Abstract:
The global transition towards sustainable transportation has positioned the Electric Vehicle (EV) as a cornerstone technology. At the heart of every EV lies its battery pack, typically based on Lithium-ion chemistry owing to its high energy density and longevity. However, the performance, safety, and lifespan of these complex electrochemical systems are critically dependent on an intermediary electronic system—the Battery Management System (BMS). This paper offers a thorough analysis of the fundamental and advanced aspects of BMS technology for EVs. It delves into the core objectives of a BMS, including monitoring, protection, state estimation, thermal management, and communication. The review systematically analyzes the prevalent circuitry configurations, namely centralized, distributed, and modular, comparing their advantages, limitations, and suitability for different vehicle classes. Furthermore, the paper explores the critical algorithms that form the intelligence of the BMS, with a particular focus on State of Charge (SoC) and State of Health (SoH) estimation techniques, ranging from traditional Coulomb counting and Open-Circuit Voltage methods to advanced model-based approaches like Kalman Filters and machine learning algorithms. The synthesis of current literature indicates a clear trend towards modular, master-slave architectures for scalability and advanced, data-driven algorithms for improved accuracy and reliability.
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