Parameter estimation of ECM model for Li-Ion battery using the weighted mean of vectors algorithm

Abstract

Accurate parameter estimation of the equivalent circuit model (ECM) for Li-Ion batteries (LiBs) allows for better behavior modeling and understanding. This is crucial for various applications, such as battery management systems (BMSs) and renewable energy systems, as it enables more precise performance prediction and optimization. This article introduces a novel artificial intelligence (AI) approach to estimate the ECM parameters of LiBs. The proposed method leverages the innovative weIghted meaN oF vectOrs (INFO) algorithm to optimize ECM parameter values. INFO combines multiple vectors, considering their relative importance, to generate a single vector. The algorithm minimizes the disparity between the ECM’s estimated voltage and the battery’s measured voltage. The effectiveness of the proposed method is assessed using a test profile based on real-world driving data. Furthermore, A comparative analysis is conducted with other state-of-the-art optimization algorithms, including Artificial Ecosystem Optimizer (AEO), Grey Wolf Optimizer (GWO), Artificial Hummingbird Algorithm (AHA), Harris Hawks Optimization (HHO), Leader Harris Hawks Optimization (LHHO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Adolescent Identity Search Algorithm (AISA). Results demonstrate that the INFO outperforms these methods in terms of accuracy and convergence speed. The proposed INFO algorithm offers a promising approach for accurate parameter estimation in LIB modeling, contributing to improved BMS and enhanced performance prediction.