An online state-of-health estimation method for lithium-ion battery based on linear parameter-varying modeling framework

The accurate estimation of state -of -health (SOH) is crucial for ensuring the safe and reliable operation of lithiumion battery systems. However, the intimate coupling between SOH and state -of -charge (SOC) is often overlooked in existing estimation methods, leading to inaccurate estimates. To address this, we propose a linear parametervarying (LPV) battery model that captures both gradual capacity degradation and rapid dynamic changes. This model integrates traditional linear models with emerging nonlinear models, providing a comprehensive online SOH estimation framework that effectively separates the effects of SOC in the LPV model structure. The model parameters are identified using a subspace algorithm with accelerated aging data. The proposed method is validated by accelerated aging experiments on two sets of battery samples, one for model development and another for model validation. The experimental data show that the LPV battery model can achieve high SOH estimation accuracy, with an average error of 2.85 % and 5.51 % for SOH, and 0.63 % and 1.20 % for capacity, respectively. The method also shows the advantages of being easy to implement and highly generalizable, making it suitable for different battery types and application scenarios.