Learning-based Characterization of Noise Statistics for SoC Estimation via Kalman Filtering

In the field of Battery Management Systems (BMS), the State of Charge (SoC) is a crucial metric that represents the available energy capacity and directly affects the operational strategy. Accurately determining SoC is inherently complex due to the electrochemical characteristics that exhibit non-linear responses under various operational conditions. BMS operates model-based on an Extended Kalman Filter (EKF). In contrast, the employed Deep Deterministic Policy Gradient (DDPG) algorithm represents a model-free reinforcement learning methodology. This study aims to investigate the learning-based characterization of the process and measurement noise statistics for state of charge estimation via Kalman filtering. The method iteratively updates a value-function based on a reward mechanism, facilitating the selection of actions that minimize estimation error without a model of the environment. The model proposed in this study enhances EKF, a method known for its robustness in tracking SoC, by incorporating a RL paradigm. This paradigm is tailored to optimize parameter estimation despite sparse datasets. The adaptive mechanism is governed by a reward function that is based on minimizing SoC estimation error. This represents a judicious calibration between model-based and data-driven estimation techniques. The integration of DDPG improves our model's adaptability to SoC dynamics, promising enhanced estimation accuracy and improved reliability and efficiency in BMS across diverse applications.