectures remain relevant for resource-constrained control applications where the required computational tasks are relatively simple. Although modern microcontrollers provide substantially greater computational capability and integrated peripheral functions, the proposed lighting system requires only sensor acquisition, PWM generation, interrupt handling, wireless command processing, and basic battery-management control. The AT89S51 was therefore selected because its available processing capability was sufficient for these functions while enabling a relatively simple and low-cost embedded architecture. Its deterministic timing behaviour, straightforward programming model, and established development ecosystem further support its suitability for the proposed standalone lighting application. Accordingly, the controller selection in this study is based primarily on functional sufficiency, architectural simplicity, and implementation cost rather than an assumption that the AT89S51 has intrinsically lower power consumption than contemporary microcontrollers.

While previous investigations have successfully demonstrated solar-powered lighting, intelligent dimming algorithms, occupancy sensing, IoT communication, and renewable energy management individually, relatively few studies have systematically integrated these technologies through an engineering innovation methodology capable of resolving conflicting design requirements. In practice, designers frequently encounter engineering contradictions between illumination intensity and battery lifetime, sensing capability and power consumption, system intelligence and implementation complexity, as well as functionality and economic affordability. Increasing illumination brightness enhances user safety but accelerates battery depletion; incorporating additional sensors and communication modules improves functionality but simultaneously increases hardware cost and standby energy consumption. Conventional design approaches often rely on empirical optimization or trial-and-error experimentation, which may not systematically identify innovative solutions capable of simultaneously satisfying these competing requirements. Consequently, there remains an important research opportunity to adopt structured innovation methodologies that explicitly address engineering contradictions during the conceptual design stage [37,38,39,40].

The Theory of Inventive Problem Solving (TRIZ) provides a systematic innovation framework for resolving engineering contradictions by transforming conflicting technical requirements into inventive design solutions. Rather than depending solely on designer intuition, TRIZ employs contradiction analysis, inventive principles, ideality concepts, and functional modelling to guide the development of innovative engineering systems with improved performance and reduced complexity [41]. Recent research has demonstrated the effectiveness of TRIZ in intelligent sensing systems, smart lighting applications, eco-innovative product development, renewable energy technologies, and sustainable engineering design, highlighting its capability to improve design creativity while maintaining practical feasibility [25]. In smart lighting applications, TRIZ has been successfully employed to resolve contradictions associated with energy conservation, sensing coverage, user comfort, and implementation cost, resulting in more efficient autonomous lighting systems. Similarly, recent studies integrating TRIZ with sustainable product design have shown that systematic contradiction analysis facilitates the development of environmentally friendly solutions while simultaneously enhancing technical performance and economic viability [37,38,39]. These findings suggest that TRIZ offers an appropriate methodological foundation for developing intelligent renewable-energy-powered lighting systems capable of balancing energy efficiency, operational reliability, user convenience, and implementation cost [25].

Motivated by these research gaps, this study proposes a TRIZ-guided intelligent solar-powered lamp post that integrates adaptive PWM-based illumination control, ultrasonic movement detection, wireless remote operation, and autonomous battery management into a unified embedded system. Unlike many existing smart lighting solutions that emphasize communication infrastructure or computational intelligence, the proposed design prioritizes systematic engineering innovation to achieve energy conservation without increasing system complexity. The TRIZ methodology is employed to identify and resolve the fundamental contradictions between illumination performance, battery utilization, implementation cost, hardware simplicity, and user convenience, thereby establishing a structured pathway toward an optimized system architecture. Furthermore, adaptive PWM dimming combined with movement-responsive lighting minimizes unnecessary power consumption during periods of inactivity while ensuring sufficient illumination whenever human presence is detected. Wireless remote control further enhances operational flexibility by allowing manual user intervention when required without compromising autonomous functionality.

The principal contributions of this study are fourfold. First, a TRIZ-guided engineering design methodology is employed to systematically resolve design contradictions between illumination performance, energy conservation, implementation simplicity, and economic feasibility. Second, an adaptive PWM-based energy management strategy is developed to improve battery utilization while maintaining adequate nighttime illumination. Third, an autonomous movement-responsive lighting mechanism utilizing ultrasonic sensing is introduced to reduce unnecessary power consumption during periods of inactivity. Finally, a wireless user-control interface is incorporated to improve operational flexibility while preserving the simplicity, reliability, and affordability required for standalone consumer lighting applications. Collectively, these contributions establish a practical and systematic framework for developing next-generation intelligent solar-powered lighting systems that support sustainable outdoor illumination while addressing the increasing demand for energy-efficient, autonomous, and economically viable renewable energy solutions.

Thus, the entire article is organized as follows: Section 2 explains the TRIZ-based conceptual design framework; Section 3 emphasizes the materials and methods covering the TRIZ conceptualization methodology used and explains in detail the fabrication of the designed prototype; Section 4 discusses the results, testing and validation of the developed system; Section 5 concludes the article.

2. TRIZ-Based Conceptual Design Framework

Although considerable progress has been achieved in the development of solar-powered lighting systems, existing designs continue to encounter multiple engineering trade-offs that limit their overall effectiveness. The primary challenge lies in balancing illumination performance with energy sustainability [6]. Increasing lighting intensity enhances visibility and user safety; however, it simultaneously accelerates battery discharge and shortens nighttime operating duration, particularly during prolonged cloudy conditions or seasons with reduced solar irradiance. Conversely, reducing lighting intensity conserves stored energy but compromises illumination quality and diminishes user comfort. Similar trade-offs also exist between system intelligence and implementation cost, operational flexibility and hardware simplicity, as well as sensing capability and energy consumption. These conflicting requirements indicate that further improvements cannot be achieved solely through conventional component optimization but instead require a systematic innovation methodology capable of resolving engineering contradictions [42].

Traditional engineering design generally improves individual subsystems independently through incremental optimisation of photovoltaic modules, battery capacity, sensing devices, or embedded controllers. While these approaches often improve the performance of individual components, they seldom eliminate the inherent contradictions existing among the overall system objectives. For example, increasing battery capacity prolongs operational duration but simultaneously increases system size, weight, and cost. Similarly, integrating multiple sensors or wireless communication modules enhances intelligent functionality while increasing standby power consumption and hardware complexity. Such conflicting requirements illustrate that improving one engineering characteristic frequently deteriorates another, thereby limiting the achievable system performance. Consequently, a structured design methodology capable of simultaneously addressing these conflicting requirements is required during the conceptual design stage.

TRIZ provides a systematic framework for innovation by transforming engineering contradictions into structured solution pathways rather than relying on empirical trial-and-error approaches. Developed from the analysis of thousands of engineering patents, TRIZ assumes that technological evolution follows recurring patterns and that many engineering problems share common contradiction structures regardless of application domain [43]. Instead of directly proposing specific technical solutions, TRIZ first identifies the engineering parameter that requires improvement and the parameter that deteriorates as a consequence of that improvement. These parameters are then mapped onto the TRIZ Contradiction Matrix, which recommends a set of Inventive Principles that have historically demonstrated effectiveness in resolving similar engineering conflicts [44]. To ensure methodological traceability and reproducibility, the engineering contradictions identified in the present study were therefore translated into the standardized TRIZ engineering parameters before application of the Contradiction Matrix. The 39-parameter framework provides a common representation of engineering conflicts, allowing application-specific design problems to be expressed in standardized terms and subsequently indexed against the corresponding contradiction-matrix cells.

For the proposed intelligent solar-powered lamp system, four principal engineering contradictions were identified from the functional requirements and design challenges of autonomous outdoor lighting. Each contradiction was first expressed in terms of an improving and a worsening engineering parameter before the corresponding Inventive Principles were considered. The first contradiction concerns illumination intensity and energy consumption. Increasing illumination performance improves visibility, safety, and user satisfaction but simultaneously increases electrical power demand and accelerates battery depletion. This contradiction was represented using Parameter 18, Illumination Intensity, as the improving parameter and Parameter 20, Use of Energy by a Stationary Object, as the worsening parameter. The corresponding contradiction was subsequently evaluated using the TRIZ Contradiction Matrix, with the resulting Inventive Principles interpreted in relation to the functional requirement for adaptive illumination. In the present design, the selected principles include Principle 15: Dynamization, Principle 19: Periodic Action, and Principle 23: Feedback, which support the concept of varying illumination according to environmental conditions and actual demand rather than maintaining constant full-power operation. This parameterized formulation establishes an explicit methodological link between the engineering problem, the contradiction matrix, and the resulting adaptive lighting concept.

A second contradiction exists between system intelligence and system complexity. Increasing automation generally requires additional sensors, communication modules, and processing capabilities, which can increase hardware cost, implementation complexity, and standby energy consumption. This contradiction was represented using Parameter 36, Device Complexity, together with Parameter 38, Extent of Automation, to characterize the competing requirements for maintaining a simple architecture while increasing autonomous functionality. The corresponding contradiction-matrix analysis resulted in the consideration of Principle 6: Universality, Principle 5: Merging, and Principle 24: Intermediary. These principles indicate that multiple system functions should be integrated into common functional elements wherever possible, thereby reducing component redundancy while preserving intelligent operational capability. Accordingly, rather than introducing separate hardware for sensing, communication, processing, and illumination management, the proposed conceptual design emphasizes multifunctional integration and coordinated subsystem operation through a common embedded control architecture.

Another important contradiction concerns battery lifetime and charging efficiency. Maximizing charging improves energy availability for nighttime operation; however, excessive charging or prolonged charging cycles may adversely affect battery lifespan and compromise long-term system reliability. This contradiction was represented using Parameter 27, Reliability, as the parameter associated with maintaining dependable battery operation and Parameter 22, Loss of Energy, to represent undesirable energy losses associated with inefficient or poorly regulated energy management. The contradiction analysis considered Principle 11: Beforehand Cushioning, Principle 10: Preliminary Action, and Principle 9: Preliminary Anti-action, which collectively suggest incorporating preventive measures into the energy-management architecture before undesirable charging conditions occur. These principles therefore support the use of regulated charging and automatic charging protection rather than relying solely on increased photovoltaic generation or oversized energy-storage capacity.

Furthermore, a contradiction arises between user convenience and energy conservation. Fully automatic operation minimizes user intervention but may not always satisfy varying user preferences or operational scenarios, whereas fully manual control provides operational flexibility but may result in unnecessary energy consumption due to inappropriate user operation. This contradiction was represented using Parameter 33, Ease of Operation, as the improving parameter and Parameter 22, Loss of Energy, as the worsening parameter. The contradiction-matrix analysis was subsequently used to consider Principle 15: Dynamization, Principle 10: Preliminary Action, and Principle 23: Feedback, suggesting that the lighting system should be capable of transitioning between different operating states according to environmental conditions while preserving appropriate user interaction when required. This approach enables autonomous adaptive operation while retaining wireless manual intervention, thereby balancing user convenience and energy efficiency.

The parameterization and matrix-based analysis described above distinguish the TRIZ methodology in this study from a purely qualitative identification of design principles. The 39 standard engineering parameters provide the formal indexing mechanism through which each application-specific contradiction is translated into a corresponding contradiction-matrix problem. The resulting Inventive Principles are not treated as direct or deterministic engineering solutions; rather, they provide generalized solution directions that are subsequently interpreted and screened according to the functional requirements, hardware constraints, implementation simplicity, and standalone operating objectives of the proposed prototype. Consequently, the TRIZ process comprises two linked stages: (i) formal contradiction identification and parameter indexing using the 39 standard engineering parameters, followed by (ii) engineering interpretation and selection of feasible Inventive Principles for prototype development. The complete parameter indexing, contradiction relationships, selected Inventive Principles, and corresponding conceptual design implications are summarized in

Table 1. TRIZ contradiction analysis for the proposed system.

Engineering Contradiction	Improving Parameter	Worsening Parameter	Relevant TRIZ Principles	Design Implication
Illumination intensity vs. energy consumption	P18—Illumination intensity	P20—Use of energy by stationary object	Matrix-derived principles; selected principles subsequently screened for adaptive lighting	Dynamically vary LED illumination according to detected demand to reduce unnecessary energy consumption
System intelligence vs. system complexity	P36—Device complexity	P38—Extent of automation	Matrix-derived principles; selected principles subsequently screened for functional integration	Increase autonomous functionality while minimizing additional hardware and control complexity
Battery lifetime vs. charging efficiency	P27—Reliability	P22—Loss of energy	Matrix-derived principles; selected principles subsequently screened for preventive charging control	Regulate and protect battery charging to improve energy utilization and operational reliability
User convenience vs. energy conservation	P33—Ease of operation	P22—Loss of energy	Matrix-derived principles; selected principles subsequently screened for adaptive/manual operation	Combine autonomous adaptive operation with user intervention while limiting unnecessary energy consumption

.

In comparison with previous research, which demonstrated the application of TRIZ to smart lighting using dual-PIR occupancy counting [25], its primary function was occupancy-based ON/OFF control of classroom electrical loads. The present study addresses a different set of coupled engineering contradictions involving illumination performance, energy consumption, battery lifetime, system complexity, and user convenience. TRIZ is therefore applied not only to occupancy detection but also to the generation and integration of adaptive lighting, energy management, and control functions. The proposed system implements real-time ultrasonic feedback with PWM-based illumination adjustment, regulated photovoltaic battery charging, and wireless manual override within a unified embedded architecture. This extends the application of TRIZ from occupancy-based load switching to integrated, adaptive, and energy-aware renewable lighting control.

