Multi-Feature State of Health Estimation for Lithium-ion Batteries Fusing Equivalent Circuit Model and Data-Driven Methods

Accurate estimation of the State of Health (SOH) is critical for ensuring the safety and reliability of Battery Management Systems (BMS) in lithium-ion batteries. The ohmic internal resistance, a parameter in the Equivalent Circuit Model (ECM), serves as a key indicator of the aging degree during battery operation. Although data-driven methods for SOH estimation are highly flexible, their reliance on externally measured parameters often limits their ability to reflect complex internal battery dynamics, resulting in limited estimation accuracy. To address this issue, this study first identifies parameters of a first-order RC ECM using the Particle Swarm Optimization (PSO) algorithm, thereby extracting the key health feature—ohmic internal resistance. Concurrently, Differential Thermal Capacity (DTC) analysis is employed to derive thermodynamic characteristics during battery charging and discharging. An input dataset integrating these ECM parameters and DTC features is constructed to enhance the physical information density of the model inputs, thereby improving the data quality for the subsequent data-driven model. Furthermore, a hybrid model combining a Bidirectional Gated Recurrent Unit (BiGRU) neural network with the eXtreme Gradient Boosting (XGBoost) algorithm is developed to further advance the estimation accuracy. Validation experiments on multiple cells from the Oxford battery aging dataset demonstrate that the proposed method achieves a maximum Root Mean Square Error (RMSE) below 0.3% and a minimum SOH prediction error of 0.01%, confirming its high estimation precision.