Multi-objective optimization for fast charging design of lithium-ion batteries using constrained Bayesian optimization

Fast charging of lithium-ion battery accounting for both charging time and battery degradation is key to modern electric vehicles. The challenges of fast charging optimization are (i) the high dimensionality of the space of possible charging protocols while the experiment budget is often limited; and (ii) the limited quantitative description of battery capacity fade mechanisms. This article proposes a data-driven multi-objective charging approach to minimize charging time while maximizing battery cycle life, in which a Chebyshev scalarization technique is used to transform the multi-objective optimization problem into a group of single objective problems, and a constrained Bayesian optimization (BO) is then utilized to effectively explore the parameter space of charging current as well as handle the constraint of charging voltage. Moreover, continuous-varied-current charging protocols are introduced into the proposed charging optimization approach by the utilization of polynomial expansion technique. The effectiveness of the proposed charging approach is demonstrated on a porous electrode theory-based battery simulator. The results show that the proposed constrained BO-based approach possesses superior charging performance and higher sample efficiency, compared with the state-ofthe-art baselines including constrained optimization by linear approximations (COBYLA) and covariance matrix adaptation evolutionary strategy (CMA-ES). In addition, the increase in the charging performance and its uncertainty with an increasing number of degrees of freedom used in charging protocols is discussed.