Development and Simulation of SOC Estimation, Thermal Management and Health Monitoring Algorithms for Optimized Battery Management Systems

Battery Management Systems (BMS) for cars driven by batteries, as well as renewable energy storage, have been much-optimized thanks to the invention and modelling of State of Charge (SOC), thermal management, and health monitoring algorithms. Integration of the Unscented Kalman Filter (UKF) for SOC prediction, a Proportional Integral Derivative (PID) controller for temperature management, and Electrochemical Impedance Spectroscopy (EIS) for health monitoring has been demonstrated to boost battery safety, lifetime, and performance of the advised method. Real-world validation for Simulink and Hardware-in-Loop (HIL) testing confirmed its effectiveness; SOC estimate accuracy climbed to within 2%, a decrease in heat fluctuations, and better predictive diagnostics assuring battery reliability revealed its value. Better than other energy management techniques, a comparison of traditional methods revealed that State of Health (SOH) retention rose by as much as 95% after 500 cycles. This study guarantees that new battery technologies have sustainable and reasonably priced energy storage options. It, therefore, provides the foundation for the next AI-driven predictive analytics, adaptive control systems, and battery management system solutions.