MRFO-AEO Based Batteries Parameter Identification for Life Prediction

Abstract

In this paper, a novel hybrid algorithm based on manta ray foraging optimization (MRFO) and artificial ecosystem-based optimization (AEO), called MRFO-AEO, is proposed to identify the battery parameters based on a third-order Thevenin equivalent circuit model. To improve the accuracy and stability of the battery parameter identification, MRFO-AEO discards the random search operation in the MRFO cyclone foraging operator and dynamically coordinates the AEO decomposition operator and the improved MRFO tumble foraging operator with the iterative process to reasonably balance the local exploration and global search. And the validity of the battery model and the feasibility of the algorithm are verified under the experimental data of battery discharge at the Kunbei converter station in Yunnan, China.