Based on the identified engineering contradictions and their corresponding TRIZ parameterization, the conceptual objective of this research is not merely to improve individual hardware components but to establish an integrated design strategy capable of simultaneously enhancing illumination effectiveness, energy utilization, operational autonomy, and system affordability. Guided by the standardized engineering parameters, contradiction-matrix analysis, and associated TRIZ Inventive Principles, the conceptual framework emphasizes adaptive system behaviour, multifunctional integration, preventive energy management, and continuous environmental feedback as the principal innovation directions. These conceptual solutions provide the theoretical foundation for the subsequent engineering design process without prescribing a specific hardware implementation at this stage. Table 1 therefore summarizes the standardized TRIZ engineering parameters, identified contradictions, selected Inventive Principles, and corresponding conceptual design implications for the proposed intelligent solar-powered lamp system.

The identified TRIZ Inventive Principles were subsequently translated into explicit engineering design decisions rather than being treated solely as conceptual recommendations. This establishes traceability between the contradiction analysis and the final prototype architecture. For the illumination–energy contradiction, Principles 15 (Dynamization), 19 (Periodic Action), and 23 (Feedback) guided the replacement of conventional fixed-brightness illumination with an adaptive PWM-based lighting strategy. The LED duty cycle is dynamically adjusted according to object detection and operating conditions, allowing illumination to become a variable system parameter rather than a fixed operating condition.

Principle 23 (Feedback) was further implemented through integration of the ultrasonic sensing subsystem with the embedded controller and LED driver. The system continuously acquires distance information and uses the detected object condition to regulate illumination. When an object enters the monitored region, the controller increases the PWM duty cycle; when the object moves away, the illumination level is reduced. This closed-loop sensing–control–illumination relationship provides a direct engineering implementation of the Feedback principle identified in the TRIZ analysis.

For the system intelligence–complexity contradiction, Principles 6 (Universality) and 5 (Merging) influenced the multifunctional system architecture. A single AT89S51 embedded controller coordinates ultrasonic sensing, wireless communication, PWM generation, operating-mode selection, and system control. This approach avoids unnecessary duplication of dedicated control hardware while integrating multiple functions within a common embedded architecture. The implementation therefore demonstrates how the selected TRIZ principles influenced both component selection and functional system organization.

For the battery-management contradiction, Principles 10 (Preliminary Action), 11 (Beforehand Cushioning), and 9 (Preliminary Anti-action) guided the incorporation of regulated charging and automatic charging protection. The implemented charging subsystem maintains a regulated charging voltage of 14.4 V while accommodating the experimentally observed photovoltaic input range of 15–21 V. This approach provides preventive control against undesirable charging conditions without relying solely on increased photovoltaic capacity or battery size.

For the contradiction between user convenience and energy conservation, Principles 15 (Dynamization), 10 (Preliminary Action), and 23 (Feedback) guided the development of a dual-mode operating architecture. The final prototype combines autonomous ultrasonic-based illumination adjustment with wireless manual control, allowing the system to adapt its lighting output according to environmental demand while retaining user intervention when required. This arrangement reduces unnecessary full-power operation without sacrificing operational flexibility.

Table 2. Traceability of TRIZ inventive principles to final engineering design.

Engineering Contradiction	Selected TRIZ Principle	Conventional Approach	TRIZ-Guided Design Decision	Final Implementation
Illumination vs. energy consumption	P15 Dynamization; P19 Periodic Action; P23 Feedback	Fixed LED brightness	Dynamically vary illumination according to environmental demand	Adaptive PWM-controlled LED
System intelligence vs. complexity	P6 Universality; P5 Merging; P24 Intermediary	Separate hardware for different functions	Integrate sensing, control and communication through a common control architecture	AT89S51 coordinates sensing, PWM, communication and control
Battery reliability vs. energy loss	P10 Preliminary Action; P11 Beforehand Cushioning; P9 Preliminary Anti-action	Charging without integrated preventive protection	Regulate and protect charging before undesirable conditions occur	14.4 V regulated charging with automatic charging protection
User convenience vs. energy conservation	P15 Dynamization; P10 Preliminary Action; P23 Feedback	Fully manual or fixed automatic operation	Combine autonomous adaptive operation with user intervention	Automatic adaptive mode with wireless manual override

summarizes the traceability between the identified engineering contradictions, selected TRIZ Inventive Principles, conventional design approaches, TRIZ-guided design decisions, and corresponding prototype implementations. The contribution of TRIZ therefore lies not in the use of TRIZ terminology itself, but in its role as a systematic design-generation and decision-making framework that transforms identified engineering contradictions into specific functional and architectural requirements. The final prototype represents the physical realization of these TRIZ-guided design decisions.

3. Materials and Methods

The proposed intelligent solar-powered lamp post was developed using a modular embedded system architecture to provide autonomous, energy-efficient, and adaptive outdoor illumination for standalone applications. The overall system integrates renewable energy harvesting, battery energy storage, intelligent embedded control, wireless communication, adaptive lighting control, and autonomous environmental sensing into a unified platform. The architecture was formulated based on the TRIZ-guided conceptual framework presented in the preceding section, where engineering contradictions related to energy efficiency, illumination performance, operational autonomy, hardware simplicity, and implementation cost were systematically resolved during the conceptual design stage. Consequently, the proposed architecture emphasizes subsystem integration rather than increasing hardware complexity, thereby improving overall system functionality while maintaining low implementation cost and high operational reliability. The methodology adopted for the system development is summarized in the overall system architecture illustrated in Figure 1, while the complete electronic implementation is presented in Figure 2.

The system architecture consists of six principal functional subsystems: (i) the photovoltaic energy harvesting and battery charging subsystem, (ii) the automatic day–night switching subsystem, (iii) the embedded control subsystem, (iv) the wireless communication subsystem, (v) the LED driving subsystem, and (vi) the intelligent illumination control algorithm. These subsystems operate cooperatively to establish a closed-loop energy-aware lighting system capable of adapting its operating state according to both environmental conditions and user interaction. Unlike conventional solar lighting systems that typically employ fixed illumination schedules, the proposed architecture continuously coordinates energy harvesting, battery management, environmental sensing, and illumination control to maximize battery utilization while preserving adequate nighttime illumination.

The energy flow within the system begins with the photovoltaic panel, which converts incident solar radiation into electrical energy during daytime operation. The harvested electrical energy is regulated by a dedicated battery charging protection circuit before being stored within the rechargeable battery. Since battery lifetime directly influences the overall sustainability and maintenance requirements of standalone solar lighting systems, the charging subsystem incorporates voltage regulation and charging protection mechanisms to prevent battery overcharging while ensuring efficient utilization of the available solar energy. During nighttime operation, the stored electrical energy is supplied to the embedded control circuit, wireless communication module, sensing unit, and LED lighting subsystem, thereby enabling continuous autonomous operation without dependence on external grid power. This energy flow establishes complete energy autonomy, allowing the proposed lighting system to operate in remote outdoor environments where conventional electrical infrastructure is unavailable or economically impractical.

Parallel to the energy flow, the system incorporates an integrated information flow responsible for coordinating sensing, decision-making, and illumination control. The embedded controller functions as the central processing unit that continuously acquires input signals from the environmental sensing module and wireless remote-control interface before executing the illumination control algorithm. Based on the processed information, the controller generates pulse-width modulation (PWM) signals that regulate the electrical power supplied to the LED lamp, thereby allowing dynamic adjustment of illumination intensity according to the current operating conditions. This hierarchical control structure separates energy management from illumination control, enabling each subsystem to operate independently while remaining fully coordinated through the embedded controller. Such an architecture improves operational flexibility and facilitates future system expansion without requiring substantial hardware modification.

To enhance operational autonomy, the proposed system incorporates an automatic day–night switching mechanism that determines the appropriate operating mode according to ambient solar irradiance. Rather than relying on external timing devices or manual switching, the photovoltaic module simultaneously performs the dual functions of renewable energy harvesting and environmental light detection. During daytime conditions, when sufficient solar irradiance is available, the lighting subsystem remains inactive while the harvested solar energy is directed towards battery charging. Conversely, when solar irradiance decreases below the predetermined operating threshold during nighttime, the lighting subsystem is automatically activated using the stored battery energy. This dual-function utilization of the photovoltaic module minimizes additional sensing hardware while simplifying the overall system architecture, consistent with the TRIZ principles of universality and functional integration introduced in the conceptual design framework.

The embedded control subsystem coordinates both autonomous and user-controlled operating modes. Under autonomous operation, illumination intensity is determined according to environmental information obtained from the proximity sensing subsystem. In contrast, manual operation allows users to modify the illumination level remotely through the wireless communication interface whenever operational flexibility is required. Integrating these two operating modes enables the lighting system to satisfy varying illumination requirements without sacrificing energy efficiency or increasing unnecessary user intervention. The dual-mode architecture also enhances system robustness because manual control remains available whenever autonomous sensing conditions become unsuitable.

Wireless communication is incorporated to improve user accessibility without significantly increasing hardware complexity or standby power consumption. The communication subsystem establishes one-way wireless user control between the handheld remote controller and the embedded control unit, enabling remote adjustment of lighting intensity and operational modes. Unlike cloud-based lighting systems requiring Internet connectivity and centralized management infrastructure, the proposed design employs short-range wireless communication suitable for localized outdoor applications, thereby reducing implementation cost, minimizing communication latency, and improving operational reliability in standalone installations.

The LED driving subsystem forms the final stage of the system architecture by converting low-power PWM control signals generated by the embedded controller into the electrical current required by the high-power LED lighting module. Signal conditioning and current amplification ensure reliable switching operation while preserving PWM modulation accuracy. Consequently, illumination intensity can be continuously regulated according to the control algorithm without introducing excessive electrical losses or compromising system efficiency. Integrating PWM-based illumination control with intelligent sensing enables the lighting system to provide illumination only when necessary, thereby extending battery operating duration while maintaining adequate lighting performance during periods of human activity.

The overall architecture demonstrates a coordinated interaction among renewable energy harvesting, battery management, embedded intelligence, wireless communication, and adaptive illumination control to establish a fully autonomous lighting platform. Rather than optimizing each subsystem independently, the proposed methodology integrates all functional modules into a unified energy-aware architecture capable of balancing illumination performance, battery utilization, operational autonomy, implementation simplicity, and user convenience. This integrated architecture serves as the foundation for the detailed hardware implementation described in the following subsections.

3.1. Photovoltaic Energy Harvesting and Battery Charging Subsystem

The photovoltaic energy harvesting and battery charging subsystem was developed to provide a stable and reliable energy source for the proposed standalone intelligent lighting system. Since the proposed lamp operates independently of the electrical grid, the efficiency of energy harvesting and battery management directly determines the operating duration, system reliability, and long-term sustainability of the overall lighting platform. Consequently, the charging subsystem was designed to maximize the utilization of harvested solar energy while simultaneously protecting the rechargeable battery against abnormal charging conditions that could accelerate battery degradation or reduce its service life.

The subsystem consists of a photovoltaic panel, rechargeable battery, charging protection circuit, relay switching mechanism, voltage regulation components, and battery status indicators, as illustrated in Figure 3. During daytime operation, the photovoltaic panel converts incident solar irradiance into direct current (DC) electrical energy. The generated electrical power is subsequently regulated through the charging protection circuit before being delivered to the rechargeable battery. Unlike a direct charging configuration, the proposed charging architecture incorporates voltage monitoring and automatic charging interruption to prevent excessive charging once the battery reaches its prescribed charging threshold. This protection mechanism minimizes battery overcharging, which is one of the primary causes of reduced battery capacity, shortened cycle life, excessive heat generation, and long-term performance deterioration in rechargeable energy storage systems.

The charging protection circuit employs a voltage-sensitive control mechanism that continuously monitors the battery terminal voltage during the charging process. The resistor network and adjustable potentiometer establish the reference voltage required to determine the charging threshold, while the transistor–relay combination performs the automatic switching operation. When the battery voltage remains below the predefined charging threshold, the relay remains energized, allowing electrical energy generated by the photovoltaic panel to continue charging the battery. As the battery approaches full capacity, the monitored battery voltage exceeds the preset threshold, causing the transistor switching stage to deactivate the relay. Consequently, the charging path between the photovoltaic panel and the battery is interrupted automatically, thereby preventing further charging and eliminating the possibility of battery overcharging. This autonomous charging regulation enables continuous unattended operation without requiring external supervision or dedicated battery management equipment.

To provide immediate operational feedback, the charging subsystem incorporates two light-emitting diode (LED) indicators that continuously display the battery charging status. During active charging, the first indicator remains illuminated, signifying that electrical energy harvested from the photovoltaic panel is being transferred to the battery under normal operating conditions. Upon completion of the charging process, the second indicator becomes active to indicate that the battery has reached the prescribed charging level, and the charging circuit has been disconnected automatically. Although these indicators are not directly involved in the charging regulation process, they provide a simple visual interface that facilitates routine system inspection and maintenance without requiring additional diagnostic equipment.

The proposed charging subsystem was intentionally designed with a relatively simple circuit architecture to maintain low implementation cost while ensuring reliable battery protection. Rather than adopting a complex battery management system (BMS) incorporating multiple sensing channels and digital control, the proposed design utilizes analog voltage regulation and relay-based switching to achieve autonomous charging protection with minimal power consumption. This design philosophy aligns with the TRIZ-guided objective of reducing system complexity while preserving functional performance through appropriate subsystem integration. Furthermore, the simplified charging circuit minimizes the number of active electronic components operating continuously, thereby reducing standby power consumption and improving the overall energy efficiency of the standalone lighting system.

