Search Results (538)

IoT devices with sensors and actuators are frequently deployed in environments without access to the power grid. These devices are battery powered and might make use of energy harvesting if battery lifetime is too limited. This article focuses on automatically adapting the duty cycle frequency to the predicted available solar energy so that a continuous operation of IoT applications is guaranteed. The implementation is based on a low-cost solar control board that is integrated with the Serverless IoT Framework (SIF), which provides an event-based programming paradigm for microcontroller-based IoT devices. The paper presents a case study where the IoT device sleep time is pro-actively adapted to a predicted sequence of cloudy days to guarantee continuous operation. Full article

In this paper, a robust optimized controller is implemented in the photovoltaic generator system (PVGS). The PVGS is composed of individual photovoltaic (PV) cells, which convert solar energy to electrical energy. To optimize the efficiency of the PVGS under variable solar irradiance and temperatures, a maximum power point tracking (MPPT) controller is necessary. Additionally, the PVGS output voltage is typically low for many applications. To achieve the MPPT and to gain the output voltage, an increasing boost converter (IBC) is employed. Then, two issues should be considered in MPPT. At first, a smooth control signal for adjusting the duty cycle of the IBC is important. Another critical issue is the PVGS and IBC unknown sections, i.e., the total system uncertainty. Therefore, to address the system uncertainties and to regulate the smooth duty cycle of the converter, a robust dynamic sliding mode control (DSMC) is proposed. In DSMC, a low-pass integrator is placed before the system to suppress chattering and to produce a smooth actuator signal. However, this integrator increases the system states, and hence, a sliding mode observer (SMO) is proposed to estimate this additional state. The stability of the proposed control scheme is demonstrated using the Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed method and provide a reliable comparison, conventional sliding mode control (CSMC) with the same proposed SMO is also implemented. Full article

Selective catalytic reduction filter (SDPF) technology constitutes a critical methodology for controlling nitrogen oxide (NOx) and particulate matter emissions from light-duty diesel vehicles. A series of SDPFs with different sulfur poisoning times and concentrations were prepared using the worldwide harmonized light vehicles test cycle (WLTC). Bench testing revealed that sulfur poisoning diminished the catalyst’s NH₃ storage capacity, impaired the transient NOx reduction efficiency, and induced premature ammonia leakage. After multiple sulfur poisoning incidents, the NOx reduction performance stabilized. Higher SO₂ concentrations accelerated catalyst deactivation and hastened the attainment of this equilibrium state. The characterization results for the catalyst indicate that the catalyst accumulated the same sulfur content after tail gas poisoning with different sulfur concentrations and that sulfur existed in the form of SO₄²⁻. The sulfur species in low-sulfur-poisoning-concentration catalysts mainly included sulfur ammonia and sulfur copper species, while high-sulfur-poisoning-concentration catalysts contained a higher proportion of sulfur copper species. Neither species type significantly altered the zeolite coating’s crystalline structure. Sulfur ammonia species could easily lead to a significant decrease in the specific surface area of the catalyst, which could be decomposed at 500 °C to achieve NOx reduction performance regeneration. In contrast, sulfur copper species required higher decomposition temperatures (600 °C), achieving only partial regeneration. Full article

As public concern over environmental pollution and the urgent need for sustainable development grow, the popularity of new-energy vehicles has increased. Hybrid electric vehicles (HEVs) represent a significant segment of this movement, undergoing robust development and playing an important role in the global transition towards sustainable mobility. Among the various factors affecting the fuel economy of HEVs, energy management strategies (EMSs) are particularly critical. With continuous advancements in vehicle communication technology, vehicles are now equipped to gather real-time traffic information. In response to this evolution, this paper proposes an optimization method for the adaptive equivalent consumption minimization strategy (A-ECMS) equivalent factor that incorporates traffic information and efficient optimization algorithms. Building on this foundation, the proposed method integrates the charge depleting–charge sustaining (CD-CS) strategy to create a combined EMS that leverages traffic information. This approach employs the CD-CS strategy to facilitate vehicle operation in the absence of comprehensive global traffic information. However, when adequate global information is available, it utilizes both the CD-CS strategy and the A-ECMS for vehicle control. Simulation results indicate that this combined strategy demonstrates effective performance, achieving fuel consumption reductions of 5.85% compared with the CD-CS strategy under the China heavy-duty truck cycle, 4.69% under the real vehicle data cycle, and 3.99% under the custom driving cycle. Full article

