Abstract
This paper presents a comparative analysis of various battery management systems (BMS) in electric vehicles, with a focus on incorporating machine learning techniques to improve battery safety and extend battery life. The study evaluates conventional BMS against machine learning-enhanced models in predicting thermal runaway, state of charge (SOC), and state of health (SOH) under diverse operating conditions. Results indicate that machine learning algorithms outperform conventional methods, providing more accurate SOC and SOH estimations, thus enhancing vehicle safety and longevity.
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