Abstract
Considering the polarization effect of lithium-ion battery, this paper establishes a second-order RC model, and proposes an adaptive global optimal guided coyote optimal identification algorithm (AGCOA). It effectively solves the problems of traditional heuristic algorithms that are easy to fall into local optima and slow convergence. The battery testing equipment NEWARE BTS-4008 is used to charge and discharge the lithium-ion battery. The parameters identified by the AGCOA are used to predict the terminal voltage, and the extended Kalman filter algorithm (EKF) is used to estimate the battery SOC. The results show that the predicted voltage obtained by the identification is basically the same as the actual voltage, and the SOC estimation error is small, which verifies the efficiency and accuracy of the algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
