A digital twin for advancing battery fast charging based on a Bayesian optimization-based method

The optimization of fast charging protocols is regarded as a key technology for promoting the use of electric vehicles because it can balance battery charging time and health. Optimizing such charging protocols through electrochemical models is a mainstream approach and can demonstrate high accuracy in simulating battery characteristics. However, the high complexity of the corresponding models makes the calculation and optimization processes difficult to perform in real time. To address this problem, this study presents an online closed-loop fast charging strategy optimization scheme that combines a battery digital twin model (BDTM) and Bayesian optimization (BO). Because the BO can quickly find a near-optimal solution in a few iterations, the optimization time is shortened, thereby reducing the computational burden incurred by highly complex models. To further improve the efficiency of the online optimization, a parallel multichannel optimization strategy is proposed, which further accelerates the process of finding the optimal protocol by simultaneously executing multiple optimization algorithms. Additionally, we analysed the effects of ageing parameters and ambient temperature on the optimization results. The results show that BO can obtain relatively stable optimization and has the highest efficiency when using four parallel channels. Specifically, the number of convergence evaluations for single-channel optimization is 2.5 times that of the four-channel optimization. Compared with the reference charging protocol, charging using the protocol optimized based on the Doyle-Fuller-Newman (DFN) model can effectively suppress the loss of lithium inventory (LLI) by up to 4.76 % within 90 cycles.