Health-Aware Battery-Fast-Charging Strategy Using Thermal-Aging Cell Model and Whale Optimization Algorithm

In this article, developing an optimized health-aware battery-fast-charging strategy is proposed using multistep constant-current constant-voltage (MSCCCV)-charging technique. First, the thermal-aging cell model (TACM) is formulated utilizing a 1D radial heat-conduction phenomenon. The utilization of this model facilitates the generation of a simulated cell model, thereby mitigating the necessity for conducting multiple experimental assays. A cycle life predictor based on multi-input elastic net regression is then developed, which forecast cell's cycle life based on inputs from TACM. The accuracy of the projected life is 87% which is validated using APR18650M1B cell cycling dataset. This is then utilized to devise an adaptive MSCCCV-charging strategy with four-step constant-current (CC), which is optimized using whale optimization algorithm, have C-rate of 5.2C, 4.4C, 5.2C, and 4.52C. An alternate cell (LGEBM26R) is used to validate the reproducibility of the proposed charging method. The second cell charging is optimized using the aforesaid steps and four-step CC, thus obtained have C-rate of 1.3C, 1.95C, 2.23C, and 1.24C. Compared to 1C-CCCV charging, the algorithm increases LGEBM26R cell cycle life by 17.23% and APR18650M1B cell cycle life by 28%. The MSCCCV technique's superiority is demonstrated through a comparison with benchmark techniques considering charging time and cycle life as performance parameters.