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26 May 2025

Intelligent Battery Management in a Hybrid Photovoltaic Using Fuzzy Logic System

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1
Department of Electronics and Computer Engineering, De La Salle University (DLSU), 2401 Taft Avenue, Malate, Manila 1004, Philippines
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Department of Mechanical Engineering, De La Salle University (DLSU), 2401 Taft Avenue, Malate, Manila 1004, Philippines
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Department of Manufacturing Engineering and Management, De La Salle University (DLSU), 2401 Taft Avenue, Malate, Manila 1004, Philippines
4
Faculty of Human and Digital Sciences, Liverpool Hope University, Hope Park Campus, Taggart Avenue, Liverpool L169JD, UK
Technologies2025, 13(6), 214;https://doi.org/10.3390/technologies13060214
This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration
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Abstract

LiFePO4 batteries need a battery management system (BMS) to improve performance, extend their lifespan, and maintain safety by utilizing advanced monitoring, control, and optimization techniques. This paper presents the design, development, and implementation of an intelligent battery management system (i-BMS) that integrates the real-time monitoring and control of batteries. The system was extensively tested using multiple datasets, and the results show that the system was able to maintain battery temperature within the set range, balance the cell voltages, and distribute energy according to load prioritization. It uses a fuzzy logic system approach to effectively manage farm energy requirements. Additionally, the proposed method embedded a three-level load prioritization algorithm woven into the fuzzy rule set to allocate energy dynamically among essential, regular, and non-essential loads.

1. Introduction

Driven by the imperative to mitigate climate change, there is an increasingly global shift to renewable energy sources, a movement inspired by the United Nations’ 2030 Sustainable Development Goal to provide clean, green, sustainable, and affordable energy for all [1]. Nevertheless, despite their abundance and cost-effectiveness, renewable resources remain inherently weather-dependent and intermittent [2]. This variability underscores the critical need for efficient energy storage to maintain a consistent power supply [3]. According to Statista 2025, it is expected that by year 2031, batteries in the solar energy systems will have the second largest market share, amounting to 154.31 billion US dollars (refer to Figure 1). Among the various battery technologies available, lithium iron phosphate (LiFePO4) has emerged as the preferred choice, due to its superior performance and enhanced safety [4]. Today, this rising demand for LiFePO4 batteries is widely observed both in the automative and renewable energy industries [5].
Figure 1. Global market value of solar energy systems, 2023–2031, by component.
A LiFePO4 battery has high power density, longer life span, better thermal stability, and low self-discharge rate, and is environmentally friendly, robust, and reliable, making it suitable for solar power systems. Although it offers numerous advantages, it also has some drawbacks, such as low cell voltage, typically 3.2 V, and a relatively flat discharge voltage [6]. The low cell voltage implies more batteries connected in a series to meet the 12 V, 24 V, 36 V, or more system design. Thus, a battery management system (BMS) is needed.
In any energy storage system, BMS is used for energy optimization, grid stabilization, peak shaving, and safety [7]. Primarily, the functionalities include (1) measurement and control; (2) charge and discharge control; (3) thermal control; (4) communications; (5) diagnostics, data logging, and authentication; and (6) protection [8]. It has also evolved from no management to advanced management [9]:
  • Low Management—This management level primarily involves the manual measurement and periodic monitoring of battery parameters, typically suited for single-cell lead–acid batteries. It necessitates regular manual maintenance to ensure proper operation and safety.
  • Basic Management—Provides the real-time online monitoring of essential external battery parameters, offering protection against overcharging and over-discharging scenarios. This level is suitable for managing small sets of batteries, ensuring improved reliability and lifespan through basic protective functionalities.
  • Enhanced Management—Explicitly designed for onboard battery systems in electric vehicles (EVs), this advanced management level encompasses comprehensive battery modeling, precise state estimation, sophisticated fault diagnosis, and optimized control strategies. It is tailored to handle battery modules, significantly enhancing performance, safety, and overall system efficiency.
The continued development of battery technology has led to intelligent battery management systems. This emerging technology incorporates artificial intelligence, big data, IoT, wireless sensing network (WSN), cloud computing, and predictive models [10]. Hence, this convergence of digital technologies led to the development of this study.
The primary objective of this study was to design and develop an intelligent battery management system for hybrid photovoltaic (PV) technology, using computational intelligence (CI) techniques to optimize energy distribution and utilization. Specifically, the research aimed to
  • Develop a comprehensive monitoring system that tracks and analyzes energy generation, distribution, and priority-based load management in real time;
  • Design and implement a real-time intelligent battery management framework using advanced sensors integrated with computational intelligence algorithms to enhance energy distribution efficiency and optimize utilization;
  • Evaluate the overall performance of the intelligent battery management system (i-BMS) by comparing the energy generated versus energy demand utilization.
This study is presented as follows: Section 2 discusses the related studies and works on battery management systems; Section 3 covers the system overview and fuzzy logic application; Section 4 includes MATLAB R2024b simulation; Section 5 shows the results and discussion; and, lastly, Section 6 presents the conclusion and recommendations.

