LAUSR

Battery State-of-Health (SOH) estimation is of utmost importance for the performance and cost-effectiveness of electric vehicles. Incremental capacity analysis (ICA) has been ubiquitously used for battery SOH estimation. However, challenges remain with regard to the characteristic parameter selection, estimation viability and feasibility for practical implementation. In this paper, a novel ICA-based method for battery SOH estimation is proposed, with the goals to identify the most effective characteristic parameters of IC curves, optimize the SOH model parameters for better prediction accuracy and enhance its applicability in realistic battery management systems. To this end, the IC curve is first derived and filtered using the wavelet filtering, with the peak value and position extracted as health factors (HFs). Then, the correlations between SOH and HFs are explored through the grey correlation analysis. The SOH model is further established based on the Gaussian process regression (GPR), in which the optimal hyper parameters are calculated through the conjugate gradient method and the multi-island genetic algorithm (MIGA). The effects of different HFs and kernel functions are also analyzed. The effectiveness of the proposed MIGA-GPR SOH model is validated by experimentation.