International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2025
p-ISSN: 2395-0072
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ELECTRIC VEHICLE BATTERY MANAGEMENT SYSTEM WITH CHARGE MONITORING AND FIRE PROTECTION Shobha Rani Konduru, Dr. M. T. Naik M. Tech Scholar, Centre for Energy Studies, JNTUH University College of Engineering, Science and Technology, Hyderabad-500085, Telangana Professor & Co-Ordinator, Centre for Energy Studies, JNTUH University College of Engineering, Science and Technology, Hyderabad-500085, Telangana ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - This paper presents a hybrid Battery Management System (BMS) designed to enhance monitoring, accuracy, and safety in electric vehicle (EV) applications. The system integrates Coulomb Counting with an Adaptive Kalman Filter (AKF) to achieve a precise State of Charge (SOC) estimation and minimize cumulative drift errors. For State of Health (SOH) monitoring, a combined cycle-counting and degradation-based model was employed to capture long-term capacity fade and resistance growth trends. A thermal and pressure monitoring subsystem is incorporated to analyze the I ²-based heat generation, temperature variations, and safety responses during charge–discharge cycles. The BMS model was implemented in MATLAB/Simulink using the parameters from the A123 ALM12V7 LiFePO4 battery. The simulation results demonstrate that the hybrid method reduces the SOC estimation error to below 0.5%, achieves smooth SOH tracking, and ensures stable thermal behavior under dynamic load conditions. Comparative analysis with conventional methods confirmed their superior accuracy, robustness, and real-time adaptability. The proposed hybrid BMS framework enhances the battery reliability, efficiency, and lifespan, providing a scalable solution for next-generation electric mobility systems.
Key Words: Battery Management System (BMS), State of Charge (SOC), State of Health (SOH), Adaptive Kalman Filter (AKF), Coulomb Counting, Thermal Modelling, A123 ALM12V7, Electric Vehicle (EV), MATLAB/Simulink)
1.INTRODUCTION The increasing adoption of electric vehicles (EVs) has intensified the need for reliable energy storage systems that ensure safety, efficiency, and extended battery life. Lithium-ion batteries are widely used because of their high energy density and long cycle life; however, their performance strongly depends on the accurate estimation of the State of Charge (SOC) and State of Health (SOH). Conventional methods, such as Coulomb counting, are simple but prone to drift, while advanced filters, such as the Kalman Filter, improve accuracy under dynamic conditions. Furthermore, battery degradation is highly temperature sensitive, making thermal modelling essential for realistic monitoring. To address these challenges, this study proposes a hybrid Battery Management System (BMS) that integrates Coulomb counting, Adaptive Kalman Filtering, and thermal analysis to improve the reliability of EV applications.
2.LITERATURE SURVEY Recent research on Battery Management Systems (BMS) has emphasized the importance of accurate SOC and SOH estimation under dynamic conditions. Traditional Coulomb counting suffers from drift, whereas Extended and Adaptive Kalman Filters offer improved accuracy by fusing sensor data with model predictions. Studies have also highlighted the role of thermal effects, where high temperatures accelerate capacity fade and internal resistance growth. Data-driven and machine learning approaches have emerged for SOH prediction, although they require large datasets. Overall, integrating electrical-, thermal-, and usage-based models provides a more comprehensive solution for reliable battery health monitoring in electric vehicles.
3.MATERIALS AND METHODOLOGY This project modelled the A123 ALM12V7 LiFePO4 battery using an equivalent electrical circuit in MATLAB/Simulink. The proposed methodology integrates Coulomb Counting with an Adaptive Kalman Filter (AKF) for accurate SOC estimation, whereas SOH is monitored using cycle counting and capacity degradation models. A thermal subsystem analyses the temperature effects and ensures safety during charge-discharge cycles. The system employs the I²R heat generation and thermal dynamics for temperature prediction. Comparative simulations with base paper data validated the performance through error metrics, such as MAE, RMSE, and percentage deviation. The integrated framework enhances the estimation accuracy, safety, and reliability for electric vehicle battery management.
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