The energy consumption caused by battery thermal management of electric vehicles can be reduced using predictive control. A predictive controller needs a prediction model of the battery temperature, for example for different battery cooling and heating thresholds. In the proposed method, cross-domain data from simulation, vehicle fleet and weather stations were analyzed and processed as training data for a Convolutional Neural Network (CNN). The CNN took data from previous road segments and predictions for following road segments as input and predicted the change in battery temperature as quantile sequences over a prediction horizon. Properties of the collected cross-domain data sets were analyzed and considered during preprocessing, before 150 models were trained, of which the best performing model was further analyzed. Point-forecast metrics and quantile-related metrics were used for model comparison and evaluation. For example, the median prediction achieved a mean absolute error (MAE) of 0.27 °C and the true values were below the median prediction in 47% of the test data. Possible improvements of the method such as increasing data size, using more complex architectures as well as optimizing the horizon sizes were discussed. In conclusion, the method was able to well predict battery temperatures for different battery cooling thresholds.

中文翻译：

---

一种开发用于跨域数据训练的电动汽车电池温度预测的分位数卷积神经网络的方法

使用预测控制可以减少电动汽车电池热管理引起的能耗。预测控制器需要电池温度的预测模型，例如针对不同的电池冷却和加热阈值。在所提出的方法中，来自模拟、车队和气象站的跨域数据被分析和处理为卷积神经网络 (CNN) 的训练数据。CNN 将先前路段的数据和对后续路段的预测作为输入，并将电池温度的变化预测为预测范围内的分位数序列。在预处理过程中分析和考虑了收集的跨域数据集的属性，然后训练了 150 个模型，其中对性能最佳的模型进行了进一步分析。点预测指标和分位数相关指标用于模型比较和评估。例如，中值预测实现了 0.27 °C 的平均绝对误差 (MAE)，并且 47% 的测试数据的真实值低于中值预测。讨论了该方法的可能改进，例如增加数据大小、使用更复杂的架构以及优化水平尺寸。总之，该方法能够很好地预测不同电池冷却阈值的电池温度。讨论了使用更复杂的架构以及优化水平尺寸。总之，该方法能够很好地预测不同电池冷却阈值的电池温度。讨论了使用更复杂的架构以及优化水平尺寸。总之，该方法能够很好地预测不同电池冷却阈值的电池温度。