Equivalent Circuit Models (ECMs) are widely used in Battery Management Systems (BMSs) for their computational efficiency, yet they often lack precision in predicting battery internal states. In contrast, Physics-Based Models (PBMs) offer detailed insights into battery behavior but are computationally intensive. The advent of cloud computing provides a promising solution to improve battery state estimation by means of a PBM-based battery digital twin. However, tracking the variation of PBM parameters during battery life is a non-trivial problem. This work proposes an approach based on a neural network, which leverages the existing BMS capability to estimate ECM parameters online by correlating them to critical electrochemical parameters of a Pseudo-Two-Dimensional PBM. The neural network is trained and validated on a synthetic dataset generated using a Pseudo-Two-Dimensional PBM to explore various degrees of battery degradation. The achieved error on electrolyte transport properties estimation is below 1 % for 99.9 % of the tested cases. Errors on electrode solid phase diffusivity are below 1 % for 94.1 % and 97.4 % of cases for negative and positive electrodes, respectively. Similarly, errors below 5 % are achieved for 93.4 % of cases in negative electrode intercalation kinetics and 90.5 % in positive electrode intercalation kinetics. Therefore, this methodology represents a practical solution to leverage Equivalent Circuit Models and Physics-Based Models synergistically, thereby enhancing battery state estimation capabilities using cloud computing approaches.

Keywords: {Aging mechanisms; Battery management system; Electrochemical model}

File: https://www.sciencedirect.com/science/article/pii/S2352152X24028433