Optimal parameter identification strategy applied to lithium-ion battery model

This paper presents an optimal parameter identification strategy of the lithium-ion (Li-ion) battery model applying a recent metaheuristic artificial ecosystem-based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root-mean-square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show.