The rapid expansion of Internet of Things (IoT) devices has heightened the demand for lightweight and secure cryptographic mechanisms suitable for resource-constrained environments. While SHA-256 remains a widely used standard, the emergence of SHA-3 particularly the KangarooTwelve variant offers potential benefits in flexibility and post-quantum resilience for lightweight resource-constrained devices. This paper presents a comparative evaluation of the energy costs associated with SHA-256 and SHA-3 hashing in Contiki 3.0, using three generationally distinct IoT platforms: Sky Mote, Z1 Mote, and Wismote. Unlike previous studies that rely on hardware acceleration or limited scope, our work conducts a uniform, software-only analysis across all motes, employing consistent radio duty cycling, ContikiMAC (a low-power Medium Access Control protocol) and isolating the cryptographic workload from network overhead. The empirical results from the Cooja simulator reveal that while SHA-3 provides advanced security features, it incurs significantly higher CPU and, in some cases, radio energy costs particularly on legacy hardware. However, modern platforms like Wismote demonstrate a more balanced trade-off, making SHA-3 viable in higher-capability deployments. These findings offer actionable guidance for designers of secure IoT systems, highlighting the practical implications of cryptographic selection in energy-sensitive environments. Full article

Due to the limitations of current battery technologies—such as lower energy density and high cost compared to fossil fuels—electric vehicles (EVs) face constraints in applications requiring extended range or heavy payloads, such as refuse trucks. As a midterm solution, hybrid electric vehicles (HEVs) combine internal combustion engines (ICEs) and electric powertrains to enable flexible energy usage, particularly in urban duty cycles characterized by frequent stopping and idling. This study introduces a model-based energy management strategy using the Equivalent Consumption Minimization Strategy (ECMS), tailored for a retrofitted series hybrid refuse truck. A conventional ISUZU NPR 10 truck was instrumented to collect real-world driving and operational data, which guided the development of a vehicle-specific ECMS controller. The proposed strategy was evaluated over five driving cycles—including both standardized and measured urban scenarios—under varying load conditions: Tare Mass (TM) and Gross Vehicle Mass (GVM). Compared with a rule-based control approach, ECMS demonstrated up to 14% improvement in driving range and significant reductions in exhaust gas emissions (CO, NO_x, and CO₂). The inclusion of auxiliary load modeling further enhances the realism of the simulation results. These findings validate ECMS as a viable strategy for optimizing fuel economy and reducing emissions in hybrid refuse truck applications. Full article

Hybrid electric vertical take-off and landing (HEVTOL) flying vehicles serve as effective platforms for efficient transportation, forming a cornerstone of the emerging low-altitude economy. However, the current lack of co-optimization methods for powertrain component sizing and energy controller design often leads to suboptimal HEVTOL performance. To address this, this paper proposes a hierarchical manifold-enhanced Bayesian evolutionary optimization (HM-BEO) approach for HEVTOL systems. This framework employs lightweight manifold dimensionality reduction to compress the decision space, enabling Bayesian optimization (BO) on low-dimensional manifolds for a global coarse search. Subsequently, the approximate Pareto solutions generated by BO are utilized as initial populations for a non-dominated sorting genetic algorithm III (NSGA-III), which performs fine-grained refinement in the original high-dimensional design space. The co-optimization aims to minimize fuel consumption, battery state-of-health (SOH) degradation, and manufacturing costs while satisfying dynamic and energy management constraints. Evaluated using representative HEVTOL duty cycles, the HM-BEO demonstrates significant improvements in optimization efficiency and solution quality compared to conventional methods. Specifically, it achieves a 5.3% improvement in fuel economy, a 7.4% mitigation in battery SOH degradation, and a 1.7% reduction in system manufacturing cost compared to standard NSGA-III-based optimization. Full article

