Journal of Advance Electrical Engineering and Devices

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

Shruti Makwan — 2026-09-16

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.

Evolution of Battery Management Systems: From Conventional Approaches to Advanced Intelligent Techniques

Sangappa K. Rajeshwar — 2026-09-28

The rapid growth of Electric Vehicles (EVs), hybrid electric vehicles, renewable-energy storage systems, and portable electronic devices has increased the need for safe, reliable, and efficient battery management. Lithium-ion batteries are widely used because of their high energy density, power capability, and relatively long cycle life; however, their performance is strongly affected by cell imbalance, temperature, ageing, operating conditions, and measurement uncertainty. A Battery Management System (BMS) therefore performs continuous monitoring, protection, state estimation, cell balancing, thermal management, and communication. This review examines the evolution of BMS technology from conventional approaches, including Coulomb counting, open-circuit-voltage estimation, fixed threshold protection, equivalent-circuit models, and passive cell balancing, to advanced intelligent techniques. Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter methods are discussed together with fuzzy logic, neural networks, machine learning, active balancing, cloud-connected BMS, digital twins, and predictive battery management. The review indicates that conventional methods remain attractive because of their simplicity, low computational requirements, and reliability, whereas advanced methods provide better adaptability and prediction capability. Hybrid methods that combine physical battery models, measured variables, and artificial intelligence provide a promising balance between interpretability and estimation performance. Remaining challenges include computational complexity, data requirements, cybersecurity, thermal safety, and real-world validation.

Comprehensive Electrical Hazard Evaluation for Industrial Plant Safety Management

Ritesh G Upadhyay — 2026-10-07

Electrical hazards are responsible for numerous deaths, injuries, and damage to equipment in industrial plants; because of this, hazard assessment and risk management are critical for plant safety. This paper proposes a systematic procedure for electrical hazard assessment in industrial plants by using international procedural regulations, such as ISO 31000, ISO 12100, ISO 45001, and IEC 60364. The procedure takes into account common electrical installations during their whole life cycle and makes a semi-quantitative risk assessment based on the probability of occurrence and severity of the hazards. The risks are then classified and ranked by using a risk matrix, which allows enterprises to prioritize hazards and allocate resources safely. Measures to reduce risks should follow the hierarchy of controls, starting with the complete elimination and substitution of hazards, then engineering controls, administrative controls, and finally PPE in place for residual risks. Further safety controls in the form of employee training, safe operating procedures, workplace observations, inspection programs, and monitoring on a periodic basis are also factored in. The findings indicate that integrating systematic hazard identification with formal risk analysis and risk prioritization can greatly enhance electrical safety risk levels and associated cost/regulatory concerns. The risk matrix offers industrial plants a valuable and readily adoptable tool for hazard prioritization and resource allocation planning. The supplemental ISO-based and life-cycle-oriented structure established by this study can facilitate compliance with electrical safety regulations, elevate the level of safety management systems, and foster a positive safety culture. The method supplies plant safety managers with a deployable visual decision tool to facilitate a targeted hazard-focused intervention and continuous improvement to electrical safety performance.