Advanced Parameter Identification in Electric Vehicles Lithium-Ion Batteries With Marine Predators Algorithm-Based Optimization

Accurate parameter identification of lithium-ion (Li-ion) battery models is critical for understanding battery behavior and optimizing performance in electric vehicle (EV) applications. Traditional methods often rely on manual adjustments or trial-and-error processes, leading to inefficiencies and suboptimal outcomes. This study introduces a novel parameter identification approach using the marine predators algorithm (MPA), applied to a Shepherd model for EV applications. The proposed technique was validated under various dynamic test conditions, including the urban dynamic driving cycle (UDDC), the new European driving cycle (NEDC), and the worldwide harmonized light vehicles test procedure (WLTP). The MPA-based method systematically identifies optimal parameters, achieving a voltage error of 2.743 × 10⁻³, a state of charge (SOC) error of 0.7693 × 10⁻³, and a root mean square error (RMSE) of 8.37 × 10⁻³ between the model and real data. Compared to other optimization techniques, the MPA demonstrated superior performance, achieving an optimization efficiency of 97.69%. These results validate the robustness and reliability of the method for accurately capturing battery dynamics under realistic driving conditions. These results highlight the potential of the MPA-based approach in improving the accuracy of Li-ion battery parameter identification, leading to more efficient energy management in EVs and contributing to enhanced battery performance and reliability.