Hybrid Electro-Thermal model based optimal charging of Lithium-ion Battery using MOGA for Enhanced State -of-Health

Abstract

Electric Vehicles (EV) have gained popularity in recent years to reduce the amount of greenhouse gas emissions and utilize renewable energy sources more effectively. Fast charging of Lithium -Ion batteries (Li-Ion) in EV is a serious issue affecting battery life. The main objective of the proposed work is to develop a Hybrid Electro-Thermal Model (H-ETM) using multiple model approach to generate an optimal charging profile to enhance State -of-Health (SoH) of Li-Ion based on Multi -Objective Genetic Algorithm (MOGA). The hybrid model is developed by integrating four local models based on a multi model approach to improve accuracy. For the dataset collected from real battery, a single model over the entire SoC range can provide terminal voltage accuracy of +10 mV and the proposed multi-model approach yields an improved accuracy of 5 mV. Further, the optimal current profiles under varying weight coefficients for charging time and temperature rise are generated using the proposed H-ETM. The suggested strategy's Pareto fronts are used as references to alter charging current rate to further satisfy diversified user demands, particularly for charging speed and temperature fluctuations in different charging applications. The proposed method provides more feasibility to select optimal charging patterns based on the requirements of the user by taking the trade-off between charging time and internal battery temperature rise while maintaining the constraints in state -of-charge, charging current, internal temperature rise, and charging time.