Accurate State of Charge Estimation for Lithium-Ion Batteries in Electric Vehicles Using PSO-Optimized ANN Approach

Lithium-ion battery State of Charge (SoC) estimate accuracy is importance for the effective and secure operation of Electric Vehicles (EVs), since it affects battery management and longevity. This study offers a revolutionary SoC estimation methodology that uses an Artificial Neural Network (ANN) optimized with Particle Swarm Optimization (PSO). The preprocessing includes resampling, outlier detection, and normalization, ensuring reliable input data. The ANN effectively captures the nonlinear dynamics of battery charge behavior, while PSO fine tunes the network’s hyperparameter to enhance prediction accuracy. The model implemented in Python, offers a reliable and efficient solution for real-world battery management applications, achieving a prediction accuracy of $94 \%$ and outperforming traditional estimation methods, thereby contributing to the advancement of reliable and intelligent battery monitoring systems in EV.