Accurate SOH Estimation for Second-Life Lithium-Ion Batteries Using a Prior-Informed Hierarchical Fusion Network

Accurate State-of-Health (SOH) estimation for second-life batteries is a critical challenge due to unavailable first-life historical data. This work introduces the Prior-Informed Hierarchical Fusion Network (PI-HFN), a flexible deep learning framework to address this information asymmetry. The model leverages a historical degradation vector from the battery’s first life as a prior condition to guide the interpretation of its current electrochemical state. This prior-conditioning is achieved by initializing a recurrent neural network, which processes instantaneous second-life data, with the historical information. The framework’s efficacy was validated across two comprehensive case studies. In Case Study 1 on NMC cells using multimodal EIS and DCIR inputs, the PI-HFN achieved a state-of-the-art average RMSE of 1.91%. Case Study 2 on a large-scale LFP dataset, using only DCIR inputs, further demonstrated the framework’s robustness and flexibility. Both studies confirmed through systematic ablation studies that the historical prior (BFN) and the hierarchical fusion strategy (PFN) are crucial for high accuracy, with the historical prior proving to be the dominant factor in mitigating errors in data-sparse SOH regions. The model also shows excellent deployment potential, with an inference latency of 15.8 ms on an RK3568 edge platform after INT8 quantization. This study validates that conditioning deep learning models with historical priors offers a robust, flexible, and practical pathway for evaluating second-life batteries, supporting their integration into the circular economy.