Li-Ion Battery State of Health Estimation and Remaining Useful Life Prediction Through a Model-Data-Fusion Method

The prognostics and health management of Li-ion batteries in electric vehicles are challenging due to the time-varying and nonlinear battery degradation. This article proposes a model-data-fusion method for battery state-of-health estimation and remaining useful life prediction. First, combined with metabolic gray model and multiple-output Gaussian process regression, a dynamic and data-driven battery degradation model is established to simulate battery complicated degradation behaviors, which takes the capacity degradation as the state variable and takes the internal resistance and polarization resistance from battery Thevenin model as the input variables. Second, to suppress the measurement noises of online battery information, a particle filter is utilized to track the battery capacity degradation for state-of-health estimation and extrapolate the degradation trajectory for remaining useful life prediction. Furthermore, battery ageing experiments are conducted to verify the proposed model-data-fusion method. The verification results show that the proposed method can provide an accurate and robustness state of health estimation and remaining useful life prediction at different temperatures.