Data-Driven Battery Health Prognosis Using Adaptive Brownian Motion Model

Degradation dynamics modeling and health prognosis play extremely important roles in system prognostics and health management. Wiener process-based degradation models and remaining useful life (RUL) prediction methods have the advantage of high flexibility and efficiency, with features such as Brownian motion with drift and scale parameters. They can also quantify prediction uncertainty through inverse Gaussian distribution. However, prior studies use offline-identified model parameters, which can result in difficulties in both model adaptability and health prognosis. To improve the performance of Wiener process models, this article proposes a new data-driven Brownian motion model that utilizes the adaptive extended Kalman filter (AEKF) parameter identification method. The proposed model can update model parameters online and adapt to uncertain degradation operations. This data-driven method has the flexibility and efficiency of Brownian motion models but avoids their shortcomings in model adaptability and health prognosis. The model parameters and drift parameter are online estimated based on AEKF using limited historical system measurements. The effectiveness of the proposed data-driven framework in degradation modeling and RUL prediction is evaluated through simulations and experimental results on lithium-ion battery degradation data. The results show that the proposed approach has significant accuracy and robustness for both model adaptability and RUL prediction.