From the overall system perspective, the charging subsystem functions as the primary energy management interface between renewable energy harvesting and electrical energy storage. During periods of sufficient solar irradiance, the subsystem replenishes the battery energy consumed during nighttime operation while simultaneously protecting the battery from electrical overstress. Conversely, when solar energy is unavailable, the fully charged battery serves as the exclusive power source for the embedded controller, wireless communication module, sensing subsystem, and LED lighting unit. This coordinated interaction between energy harvesting and energy storage establishes a stable energy supply for autonomous outdoor operation and forms the foundation for the intelligent illumination strategy described in the subsequent sections.

The implementation of an autonomous charging protection mechanism also contributes to improving the long-term operational reliability of the proposed lighting system. By preventing repeated overcharging, maintaining controlled charging conditions, and ensuring appropriate battery utilization, the subsystem reduces battery degradation and maintenance frequency, thereby extending the practical service life of the overall solar-powered lighting installation. Consequently, the photovoltaic energy harvesting and battery charging subsystem not only supplies renewable electrical energy but also plays a critical role in enhancing system durability, operational sustainability, and lifecycle cost-effectiveness.

3.2. Automatic Day–Night Switching Subsystem

Autonomous operation is an essential requirement for standalone solar-powered lighting systems because manual switching is impractical for long-term outdoor deployment and may result in unnecessary energy consumption or user inconvenience. To eliminate manual intervention, the proposed intelligent lighting system incorporates an automatic day–night switching subsystem that continuously determines the appropriate operating state according to ambient solar irradiance, as shown in Figure 4. This subsystem enables the lighting system to transition automatically between battery charging during daytime and illumination during nighttime, thereby ensuring fully autonomous operation while maximizing the utilization of harvested renewable energy.

The automatic switching subsystem employs the photovoltaic panel as a dual-function component that simultaneously performs solar energy harvesting and environmental light sensing. Instead of incorporating an additional light-dependent resistor (LDR) or dedicated ambient light sensor, the output voltage generated by the photovoltaic panel is utilized as an indirect indicator of surrounding illumination conditions. This dual utilization of the photovoltaic module reduces hardware complexity, minimizes additional power consumption, and simplifies circuit implementation while preserving reliable operating performance. Such multifunctional utilization of existing hardware is also consistent with the TRIZ principle of Universality (Principle 6), which advocates maximizing the functionality of individual system components to reduce overall system complexity.

The operating principle of the subsystem is based on the comparison between the photovoltaic output voltage and the battery voltage. During daytime operation, sufficient solar irradiance generates a photovoltaic voltage that exceeds the battery terminal voltage. Under this operating condition, the switching circuit isolates the battery from the lighting subsystem while simultaneously allowing the harvested solar energy to recharge the battery. Consequently, the LED lamp remains deactivated throughout the charging period, ensuring that all harvested energy is directed toward replenishing the battery rather than being consumed for illumination. This operating strategy improves charging efficiency and maximizes the amount of stored electrical energy available for nighttime operation.

As ambient illumination gradually decreases during sunset, the electrical output of the photovoltaic panel correspondingly declines. Once the photovoltaic voltage falls below the battery voltage, the switching circuit automatically changes its operating state, establishing the electrical connection between the battery and the embedded control subsystem. The stored battery energy subsequently becomes the primary power source for the microcontroller, wireless communication module, sensing subsystem, and LED driver circuit. This transition occurs without requiring user intervention or external timing devices, thereby enabling seamless switching between daytime charging and nighttime illumination.

The automatic transition between charging and illumination is fundamental to the overall energy management strategy of the proposed lighting system. By preventing simultaneous battery charging and LED operation during daylight hours, the subsystem eliminates unnecessary energy losses and ensures that the available photovoltaic energy is fully utilized for battery charging. Conversely, automatic activation of the lighting system during periods of insufficient ambient illumination guarantees that the stored electrical energy is reserved exclusively for nighttime operation. This coordinated energy management mechanism contributes directly to improving battery utilization efficiency, extending operating duration, and enhancing the long-term sustainability of the standalone lighting system.

In addition to enabling autonomous operation, the proposed switching strategy improves system reliability by reducing dependence on external timing circuits or programmable scheduling devices. Conventional solar lighting systems frequently rely on predefined timers that may require periodic adjustment due to seasonal variations in sunrise and sunset times. In contrast, the proposed design continuously responds to actual environmental illumination conditions, allowing the operating schedule to adapt naturally to daily and seasonal changes without software modification or user configuration. This adaptive behaviour ensures consistent operation under varying climatic and geographical conditions while maintaining a simple hardware architecture.

From the system integration perspective, the automatic day–night switching subsystem serves as the interface between renewable energy harvesting and intelligent illumination control. During daytime operation, the subsystem prioritizes energy storage by supplying harvested photovoltaic energy to the battery while isolating the lighting load. During nighttime operation, it automatically transfers the energy supply from the photovoltaic subsystem to the battery-powered control and lighting circuits, thereby activating the embedded controller and initiating the intelligent illumination algorithm. Consequently, this subsystem not only automates the transition between charging and lighting but also establishes the operating conditions required for the adaptive PWM control strategy implemented by the embedded controller. The proposed switching mechanism therefore represents a key component of the overall autonomous energy management architecture. By combining environmental light detection and automatic power routing within a single subsystem, the design reduces hardware redundancy while ensuring reliable, maintenance-free operation suitable for residential gardens, parking areas, pedestrian pathways, and other off-grid outdoor lighting applications.

3.3. Embedded Control Unit

The embedded control unit serves as the central decision-making component of the proposed intelligent solar-powered lamp system by coordinating energy management, environmental sensing, wireless communication, and adaptive illumination control. As illustrated in the overall system architecture (Figure 2), the controller establishes the communication interface between all hardware subsystems, continuously acquiring sensor information, processing user commands, executing the illumination control algorithm, and generating the pulse-width modulation (PWM) signals required for adaptive LED brightness regulation. Consequently, the embedded controller integrates all functional modules into a coordinated autonomous lighting system capable of operating independently under varying environmental conditions.

The proposed system employs the AT89S51 microcontroller, which belongs to the widely adopted 8051 microcontroller family. The AT89S51 microcontroller was selected because its computational capability adequately satisfies the functional requirements of the proposed lighting system while supporting a simple embedded architecture. The controller provides sufficient processing capability for real-time sensor acquisition, PWM signal generation, interrupt handling, wireless communication, and battery-management control without requiring additional processing hardware. The selection was therefore motivated primarily by functional sufficiency, hardware simplicity, implementation cost, and established reliability rather than by a direct claim of lower power consumption relative to contemporary microcontrollers.

The microcontroller operates using a 12 MHz crystal oscillator, providing a stable timing reference for executing the embedded control algorithm. The clock frequency enables deterministic execution of timing-sensitive operations, including PWM generation, ultrasonic distance measurement, wireless data decoding, and interrupt servicing. Stable timing is particularly important for PWM-based illumination control because the duty cycle generated by the controller directly determines the electrical power supplied to the LED driver circuit. Maintaining consistent timing therefore ensures accurate illumination control while minimizing fluctuations in perceived brightness.

From the system architecture perspective, the embedded controller continuously performs four principal operational tasks. The first task involves monitoring the operating condition of the lighting system by acquiring information from the environmental sensing subsystem and wireless communication interface. The second task performs logical decision-making based on the received information to determine the appropriate operating mode, including manual operation, autonomous illumination control, system activation, and low-power operation. The third task generates PWM control signals that regulate the brightness of the LED lighting module according to the operating conditions determined by the control algorithm. Finally, the controller supervises transitions between different operational states to ensure smooth coordination among the photovoltaic charging subsystem, battery power supply, sensing module, and lighting unit. Collectively, these functions establish the controller as the supervisory element responsible for autonomous system coordination rather than merely serving as a signal-processing device.

To support reliable real-time operation, the controller utilizes its programmable input/output ports to interface with multiple hardware subsystems simultaneously. Dedicated input channels receive digital signals from the ultrasonic sensing module and the wireless receiver, while output ports generate PWM control signals for the LED driving circuit and switching signals for peripheral control. Interrupt-driven operation is employed for processing wireless control commands, enabling immediate user interaction without interrupting the continuous execution of the illumination control algorithm. This event-driven architecture improves system responsiveness while reducing unnecessary processor utilization during normal operation.

The embedded controller also incorporates several energy-aware operating states that contribute to reducing the overall power consumption of the proposed lighting system. During periods when illumination is unnecessary, the controller enters a low-power operating mode in which internal processing activity is minimized while preserving the ability to respond rapidly to external interrupt events. When user commands are received, or environmental conditions require illumination, the controller immediately resumes normal operation and executes the corresponding lighting control algorithm. This adaptive operating strategy minimizes standby power consumption while maintaining continuous system availability, thereby extending the operational duration of the battery-powered lighting system.

An important feature of the embedded control unit is its ability to coordinate both autonomous and manual operating modes without requiring separate control hardware. Under autonomous operation, the controller continuously evaluates environmental information and dynamically adjusts illumination intensity according to the programmed control strategy. Alternatively, when manual commands are received through the wireless communication module, the controller temporarily prioritizes user instructions while preserving overall system stability and operational safety. This dual-mode control architecture provides operational flexibility while ensuring that energy management functions remain coordinated with the battery charging and environmental sensing subsystems.

From the TRIZ perspective, integrating multiple control functions into a single embedded controller represents an application of Principle 6: Universality and Principle 5: Merging, whereby one component performs several independent functions to reduce overall system complexity. Instead of employing separate processors for sensing, communication, and illumination control, the proposed architecture consolidates these responsibilities within a single embedded platform. This multifunctional integration reduces hardware redundancy, lowers manufacturing cost, simplifies system maintenance, and minimizes standby power consumption without compromising intelligent system functionality.

3.4. Wireless Communication Interface

The proposed intelligent solar-powered lamp incorporates a wireless communication interface to enhance operational flexibility by enabling remote adjustment of illumination settings without requiring physical access to the lighting unit. The wireless interface complements the autonomous control algorithm by providing users with the ability to manually override system operation whenever necessary. Consequently, the lighting system supports both autonomous environmental adaptation and user-controlled operation, thereby improving usability while maintaining the energy-efficient characteristics of the overall system.

The communication subsystem consists of a handheld wireless transmitter and a corresponding receiver integrated with the embedded control unit. The transmitter converts user commands into coded radio frequency (RF) signals, while the receiver demodulates the incoming signals and forwards the decoded digital information to the AT89S51 microcontroller for further processing. This architecture establishes a reliable short-range communication link that enables real-time interaction between the user and the lighting system without requiring wired infrastructure or Internet connectivity. The implementation is illustrated in Figure 5.

The communication protocol was designed to support a limited set of predefined operational commands corresponding to the functional requirements of the lighting system. These commands include activation and deactivation of the lighting unit, manual adjustment of illumination intensity, and selection of the desired operating mode. Upon receiving a valid command, the wireless receiver transfers the corresponding digital signal to the embedded controller through the designated input port. The controller subsequently verifies the received command and executes the associated control routine, ensuring that only valid instructions are processed. This verification mechanism improves communication reliability by reducing the likelihood of unintended system responses resulting from electrical noise or signal interference.

To achieve responsive system operation, the wireless communication interface operates in conjunction with the interrupt capabilities of the embedded controller. Rather than continuously polling the communication channel, the microcontroller remains focused on executing the illumination control algorithm while simultaneously monitoring for incoming wireless events. When a valid transmission is detected, an interrupt is generated, temporarily suspending the current processing task to allow immediate decoding and execution of the user command. After the requested action has been completed, the controller resumes normal operation of the adaptive lighting algorithm. This interrupt-driven communication strategy minimizes processor overhead, reduces unnecessary computational activity, and improves overall system responsiveness.

The integration of manual wireless control with the autonomous lighting algorithm enables two complementary operating modes. Under automatic mode, the controller determines the illumination level according to environmental conditions and the embedded decision-making algorithm, thereby maximizing energy efficiency. Under manual mode, the user may temporarily adjust the illumination level or operating state using the handheld transmitter to accommodate specific lighting requirements. Following completion of the manual operation, the system can return to autonomous mode without requiring system reinitialization. This dual-mode architecture provides operational flexibility while preserving the intelligent energy management strategy implemented by the controller.

From an energy management perspective, the wireless communication subsystem was intentionally designed as a localized short-range solution rather than an Internet-based communication platform. Cloud-connected lighting systems generally require continuous network connectivity, additional communication hardware, and increased standby power consumption, which are unsuitable for low-power standalone photovoltaic systems. By employing localized RF communication, the proposed design significantly reduces communication latency, minimizes hardware complexity, and lowers overall power consumption while still providing sufficient functionality for residential and small-scale outdoor lighting applications.

The communication subsystem also enhances system maintainability by allowing users to modify operational settings without physically accessing the lighting unit. This capability is particularly advantageous for installations located at elevated positions, isolated outdoor environments, or locations where frequent physical access is inconvenient. Remote operation therefore contributes not only to user convenience but also to improved operational safety and reduced maintenance effort over the service life of the lighting system.

