An Intelligent Charging Scheme for Lithium-Ion Batteries of Electric Vehicles Considering Internal Attenuation Modes
Jiaqiang Tian, Duo Yang, Xu Zhang, Jianning Yin, Qingping Zhang
Abstract
Charging control is one of the essential functions of battery management systems. Battery charging involves behavioral changes such as electricity, heat, and aging. Balancing these factors is crucial for the safe and efficient operation of batteries. This work proposes an intelligent charging scheme for lithium-ion batteries that considers charging time, temperature rise, and health losses. First, charging aging experiments are conducted to investigate the effect of charging rate on battery aging. Specifically, half-cell experiments are carried out to construct an electrode open circuit voltage model to explore battery aging modes. The aging mode attenuation model is proposed based on empirical formulas. Then, the charging multiobjective function is established based on the equivalent circuit, thermal, and aging empirical models (AEMs). A multistage constant current (CC) charging optimization scheme has been developed based on the multiobjective gray wolf optimizer (MOGWO). Finally, the feasibility of three targets and dual targets fast charging schemes is verified through simulation and experiments.