Federated Learning-Based State of Charge Estimation in Electric Vehicles Using Federated Adaptive Client Momentum

Batteries are essential for Electric Vehicles (EVs). Traditional Battery Management System (BMS) algorithms can be inadequate for State of Charge (SoC) estimation due to incorrect measurements and unobservable battery characteristics. Centralized machine learning methods are used to improve SoC estimation. Both privacy and high bandwidth requirements are the main disadvantages during the implementation. Federated Learning (FL) solves these issues by performing local learning on devices, protecting data privacy, and only aggregating model updates at the central server. While FL approaches can help preserve data privacy during model training, collaborative learning can facilitate the integration of priori data learned by the agents in the fleet with the rest of the fleet members to improve charge prediction. This study proposes a new aggregation rule named Federated Adaptive Client Momentum (FedACM) to handle data imbalance and heterogeneity in SoC estimation. The proposed method is initially validated via experimental results using the collected data from Musoshi L5 type EV. It is also tested using publicly known datasets such as NASA, BMW i3, and Standford University Battery Datasets. These experiments show that the proposed aggregation rule performs better than current state-of-the-art rules for FL.