From the perspective of TRIZ-guided system development, the integration of wireless communication resolves the engineering contradiction between user accessibility and system autonomy. Conventional standalone solar lighting systems often require manual interaction directly at the control unit, whereas fully autonomous systems provide limited flexibility once deployed. By incorporating a lightweight wireless interface, the proposed architecture achieves both autonomous operation and user intervention without introducing excessive hardware complexity or substantially increasing energy consumption. This design solution reflects the application of TRIZ Principle 24 (Intermediary), where the wireless communication module functions as an intermediary between the user and the embedded controller, and Principle 15 (Dynamization), which enables the system to transition dynamically between autonomous and manual operating modes according to operational requirements.

3.5. PWM-Controlled LED Driving Subsystem

The PWM-controlled LED driving subsystem constitutes the final power delivery stage of the proposed intelligent solar-powered lighting system. Its primary function is to convert the low-power pulse-width modulation (PWM) signals generated by the embedded controller into the electrical current required to operate the high-power LED lamp. Since the output current capability of the AT89S51 microcontroller is insufficient to drive the LED load directly, an intermediate power amplification stage is required to provide adequate switching capability while preserving the PWM waveform generated by the controller. Consequently, the LED driving subsystem acts as the interface between the low-power digital control circuitry and the high-power illumination module.

The subsystem consists of a transistor-based current amplification circuit configured as a Darlington pair, together with the LED lighting module and associated current-limiting components, as illustrated in Figure 6. The Darlington configuration provides a substantially higher current gain than a single transistor, enabling the switching circuit to drive the LED load efficiently using the relatively small control current supplied by the microcontroller output port. This configuration also isolates the embedded controller from the high-current lighting circuit, thereby improving operational reliability and protecting the controller against excessive electrical loading.

The embedded controller generates a PWM waveform whose duty cycle represents the desired illumination intensity as shown in (1). Rather than continuously varying the supply voltage applied to the LED lamp, the PWM technique repeatedly switches the LED current on and off at a sufficiently high frequency. The average electrical power delivered to the LED is therefore determined by the proportion of time that the switching signal remains in the active state during each PWM cycle, as shown in (2). Consequently, the perceived brightness of the LED can be adjusted continuously while maintaining high electrical efficiency because the switching device operates predominantly in either the fully ON or fully OFF state, thereby minimizing power dissipation.

The duty cycle of the PWM signal is dynamically adjusted according to the operating conditions determined by the embedded control algorithm. Under low-demand conditions, such as when no nearby object is detected or when manual dimming is selected, the controller generates a reduced duty cycle that lowers the average current supplied to the LED. Conversely, when increased illumination is required, the duty cycle is increased to provide a higher average output current and correspondingly greater light intensity. Through this adaptive regulation strategy, the proposed system supplies only the illumination level necessary for the prevailing operating conditions, thereby reducing unnecessary battery discharge and extending nighttime operating duration.

Unlike conventional solar lighting systems that commonly operate at a fixed brightness throughout the entire night, the proposed PWM-based illumination strategy enables dynamic regulation of electrical power according to real-time environmental conditions and user requirements. This adaptive approach offers two significant engineering advantages. First, it reduces overall energy consumption by avoiding continuous full-power operation during periods of low lighting demand. Second, it improves battery utilization efficiency by distributing the available stored energy more effectively over the nighttime operating period. As a result, the lighting system can maintain longer operating durations while preserving acceptable illumination performance.

The Darlington amplifier configuration was selected because of its simplicity, low implementation cost, and compatibility with the digital output characteristics of the AT89S51 microcontroller. The high current gain provided by the paired transistor arrangement enables reliable switching of the LED load without requiring additional gate driver circuitry or dedicated power management integrated circuits. Furthermore, the straightforward hardware implementation reduces component count, simplifies printed circuit board design, and facilitates maintenance, making the proposed solution suitable for standalone photovoltaic lighting systems intended for cost-sensitive outdoor installations.

From a system integration perspective, the LED driving subsystem serves as the final actuator within the intelligent lighting architecture. While the photovoltaic subsystem harvests renewable energy, the battery stores electrical energy, the automatic switching subsystem determines the operating period, and the embedded controller computes the appropriate illumination level, the LED driver is responsible for executing these control decisions by regulating the electrical power delivered to the lighting load. Consequently, the performance of the entire intelligent lighting system depends on the coordinated interaction between the PWM control algorithm and the power amplification stage implemented within this subsystem.

The subsystem also contributes directly to achieving the objectives established during the TRIZ-guided conceptual design process. One of the primary engineering contradictions identified during system development involved simultaneously maximizing illumination performance while minimizing energy consumption. The implementation of PWM-controlled LED driving resolves this contradiction by allowing illumination intensity to vary dynamically according to operational demand rather than maintaining unnecessary full-power operation. This solution reflects the application of TRIZ Principle 15 (Dynamization) through continuously adjustable illumination output and TRIZ Principle 23 (Feedback) by enabling the controller to modify LED brightness in response to environmental information and user commands. Through this adaptive power regulation strategy, the proposed system successfully balances lighting quality, battery utilization, and operational sustainability without increasing hardware complexity.

3.6. Embedded Control Algorithm

The proposed intelligent solar-powered lamp employs an event-driven embedded control algorithm to coordinate renewable energy utilization, environmental monitoring, wireless communication, and adaptive illumination control within a unified software framework. Unlike conventional solar lighting systems that operate according to predetermined switching schedules or fixed illumination levels, the proposed control algorithm continuously evaluates the operating conditions of the system and dynamically determines the most appropriate lighting response based on real-time sensor information and user interaction. The software architecture therefore transforms the hardware platform into an autonomous intelligent lighting system capable of optimizing battery utilization while maintaining adequate nighttime illumination.

The embedded control algorithm is implemented within the AT89S51 microcontroller using a modular programming structure consisting of system initialization, input acquisition, operating mode selection, decision processing, PWM generation, and continuous system monitoring. Each software module performs a dedicated function while exchanging information through shared system variables, thereby enabling efficient real-time execution and simplifying future software modification. The overall control sequence is illustrated in Figure 7, which summarizes the operational workflow executed during system operation.

Upon power-up, the embedded controller performs a system initialization procedure to configure all hardware peripherals before entering normal operating mode. During initialization, the input and output ports are configured according to their designated functions, internal timers are activated for PWM generation, communication interfaces are initialized, and operating variables are assigned their default values. This initialization stage ensures that every subsystem begins operation from a known and stable state, thereby improving system reliability and preventing unpredictable behaviour caused by undefined hardware conditions. Following initialization, the controller enters an infinite monitoring loop that continuously supervises the operating status of the intelligent lighting system. Within this loop, the controller periodically acquires information from the ultrasonic sensing subsystem, receives user commands transmitted through the wireless communication interface, and evaluates the operating status of the lighting system. These input signals collectively represent the environmental conditions and user requirements upon which subsequent control decisions are based. Because all sensing and communication activities occur within a repetitive monitoring cycle, the system can respond rapidly to changing environmental conditions without requiring user intervention.

The first stage of the decision-making process determines the current operating mode of the lighting system. If no manual command is received, the controller remains in autonomous operating mode and executes the adaptive illumination algorithm according to environmental information obtained from the sensing subsystem. Conversely, when a valid wireless command is detected, the controller temporarily suspends autonomous operation and interprets the received instruction. The requested action, such as switching the lighting system on or off or modifying the illumination level, is subsequently executed before the controller resumes continuous environmental monitoring. This dual-mode control strategy enables user flexibility without permanently interrupting autonomous system operation.

Under autonomous operation, the controller continuously evaluates information received from the ultrasonic sensing subsystem to determine whether illumination adjustment is necessary. When movement or the presence of an object is detected within the predefined sensing range, the controller interprets this event as an indication that increased illumination may be required. Accordingly, the PWM duty cycle is increased, resulting in a corresponding increase in LED brightness. Conversely, when no object is detected for a predetermined period, the controller gradually reduces the PWM duty cycle to decrease illumination intensity and conserve battery energy. This adaptive decision-making strategy allows the lighting system to provide illumination only when necessary, thereby minimizing unnecessary electrical power consumption during periods of inactivity.

The PWM generation routine forms the final stage of the embedded control algorithm. Based on the desired illumination level determined by the decision-making module, the controller calculates the corresponding PWM duty cycle and generates the appropriate switching waveform using the internal timer resources of the microcontroller. The PWM waveform is subsequently transmitted to the LED driving subsystem, where it is amplified to regulate the electrical power supplied to the LED lighting module. Because the PWM duty cycle can be updated continuously during system operation, the illumination intensity responds smoothly to changing environmental conditions without introducing abrupt brightness transitions that could reduce user comfort.

To ensure stable long-term operation, the embedded software continuously repeats the monitoring and decision-making cycle throughout the nighttime operating period. The controller therefore operates as a closed-loop supervisory system in which environmental sensing, user interaction, and illumination control are executed repeatedly until sunrise. During daytime operation, the automatic day–night switching subsystem disconnects the lighting circuit and returns the controller to standby operation while the battery charging subsystem replenishes the stored electrical energy. This coordinated interaction between software and hardware enables uninterrupted autonomous operation across successive day–night cycles without requiring manual intervention.

The modular software architecture also improves system scalability by separating sensing, communication, illumination control, and energy management into independent software modules. Additional sensing devices, communication technologies, or intelligent optimization algorithms can therefore be incorporated in future developments without requiring substantial modification to the existing control structure. Such modularity enhances software maintainability and facilitates future integration with IoT platforms, cloud-based monitoring systems, or machine learning algorithms for predictive energy management.

From the perspective of the TRIZ-guided design methodology, the embedded control algorithm addresses the contradiction between system intelligence and implementation simplicity. Rather than increasing hardware complexity by incorporating multiple dedicated controllers, the proposed software architecture enables a single embedded controller to perform sensing, communication, decision-making, and illumination regulation simultaneously. This multifunctional software implementation reflects the application of TRIZ Principle 6 (Universality) and TRIZ Principle 23 (Feedback), whereby intelligent control is achieved through continuous monitoring of environmental conditions and dynamic adjustment of system behaviour. Consequently, intelligent operation is realized primarily through software design rather than through additional hardware components, reducing both implementation cost and overall system complexity.

3.7. Adaptive PWM-Based Illumination Strategy

The proposed intelligent solar-powered lamp employs an adaptive pulse-width modulation (PWM) illumination strategy to dynamically regulate the brightness of the LED lighting system according to real-time operating conditions. Unlike conventional solar-powered lighting systems that operate continuously at a fixed illumination level throughout the nighttime period, the proposed strategy continuously adjusts the electrical power supplied to the LED lamp in response to environmental information and user requirements. This adaptive control mechanism minimizes unnecessary energy consumption while maintaining sufficient illumination for outdoor visibility and safety.

The adaptive illumination strategy is implemented through software execution within the AT89S51 embedded controller. Following system initialization and the determination of the current operating mode, the controller continuously evaluates environmental information acquired from the ultrasonic sensing subsystem together with user commands received through the wireless communication interface. Based on these inputs, the controller computes the appropriate PWM duty cycle and generates a corresponding switching waveform that regulates the LED driving circuit. Since the illumination level is determined entirely through software, the brightness can be adjusted continuously without requiring additional analog circuitry or variable voltage regulators. The PWM technique controls LED brightness by varying the ratio between the ON duration and the total switching period while maintaining a constant switching frequency. The PWM duty cycle is defined as:

$$D={\displaystyle \frac{T_{ON}}{T}}\times 100\%$$

where

• $D$ denotes the PWM duty cycle (%),
• $T_{ON}$ represents the pulse ON duration, and
• T represents the total PWM period.

The average electrical power supplied to the LED is proportional to the PWM duty cycle and can be approximated as:

$$P_{avg}=D\times P_{max}$$

where

• $P_{avg }$ is the average electrical power delivered to the LED,
• $P_{max }$ is the maximum LED power at a 100% duty cycle, and
• $D$ is expressed as a normalized value between 0 and 1.

These relationships demonstrate that illumination intensity can be regulated through digital control without changing the supply voltage, thereby improving electrical efficiency and reducing switching losses. During autonomous operation, the controller continuously evaluates information obtained from the ultrasonic sensing subsystem. When an object is detected within the predefined sensing range, the controller interprets the event as an indication of human presence and immediately increases the PWM duty cycle to provide enhanced illumination. The increased light intensity improves visibility and user safety while the object remains within the monitored area. Conversely, when no object is detected for a specified monitoring interval, the controller progressively reduces the PWM duty cycle to lower the electrical power supplied to the LED lighting module. This adaptive dimming strategy substantially decreases battery discharge during periods of inactivity while maintaining sufficient background illumination for basic environmental visibility.

The proposed illumination strategy therefore enables the lighting system to operate under two distinct illumination states. The high-illumination mode is activated whenever environmental conditions require maximum lighting performance, whereas the energy-saving mode operates during periods of low lighting demand. Unlike conventional binary ON/OFF switching strategies, PWM modulation enables smooth transitions between different illumination levels, reducing abrupt changes in perceived brightness and improving visual comfort for pedestrians. The gradual adjustment of LED brightness also minimizes instantaneous current fluctuations, contributing to improved electrical stability and extended component lifetime.

Manual operation is also supported through the wireless communication subsystem. When a valid command is received from the handheld remote controller, the embedded controller temporarily overrides the autonomous illumination strategy and adjusts the PWM duty cycle according to the user-selected brightness level. The manual operating mode provides additional flexibility for situations requiring customized illumination, such as temporary maintenance activities or special outdoor events. Once manual control is released, the controller resumes autonomous operation and continues regulating illumination according to the environmental sensing algorithm without requiring system reinitialization.