3. Materials and Method

3.1. Dataset

The dataset comprises high-resolution operational records from the i-BMS battery voltage and temperature monitoring platform installed on a hybrid PV smart farm testbed. Continuous measurements were logged once per minute over 11 consecutive days—from 19 September 2024 to 29 September 2024—yielding approximately 4700 time-stamped entries.
At each time stamp, the following variables were captured:
  • Battery Pack Health
1. Individual cell voltages (V1–V16) of the 16 series connected LiFePO4 cells, expressed in volts (V);
2. Individual cell surface temperatures (T1–T16) of the same cells, expressed in degrees Celsius (°C).
  • Load Category Power Metrics
Essential, regular, and non-essential loads consist of an aggregate line current (Iₑ), line voltage (Vₑ), and instantaneous active power (Pₑ).
In summary, each data point stores 16 cell voltages, 16 cell temperatures, and 9 load parameters, a total of 41 features per minute. The resulting time series dataset supports detailed analyses such as cell-level balancing, thermal management, and prioritized demand-side energy optimization across all load types.

3.2. System Calibration

Calibration of the voltage sensor follows this regression equation:
Vcalibrated = 0.3402Vsensor_raw + 1.7876, (volts)
The model summary has a standard error of the regression (S) of 0.0480807, an R-squared (R2) of 86.32%, an R-squared adjusted of 85.99, and an R-squared predicted of 83.17%. This S value is within the accuracy resolution of the voltage sensor used in this study. The high R2 value means an excellent result for sensor calibration, where some noise is unavoidable. The predicted R-squared confirms that the calibration should work on readings, not in the training set.
Calibration of the temperature sensor follows this regression equation:
Tcalibrated = 1.008Tsensor_raw − 0.2405, (Celsius)
With a regression standard error of 0.361 °C and an R2 of 99.69% (adjusted R2 = 99.67%), the calibration model captures virtually all the variation in the reference readings. In practical terms, each calibrated temperature measurement deviates from the reference thermometer by only about ±0.36 °C on average, confirming that the sensors are highly accurate across the tested range.

3.3. Site Climate and Environmental Conditions

The battery bank was housed in a roofed, well-shaded battery shed in Morong, Rizal, Philippines (14.5181° N, 121.2390° E). The ambient conditions during the test phase (19–29 September 2024) data were extracted from gridded surface meteorology taken from NASA POWER v 2.4.14. Averaged over 11 days, the site experienced the following:
  • A mean daily maximum air temperature (T2M_MAX) of 29.6 °C;
  • A mean daily minimum temperature (T2M_MIN) of 24.7 °C;
  • An average 2 m relative humidity of 88.5%.
These figures indicate an optimal operating temperature and a highly humid environment that remained well below the LiFePO4 manufacturer’s critical thermal threshold, thereby ensuring that the observed battery behavior primarily reflects the BMS control logic rather than external heat stress [44].

