A Canonical Correlation Analysis-Based Approach for Battery State of Charge Estimation

This brief presents a novel method based on canonical correlation analysis (CCA) and particle filter (PF) for battery state of charge (SOC) estimation. More specifically, CCA is adopted to provide a universal way for battery SOC estimation with PF for error correction. Technically, the input data are first mapped to polynomial terms before training to find the intrinsic features. Afterwards, ℓ2-norm regularization is further added to the loss function in order to prevent overfitting. Finally, the SOC estimation result from CCA training is combined with the Coulomb counting model and updated by PF for error correction. Experimental results using battery testing data at different testing profiles and temperatures show the high accuracy of estimation and robustness to wrong initial guess. In particular, compared with linear regression, support vector regression, and neural network, the error of the proposed method is reduced to 1.47%.