Development of DS-CC-CT-CV Charging Strategy for Health-Aware Battery Fast Charging Using Quad Objective Genetic Algorithm

This paper proposes a novel dual step-constant current-constant temperature-constant voltage (DS-CC-CT-CV) charging strategy for a health-aware battery fast charging (HABFC). Firstly, a highly robust thermo-electric ageing cell model (TEACM) is created where the impedance, thermal, and ageing models are intercoupled to obtain a semi-empirical model. These models are verified using a cell cycling test bench where the accuracy of TEACM is 91% for voltage detection, 97% for current detection and 99% for cycle life prediction. Second, these parameters obtained in TEACM are used to optimize the proposed charging strategy with quad objective genetic algorithm (QOGA). This helps in providing a customized charging strategy for every charging cycle. The QOGA helps to reduce the charging time, degradation rate, energy loss and temperature rise by setting the values of maximum permissible temperature and charging rate. The charging strategy is fed into the charger, and the cells are cycled up to 80% of State of Health condition. The results indicate that the cell charges 17.6% faster and has 17.23% more cycle life than a cell charged at 1C using CC-CV. Finally, the superiority of the DS-CC-CT-CV technique is proved by comparing it with the benchmark techniques with respect to charging time, temperature rise, and cycle life.