A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles

Abstract

Electric vehicles (EV) are gaining wide traction and popularity despite the operational range and charging time limitations. Therefore, to ensure the reliability of EVs for realizing improved customer satisfaction, it is necessary to monitor and track its battery condition. This paper introduces a novel federated & ensembled learning (FEL) algorithm for precise estimation of battery State of Health (SoH). FEL algorithm leverages real-world data from diverse stakeholders and geographical factors like traffic and weather data. A Long-Short Term Memory (LSTM) model has been implemented as a base-model for SoH estimation, continuously updating for each trip as an edge scenario using data-centric federated learning strategy. A stacked ensemble learning algorithm is employed to combine data from heterogenous data sources for retraining the base-model. The effectiveness of the proposed FEL algorithm has been evaluated using NASA battery dataset, showing significant improvement in SoH estimations with a mean average error of 3.24% after 30 iterations. Comparative analysis, including LSTM model with and without ensembled stakeholder data, reveals up to 75% accuracy improvement. The proposed model-agnostic FEL algorithm shows its effectiveness in precise SoH estimation through efficient data sharing among stakeholders and could bring significant benefits for realizing data-centric intelligent solutions for connected EVs.

Keywords

• Data-centric AI
• federated learning
• state of health (SoH)
• connected vehicles