The advancement of electric vehicles (EVs) hinges on the efficiency, safety, and longevity of lithium-ion battery systems. This study proposes A-BMS-LCP, a novel adaptive Battery Management System (BMS) architecture designed to enhance lifecycle performance through a control-oriented strategy. Integrating state estimation algorithms with real-time control feedback, the system manages key battery parameters such as State of Charge (SoC), State of Health (SoH), and thermal regulation. The architecture leverages intelligent control layers to address challenges like cell inconsistency, overcharging, and thermal runaway. By incorporating predictive analytics and high-precision monitoring technologies, A-BMS-LCP enables dynamic response to changing operational conditions, thus extending battery life and improving system reliability. This framework also supports seamless communication with external infrastructures, including smart grid systems, enabling energy optimization through vehicle-to-grid (V2G) and vehicle-to-home (V2H) strategies. The proposed system marks a significant step toward intelligent, sustainable, and safe EV energy management.
Mehta et al. (2025) studied this question.
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