The four-switch buck–boost (FSBB) converter, which possesses both step-up and step-down capabilities, is highly suitable for applications where input and output voltages have overlapping ranges. Correspondingly, the current shaping control (CSC) strategy is investigated for the FSBB converter, which shapes a quadrilateral inductor current waveform featuring the minimum RMS value to improve efficiency and power density. However, the small-signal model for the CSC algorithm has not yet been established, and the traditional and common modeling method requires considering multiple duty cycles and phase shifts of the FSBB converter, whose calculation is complex and inconvenient to use. For the special case of the CSC strategy using cycle-by-cycle current detection, an additional constraint of the averaged volt-second on the inductor can be regarded as zero, making the inductor current no longer a variable of the state-space, which eliminates the pole generated by the inductor and reduces the order of the small-signal model. Thus, this paper greatly simplifies the computation and design of the compensator by using the constraint condition mentioned above. This one-pole first-order model is simplified, maintains enough accuracy in the low-frequency domain, and can be corrected using only a simple PI controller. Finally, a prototype of the 300 W FSBB converter under the digital CSC algorithm was built to validate the precision and dynamic performance of the proposed first-order small-signal model. Full article

Conventional continuous pesticide application remains prevalent in agriculture, but its limitations in addressing the spatial–temporal variability of biotic stressors have led to excessive chemical inputs and inefficiency. The emergence of precision agriculture has catalyzed significant advancements in variable-rate spray systems to optimize agrochemical deployment through real-time modulation. This technology demonstrates critical advantages in minimizing the environmental footprint while maintaining crop protection efficacy. Our systematic review analyzes three foundational variable-rate spray architectures—pressure-regulated, flow rate-regulated, and pesticide concentration-regulated mechanisms—evaluating their maturity and implementation paradigms. Pressure-regulated technology relies on the pressure–flow relationship to achieve regulation, but there is a narrow range in flow regulation, atomization stability is insufficient, and there are other defects. Flow rate-regulated technology achieves precise control through the dynamic adjustment of the nozzle orifice area or Pulse-Width Modulation duty cycles, but this technology faces mechanical wear, a nonlinear flow–duty cycle relationship, and other challenges. Pesticide concentration-regulated technology is centered on real-time mixing, which can avoid the residue of chemicals but is highly dependent on fluid characteristics and mixing efficiency. This study proposes improvement paths from the perspectives of hardware optimization, control strategy integration, and material innovation. Through the summary and analysis of this paper, we hope to provide valuable references for future research on variable-rate spray technology. Full article

In order to meet the demand for high-voltage architectures of 400 V and 800 V in electric vehicle systems, high-power DC-DC converters have become a key focus of research. The Four-Switch Buck–Boost converter has gained widespread application due to its wide voltage conversion range, consistent input and output polarity, and the capability of bidirectional power transfer. This paper focuses on the energy conversion requirements in high-voltage scenarios for electric vehicles, analyzing the working principle of this converter and typical control strategies. It summarizes the issues encountered under different control strategies and presents improvements. Hard-switching multi-mode control strategies aim to improve control algorithms and logic to mitigate large duty cycle variations and voltage gain discontinuities caused by dead zones. For control strategies based on controlling the inductor current to achieve soft-switching, the discussion mainly focuses on optimizing the implementation of soft-switching, reducing overall system losses, and improving the computation speed. Finally, the paper summarizes FSBB control strategies and outlines future directions, providing theoretical support for high-voltage fast charging and onboard power supplies in electric vehicles. Full article

Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by $5.19\%$ in autonomous mode and $0.534\%$ in manual mode compared to traditional fixed-gain approaches. Full article

