Abstract
Electric vehicle batteries must possess fast rechargeability. However, fast charging of lithium-ion batteries remains a great challenge. This paper develops a feature-driven closed-loop optimization (CLO) methodology to efficiently design health-conscious fast-charging strategies for batteries. To avoid building an early outcome predictor, the feature highly related to battery end-of-life is used as the optimization objective instead of using the predicted lifetime. This feature is extracted from the battery’s early cycles and the experimental cost is thus reduced. By developing closed-loop multi-channel experiments with Bayesian optimization (BO), the optimal charging protocols with long cycle lives are located quickly and efficiently among 224 four-step, 10 min fast-charging protocols. Experimental results show that BO performs well with different acquisition functions, and a minimum of 12 paralleled channels for each round of experiments are recommended to obtain stable optimization results. Compared with the benchmark, the developed method recommends similar fast-charging protocols with long cycle lives based on much less experimental cost.
Funder
Sichuan Province Science and Technology Support Program
National Key Technology Research and Development Program of China
Publisher
The Electrochemical Society
Subject
Materials Chemistry,Electrochemistry,Surfaces, Coatings and Films,Condensed Matter Physics,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献