Charging strategy optimization using a dynamic programming and physics-based model for fast and safe battery charging at low temperatures

Although fast-charging technology for lithium-ion batteries is being developed for the continued commercialization of electric vehicles (EVs), fast charging at low temperatures can substantially shorten battery life and cause fires. Therefore, it is crucial to develop a technology that can balance the trade-off relationship between battery degradation and reduced charging time. This research offers a model-based optimization methodology for charging strategies to control the battery-charging time and lithium plating at low temperatures. A dynamic programming algorithm that guarantees a global optimum is used as an optimization method. To formulate the optimization problem for dynamic programming (DP), the electrochemical model of the battery was converted to a control-oriented model with model reduction methods. To overcome the high computational burden of DP, we developed a good fidelity model including single-particle model with electrolyte (SPMe), thermal model, and plating model with a small number of states. The conscious factor was defined as a weighting factor between the two costs of charging time and lithium plating thickness, and the algorithm was performed at various conscious factors and ambient temperature conditions. The optimization result was verified by simulating the optimized charging profile of the algorithm using a full electrochemical model. The final result was analyzed and discussed using Pareto frontier and sensitivity analysis. In all the optimizations performed, a cost reduction of at least 7 % and up to 57 % was achieved compared to conventional 1C-rate constant-current-constant-voltage (CCCV) charging strategy. This result indicates that the proposed charging strategy offers an effective optimization method that can easily handle the trade-off between degradation and charging time to achieve fast and safe charging under low-temperature conditions.