Advanced SOH and SOC Prediction Models for Lithium-Ion Batteries: Integrating Machine Learning with Battery Management Systems

This paper focuses on the development and validation of predictive models for battery management systems, specifically targeting State of Health (SOH) and State of Charge (SOC) estimation, as well as the design of a comprehensive Battery Management System (BMS). The study begins by establishing and evaluating SOH prediction models, employing both linear regression and Long Short-Term Memory (LSTM) algorithms. Comparative analysis is conducted to assess the prediction accuracy between Recurrent Neural Networks (RNN) and LSTM, highlighting the superior performance of the LSTM algorithm in forecasting battery health. The second part of the paper addresses SOC estimation, outlining common methods and introducing an Extended Kalman Filter (EKF) algorithm for real-time SOC prediction. The EKF model is constructed through three primary stages: the establishment of the observed signal section, the ECU section, and the algorithmic structure itself. Rigorous validation confirms the EKF model’s effectiveness in providing accurate SOC predictions. Lastly, the paper delves into the design and validation of a robust BMS model. Key components such as the high- and low-limit trigger signal module and SOC subscription module are integrated into the BMS framework. Validation results demonstrate that the proposed BMS model can efficiently monitor and manage battery performance, ensuring reliability and safety. The paper concludes with a discussion of future work aimed at enhancing the predictive capabilities and real-time applications of these models in battery management systems.