Abstract

The advancement towards electrified transportation calls for the critical need for advanced Battery Management Systems (BMS) that can reliably enhance the performance, safety, and increase the efficiency and lifespan of lithium-ion battery used in electric vehicles (EVs). This paper introduces a comprehensive BMS framework that seamlessly integrates intelligent thermal management with data-driven battery life optimization techniques. Conventional BMS architectures treat thermal regulation and lifecycle extension as separate entities, the proposed system employs a holistic approach continuously monitoring, predicting, and controlling battery health, charge, and thermal conditions in real time. The core focus of this research lies in the development of an adaptive thermal control mechanism, that dynamically adjusts cooling and heating strategies based on operating conditions, ambient temperature, and repetitive usage patterns. Simultaneously, the system utilizes machine learning algorithms to optimize charge and discharge cycles, minimize degradation rates, and extend usable battery life without compromising vehicle performance. Aligning thermal control and lifecycle optimization under a unified intelligent platform, this work provides a modular and energy-efficient solution for next-generation EVs. The proposed system not only addresses immediate performance and safety concerns but also paves the way for future advancements in smart, connected energy systems within the transportation sector.