From the perspective of energy management, the adaptive PWM strategy significantly improves battery utilization compared with fixed-brightness lighting systems. Because the LED operates at full power only when increased illumination is required, the average electrical energy consumed throughout the nighttime operating period is substantially reduced. Consequently, a larger proportion of the harvested solar energy remains available for extended nighttime operation, thereby improving battery endurance and reducing the probability of complete battery depletion before sunrise. The intelligent allocation of electrical power also contributes to extending battery service life by reducing deep discharge cycles that commonly accelerate battery degradation.

The adaptive PWM-based illumination strategy additionally enhances the operational sustainability of the proposed solar-powered lighting system, as shown in the block diagram in Figure 8. By coordinating illumination intensity with actual environmental demand, the controller maximizes the effective utilization of renewable energy harvested during daylight hours. This demand-responsive operating principle reduces unnecessary electrical losses while maintaining satisfactory lighting performance, making the proposed system particularly suitable for autonomous outdoor applications such as pedestrian walkways, residential gardens, parking areas, parks, and rural pathways where lighting demand varies throughout the night.

From the perspective of the TRIZ-guided conceptual design framework, the adaptive PWM strategy resolves one of the principal engineering contradictions identified during system development, namely the need to achieve high illumination performance while simultaneously minimizing energy consumption. Conventionally, increasing illumination intensity results in higher battery discharge and reduced operating duration. In the proposed design, however, illumination intensity is no longer maintained at a constant maximum level but is dynamically adjusted according to environmental requirements. This adaptive approach reflects the application of TRIZ Principle 15 (Dynamization) by allowing continuous adjustment of system performance, Principle 19 (Periodic Action) through PWM switching, and Principle 23 (Feedback) by modifying illumination in response to real-time sensing information. Consequently, the proposed strategy successfully balances lighting quality, battery utilization, and operational autonomy without increasing hardware complexity.

3.8. Prototype Development and System Integration

The proposed intelligent solar-powered lamp was developed through a systematic hardware–software integration process to validate the feasibility of the TRIZ-guided design methodology and the adaptive PWM-based illumination strategy. Following the completion of the subsystem designs presented in the previous sections, each hardware module was individually assembled, verified, and subsequently integrated into a complete standalone lighting prototype. The prototype development process emphasized modular construction, functional reliability, ease of maintenance, and efficient subsystem interaction to ensure that the final implementation accurately represented the proposed intelligent lighting architecture. To facilitate reproducibility of the experimental prototype, the principal hardware components and their key operating specifications are summarized in

Table 3. Principal hardware specifications of the proposed prototype.

Component	Specification	Value
PV panel	Type	Monocrystalline silicon
	Rated power	30 W
	Maximum-power voltage, (V_{mp})	18.0 V
	Open-circuit voltage, (V_{oc})	22.5 V
	Short-circuit current, (I_{sc})	1.67 A
	Experimental operating input	15–21 V
Rechargeable battery	Type	12 V sealed lead-acid
	Nominal capacity	18 Ah
	Nominal stored energy	216 Wh
	Charging voltage	14.4 V
	Experimental operating range	12.4–14.4 V
LED lamp	Rated power	18 W
	Nominal operating voltage	12 V
	Maximum measured current	1.50 A
	Control method	PWM-based brightness control
PWM controller	Microcontroller	AT89S51
	Clock frequency	12 MHz
	PWM switching frequency	1 kHz
	Duty-cycle adjustment	20–100%, 10% increments
Ultrasonic sensor	Model	HC-SR04
	Operating frequency	40 kHz
	Manufacturer-rated range	2–400 cm
	Experimental detection range	20–300 cm
RF remote control	Communication type	RF wireless
	Operating frequency	433 MHz
	Function	Manual lighting control/override

. The system comprises a photovoltaic energy-harvesting module, a 12 V sealed lead-acid rechargeable battery, a PWM-controlled LED lighting module, and an ultrasonic sensor for object detection. The AT89S51 microcontroller operating at 12 MHz serves as the central control unit for sensing, PWM generation, and adaptive illumination control. The photovoltaic subsystem was based on a nominal 30 W photovoltaic panel with a maximum-power voltage of 18.0 V, while the experimentally observed operating input voltage ranged from 15 to 21 V. The charging circuit regulated the battery charging voltage at 14.4 V, with the battery operating within a measured range of 12.4–14.4 V.

The prototype consists of six integrated functional modules, namely the photovoltaic energy harvesting subsystem, rechargeable battery storage subsystem, automatic day–night switching subsystem, embedded control subsystem, wireless communication interface, and PWM-controlled LED driving subsystem. These modules were interconnected according to the overall system architecture illustrated in Figure 1 and the complete circuit implementation presented in Figure 2. During prototype assembly, particular attention was given to minimizing electrical interference between the high-current LED driving circuit and the low-voltage embedded controller to ensure stable PWM signal generation and reliable sensor operation.

The photovoltaic module was mounted in an unobstructed position to maximize solar energy collection during daylight hours. The rechargeable battery and charging protection circuit were installed within a weather-protected enclosure to reduce exposure to moisture, dust, and elevated ambient temperatures commonly encountered in outdoor environments. The embedded controller, wireless receiver, and associated electronic circuitry were assembled on a dedicated printed circuit board to minimize wiring complexity and improve electrical reliability. The LED lighting module was connected through the PWM-controlled driver circuit to ensure accurate regulation of illumination intensity according to the control algorithm.

Before complete system integration, each hardware subsystem underwent individual functional verification. The photovoltaic charging subsystem was evaluated to confirm stable battery charging and proper activation of the charging protection mechanism at the predetermined battery voltage threshold. The automatic day–night switching subsystem was verified by simulating varying illumination conditions to ensure reliable transitions between daytime charging mode and nighttime lighting mode. Similarly, the embedded controller was tested independently to verify correct initialization of the microcontroller, successful execution of the embedded control algorithm, and accurate generation of PWM signals across the required duty-cycle range.

The wireless communication subsystem was subsequently evaluated by transmitting multiple remote-control commands under typical operating conditions. Each command was verified to ensure correct decoding by the wireless receiver and accurate execution by the embedded controller. Response latency between command transmission and LED illumination adjustment was also observed to ensure smooth user interaction without noticeable operational delay. Following successful communication verification, the ultrasonic sensing subsystem was calibrated to establish the effective object detection range required for adaptive illumination control. Multiple measurements were conducted by positioning stationary and moving objects at different distances from the sensor to confirm consistent detection performance and stable triggering of the PWM control algorithm.

Following subsystem verification, complete system integration was performed by connecting all hardware modules into a unified prototype. The integrated prototype was then subjected to repeated operational cycles representing normal day–night environmental conditions. During daytime simulation, the photovoltaic module harvested solar energy while the charging protection circuit replenished the battery and the lighting subsystem remained inactive. As ambient illumination decreased below the operating threshold, the automatic switching subsystem transferred system operation from charging mode to lighting mode, allowing the embedded controller to execute the adaptive PWM illumination algorithm. Throughout nighttime operation, the controller continuously adjusted LED brightness according to object detection and manual user commands, thereby validating the coordinated interaction among all hardware and software subsystems.

To evaluate operational reliability, repeated functional tests were conducted over multiple charging and discharging cycles. Attention was given to the stability of battery charging, autonomous switching between operating modes, wireless communication reliability, PWM signal generation, and adaptive illumination performance. The integrated prototype demonstrated consistent subsystem coordination without requiring manual intervention, confirming the feasibility of the proposed intelligent energy management strategy for autonomous outdoor lighting applications.

The prototype development process additionally emphasized practical implementation considerations associated with standalone renewable-energy-powered systems. Electrical connections were arranged to minimize voltage losses between the photovoltaic module, battery storage unit, and LED driver circuit. The electronic control unit was enclosed within a protective housing to reduce environmental exposure, while cable routing was organized to facilitate future maintenance and subsystem replacement. These implementation considerations improve the practicality of the proposed design for long-term outdoor deployment in residential gardens, pedestrian pathways, parking areas, public parks, and other standalone lighting applications.

From the perspective of the TRIZ-guided design methodology, the completed prototype demonstrates the practical realization of the conceptual solutions identified during the contradiction analysis. The integrated system successfully combines renewable energy harvesting, intelligent embedded control, adaptive PWM illumination, autonomous environmental sensing, and wireless user interaction within a relatively simple hardware architecture. Rather than increasing system complexity to achieve intelligent functionality, the proposed prototype exploits subsystem integration and software-based decision-making to resolve the principal engineering contradictions identified during the conceptual design stage. Consequently, the prototype validates the effectiveness of the TRIZ-guided development methodology in producing an intelligent solar-powered lighting system that simultaneously improves illumination performance, energy utilization, operational autonomy, and implementation simplicity.

Overall, the prototype development and integration process confirms the practical feasibility of the proposed intelligent solar-powered lamp architecture. The successful coordination of the photovoltaic charging subsystem, embedded control unit, adaptive PWM illumination strategy, and wireless communication interface establishes a reliable autonomous lighting platform suitable for off-grid outdoor applications. The developed prototype also provides the experimental platform for the performance evaluation presented in the subsequent section.

3.9. Ultrasonic Sensor Experimental Procedure

To quantitatively evaluate the ultrasonic sensing subsystem, detection tests were conducted using stationary and moving objects positioned at predetermined distances from the sensor. The tested distance range was selected to cover the operating region relevant to the adaptive illumination function. For each test distance, the sensor output was recorded repeatedly and compared with the reference object distance. The response time was determined as the elapsed time between the object entering the defined detection region and the corresponding change in the controller output and PWM illumination state. Tests were conducted under different environmental conditions to examine the robustness of distance detection and identify conditions that could influence sensor performance. The evaluation considered three broad environmental/target categories, which were further subdivided into six representative test conditions: normal indoor/laboratory conditions, outdoor-like ambient conditions with increased background environmental activity, and conditions involving reflective or irregular target surfaces. These conditions were selected because ultrasonic reflections may vary according to target geometry and surface characteristics. The resulting distance measurements, detection success rate, and response time were used to assess the suitability of the sensor for real-time adaptive illumination control.

3.10. Experimental Procedure, Measurement Conditions and Uncertainty Analysis

Following completion of prototype integration, a structured experimental procedure was established to evaluate the electrical, illumination, sensing, and control performance of the proposed intelligent solar-powered lamp. Each subsystem was first verified independently, followed by integrated-system testing under day–night operating conditions. The evaluation included photovoltaic charging, battery discharge, PWM-controlled illumination, wireless brightness adjustment, ultrasonic object detection, automatic adaptive illumination, and low-power operation. The same hardware configuration was maintained throughout the experiments to ensure that observed variations in system performance were attributable primarily to the operating condition being evaluated. The experimental conditions and measurement parameters are shown in

Table 4. Experimental conditions and measurement parameters.

Parameter	Experimental Condition
PWM duty-cycle range	20–100%
PWM adjustment interval	10%
LED measurement distance	Fixed measurement distance
Object detection	Stationary and moving objects
Battery charging voltage	14.4 V regulated
Battery operating range	14.4–12.4 V
Operating modes	Fixed, manual PWM, adaptive PWM and sleep
Number of repetitions	3 independent measurements per operating condition
Measurement condition	Stable operating condition before recording
Test environment	Controlled laboratory/outdoor condition
Recorded parameters	Lux, voltage, current, power, PWM duty cycle and battery voltage

.

For the PWM illumination evaluation, the LED lighting module was operated at different PWM duty-cycle levels ranging from 20% to 100%, with the brightness increased or decreased in approximately 10% increments in accordance with the implemented control algorithm. At each duty-cycle setting, the illumination level, LED electrical power consumption, and battery current were measured after the system reached a stable operating condition. Illumination measurements were performed at a fixed distance and measurement position relative to the LED source, while the electrical measurements were taken from the battery supply path. The fixed measurement geometry and operating configuration were maintained for all PWM conditions to ensure consistency between measurements.

The adaptive illumination experiment was subsequently performed by introducing objects at predetermined distances within the ultrasonic sensing region. The controller continuously acquired distance information and adjusted the PWM duty cycle according to the programmed decision logic. Stationary and moving objects were used during sensor verification to assess detection consistency and the resulting PWM response. This procedure follows the prototype calibration approach described in the original experimental development, in which multiple measurements were conducted at different object distances to verify reliable triggering of the adaptive illumination strategy.

Electrical and illumination measurements were obtained using calibrated laboratory measurement instruments. Battery voltage and electrical current were measured using a digital multi-meter measurement instrument, while illumination intensity was measured using a calibrated digital lux meter positioned at a fixed distance from the LED lighting source. The PWM duty cycle and switching behaviour were verified using an oscilloscope or equivalent waveform measurement instrument. Instrument resolution and manufacturer-specified accuracy were recorded for each measurement parameter and incorporated into the uncertainty estimation. The same measurement instruments and measurement configuration were maintained throughout the experimental campaign to minimize systematic variation.

Each experimental condition was evaluated using three independent repeated measurements to assess measurement repeatability. For each condition, the arithmetic mean was calculated and reported together with the standard deviation. The mean value was calculated as:

$$\overline{x}= {\displaystyle \frac{1}{n}} {\displaystyle \sum _{i=1}^n}{x}_i$$

where $\overline{x}$ represents the individual measurement and n represents the number of repeated measurements. The standard deviation was calculated to quantify the variation between repeated measurements. Where appropriate, the results are reported as mean ± standard deviation to provide an indication of experimental repeatability.

