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首页> 外文期刊>SAE International Journal of Electrified Vehicles >A Neural Network-Based Regression Study for a Hybrid Battery Thermal Management System under Fast Charging
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A Neural Network-Based Regression Study for a Hybrid Battery Thermal Management System under Fast Charging

机译：基于神经网络的快速充电下混合电池热管理系统回归研究

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摘要

Fast charging is significant for the driving convenience of an electric vehicle (EV). However, this technology causes lithium (Li)-ion batteries' massive heat generation under such severe current rates. To ensure the thermal performance and lifespan of a Li-ion battery module under fast charging, an artificial neural network (ANN) regression method is proposed for a hybrid phase change material (PCM)-liquid coolant-based battery thermal management system (BTMS) design. Two ANN regres-sion models are trained based on experimental data considering two targets: maximum temperature (Tmax) and temperature standard deviation (TSD) of the hybrid cooling-based battery module. The regression accuracy reaches 99.942% and 99.507%, respectively. Four sets of experimental data are employed to validate the reliability of this method, and the cooling effect (Tmax and TSD) of the hybrid BTMS are predicted using the trained ANN regression models. Comparison results indicate that the deviations between the predicted value and the experimental value are acceptable, which prove the accuracy of the ANN regression models. This proposed method combines regression modelling with experimental tests to achieve the desired design efficiency and control, which can be utilized for efficient BTMS design, especially with more complex factors such as the future fast -charging requirements.
机译：快速充电很重要的驾驶方便电动汽车(EV)。这种技术会导致锂离子(李)电池的一代在这样巨大的热量严重的当前的利率。锂离子电池的性能和寿命模块在快速充电,一个人工神经网络(安)回归方法提出了一个混合相变材料(PCM)的液体coolant-based电池热管理系统(btm)设计。根据实验数据考虑训练两个目标:最高温度(达峰时间)和温度标准差(TSD)混合cooling-based电池模块。回归精度达到99.942%和99.507%,分别。用来验证的可靠性法,冷却效果(最高温度和TSD)混合btm预计使用培训安回归模型。表明之间的偏差预测值和实验值可接受的,这证明安的准确性回归模型。回归模型与实验测试达到预期的设计效率和控制,可用于高效btm设计,尤其是在更复杂的因素作为未来快速充电的需求。

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