A novel composite fractional order battery model with online parameter identification and truncation approximation calculation

Fractional order model (FOM) has proven effective in battery state estimation, but their broader application is hindered by three critical limitations: accuracy constraints, limited operational adaptability, and computational complexity impeding embedded deployment. This work designs a composite FOM based on the main reactions inside battery to further improve the model accuracy of conventional FOM. An online parameter identification method is developed to improve the model's adaptability, and a truncation approximation calculation strategy for fractional-order calculation is proposed to significantly reduce computational complexity. Experimental results demonstrate that the designed composite FOM achieves high accuracy, and the truncation approximation strategy reduces computation time by approximately 60 % with only a 1.43 % loss in accuracy.