3.4. System Architecture Overview of i-BMS

A conventional hybrid-PV installation typically includes PV modules, a solar charge controller, a battery bank, and an inverter. The proposed system upgrades this architecture by embedding the i-BMS driven by a fuzzy logic controller that supervises and optimizes the energy flow between the batteries, the utility grid, and the loads. This smart farm integrates several subsystems, including irrigation, lighting, computer vision, CCTV surveillance, cooling systems, aquaponics, and energy management. These units draw their primary power from a PV array with battery storage, while the utility grid automatically covers any remaining demand. The authors developed the i-BMS, which was deployed in a smart farm, in this study.
Figure 5 depicts the complete i-BMS architecture for the hybrid PV system, comprising four key control units—the solar management control unit (SMCU), battery management control unit (BMCU), load management control unit (LMCU), and the fuzzy logic engine—as well as load prioritization logic and a web-based monitoring dashboard. The smart farm’s primary power source is the SMCU, which comprises a PV array, charge controller, and inverter. The BMCU, consisting of the battery bank, voltage, current, and temperature sensors, ensures that cell operation remains within safe limits. The LMCU allocates energy to loads based on their assigned priority levels. The fuzzy logic controller is at the heart of the system, processing inputs from all units to regulate charge, discharge, and load switching commands in real time. Finally, every metric, including cell temperatures and voltages, pack currents, and load usage by load category, is streamed live to a web interface for remote visualization and analysis.
Figure 5. The overall architecture of the i-BMS for the hybrid PV system.
To make real-time decisions, the i-BMS continuously acquires the variables listed in Table 1.
Table 1. Data acquired by the fuzzy logic engine.
Based on those inputs, the fuzzy logic controller issues four high-level commands, which are as follows:
  • PV coupling—Connect or isolate the PV array from the charge controller;
  • Battery dispatch—Engage or disconnect batteries for charging or discharging;
  • Load prioritization—Energize or defer loads according to their priority class;
  • Grid interchange—Transfer the bus to or from the utility grid via an automatic transfer switch (ATS).
The i-BMS subsystem consists of the central processing unit (CPU), BMCU, SMCU, LMCU, and ATSCU. Table 2 summarizes the function of each subsystem.
Table 2. i-BMS subsystem architecture.
Together, these coordinated modules allow for the i-BMS to allocate solar energy efficiently, maintain battery health, guarantee uninterrupted power for essential loads, and call on the grid only when necessary, all without manual intervention.

