Advanced Battery Management Algorithms for Enhanced Safety and Lifespan in Electric Vehicles

Abstract

Battery systems that provide high energy density and performance, unwavering safety, and an extended operating lifetime are required due to the growing popularity of electric vehicles (EVs).  Despite their efficiency, lithium-ion batteries are vulnerable to safety hazards and degradation processes brought on by deep draining, overcharging, temperature imbalances, and uneven cell ageing.  To overcome these difficulties, this work explores sophisticated battery management algorithms that use improved monitoring, prediction, and control techniques.  The suggested methods allow proactive defect identification, adaptive charging profiles, intelligent thermal control, and precise state-of-charge (SOC) and state-of-health (SOH) estimates by using model-based estimation, machine learning techniques, and real-time optimization.  Results from simulations and experimental validation show that these algorithms can substantially decrease capacity fading, increase energy efficiency, and lessen safety risks in a variety of operating scenarios.  The results demonstrate how important algorithm-driven Battery Management Systems (BMS) are to guarantee the lifespan, safety, and dependability of EV battery packs and hasten the shift to environmentally friendly transportation.