Abstract

The rapid commercial expansion and increasing market penetration of electric vehicles (EVs) have spurred extensive research into the engineering of battery systems. Current investigations primarily target enhancements in energy efficiency, thermal regulation, and the structural optimization of multi-material enclosures. Furthermore, the paradigm of simulation-based optimization for battery packs and Battery Management Systems (BMS) is shifting toward the integration of artificial intelligence and machine learning (AI/ML). These computational advancements aim to streamline design, manufacturing, and operational efficiencies for EVs and energy storage applications. Within the context of BMS, these technologies facilitate rigorous estimation of critical metrics, specifically State of Health (SOH), State of Charge (SOC), and State of Power (SOP). Consequently, this paper provides a comprehensive review of state-of-the-art developments in battery architecture, thermal management strategies, and the deployment of AI algorithms in EV Battery Management Systems.