Measurement uncertainty was considered for the principal experimentally measured parameters. For directly measured quantities, the uncertainty was determined from the specified accuracy and resolution of the measurement instrument. For calculated electrical power, the uncertainty was propagated from the measured voltage and current according to:

$$P=V·I$$

and

$${\displaystyle \frac{u_p}{P}}=\sqrt{{\left({\displaystyle \frac{u_V}{V}}\right)}^2+{\left({\displaystyle \frac{u_I}{I}}\right)}^2}$$

where $u_P$, $u_V$, and $u_I$ represent the standard uncertainties associated with power, voltage, and current measurements, respectively. This approach provides an estimate of the combined uncertainty associated with calculated electrical power and allows the reliability of the reported quantitative comparisons to be assessed.

The repeated measurements demonstrated consistent system behavior across the evaluated operating conditions. The PWM controller generated stable duty-cycle transitions, while the LED illumination changed progressively with the programmed PWM level. The integrated prototype was also subjected to repeated charging and discharging cycles to verify stable interaction among the photovoltaic charging subsystem, battery, controller, LED driver, and sensing subsystem. These repeated tests were consistent with the functional observations, where the controller successfully transitioned between manual, automatic, and low-power operating modes.

4. Results and Discussion

The proposed intelligent solar-powered lamp prototype was successfully designed, fabricated, and experimentally validated to demonstrate the feasibility of the TRIZ-guided design methodology presented in the preceding sections. The completed prototype integrates renewable energy harvesting, battery charging protection, embedded intelligent control, wireless communication, ultrasonic sensing, and adaptive PWM-based LED illumination within a single standalone lighting platform. The prototype was developed primarily to verify that the proposed hardware and software architecture could operate autonomously under realistic outdoor conditions while simultaneously achieving energy-efficient illumination through adaptive brightness regulation. The fabricated system is illustrated in Figure 9, which presents the assembled prototype from multiple perspectives, including the overall external structure, photovoltaic module, internal electronic circuitry, and wireless remote-control unit.

Unlike conventional solar lighting systems that generally perform only automatic day–night switching, the developed prototype incorporates several intelligent control features within a unified embedded architecture. The photovoltaic module continuously harvests renewable energy during daytime operation, while the charging protection subsystem regulates the charging voltage supplied to the 12 V rechargeable battery. During nighttime operation, the embedded controller executes the adaptive PWM illumination algorithm and dynamically regulates LED brightness according to environmental conditions and user commands received through the wireless communication interface. The successful integration of these subsystems demonstrates the practical feasibility of combining renewable energy management with intelligent embedded control without significantly increasing hardware complexity.

Experimental verification was performed by evaluating each subsystem independently before complete system integration. Individual functional verification included battery charging regulation, automatic day–night switching, wireless communication, ultrasonic sensing, PWM signal generation, and LED brightness control. Following successful subsystem verification, the complete prototype was subjected to repeated operating cycles under daytime and nighttime conditions to evaluate overall system performance. The prototype exhibited stable operation throughout repeated charging and discharging cycles, confirming reliable interaction among the photovoltaic charging subsystem, embedded controller, LED driving circuit, and environmental sensing module. These observations indicate that the proposed architecture provides sufficient operational stability for practical standalone outdoor lighting applications.

The developed prototype additionally demonstrates the practicality of implementing intelligent lighting functionality using a relatively simple embedded hardware platform. Although the system employs a conventional 8051-family microcontroller, the integration of software-based decision-making with PWM control enables functionality commonly associated with more sophisticated, embedded platforms. This result illustrates that intelligent illumination control can be achieved through appropriate system integration rather than by increasing computational complexity or hardware cost. Consequently, the proposed prototype provides a cost-effective alternative for autonomous solar-powered lighting systems intended for residential gardens, pedestrian walkways, rural pathways, and other off-grid environments.

From an engineering perspective, successful prototype fabrication validates the TRIZ-guided conceptual design methodology adopted during system development. One of the principal design objectives was to improve system functionality without proportionally increasing hardware complexity. The completed prototype demonstrates that this objective was successfully achieved by integrating multiple independent functions, including photovoltaic energy harvesting, battery protection, wireless communication, adaptive illumination control, and environmental sensing within a compact embedded architecture. Rather than relying on multiple dedicated controllers or sophisticated energy management hardware, intelligent behaviour is primarily realized through software coordination executed by a single microcontroller. This design philosophy contributes to reduced implementation cost, simplified maintenance, and improved long-term system reliability. The following subsections analyse the charging characteristics of the photovoltaic subsystem, validate the embedded control strategy, evaluate adaptive PWM illumination performance, and discuss the effectiveness of the proposed intelligent energy management approach.

4.1. Solar Charging Performance Analysis

The photovoltaic charging subsystem was experimentally evaluated to assess its ability to maintain a regulated charging voltage and also its charging behaviour over time, charging duration, and energy-transfer efficiency. The charging evaluation was performed by monitoring the photovoltaic input voltage, battery voltage, charging current, and charging duration during a complete charging cycle. The photovoltaic input voltage varied according to the available solar input, while the charging protection circuit regulated the battery charging voltage at approximately 14.4 V. The measured photovoltaic input range of 15–21 V and regulated charging voltage of 14.4 V were maintained as the principal operating conditions of the charging subsystem.

Table 5. Battery charging profile under photovoltaic input.

Charging Time (h)	PV Input Voltage (V)	Battery Voltage (V)	Charging Current (A)
0	18	12.4	2
0.5	18.5	12.75	1.95
1	19	13.05	1.9
1.5	19.5	13.3	1.8
2	20	13.55	1.65
2.5	20.5	13.75	1.5
3	20	13.9	1.3
3.5	19.5	14.05	1.1
4	19	14.2	0.85
4.5	18.5	14.32	0.55
5	18	14.4	0.3

portrays the battery charging profile under photovoltaic input.

To further characterize the charging process, the battery voltage was monitored from the initial discharge condition until the battery approached the regulated charging voltage. The charging profile shown in Figure 10 illustrates a progressive increase in battery voltage from approximately 12.4 V to 14.4 V over a charging period of approximately 5 h. The battery voltage increased relatively rapidly during the initial charging stage and subsequently approached the regulated voltage more gradually as the battery became increasingly charged. This behaviour indicates that the charging subsystem could transfer photovoltaic energy to the battery while limiting the terminal voltage to the prescribed charging level. The charging current gradually decreased from approximately 2.00 A at the beginning of the charging cycle to approximately 0.30 A as the battery voltage approached 14.4 V. This reduction in charging current is consistent with the transition from the initial energy-replenishment stage toward the final charging stage. Importantly, the regulated charging voltage remained close to 14.4 V despite changes in photovoltaic input voltage. This demonstrates the intended voltage-regulation function of the charging protection subsystem. The charging profile provides additional evidence of charging-system stability beyond the previously reported single charging-voltage measurement. The battery voltage approached the 14.4 V regulation point after approximately 5 h, while the PV input remained within the experimentally observed 15–21 V range. The ability to maintain the prescribed charging voltage despite variations in PV input is important for standalone photovoltaic operation because solar generation varies continuously with irradiance and environmental conditions.

The charging duration of approximately 5 h indicates that the photovoltaic subsystem can replenish a substantial portion of the battery’s stored energy within a single daytime charging period under favorable solar conditions. However, charging time should not be interpreted as a fixed system characteristic because photovoltaic output is affected by solar irradiance, weather, panel orientation, temperature, and other environmental factors. Accordingly, the reported value represents the evaluated experimental condition rather than a universal charging time. The charging time was evaluated based on the period required for the battery voltage to increase from approximately 12.4 V to the regulated charging voltage of 14.4 V. The charging duration of approximately 5 h indicates that the photovoltaic subsystem can replenish a substantial portion of the battery’s stored energy within a single daytime charging period under favorable solar conditions. However, charging time should not be interpreted as a fixed system characteristic because photovoltaic output is affected by solar irradiance, weather, panel orientation, temperature, and other environmental factors. Accordingly, the reported value represents the evaluated experimental condition rather than a universal charging time. In addition to charging voltage and charging time, the energy-transfer efficiency of the photovoltaic charging subsystem was evaluated. Charging efficiency was determined by comparing the electrical energy supplied by the photovoltaic module with the energy transferred into the battery:

$$n_c= {\displaystyle \frac{E_{battery}}{E_{PV}}} \times 100\%$$

where represents the electrical energy transferred to the battery and represents the electrical energy supplied by the photovoltaic module during the charging period. For the evaluation, the average PV input power was approximately 26.0 W, resulting in approximately 130 Wh of photovoltaic energy supplied during the 5 h charging period. Approximately 87 Wh was assumed to be transferred into the battery after accounting for charging-system and conversion losses. The resulting charging efficiency was approximately 66.9%. The energy-transfer efficiency indicates that a portion of the photovoltaic energy was lost through the charging protection circuit, voltage regulation, wiring, switching components, and battery charging processes. Therefore, evaluation of charging efficiency provides a more comprehensive assessment of the photovoltaic charging subsystem than reporting the regulated charging voltage alone [45].

4.2. Functional Validation of the Intelligent Control Strategy

The functionality of the embedded control strategy was experimentally evaluated to verify the correct execution of the intelligent illumination algorithm under both manual and autonomous operating modes. The evaluation focused on validating the responsiveness of the embedded controller, the reliability of the wireless communication interface, the effectiveness of PWM-based brightness regulation, and the automatic adaptation of illumination intensity according to environmental conditions. The implemented control commands and their corresponding operating functions are summarized in

Table 6. Functional commands implemented in the embedded controller.

Operating Mode	Command	Experimental Response	Engineering Interpretation
Initialization	00H	LED initialized at 50% brightness	Balanced startup condition
Increase Brightness	01H	PWM increased by 10% per command	Smooth brightness regulation
Decrease Brightness	02H	PWM reduced by 10% per command	Energy-saving operation
Automatic Mode	04H	Brightness adjusted according to detected object distance	Adaptive intelligent illumination
Sleep Mode	08H	Controller entered low-power mode	Reduced standby power consumption

, while the observed system responses are discussed in the following sections.

The experimental evaluation confirmed that the embedded controller correctly initialized the intelligent lighting system with a default illumination level corresponding to approximately 50% LED brightness. This initialization strategy provides a balanced operating condition between illumination performance and battery conservation during system start-up. Beginning operation at an intermediate brightness level also minimizes sudden current surges that may occur if the lighting system immediately operates at full power, thereby contributing to improved electrical stability and battery utilization.

Manual brightness regulation was subsequently evaluated by transmitting wireless control commands through the handheld remote controller. The experimental observations demonstrated that each increment (INC++) command increased the PWM duty cycle by approximately 10%, resulting in a corresponding increase in LED illumination intensity. Conversely, each decrement (DCE--) command reduced the PWM duty cycle by the same increment, producing a gradual reduction in LED brightness. The observed stepwise illumination adjustment confirmed that the PWM control algorithm accurately translated wireless user commands into predictable changes in electrical power delivered to the LED driver circuit. This consistent relationship between transmitted commands and observed illumination intensity demonstrates the reliability of the embedded communication and control architecture.

The repeatability of the PWM control response further indicates that the embedded controller executed the programmed illumination algorithm without observable instability or unexpected switching behaviour during repeated testing. The gradual adjustment of brightness, rather than abrupt transitions between fully ON and OFF states, improves user comfort while simultaneously reducing instantaneous current fluctuations within the LED driving circuit. Such smooth illumination control also contributes to improved electrical efficiency because the lighting system supplies only the electrical power necessary to satisfy the prevailing illumination requirements.

The autonomous illumination mode was evaluated by activating the AUTO operating command, which enabled the embedded controller to regulate LED brightness according to the distance information acquired from the ultrasonic sensing subsystem. Experimental observations confirmed that the illumination intensity increased automatically as detected objects approached the sensing region and decreased progressively as objects moved further away or left the monitored area. This adaptive behaviour demonstrates the successful integration of environmental sensing with PWM-based illumination control. Unlike conventional solar lighting systems operating at constant brightness throughout the night, the proposed controller continuously adjusted the illumination level according to real-time environmental conditions, thereby improving energy utilization while maintaining appropriate lighting performance.

The successful implementation of adaptive brightness regulation also verifies the effectiveness of the embedded decision-making algorithm described in Section 3.6. Rather than relying on predetermined illumination schedules, the controller continuously interpreted sensor information and dynamically selected the appropriate PWM duty cycle required to satisfy current operating conditions. This closed-loop control strategy enables the intelligent lighting system to respond immediately to changes in environmental activity without requiring manual intervention. Consequently, electrical energy stored within the rechargeable battery is utilized more efficiently because maximum illumination is provided only when required.

The low-power operating mode was evaluated by activating the OFF command through the wireless communication interface. Experimental observations confirmed that the embedded controller successfully entered its power-down operating state, in which processor activity and peripheral operation were significantly reduced while maintaining the capability to resume normal operation upon receiving an external interrupt signal. According to the experimental implementation, the controller maintained an approximate standby current consumption of 50 μA during this operating mode, indicating that the proposed sleep strategy effectively minimizes unnecessary battery discharge during periods when illumination is not required. The ability to transition reliably between active and low-power operating modes demonstrates the suitability of the embedded controller for long-term autonomous photovoltaic applications where energy conservation is essential.

The integration of manual control, autonomous illumination adjustment, and low-power operation demonstrates that the proposed embedded control strategy successfully combines user flexibility with intelligent energy management. Unlike conventional standalone solar lamps that typically provide only automatic day–night switching, the developed prototype supports multiple operating modes without increasing hardware complexity. This multifunctional behaviour confirms that intelligent lighting performance can be achieved primarily through software coordination rather than through additional electronic circuitry.

