Multi-time scale identification of key kinetic processes for lithium-ion batteries considering variable characteristic frequency

The electrification of vehicles puts forward higher requirements for the power management efficiency of integrated battery management systems as the primary or sole energy supply. In this paper, an efficient adaptive multi-time scale identification strategy is proposed to achieve high-fidelity modeling of complex kinetic processes inside the battery. More specifically, a second-order equivalent circuit model network considering variable characteristic frequency is constructed based on the high-frequency, medium-high-frequency, and low-frequency characteristics of the key kinetic processes. Then, two cou-pled sub-filters are developed based on forgetting factor recursive least squares and extended Kalman fil-tering methods and decoupled by the corresponding time-scale information. The coupled iterative calculation of the two sub-filter modules at different time scales is realized by the voltage response of the kinetic diffusion process. In addition, the driver of the low-frequency subalgorithm with the state of charge variation amount as the kernel is designed to realize the adaptive identification of the kinetic diffusion process parameters. Finally, the concept of dynamical parameter entropy is introduced and advocated to verify the physical meaning of the kinetic parameters. The experimental results under three operating conditions show that the mean absolute error and root-mean-square error metrics of the pro-posed strategy for voltage tracking can be limited to 13 and 16 mV, respectively. Additionally, from the entropy calculation results, the proposed method can reduce the dispersion of parameter identification results by a maximum of 40.72% and 70.05%, respectively, compared with the traditional fixed character-istic frequency algorithms. The proposed method paves the way for the subsequent development of adap-tive state estimators and efficient embedded applications.(c) 2023 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. All rights reserved.

Automated summary

An efficient adaptive multi-time scale identification strategy is proposed in this paper to achieve high-fidelity modeling of complex kinetic processes inside the battery. A second-order equivalent circuit model network is constructed, and two coupled sub-filters are developed to decouple the kinetic processes based on the time-scale information. The proposed method can reduce the dispersion of parameter identification results and pave the way for adaptive state estimators and efficient embedded applications.