Battery Management System (BMS) with State of Charge (SOC) and State of Health (SOH) Estimation for Electric Vehicles using MATLAB/Simulink

Abstract

The rapid adoption of Electric Vehicles (EVs) has significantly increased the demand for efficient Battery Management Systems (BMS) capable of ensuring battery safety, reliability, and optimal performance. Lithium-ion batteries are widely used in EVs because of their high energy density, long cycle life, and excellent charging characteristics. However, their performance gradually degrades due to repeated charge–discharge cycles, temperature variations, high current loads, and aging effects. Accurate estimation of the State of Charge (SOC) and State of Health (SOH) is therefore essential for maximizing battery utilization, extending battery lifespan, and preventing unsafe operating conditions. This research presents the design and simulation of a BMS with SOC and SOH estimation for EVs using the MATLAB/Simulink environment. The proposed BMS model continuously monitors battery voltage, current, and temperature to estimate SOC using the Coulomb Counting method, while SOH is determined by evaluating the available battery capacity relative to its rated capacity. The MATLAB/Simulink model incorporates battery monitoring, charge and discharge control, battery protection, and performance analysis under different operating conditions, including constant load, variable load, charging, and discharging cycles. The simulation results demonstrate that the proposed system accurately tracks battery behavior and provides reliable SOC and SOH estimation throughout the battery operating range. The estimated values closely follow the actual battery conditions, enabling effective energy management and reducing the risk of overcharging, over-discharging, and thermal stress. The developed BMS improves battery safety, operational efficiency, and overall electric vehicle performance by providing real-time battery condition monitoring and intelligent energy management. Furthermore, the proposed model offers a flexible simulation platform for evaluating battery performance without extensive experimental testing, making it suitable for research, academic studies, and industrial development. The presented methodology can be extended by integrating advanced estimation algorithms, artificial intelligence, machine learning techniques, Internet of Things (IoT)-based remote monitoring, and cloud computing for next-generation intelligent battery management systems. Overall, the proposed BMS provides an effective and reliable solution for enhancing the performance, durability, and sustainability of lithium-ion batteries used in modern electric vehicles.