3.5. Application of Fuzzy Logic in i-BMS

The i-BMS primarily aims to track the battery’s condition and allocate power to the smart farm based on predetermined priority levels. The system automatically transitions to utility grid power if the battery lacks sufficient energy to maintain continuous farm load operations. Figure 6 illustrates the proposed i-BMS that utilizes fuzzy logic to optimize battery operations. The system processes multiple input parameters through an intelligent decision-making model to regulate charging and discharging.
Figure 6. (a) i-BMS using fuzzy logic. (b) Fuzzy inference system of i-BMS.
Input parameters include SOC expressed as a percentage, cell voltage (Vcell), and cell temperature (Tcell). Individual battery voltage should be balanced regularly to extend the battery lifespan [45], whereas cell temperature monitoring is essential to avoid thermal runaway [46,47].
The i-BMS controls battery functions based on its fuzzy logic-based decisions, such as charging, discharging, and utility grid connection. Charging will be initiated when SOC is low, and temperature conditions are safe. Discharging will be initiated when load demand is high and SOC is sufficient to supply power to smart farm loads. Lastly, the system can intelligently decide whether to store or distribute energy based on demand and available resources.
Table 3 shows the input linguistic variables and their respective conditions for an i-BMS using fuzzy logic. The SOC is classified into low (0–30%), moderate (20–80%), and high (70–100%). Vcell is one of the indicators of battery health status. This system is subcategorized into low (0–2.5 V), moderate (2.3–3.5 V), and high (3.2–3.6 V). Tcell is categorized as low (0–20 °C), moderate (15–45 °C), and high (40–60 °C). This variable helps the i-BMS regulate energy distribution efficiently based on consumption patterns.
Table 3. Input linguistic variables of i-BMS.
Figure 7 displays the plots of the three fuzzy input variables—SOC, Vcell, and Tcell —categorized as low, moderate, and high using trapezoidal and triangular membership functions.
Figure 7. Input membership function (a) SOC, (b) Vcell, and (c) Tcell.
SOC is divided into three conditions:
  • Low, where batteries are almost depleted;
  • Moderate, where the battery is partially charged;
  • High, indicating a battery close to full capacity.
The Vcell has also three linguistic levels:
  • Low, which corresponds to an undervoltage region where the battery risks deep discharge;
  • Moderate refers to the typical operational range; and
  • High is the near or at maximum charge voltage, signifying a fully charged cell.
Tcell is subcategorized as follows:
  • Low is the level where battery performance may be reduced but not dangerously high;
  • Moderate pertains to the optimal band for regular operation;
  • High temperatures are approaching the upper safety threshold, potentially risking battery degradation.
Table 4 presents the output linguistic values used in the i-BMS, which utilize fuzzy logic to determine appropriate charging or discharging actions based on SOC, Vcell, and Tcell, presented as follows:
Table 4. Output linguistic variables of i-BMS.
Figure 8 depicts the output-side membership function diagram used by the fuzzy controller to classify the i-BMS charging regime. Three triangular membership functions define the linguistic states: charge (C), idle (ID), and discharge (D). When the cell temperature rises above its safe limit, the controller forces the system into the idle state, disconnects the battery, and draws power from the utility grid to keep the farm running. Once the temperature falls back to the low or moderate bands, the battery is returned online, and the controller selects either charging or discharging according to the real-time SOC, Vcell, and Tcell.
Figure 8. Output membership function plot: (a) battery charging rate and (b) load prioritization.
Load allocation decisions are made simultaneously through three priority levels—essential (E), essential + regular (E + R), and all loads—ensuring that limited energy is routed first to critical services. At every control step, the fuzzy inference engine evaluates the three inputs, assigns graded degrees of membership to each of the three charging states, and defuzzifies the result into a single actionable command for the battery system.
Table 5 presents a fuzzy rule-based system for determining the i-BMS’s battery status. The decision is based on three input conditions: SOC, Vcell, and Tcell. These inputs are processed using IF-THEN rules to determine the appropriate battery status and load prioritization. For instance, if SOC,Vcell, and Tcell are low, the system will only direct the battery to the charging state and supply energy to essential loads.
Table 5. Fuzzy logic rules of i-BMS.

3.6. The i-BMS Electrical Circuit Design

In Figure 9, a 220 VAC-to-5 VDC switch mode supply powers the ESP32 via its Vin pin and feeds two 16-channel analog multiplexers that sit on the same 5VDC supply and ground. Four shared GPIO lines (D14, D26, D25, D32) provide the S3–S0 address bits, while GPIO 17 independently enables or disables each multiplexer so that only one connects to the signal bus at a time. The active multiplexer routes the selected cell tap (C0–C15) to the ESP32′s ADC input on GPIO 34, allowing a single ADC channel to scan up to 16 cell voltages in sequence. Detailed pin assignments for the voltage sensor, including relay connections, voltage and temperature sensor connections, and the current sensor interface, are shown in Figure 10, Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15.
Figure 9. ESP32 −based cell voltage and temperature acquisition front-end circuit connection.
Figure 10. Voltage sensor–relay circuit connection.
Figure 11. Voltage sensors circuit connection.
Figure 12. Temperature sensors circuit connections.
Figure 13. (a) Battery balancer A circuit connection. (b) Battery balancer B circuit connection.
Figure 14. (a) Input current monitoring circuit. (b) Output current monitoring circuit.
Figure 15. LMCU circuit connection.

