Improved Modeling and Parameter Optimization of Li-Ion Batteries for Electric Vehicles Using Artificial Lemming Algorithm

Abstract

In electric vehicles (EVs), the battery management system (BMS) plays a central role in ensuring safe, efficient, and reliable battery operation under varying driving and environmental conditions. The effectiveness of a BMS largely depends on the availability of an accurate battery model, whose performance is strongly influenced by the precision of its identified parameters. However, estimating these parameters remains a difficult nonlinear optimization problem, especially under low state of charge (SOC) operation. Classical identification approaches often have limited robustness under such conditions, while metaheuristic algorithms provide a promising alternative because of their ability to handle nonlinear and multimodal search spaces. Even so, many existing methods still encounter drawbacks related to convergence speed and susceptibility to local optima. Motivated by these challenges, this study investigates the recently introduced Artificial Lemming Algorithm (ALA) for parameter identification of a second-order equivalent circuit model (2RC-ECM) under EV-oriented low-SOC operating conditions. Experimental validation is conducted using two independent dynamic datasets, namely the High Dynamic Profile (HDP) at 25 °C and the Urban Dynamometer Driving Schedule (UDDS) at −5 °C, involving different lithium-ion cells and operating conditions. ALA is benchmarked against nine competing metaheuristic algorithms under identical search boundaries and computational settings. Performance is assessed using RMSE, MAE, MaxAE, bias, convergence behavior, error distributions, execution time, and sensitivity to the number of independent runs, population size, and maximum number of iterations. The results show that ALA achieves the lowest minimum, mean, and maximum RMSE for both datasets, with minimum RMSE values of 0.01075 V for HDP and 0.03534 V for UDDS. Unseen-data validation further yields RMSE and MAE values of 0.0082 and 0.0061 V, respectively, for HDP, and 0.0416 and 0.0299 V, respectively, for UDDS. In addition, convergence, error-distribution, and sensitivity analyses show that ALA maintains competitive and consistent performance across the investigated configurations. Overall, the results demonstrate that ALA provides a favorable balance between estimation accuracy, robustness, convergence behavior, and computational cost for offline lithium-ion battery parameter identification.