Artificial Search Algorithm for Parameters Optimization of Li-Ion Battery Electrical Model

Leveraging AI (Artificial Intelligence) for parameter identification in battery models aids in making informed decisions about battery design, efficiency, and performance, which is crucial for electric vehicles and renewable energy systems. This article proposes the use of the Adolescent Identity Search Algorithm (AISA) to estimate parameters of the third-order Equivalent Circuit Model (ECM) for Li-ion battery. AISA introduces an innovative metaheuristic optimization technique inspired by the process of adolescent identity formation. This novel AISA-driven method achieves high accuracy with a low computational cost, making it suitable for real-world applications. The resulting RMSE of 8.50988265e-03 obtained in this study demonstrates the efficiency of the introduced approach for battery modeling and its potential for practical use in intelligent systems and sustainable applications.