On-line adaptive asynchronous parameter identification of lumped electrical characteristic model for vehicle lithium-ion battery considering multi-time scale effects

Abstract

The accurate modeling of lithium-ion batteries is extremely important to improve the reliability of battery management systems, and solving the problem of multi-time scales is extremely beneficial for high-accuracy battery modeling and adaptive asynchronous parameter identification. This paper distinguishes the fast and slow change characteristics of the model resistor-capacitor link parameters, a strong applicability model for the aggregate electrical characteristics of vehicle-mounted lithium-ion batteries based on multi-time scales is established. By combining the advantages of different identification algorithms, an adaptive asynchronous parameter identification strategy is proposed, which solves the problem of data saturation caused by the time scale identification strategy. Then, the complex charge-discharge pulse and the mixed discharge pulse tests are designed explicitly, and the parameter results and terminal voltage tracking effects under different identification strategies are compared. Moreover, the consistency results of the parameter identification test under single-time scale forgetting factor recursive least squares and multi-time scale adaptive asynchronous parameter identification strategy are analyzed. The results show that under different working conditions, the identification precision of the terminal voltage based on the adaptive asynchronous parameter identification strategy is increased by 0.420% and 1.114% respectively, and the maximum error of parameter consistency is reduced by 158.300%. • An adaptive online identification strategy for asynchronous parameters is proposed. • Different dynamic characteristics are characterized by different time scales. • Realized the hysteresis effect modeling of the battery open-circuit voltage. • The hysteresis effect equation is coupled iteratively with the state-space equation. • Combine FFRLS and EKF to complete the asynchronous identification of parameters.