Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles. However, lithium-ion cells generate immense heat at high-current charging rates. In order to address this problem, an efficient fast charging–cooling scheduling method is urgently needed. In this study, a liquid cooling-based thermal management system equipped with mini-channels was designed for the fast-charging process of a lithium-ion battery module. A neural network-based regression model was proposed based on 81 sets of experimental data, which consisted of three sub-models and considered three outputs: maximum temperature, temperature standard deviation, and energy consumption. Each sub-model had a desirable testing accuracy (99.353%, 97.332%, and 98.381%) after training. The regression model was employed to predict all three outputs among a full dataset, which combined different charging current rates (0.5C, 1C, 1.5C, 2C, and 2.5C (1C = 5 A)) at three different charging stages, and a range of coolant rates (0.0006, 0.0012, and 0.0018 kg·s⁻¹). An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments. The results indicated that the battery module’s state of charge value increased by 0.5 after 15 min, with an energy consumption lower than 0.02 J. The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8 °C, respectively. The approach described herein can be used by the electric vehicles industry in real fast-charging conditions. Moreover, optimal fast charging–cooling schedule can be predicted based on the experimental data obtained, that in turn, can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.

延长电动汽车的续航里程需要高效的快速充电技术。然而，锂离子电池在高电流充电率下会产生巨大的热量。为了解决这个问题，迫切需要一种高效的快速充电-冷却调度方法。在这项研究中，设计了一种配备微型通道的基于液体冷却的热管理系统，用于锂离子电池模块的快速充电过程。基于81组实验数据提出了基于神经网络的回归模型，该模型由三个子模型组成，考虑了三个输出：最高温度、温度标准差和能耗。每个子模型在训练后都有理想的测试准确率（99.353%、97.332% 和 98.381%）。^-1 )。从预测的数据集中选择了最佳的充电-冷却计划，并通过实验进行了验证。结果表明，电池模块的荷电状态在15 min后提高了0.5，能耗低于0.02 J，最高温度和温度标准偏差分别控制在33.35和0.8°C以内。电动汽车行业可以在真正的快速充电条件下使用此处描述的方法。此外，可以根据获得的实验数据预测最佳快速充电-冷却时间表，这反过来可以显着提高充电过程设计的效率以及控制冷却过程中的能耗。