Transfer Learning-Based Data-Fusion Model Framework for State of Health Estimation of Power Battery Packs

Abstract

Accurate State of Health (SOH) estimation is critical for battery management systems (BMS) in electric vehicles (EVs). However, the absence of a universal aging model for power batteries presents significant challenges. This study leverages the open-source battery cell data set from the University of Maryland and focuses on private battery packs to address the aging model SOH estimation. Two aging features indicative of capacity degradation are extracted from constant current charging data using incremental capacity analysis (ICA). To handle nonlinearity and feature coupling, a flexible data-driven aging model is proposed, employing dual Gaussian process regressions (GPRs) and transfer learning to enhance model efficiency and accuracy. Adaptive filtering via the Particle filter (PF) further refines the model by integrating aging features and output capacity, resulting in a closed-loop data fusion approach for precise SOH estimation. Battery pack aging experiments validate the proposed method, demonstrating that transfer learning effectively improves estimation accuracy. The proposed method achieves closed-loop SOH estimation with a mean root mean square error (RMSE) of 0.87, underscoring its reliability and precision.

Keywords

data-fusion-model / Gaussian process regression / Li-ion battery packs / particle filter / State of Health / transfer learning

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2025 The Author(s). Battery Energy published by Xijing University and John Wiley & Sons Australia, Ltd.