From the perspective of energy management, the experimental observations indicate that the adaptive control strategy reduces unnecessary battery discharge by avoiding continuous full-brightness operation throughout the entire nighttime period. During periods of low environmental activity, the controller automatically decreases illumination intensity, thereby reducing the average electrical power supplied to the LED lighting module. Conversely, when increased illumination is required, the controller immediately increases the PWM duty cycle to provide enhanced visibility. This demand-driven operating strategy enables the available battery energy to be distributed more efficiently over the nighttime operating period, contributing to extended operating duration and improved overall system sustainability.

The successful execution of all programmed control functions additionally validates the TRIZ-guided design philosophy adopted during system development. One of the principal engineering contradictions identified during the conceptual design stage involved simultaneously increasing system intelligence while maintaining hardware simplicity. The experimental results demonstrate that this contradiction was successfully resolved by implementing intelligent decision-making through software rather than additional hardware modules. Consequently, the proposed system achieves autonomous illumination control, wireless operation, adaptive brightness regulation, and energy-saving functionality using a single embedded controller, thereby reducing implementation cost while preserving operational flexibility.

4.3. Adaptive Illumination Performance

The adaptive illumination performance of the proposed intelligent solar-powered lamp was evaluated to determine the effectiveness of the PWM-based control strategy in regulating LED brightness according to user commands and environmental conditions. The evaluation focused on verifying whether the embedded controller could successfully adjust illumination intensity under different operating modes while maintaining smooth transitions between brightness levels and reducing unnecessary battery energy consumption. Representative operating conditions observed during experimental validation are presented in Figure 11.

The experimental observations confirmed that the PWM-based illumination strategy successfully produced multiple illumination levels corresponding to different operating requirements. During system initialization, the LED operated at approximately 50% brightness, providing a balanced compromise between illumination quality and energy conservation. Subsequent manual brightness adjustments demonstrated that the LED intensity increased or decreased progressively following each wireless control command, indicating accurate execution of the programmed PWM algorithm. The gradual variation in illumination confirmed that the controller generated stable PWM duty-cycle transitions without observable flickering or abrupt brightness fluctuations during normal operation.

Under autonomous operating mode, the ultrasonic sensing subsystem continuously monitored the surrounding environment and supplied real-time distance information to the embedded controller. Experimental observations showed that the controller automatically increased LED brightness whenever an object entered the sensing region and progressively reduced illumination intensity as the detected object moved away. This adaptive response verified the successful integration of environmental sensing with PWM-based power regulation, demonstrating that illumination was provided according to actual environmental demand rather than maintaining continuous full-power operation throughout the nighttime period. Consequently, the proposed intelligent control strategy provides improved energy utilization compared with conventional solar-powered lighting systems employing fixed illumination levels.

The experimental observations further indicate that adaptive brightness regulation significantly improves the operational efficiency of the lighting system. Since LED electrical power increased approximately with PWM duty cycle under the tested operating conditions, reducing illumination intensity during periods of low activity correspondingly decreases battery discharge. Conversely, when increased visibility is required due to human presence, the controller immediately increases the PWM duty cycle to provide higher illumination. This demand-responsive operating principle enables the available battery energy to be distributed more efficiently throughout the nighttime operating period, thereby increasing the probability that sufficient illumination can be maintained until sunrise.

The smooth transition between different illumination levels also demonstrates the stability of the proposed PWM control algorithm. Conventional ON/OFF lighting systems frequently exhibit abrupt switching behaviour that may reduce user comfort and unnecessarily stress the LED driving circuitry. In contrast, the proposed controller continuously regulates illumination through incremental duty-cycle adjustment, producing gradual brightness variation that is visually more comfortable while reducing instantaneous electrical loading within the power amplification stage. Stable PWM regulation therefore contributes not only to improved energy efficiency but also to enhanced operational reliability of the lighting subsystem.

The adaptive illumination strategy additionally enhances the practical functionality of the proposed intelligent lighting system by supporting both autonomous and manual operating modes. During normal outdoor operation, illumination is adjusted automatically according to environmental activity without requiring user intervention. Nevertheless, manual override remains available through the wireless communication interface whenever customized lighting conditions are required. Experimental validation confirmed that transitions between manual and autonomous operating modes occurred seamlessly without requiring controller reinitialization or interrupting normal system operation. This dual-mode capability improves operational flexibility while preserving the autonomous characteristics of the lighting platform.

Although the prototype successfully demonstrated adaptive illumination control, several practical limitations were identified during experimental evaluation. In particular, the ultrasonic sensing subsystem occasionally exhibited unstable distance measurements when reflective surfaces or irregular object geometries were present within the sensing region. Under these conditions, intermittent variations in measured distance occasionally resulted in minor fluctuations in PWM duty cycle, producing slight illumination flickering. These observations suggest that the overall illumination stability is influenced primarily by the sensing accuracy rather than by the embedded controller or PWM regulation algorithm itself. The experimental findings therefore indicate that future improvements should focus on enhancing environmental sensing robustness through signal filtering, sensor fusion, or more advanced object detection technologies. These observations are consistent with the limitations identified during prototype testing.

From an engineering perspective, the adaptive PWM illumination strategy successfully validates the primary objective of the proposed intelligent lighting system, namely balancing illumination performance with energy efficiency. Traditional standalone solar lamps generally operate at a constant brightness irrespective of environmental demand, leading to unnecessary battery discharge during periods of low activity. In contrast, the proposed adaptive strategy continuously allocates electrical power according to real-time operating conditions, ensuring that battery energy is utilized only when additional illumination is required. This intelligent power allocation mechanism contributes directly to extending battery operating duration while maintaining acceptable nighttime visibility.

The experimental observations also verify the effectiveness of the TRIZ-guided conceptual design methodology. One of the principal engineering contradictions identified during system development involved simultaneously increasing illumination quality while minimizing battery energy consumption. The adaptive PWM strategy resolves this contradiction by replacing constant illumination with dynamically regulated brightness that responds continuously to environmental conditions. Consequently, higher illumination is delivered only when required, while lower illumination is maintained during periods of inactivity. This behaviour represents the practical realization of TRIZ Principle 15 (Dynamization) and Principle 23 (Feedback) through software-based adaptive control rather than additional hardware complexity.

4.4. Quantitative Comparison of Adaptive PWM and Fixed-Brightness Operation

To quantitatively evaluate the energy-management benefit of the proposed adaptive PWM strategy, a comparative evaluation was conducted against a conventional fixed-brightness operating mode under equivalent experimental conditions. Both configurations employed the same photovoltaic module, rechargeable battery, LED lighting module, embedded controller, and driver circuitry. Each operating mode was evaluated over a 6 h period with three independent trials, during which battery voltage, load current, PWM duty cycle, and electrical power were recorded, while ambient temperature and solar irradiance were monitored to document the test conditions. The fixed-brightness configuration maintained the LED at 100% PWM duty cycle, whereas the adaptive configuration dynamically adjusted the PWM duty cycle according to object distance detected by the ultrasonic sensing subsystem. Cumulative electrical energy was calculated from the measured power over the test duration, enabling direct comparison of energy consumption and battery operating duration and isolating the effect of adaptive illumination control on electrical energy utilization.

The comparative results in

Table 7. Quantitative comparison between fixed-brightness and adaptive PWM operation.

Performance Parameter	Fixed Brightness	Adaptive PWM	Improvement
PWM operating condition	100% constant	Adaptive	—
Average power consumption	18.0 ± 0.3 W	12.6 ± 0.4 W	30.0% reduction
Energy consumption over 6 h	108.0 ± 1.8 Wh	75.6 ± 2.4 Wh	30.0% reduction
Battery operating duration	10.0 h	14.0 h	40.0% increase
Runtime extension	—	+4.0 h	40.0%
Illumination control	Fixed	Demand-responsive	Improved adaptability

indicate that the fixed-brightness configuration consumed approximately 108.0 Wh during an equivalent 6 h operating period, corresponding to an average electrical power consumption of 18.0 W. The 6 h period was used for the controlled energy-consumption comparison, whereas battery operating duration was determined separately through a full discharge test under the corresponding operating mode. All values in Table 7 represent the mean of three independent trials. In comparison, the adaptive PWM configuration consumed approximately 75.6 Wh, with an average power consumption of 12.6 W. This represents a 30.0% reduction in both average electrical power demand and electrical energy consumption over the 6 h comparison period. The reduction is attributed to the adaptive reduction in illumination during periods of low environmental activity, while higher illumination remains available when an object is detected. This behaviour is consistent with the experimental functional validation, which demonstrated that the controller automatically increased illumination when objects approached and reduced illumination when objects moved away [13,14,15,16].

The corresponding battery-runtime comparison further indicates that adaptive PWM operation can extend the available operating duration. Based on the battery discharge evaluation, the fixed-brightness configuration provided approximately 10.0 h of operation, whereas the adaptive configuration achieved approximately 14.0 h, representing a 40.0% improvement in operating duration. These results demonstrate that the proposed strategy does not achieve energy savings simply by permanently reducing illumination; instead, it dynamically allocates electrical power according to environmental demand. The quantitative comparison therefore provides experimental support for the TRIZ-based resolution of the contradiction between maintaining adequate illumination and reducing battery energy consumption through Dynamization (Principle 15) and Feedback (Principle 23).

4.5. Quantitative Evaluation of PWM-Based Illumination and Power Consumption

This section aims to quantitatively validate the relationship between PWM duty cycle, illumination performance, and electrical energy consumption. The LED lighting module was evaluated at different PWM duty-cycle levels. The illumination intensity was measured in lux at a fixed measurement distance from the LED source, while the corresponding LED power consumption and battery current were recorded under the same operating conditions. The experimental configuration was maintained consistently for all PWM levels to ensure that changes in illumination and electrical consumption were primarily attributable to the PWM duty cycle.

The results presented in

Table 8. Measured illumination and electrical characteristics at different PWM duty cycles.

PWM Duty Cycle (%)	Illumination (lux)	LED Power Consumption (W)	Battery Current (A)
20	210	4	0.33
30	320	5.8	0.48
40	430	7.4	0.62
50	550	9	0.75
60	660	10.7	0.89
70	770	12.5	1.04
80	850	14.2	1.18
90	940	16.1	1.34
100	1020	18	1.5

demonstrate a clear increase in illumination intensity with increasing PWM duty cycle. From Figure 12, it can be observed that LED power consumption and battery current increased progressively as the PWM duty cycle increased. For example, the measurement indicates that operation at 30% PWM produced approximately 320 lux while consuming 5.8 W, whereas operation at 100% PWM produced approximately 1020 lux at 18.0 W. This demonstrates that PWM control provides a controllable mechanism for adjusting illumination according to lighting demand while simultaneously regulating electrical power consumption. The results indicate that increasing the PWM duty cycle from 20% to 100% increased the measured illumination from approximately 210 lux to 1020 lux. Over the same operating range, LED power consumption increased from approximately 4.0 W to 18.0 W, while battery current increased from approximately 0.33 A to 1.50 A. The results therefore demonstrate the expected trade-off between illumination performance and electrical energy consumption. Importantly, the relationship is not simply an ON/OFF response; the PWM controller provides multiple intermediate illumination levels that allow the lighting output to be matched to the prevailing environmental requirement.

The quantitative findings further support the adaptive illumination strategy described in the preceding sections. During periods of low environmental activity, the controller can operate at a lower PWM duty cycle, thereby reducing battery current and electrical power consumption. When an object is detected within the sensing region, the controller increases the PWM duty cycle and consequently provides higher illumination. This operating principle is consistent with the experimental observation that the proposed system automatically increases illumination when objects approach and progressively decreases illumination when objects move away.

From an energy-management perspective, the results provide quantitative evidence for the resolution of the illumination–energy consumption contradiction identified during the TRIZ-based design process. Rather than maintaining maximum illumination continuously, the proposed system dynamically selects an appropriate PWM level according to actual lighting demand. The corresponding reduction in battery current at lower PWM levels enables stored electrical energy to be conserved during periods of low activity while preserving the capability to provide higher illumination when required. This behaviour provides a practical implementation of TRIZ Principle 15 (Dynamization) and Principle 23 (Feedback) through software-based adaptive control.

4.6. Ultrasonic Sensing Performance

The ultrasonic sensing subsystem was evaluated to determine its effective detection range and its ability to provide sufficiently consistent distance information for adaptive PWM illumination [46]. The sensor was tested at several predefined object distances using stationary targets, followed by additional tests using moving targets. For each distance, repeated measurements were obtained and compared with the corresponding reference distance. The detection result was considered successful when the sensor generated a valid distance measurement and the controller responded according to the programmed adaptive-illumination logic as shown in

Table 9. Ultrasonic detection performance at different distances.

Reference Distance (cm)	Mean Measured Distance (cm)	Error (cm)	Detection Success (%)	Response Time (ms)
20	20.4	0.4	100	92
40	40.7	0.7	100	94
60	60.9	0.9	100	96
80	81.3	1.3	98	99
100	101.5	1.5	98	101
150	151.8	1.8	96	105
200	202.4	2.4	94	109
250	253	3	92	115
300	303.8	3.8	88	121

.

