Co-Estimation of SOC and SOT in Lithium-Ion Batteries Using an RLS-Based Heat Generation Model

Batteries are widely utilized in various applications, such as electric vehicles and energy storage systems, where they operate under diverse environmental conditions. High temperatures can decrease the stability of batteries and accelerate aging processes, while low temperatures reduce conductivity and diffusion rates, leading to diminished capacity and output. Therefore, maintaining a stable temperature during battery operation is critical for optimal performance and longevity. This paper focuses on analyzing battery heat generation prior to the implementation of thermal management strategies through a battery management system (BMS). To achieve this, the hybrid pulse power characterization (HPPC) test is conducted to assess the initial characteristics of the battery, including heat generation across different temperature ranges. Additionally, the recursive least squares (RLS) method is employed to inversely estimate the parameters of the battery model from voltage and current data, allowing for an accurate prediction of heat generation. Furthermore, the study integrates the state-of-charge (SOC) estimation into the analysis, enhancing the understanding of battery behavior under varying operational conditions. The designed battery heat generation model is subsequently validated by comparing the results with state-of-temperature (SOT) estimates and experimental data. This comprehensive approach not only improves the accuracy of thermal management but also contributes to more effective SOC estimation, ultimately leading to better battery performance and safety.