Enhancing parameter identification and state of charge estimation of Li-ion batteries in electric vehicles using an improved marine predators algorithm

The challenge of accurately determining the unknown parameters of Lithium -ion batteries (LiBs) is primarily attributed to the intricate dynamics of these batteries, their non-linear characteristics, and the absence of straightforward measurement methodologies. This necessitates the deployment of advanced estimation methodologies. Among these, state -of -charge (SoC) estimation algorithms are particularly critical, offering a viable solution to the complexities inherent in this identification problem. This paper proposes an Improved Marine Predators Technique (IMPT) as a methodological approach for elucidating the unidentified attributes of LiBs. This technique represents a novel advancement in the field, aiming to provide a more effective means of parameter determination for these complex energy storage devices. It is influenced by how marine predators forage in the ocean habitat, where they demonstrate flexibility and effective hunting techniques. It simulates the hunting process by iteratively increasing the fitness of candidate solutions through predation, scouting, and communication mechanisms. This is done by combining individual search with group collaboration to optimize solutions. By accounting for the potential for variation in the climate and environmental variables, the proposed IMPT supports the predator's tactics. The objective function and standard deviation error on the LiB dynamic model are validated by the developed IMPT. Its results are also compared with several novel optimizers. Experiments on the 40 Ah Kokam LiBs and the ARTEMIS driving cycle pattern are paired with simulation studies. The statistical results show how effective the suggested IMPT is as an identification method. Moreover, the proposed IMPT demonstrates significant precision in comparison to other existing optimization methods for the Kokam LiBs and the ARTEMIS drive cycle pattern.