With the growing demand for high-efficiency DC-DC converters with a wide input voltage range for wireless power transmission, four-switch boost converters (FSBBs) are attracting attention due to their low current stress and flexible mode switching characteristics. However, their complex operating modes and nonlinear dynamic characteristics lead to high switching losses and limited efficiency of the system under conventional control. In this paper, an optimization algorithm is combined with the multi-mode control of an FSBB converter for the first time, and a combined optimization and voltage closed-loop control strategy based on the Whale Migration Algorithm (WMA) is proposed. Under the four-mode operation conditions of the FSBB converter, the duty cycle and phase shift parameters of the switching devices are dynamically adjusted by optimizing the values to maximize the efficiency under different operation conditions, with the premise of achieving zero-voltage switching (ZVS) and the optimization objective of minimizing the inductor current as much as possible. Simulation results show that the proposed FSBB switching control strategy combined with the WMA algorithm improves the efficiency significantly over a wide voltage range (120–480 V) and under variable load conditions, and the transfer efficiency is improved by about 1.19% compared with that of the traditional three-mode control, and the maximum transfer efficiency is 99.34%, which verifies the validity and feasibility of the proposed strategy and provides a new approach to the high-efficiency control and application of FSBB converters. Full article

Electromagnetic interference (EMI) remains a difficult task in the design and operation of contemporary power electronic systems, especially in those applications where signal quality has a direct impact on the overall performance and efficiency. Conventional control schemes that have evolved to counteract the effects of EMI generally tend to have greater design complexity, greater error rates, poor control accuracy, and large amounts of harmonic distortion. In order to overcome these constraints, this paper introduces an intelligent and advanced control approach founded on the signal randomization principle. The suggested approach controls the switching activity of a DC–DC converter by dynamically tuned parameters like duty cycle, switching frequency, and signal modulation. A boost interleaved topology is utilized to maximize the current distribution and minimize ripple, and an innovative space vector-dithered sigma delta modulation (SV-DiSDM) scheme is proposed for cancelling harmonics via a digitalized control action. The used modulation scheme can effectively distribute the harmonic energy across a larger range of frequencies to largely eliminate EMI and boost the stability of the system. High-performance analysis is conducted by employing significant measures like total harmonic distortion (THD), switching frequency deviation, switching loss, and distortion product. Verification against conventional control models confirms the increased efficiency, less EMI, and greater signal integrity of the proposed method, and hence, it can be a viable alternative for EMI-aware power electronics applications. Full article

This paper proposes a digital predictive peak current control (PPCC) strategy for superbuck converters. The proposed strategy incorporates a current predictor to calculate the output current and a peak current controller to calculate the required duty ratio for the next switching cycle. The duty ratio is precalculated ahead of a switching cycle, which creates a switching cycle for signal samplings and digital calculations. At the end of the next switching cycle, the output current peak value is regulated to match the reference value. The proposed strategy regulates the output current peak value to the reference value within two switching cycles. This increases the current loop bandwidth to π/T rad/s, which optimizes the transient performance. Moreover, a new damping parameter design method based on the damping ratio is given. Furthermore, a simplified version is proposed to facilitate digital realization. This version directly calculates the required duty ratio, which significantly reduces digital calculations. Finally, the experimental results demonstrate the effectiveness of the proposed control strategy in improving the transient performance of the superbuck converter. Full article

The power output of a photovoltaic (PV) system is inherently dependent on climatic factors. To maximize the energy harvested from PV arrays, maximum power point tracking (MPPT) algorithms are employed. These algorithms dynamically adjust the operating point of the system to extract the maximum available power. However, under partial shading conditions (PSCs), conventional MPPT algorithms often fail to locate the global maximum power point, leading to suboptimal power extraction. In this study, a robust MPPT technique based on sliding mode control (SMC) is proposed to enhance tracking efficiency and optimize power extraction from PV arrays under PSC. Particle swarm optimization (PSO) is incorporated into the MPPT framework, enabling the dynamic tuning of SMC parameters for improved adaptability and performance. The proposed SMC structure is designed to regulate the duty cycle of a boost converter, ensuring effective power conversion. The system is simulated in Matlab/Simulink for various PSCs. The simulation results demonstrate that the PSO-tuned SMC methodology exhibits superior tracking performance, enabling the PV system to rapidly and accurately converge to the true MPP under varying weather and shading scenarios. The findings indicate that the proposed technique enhances the efficiency and reliability of PV energy harvesting in PSCs. Full article