Advanced Algorithms in Battery Management Systems for Electric Vehicles: A Comprehensive Review
Anith Khairunnisa Ghazali, Nor Azlina Ab. Aziz, Mohd Khair Hassan
Abstract
Electric vehicles and hybrid electric vehicles (EV) are increasingly common on roads today compared to a decade ago, driven by advancements in technology and a growing focus on sustainable transportation. These vehicles are powered by rechargeable lithium-ion batteries. A battery management system (BMS) is indispensable for ensuring the optimal performance, safety, and longevity of the EV’s batteries. In this review, the latest algorithm trends for BMS software are discussed. This work also focuses on several key functionalities of BMS like the state of charge (SOC) estimation, state of health (SOH) monitoring, state of energy (SOE), and state of power (SOP). Advanced algorithms for BMS are comprehensively reviewed, including those designed for specific functionalities, as well as those developed based on existing optimization, artificial intelligence, and estimation algorithms. These algorithms address critical challenges such as maintaining symmetry during charging and discharging, preventing thermal runaway, and managing battery faults in EV systems. This work provides valuable insights for researchers and practitioners in the field of EV design and development, particularly those focusing on the advancement of BMS technologies.