EKF-Based Machine Learning Approach for Battery SOH Prediction in Commercial Vehicles Fleet

Estimating battery State of Health (SOH) from field data is essential to ensure successful operation and to increase the uptime of battery electric vehicles (BEVs). Most studies in the literature have proposed several methods for battery SOH estimation, relying on datasets acquired under controlled laboratory conditions. However, SOH estimation becomes significantly more challenging when dealing with real-world driving data due to the increased variability and complexity of operating conditions. In this work, CAN telematics data, sampled at 1 Hz, have been collected across approximately 16 months of operation from 10 electric commercial vehicles. During this period, a battery degradation of up to 2.5 - 3% is observed within the fleet. Firstly, a model-based framework is introduced, in which a battery second-order Equivalent Circuit Model (ECM) has been coupled with an Extended Kalman Filter (EKF) to estimate the battery SOH. The results confirmed that the EKF accurately captures the physical behavior and degradation of the battery, providing accurate SOH estimates. However, it requires accurate model parameter identification and high-frequency measurement data, leading to increased computational costs. To bridge these gaps, in this paper, the SOH estimates obtained from EKF are used to train and validate a Machine Learning (ML) model, specifically designed to operate on aggregated indicators. This approach combines the interpretability of model-based methods with the scalability and reduced data dimensionality of ML approaches. Nevertheless, it can provide accurate SOH estimates while preserving the robustness and physical consistency of model-based approaches, making it suitable for scalable and long-term fleet monitoring applications.