3.7. On-Site Deployment of the Hybrid PV Systems with the i-BMS

Figure 16 illustrates the overall system setup. The experiment platform operates on a hybrid PV system, where the utility grid provides backup energy. The system consists of several key components:
Figure 16. Actual deployment of the hybrid PV system.
  • Solar charge controller (top right)—Regulates the voltage and current coming from the solar panels to prevent overcharging [48];
  • Automatic transfer switch (middle section)—Controls the transfer of energy sources between solar and utility grid [49];
  • Inverter (center, blue box)—Converts the stored DC power from the batteries into ac [50];
  • Battery bank (Refer to Figure 17)—Stores the energy collected from the solar panels for later use, ensuring power availability even when sunlight is not present [51].
    Figure 17. Actual deployment of the i-BMS.
Figure 17 presents the on-site deployment of the i-BMS, showcasing the LMCU, the cell balancing module, the i-BMS control board, and the associated battery banks.
Sixteen pieces of LiFePO4 batteries were deployed on-site in an eight–series–two-parallel (8S-2P) configuration (refer to Figure 18a), with a 24-V, 404 Ah capacity system design. Sixteen voltage and temperature sensors were used to measure and monitor individual cells. Two current sensors were deployed at the load input and output terminals. The cell balancing module monitors and regulates the voltage of each cell to ensure the batteries remain balanced [52]. This i-BMS uses an active balancing circuit in Figure 18b.
Figure 18. Actual deployment of i-BMS: (a) 8s-2P LiFePO4 configuration; (b) cell balancing system; (c) LMCU.
Table 6 presents the summary of the battery specification used in this study.
Table 6. LiFePO4 battery specification.
The LMCU (refer to Figure 18c) prioritizes whether the farm’s load demands are essential, regular, or non-essential. The system responds based on the priority level. It also validates the state of the batteries before supplying the energy demand.
The load prioritization categories are as follows:
  • Essential loads—These loads are mission-critical and require uninterrupted power supply due to their operational requirements. They include life support systems for aquaculture tanks and security systems such as CCTV. They are last to shed, first to restore.
  • Regular loads—These are operationally necessary loads, and short outages are tolerable, leaving no severe damage or impact on system operations. Loads include irrigation pumps, data logging servers, and IoT gateways.
  • Non-essential loads—These loads are usually not vital to smart farm operations and thus pertain to convenience or discretionary loads. They may operate only when surplus energy exists. This may include electric vehicles, decorative lighting, or household loads.
Furthermore, Table 7 summarizes the sensors and control modules utilized in this study, along with their operating range, resolution, and accuracy.
Table 7. Sensors, power electronics, and control modules utilized in the i-BMS.

3.8. Real-Time i-BMS Monitoring System

Figure 19 shows the mobile dashboard of the t-BMS, which is used for monitoring the battery status. In contrast, Figure 20, when viewed on a laptop or desktop, displays a navigation panel on the left side that lists various monitoring and management functions, including battery monitoring, load monitoring, and others. Within the main window, there are two bar charts: (1) battery voltage monitoring, which displays the 16 individual voltages in bars, and (2) battery temperature monitoring, a bar chart that shows temperature readings for the same set of batteries.
Figure 19. Mobile monitoring of the i-BMS.
Figure 20. Web monitoring of the i-BMS.
This dashboard provides a comprehensive view of key battery parameters across multiple cells, enabling a quick assessment of the health and status of each battery in the system.