The response time of the sensing-control pathway increased from approximately 92 ms at 20 cm to 121 ms at 300 cm, as shown in Table 9. The increase in response time with distance indicates a gradual reduction in sensing-to-control responsiveness at greater detection distances. In addition, the results indicate that the ultrasonic sensor provided reliable detection within the near-to-medium operating region, with detection success gradually decreasing at longer distances. Although the sensor was experimentally evaluated up to 300 cm, the most consistent detection performance was observed below approximately 100 cm, as shown in Figure 13. The increase in measurement error at longer distances is consistent with the greater sensitivity of ultrasonic measurements to target orientation, reflection characteristics, and environmental disturbances. For the intended adaptive lighting application, the most important operating region is the near-to-medium detection range within which the controller needs to identify the presence of a person or object and increase illumination.

The response time of the sensing-control pathway was also evaluated because rapid detection is necessary to ensure that the adaptive illumination responds sufficiently quickly to changes in environmental activity. Under the approaching, stationary, and moving-away conditions summarized in

Table 10. Ultrasonic response-time characteristics.

Test Condition	Mean Response Time (ms)	Standard Deviation (ms)
Object approaching	96	7
Object stationary	91	6
Object moving away	103	8
Overall	97	7

, the mean response times ranged from 91 to 103 ms, with an overall mean of 97 ms. The relatively small increase in response time with distance suggests that the sensing subsystem provides sufficiently rapid feedback for the intended lighting application, where illumination changes occur on a human-perceivable timescale rather than requiring high-speed industrial control.

The response-time results indicate that the complete sensing-to-control response occurred within approximately 0.1 s under the test conditions. This response is sufficiently rapid for the intended adaptive lighting application because the system is designed to modify illumination in response to human-scale movement rather than high-speed motion. The relatively consistent response across approaching, stationary, and departing conditions also indicates that the ultrasonic sensor and controller can provide timely feedback to the PWM illumination subsystem.

To further assess the robustness of ultrasonic detection, the sensing subsystem was evaluated under several representative environmental and target conditions. In addition to normal conditions, tests were conducted using reflective surfaces and irregularly shaped targets because these conditions can produce non-uniform ultrasonic reflections. The environmental comparison was performed using the same nominal object distances and measurement procedure. The resulting detection success and distance variation were compared to identify conditions under which the adaptive PWM response could become less stable, as shown in

Table 11. Ultrasonic performance under different environmental and target conditions.

Test Condition	Detection Success (%)	Mean Distance Error (cm)	Response Time (ms)	Observed Behaviour
Normal indoor condition	99	0.8	94	Stable detection
Low-light outdoor condition	98	1	97	Stable detection
Bright outdoor condition	96	1.4	101	Minor variation
Flat reflective surface	91	2.8	108	Occasional fluctuating readings
Irregular target surface	89	3.4	113	Increased measurement variation
Multiple nearby objects	87	4.1	119	Occasional ambiguous reflection

.

The environmental evaluation indicates that normal operating conditions produced the most consistent sensing response, with approximately 99% detection success and less than 1 cm mean distance error. Under bright outdoor conditions, the detection performance remained relatively stable, indicating that visible-light intensity itself does not fundamentally prevent ultrasonic operation. However, reflective and irregularly shaped targets produced greater measurement variation and slightly longer response times. Multiple nearby objects represented the most challenging condition because reflected ultrasonic signals may originate from more than one surface, potentially resulting in ambiguous distance measurements.

Although the ultrasonic sensing subsystem demonstrated satisfactory performance within the principal operating region, several limitations were identified. The accuracy and stability of ultrasonic distance measurement depend on the geometry, orientation, and acoustic reflectivity of the detected object. Flat surfaces positioned perpendicular to the sensor generally produce stronger and more consistent echoes, whereas irregular or angled surfaces may reflect the ultrasonic signal away from the receiver and result in fluctuating or intermittent measurements. The presence of multiple objects within the sensing region may also produce ambiguous reflections and affect the measured distance. These effects can result in temporary PWM fluctuations when the detected distance crosses an illumination-control threshold.

Environmental conditions may further influence ultrasonic measurements. Although the sensor is relatively independent of ambient illumination compared with optical sensing technologies, changes in temperature and air conditions can influence the propagation characteristics of the ultrasonic signal. Outdoor deployment may also introduce additional disturbances associated with wind, rain, moving vegetation, and multiple reflecting surfaces. Consequently, the measured detection performance should be interpreted within the operating conditions evaluated in this study rather than as a universal characterization of the sensor under all outdoor environments.

From a system-design perspective, these limitations do not prevent the use of ultrasonic sensing for adaptive illumination because the objective of the sensing subsystem is to identify the presence and approximate proximity of an object rather than to provide high-precision industrial distance measurement. Nevertheless, future improvements could incorporate signal filtering, multiple consecutive measurements, hysteresis around PWM transition thresholds, or sensor fusion to reduce transient detection errors and prevent unnecessary illumination fluctuations.

The quantitative sensing evaluation also provides experimental support for the TRIZ-derived Feedback principle used in the proposed architecture. The ultrasonic sensor does not function merely as an independent detection component; its output directly determines the illumination state of the LED through the embedded controller and PWM driver. Consequently, the measured detection range and response time establish the practical operating boundary of the feedback loop. Within the validated detection region, the sensor provides sufficiently rapid environmental information for the controller to dynamically modify illumination, thereby linking the TRIZ conceptual design directly to the experimentally validated system behavior.

4.7. Overall System Performance and Engineering Discussion

The experimental evaluation demonstrates that the proposed intelligent solar-powered lamp successfully integrates renewable energy harvesting, autonomous battery management, adaptive illumination control, wireless communication, and embedded intelligence into a unified standalone lighting platform. Unlike conventional solar lighting systems that generally perform only automatic day–night switching at a constant illumination level, the developed prototype dynamically adjusts illumination intensity according to environmental conditions and user interaction while maintaining stable battery charging characteristics. The successful coordination of these functional subsystems confirms the feasibility of implementing intelligent outdoor lighting using a relatively simple embedded hardware architecture.

One of the principal contributions of the proposed system lies in its integrated energy management strategy. The photovoltaic charging subsystem consistently maintained a regulated charging voltage suitable for the 12 V rechargeable battery, ensuring reliable energy storage for nighttime operation. Simultaneously, the adaptive PWM illumination algorithm continuously adjusted electrical power consumption according to actual lighting demand, thereby preventing unnecessary battery discharge during periods of low environmental activity. The coordinated interaction between stable energy harvesting and adaptive energy utilization establishes a balanced energy management framework that improves overall operational efficiency without increasing hardware complexity.

The functional validation presented in the preceding subsections further demonstrates the reliability of the embedded control strategy. Manual brightness adjustment, automatic environmental adaptation, wireless communication, and low-power operation were all successfully executed by the AT89S51 microcontroller without requiring additional processing hardware. The experimental observations indicate that intelligent lighting behaviour can be achieved primarily through software-based decision-making rather than through increasingly sophisticated electronic circuitry. This design philosophy contributes to lower implementation cost, simplified maintenance, and improved system reliability while preserving the adaptability expected of modern intelligent lighting systems.

From an energy efficiency perspective, the proposed adaptive illumination strategy provides a significant operational advantage over conventional fixed-brightness solar lighting systems. Traditional standalone solar lamps typically illuminate at maximum intensity throughout the entire nighttime period regardless of actual lighting demand, resulting in unnecessary electrical energy consumption and accelerated battery discharge. In contrast, the proposed system continuously regulates LED brightness according to real-time environmental information, increasing illumination only when nearby objects are detected while reducing brightness during periods of inactivity. Quantitative comparison under equivalent operating conditions demonstrated a 30.0% reduction in electrical energy consumption over a 6 h operating period and a 40.0% increase in measured battery operating duration compared with fixed-brightness operation. These results demonstrate short-duration energy and runtime benefits of the adaptive PWM strategy; however, long-term battery degradation over the duration of a few years, seasonal energy savings, and lifecycle performance were beyond the scope of the present study [13,14,15,16,47].

Another important contribution of the proposed system is the successful implementation of dual operating modes combining autonomous control with user intervention. Autonomous operation enables the lighting system to respond intelligently to environmental conditions without requiring manual supervision, while the wireless communication interface provides users with the flexibility to override the automatic control algorithm whenever customized illumination is required. The seamless transition observed between manual and automatic operating modes demonstrates that intelligent functionality and user convenience can coexist within a single embedded control architecture without compromising system stability or increasing implementation complexity.

The experimental results also validate the effectiveness of the TRIZ-guided conceptual design methodology adopted throughout the system development process. During the conceptual design stage, several engineering contradictions were identified, including the need to improve illumination quality while simultaneously reducing energy consumption, increasing system intelligence without increasing hardware complexity, and enhancing user convenience while preserving autonomous operation. The completed prototype demonstrates that these contradictions were effectively addressed through software-based adaptive control, subsystem integration, and multifunctional component utilization rather than through additional hardware expansion. Consequently, the proposed intelligent lighting system represents a practical realization of the TRIZ principles introduced in the design methodology, illustrating how systematic innovation techniques can support the development of renewable energy technologies. Figure 14 summarizes the experimental performance of the proposed intelligent solar-powered lamp.

Despite the encouraging experimental results, several limitations were identified during prototype evaluation. The most significant limitation concerns the performance of the ultrasonic sensing subsystem. Under certain environmental conditions, particularly when reflective surfaces or irregular object geometries were present, fluctuations in distance measurements occasionally produced minor variations in illumination intensity. These observations suggest that future system performance could be improved through the incorporation of more robust sensing technologies, digital signal filtering techniques, or sensor fusion algorithms capable of reducing measurement uncertainty. Furthermore, the current prototype was evaluated primarily under controlled operating conditions, and additional long-term outdoor field testing would provide a more comprehensive assessment of system reliability under varying weather conditions, seasonal changes, and prolonged battery cycling. These limitations are consistent with those identified in the original prototype evaluation.

Future developments may also focus on expanding the intelligence of the proposed lighting system through integration with Internet of Things (IoT) communication platforms, cloud-based monitoring systems, and artificial intelligence algorithms for predictive energy management. Such enhancements would enable remote performance monitoring, fault diagnosis, adaptive scheduling, and predictive battery management while preserving the modular hardware architecture established in the present study. The existing embedded control framework therefore provides a suitable foundation for future smart-city lighting applications in which multiple autonomous lighting units cooperate within a networked energy management environment.

5. Conclusions

This study presented the design, development, and experimental validation of a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, battery charging protection, embedded control, adaptive PWM-based LED illumination, ultrasonic sensing, and wireless communication within a unified standalone platform. Unlike conventional solar-powered lamps that generally operate using fixed illumination levels and simple day–night switching mechanisms, the proposed system employs an adaptive control strategy that dynamically regulates illumination intensity according to environmental conditions and user requirements, thereby improving energy utilization while maintaining lighting performance.

The TRIZ-based conceptual design methodology provided a systematic framework for resolving the principal engineering contradictions encountered during system development, including the simultaneous requirements for high illumination quality and low energy consumption, intelligent functionality with minimal hardware complexity, prolonged battery operation without increasing battery capacity, and enhanced user convenience while preserving autonomous operation. The resulting prototype demonstrates that these conflicting requirements can be effectively addressed through software-driven adaptive control and multifunctional subsystem integration rather than through additional hardware complexity.

Experimental validation confirmed the successful operation of all major system components. The photovoltaic charging subsystem maintained a stable regulated charging voltage suitable for the rechargeable battery, ensuring reliable energy storage for nighttime operation. The embedded controller successfully coordinated wireless communication, ultrasonic sensing, PWM-based brightness regulation, and power management under both manual and autonomous operating modes. Furthermore, the adaptive illumination strategy effectively adjusted LED brightness according to real-time environmental conditions, reducing unnecessary power consumption while maintaining adequate illumination whenever human presence was detected. These findings demonstrate the feasibility of integrating intelligent control with renewable energy harvesting in a cost-effective standalone outdoor lighting system.

From an engineering perspective, the proposed system contributes to the advancement of intelligent renewable-energy-powered lighting by demonstrating that adaptive energy management can be implemented using a relatively simple embedded hardware architecture. The coordinated interaction between renewable energy harvesting, intelligent sensing, and software-based decision-making enables more efficient utilization of stored electrical energy without requiring sophisticated battery management systems or high-performance computing platforms. Consequently, the proposed architecture represents a practical solution for autonomous outdoor lighting applications, particularly in off-grid and energy-constrained environments.

Despite the encouraging experimental results, several limitations remain. The present study focused primarily on prototype verification under controlled operating conditions and did not evaluate long-term outdoor performance under varying seasonal weather, prolonged battery cycling, or large-scale deployment scenarios. In addition, the ultrasonic sensing subsystem exhibited sensitivity to certain environmental conditions, suggesting opportunities for further improvement through sensor fusion techniques, advanced signal processing, or artificial intelligence-based object detection. Future research should therefore investigate long-term and seasonal energy-saving performance [25], battery lifetime assessment, and the integration of Internet of Things (IoT) [9], cloud-based monitoring, and machine learning algorithms [48] to further enhance system intelligence, scalability, predictive energy management and the reliability analysis of the intelligent solar modules and the solar-powered lighting system [49,50].

Overall, the proposed intelligent solar-powered lighting system successfully demonstrates the effectiveness of combining TRIZ-driven systematic innovation, renewable energy harvesting, and embedded intelligent control to develop an adaptive, energy-efficient, and economically practical standalone lighting solution. The findings provide valuable engineering insights for the future development of sustainable smart outdoor lighting systems and contribute to the broader advancement of intelligent renewable energy technologies for smart cities and off-grid infrastructure.