Parameter identification method of lithium battery based on fuzzy forgetting factor recursive least square method

Aiming at the problem that the stability of the lithium battery parameter identification results and the accuracy of the lithium battery equivalent circuit model cannot be achieved simultaneously due to the fixed value of the forgetting factor for the forgetting factor recursive least squares method, the paper proposes an integrated algorithm that combines the fuzzy algorithm with the forgetting factor recursive least square method——the fuzzy forgetting factor recursive least squares method——thus to help make the forgetting factor dynamically changeable under the effect of the fuzzy controller. The first-order RC model is selected as the equivalent circuit model of Li-ion battery, and the fixed forgetting factor recursive least squares method and the fuzzy forgetting factor recursive least squares method are both adopted to perform parameters identification for first-order RC models, and then the results of the parameter identification are respectively input into the model for the calculation of the model end-voltage error. The simulation results show that compared with the fixed forgetting factor recursive least squares algorithm with a forgetting factor value of 1, the average absolute error value for the end voltage of the fuzzy-forgetting-factor-recursive-least-squares-based lithium battery model decreases by 0.00085 volts, and the absolute value of the maximum error decreases by 0.0742 volts. Compared with the fixed forgetting factor recursive least squares algorithm with a value of 0.9, the stability of the parameter identification results of the fuzzy forgetting factor recursive least squares algorithm is significantly improved.