3.9. i-BMS Data Transmission

The i-BMS consists of three Wi-Fi nodes: an ESP32 for multiplexed cell voltage and DS18B20 temperature measurements, an ESP8266 with an ACS712 Hall sensor for pack current readings, and a second ESP8266 equipped with dual PZEM-004T meters, with a relay for AC-side monitoring and load control. Each board communicates directly with an SQL database over plain HTTP. Sensor data are sent as HTTP GET requests to designated endpoints, and control commands are retrieved by polling another URL. When a load is toggled in the web app, the change is written to the SQL table. On the next polling cycle, the ESP8266 reads the updated flag and flips its GPIO relay accordingly. This direct HTTP → SQL → HTTP loop—is implemented entirely on the free hosting account and delivers real-time telemetry and remote load control.

4. Fuzzy Logic-Based i-BMS Learning Structure

MATLAB was the primary platform for designing, simulating, and validating i-BMS configurations. A classical Mamdani-type fuzzy inference system (FIS) was implemented, following the standard workflow of fuzzification → rule evaluation → aggregation → defuzzification. As illustrated in Figure 21 (Property Editor snapshot), the controller applies the minimum (min) operator for both the AND condition and rule implication, the maximum (max) operator for the OR condition and rule aggregation, and the centroid technique for defuzzification. The FIS accepts three input variables and produces two distinct outputs, yielding a complete rule base of 27 (33) rules that comprehensively cover all possible operating states of the battery system.
Figure 21. i-BMS property editor—FIS.
Figure 22 illustrates that the i-BMS controller with SOC = 83.8%, Vcell = 2.8 V, and Tcell = 36.7 °C produces fuzzy outputs of battery status = 2.58 and load prioritization = 2.58. On the scale defined for this Mamdani FIS, a value near three corresponds to the Discharge–-High state and the All-Loads category. Hence, the i-BMS interprets these inputs as follows:
Figure 22. Rule inference—Mamdani Type-1.
  • The battery is healthy within its safe operating limits;
  • Ample stored energy is available to power the entire farm—essential, regular, and non-essential loads—without drawing from the grid.
Thus, the controller authorizes full discharge to satisfy total demand while continuously monitoring temperature and cell voltage to prevent unsafe conditions.
Figure 23 illustrates the control surface maps produced by the Mamdani Type-1 i-BMS. For each input pair plotted on the X- and Y-axes (SOC × Tcell, SOC × Vcell, and Tcell × Vcell), the Z-axis shows the following:
Figure 23. Control Surface—Mandani Type-1: (a) Tcell vs. SOC—battery status; (b) Tcell vs. SOC—load prioritization; (c) Vcell vs. SOC—battery status; (d) Vcell vs. SOC—load prioritization; (e) Vcell vs. Tcell—battery status; (f) Vcell vs. Tcell—load prioritization.
  • Battery operating mode—charge, idle, or discharge—in Figure 23a,c,e;
  • Load prioritization level—essential only, essential + regular, or all loads—in Figure 23b,d,f.
These surfaces visualize how the controller simultaneously decides the pack’s dispatch state and the farm’s load mix across every combination of state-of-charge, cell voltage, and temperature.
In Figure 23a, the blue–turquoise trough at z = 0–1 corresponds to a low-SOC region (≤25%). Here, the fuzzy controller explicitly issues a charge command—overriding cell temperature considerations—to restore the battery’s reserve. Figure 23b shows a similar trough: while the pack is being replenished, the system restricts power delivery to essential loads only, shedding all non-critical demand until the SOC rises above the recovery threshold. A moderate SOC, around 25–60%, and safe Tcell (<≈45 °C) sloping face, colored blue, where z rises toward 2, is when the pack is allowed to discharge at a moderate rate; the higher the SOC, the stronger the discharge command. Also, the same corresponding face is where the system gradually enables regular loads in addition to the essential ones. A high SOC of greater than 60% and safe Tcell, the broad yellow plateau approximately z = 2.3–2.4, occurs when the controller authorizes full-rate discharge. A similar graph (Figure 23b) also shows a yellow plateau, enabling all three load categories. Lastly, when the Tcell surpasses 45 °C, both control surfaces flatten into a distinctive teal shelf at z = 1.5. This plateau activates the FIS’s temperature override safeguard, idle state, and essential loads only. No further energy exchange resumes until the temperature falls back into the safe operating range.
Figure 23c–f confirms that the controller behaves consistently across the remaining input planes:
  • Battery status surfaces (Figure 23c: Vcell × SOC, Figure 23e: Vcell × Tcell) progress smoothly from charge through idle to discharge as the operating point moves from low-energy/low-voltage zones to high-energy thermally safe regions, provided the point lies inside the permissible envelope;
  • Load prioritization companions (Figure 23d,f) mirror this progression, sequentially enabling essential, then regular, and finally non-essential loads in lock-step with the battery’s dispatch state.
Taken together, these maps demonstrate that i-BMS applies a uniform rule set to every combination of inputs, ensuring predictable, coordinated decisions throughout the safe operating area.

