A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve
Xingzi Qiang, Wenting Liu, Zhiqiang Lyu, Haijun Ruan, Xiaoyu Li
Abstract
The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.