An enhanced social network search algorithm for accurate dynamic parameter identification of Li-ion batteries in electric vehicles

Abstract

Accurate modeling of lithium-ion batteries (LIBs) is crucial for enhancing the performance and safety of electric vehicles (EVs) and optimizing energy storage systems. This study proposes an Enhanced Social Networking Search Algorithm (ESNSA) for precise dynamic parameter identification in LIBs models. Building on the original SNSA, ESNSA introduces an Effective Exploitation Technique (EET) and adaptive parameter adjustment to achieve a more effective balance between global exploration and local exploitation. The algorithm’s performance was rigorously evaluated using experimental and simulation data from a 40-Ah Kokam LIB under the assessment and reliability of transport emission models inventory systems’ driving cycle. ESNSA achieved minimum objective function values of 0.007638 and 0.004394 in the first and second case studies, respectively, substantially outperforming conventional and state-of-the-art algorithms, such as the Arithmetic Technique, Jellyfish Search Technique, and Grey Wolf Optimizer. The proposed approach also delivered the lowest mean error 0.00801 and standard deviation of 0.000177 across comparative tests, confirming its superior accuracy and robustness. Statistical analyses (Friedman and Wilcoxon tests) demonstrated significant performance improvements over 9 out of 10 competing algorithms. The results affirm the ESNSA as a highly effective tool for robust, accurate LIB parameter estimation, offering tangible benefits for advanced battery management systems in EVs and renewable energy applications.