5. Results and Discussion

i-BMS and traditional BMS were implemented in a real physical system. Its operation is remotely monitored in real time via a website.
Figure 24a plots the voltage of each cell at successive time stamps (colored markers) with no balancing circuitry in place. As the test progressed, the pack visibly drifted: the highest charged cells climbed to about 3.13–3.55 V, whereas the weakest sagged to roughly 2.68–2.90 V. These disparities persisted, showing that passive equalization alone could not bring the cells to a standard level. In sharp contrast, Figure 24b captures the same pack after enabling the i-BMS balancing algorithm. The out-of-tolerance cells were pulled back into line, and all voltages rapidly converged within a tight window, demonstrating effective active balancing across the stack.
Figure 24. Cell output voltage: (a) without balancer circuit; (b) with balancer circuit.
As summarized in Table 8, the i-BMS uses the voltage readings from the 16 voltage sensors to show the minimum voltage, maximum voltage, and standard deviation for each battery. This experiment used around 4700 datasets.
Table 8. Descriptive statistics of individual cell voltages for a 16-cell LiFePO4 battery pack.
The following are the findings about the performance of the cell balancing circuit:
  • There is a sufficient dataset for the experiment.
  • The mean for all the batteries is within the safe region. However, the minimum cell voltage for batteries numbered 7, 9, and 14 is below the lower cut-off voltage.
  • The maximum voltage for batteries numbered 1–4, 7,9, and 13–14 goes beyond the upper threshold voltage.
  • The pack is not well balanced: the difference between the highest and lowest mean cell voltages is ~0.35 V.
  • Several cells have experienced unsafe voltage extremes (>3.65 V or <2.5 V), which can shorten life or pose safety risks.
  • Cells 9, 12, 13, and 14 stand out for their high variability; Cell 14 is the weakest, and Cell 13 is the most overcharged.
  • To restore uniformity and pack longevity, a balancing routine, stricter charge/discharge cut-offs, or replacing the outlier cells is advisable.
These findings are further confirmed by the boxplot shown in Figure 25, where there are several outliers(*) for almost all the batteries.
Figure 25. Boxplot of individual cell voltage (volts).
Table 9 shows that the cells remained within the safe thermal band: the overall mean operating temperature was 27.10 °C, with a minimum of 18.94 °C and a maximum of 31.31 °C, which is inside the −20 to 60 °C range recommended for LiFePO4 batteries. The boxplot in Figure 26 reinforces this finding, with its tight interquartile range and absence of outliers across all cells confirming consistently stable temperature behavior during the entire test.
Table 9. Descriptive statistics of individual cell temperature for a 16-cell LiFePO4 battery pack.
Figure 26. Boxplot of individual cell temperatures (Celsius).
Table 10 shows power is measured at around 53 to 54 watts for the regular load category. The essential and non-essential load categories draw the same amount of power at a particular time. The farm’s energy supply requirement was delivered based on its priority level. The three load categories show consistency and steady supply.
Table 10. Fuzzy logic rules of intelligent battery management system.
Based on Figure 27, essential loads remained relatively stable, indicating consistent demand. Regular loads showed a slight dip but stay close to the 53–54 W range over the monitored interval. Lastly, non-essential loads trended slightly downward in power usage, yet remained near the same overall level.
Figure 27. Load monitoring graphs.
The percentage chart reveals that essential loads comprised the most significant portion of total consumption, followed by regular loads at roughly a quarter and non-essential loads at 18%. Overall, all three load categories displayed minimal fluctuation in power draw, with essential loads dominating the total share.
Sensors 1 to 8 were dipped into cold water with a food thermometer attached, as shown in Figure 28, to verify that the temperature sensors were working correctly.
Figure 28. Temperature Sensors 1 to 8 dipped into cold water.
Figure 29 compares the reference food thermometer reading with the i-BMS sensor data. At around 14:36, Temperature Sensors 1–8 were deliberately immersed in cold water, producing a sharp, synchronous drop that the i-BMS captured in real time. Temperature Sensors 9–16, which remained adhered to the battery surface, recorded virtually unchanged values over the same interval. The close correspondence between the reference instrument and Sensors 1–8 during the transient and the stability of Sensors 9–16 demonstrates the i-BMS module’s accuracy and consistency throughout the test.
Figure 29. Battery temperature monitoring system output for Sensors 1 to 16.
In this research, the developed prototype accomplished the following:
  • The system successfully balanced the voltage across the batteries, ensuring uniform energy distribution and reducing the risk of overcharge or undercharge conditions.
  • This monitoring system remotely tracked battery voltage, temperature, and power distribution. By prioritizing power distribution based on predefined levels, the system maintained optimal performance under varying operational loads.
  • This intelligent control system is effective and reliable in decision-making, handling uncertainties in the sensor data, and adapting to fluctuating load operating conditions without compromising performance.
Developing this i-BMS presented several challenges, which are as follows:
  • Voltage sensors experienced frequent malfunctions, leading to replacement.
  • Loose circuit connections resulted in repetitive system failures.
  • The automatic transfer switch malfunctioned two times, resulting in replacement.
  • Battery terminals began to show signs of rust.
  • Batteries also experienced imbalances, most of the time.
  • Several batteries exhibited bloating.
  • The prepaid Wi-Fi limits the number of devices that can be connected simultaneously, hindering the whole system operation at the farm.

6. Conclusions and Recommendations

In this study, we successfully deployed the i-BMS with 16 temperature sensors, 16 voltage sensors, and two current sensors. The i-BMS balanced the voltage across the batteries to ensure uniform energy distribution, monitor battery parameters, and execute power distribution based on priority levels.
The i-BMS efficiently and effectively delivers the required energy demand of the smart farm through a fuzzy logic system for its management and control. The website lets users view the farm’s battery status, available energy, and load demands in real time.
Even though the prototype of the i-BMS exhibited significant improvements over traditional battery management approaches, there are still several areas where the system can be enhanced to optimize its performance, efficiency, and reliability fully. Future researchers may consider incorporating advanced machine learning models to improve real-time analysis to predict battery degradation. Another area of study that can be explored is developing a more robust fault detection algorithm that monitors battery health and anticipates potential safety issues.

Author Contributions

Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, J.-A.V.M.; validation, A.A.B.; validation, A.B.C.; validation, E.S.; validation, R.R.P.V.; validation, R.N.; and project administration, supervision, validation, writing—review and editing, E.P.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Department of Science and Technology-Engineering Research and Development for Technology (DOST-ERDT) and De La Salle University.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data are available upon request.

Acknowledgments

The research team gratefully acknowledges the invaluable support provided by DOST-ERDT and De La Salle University.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BMSBattery Management System
IBMSIntelligent Battery Management System
PVPhotovoltaic
SFSmart Farming

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