Jeng Shyr-Long
Department of Mechanical Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan.
Sensors (Basel). 2025 Jan 26;25(3):749. doi: 10.3390/s25030749.
This paper presents a time-series point-to-point generative adversarial network (TS-p2pGAN) for synthesizing realistic electric vehicle (EV) driving data. The model accurately generates four critical operational parameters-battery state of charge (SOC), battery voltage, mechanical acceleration, and vehicle torque-as multivariate time-series data. Evaluation on 70 real-world driving trips from an open battery dataset reveals the model's exceptional accuracy in estimating SOC values, particularly under complex stop-and-restart scenarios and across diverse initial SOC levels. The model delivers high accuracy, with root mean square error (RMSE), mean absolute error (MAE), and dynamic time warping (DTW) consistently below 3%, 1.5%, and 2.0%, respectively. Qualitative analysis using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) demonstrates the model's ability to preserve both feature distributions and temporal dynamics of the original data. This data augmentation framework offers significant potential for advancing EV technology, digital energy management of lithium-ion batteries (LIBs), and autonomous vehicle comfort system development.
本文提出了一种用于合成逼真电动汽车（EV）驾驶数据的时间序列点对点生成对抗网络（TS-p2pGAN）。该模型能够准确生成四个关键运行参数——电池荷电状态（SOC）、电池电压、机械加速度和车辆扭矩——作为多变量时间序列数据。对一个开放电池数据集的70次实际驾驶行程进行评估后发现，该模型在估计SOC值方面具有卓越的准确性，尤其是在复杂的启停场景以及不同的初始SOC水平下。该模型具有很高的准确性，均方根误差（RMSE）、平均绝对误差（MAE）和动态时间规整（DTW）分别始终低于3%、1.5%和2.0%。使用主成分分析（PCA）和t分布随机邻域嵌入（t-SNE）进行的定性分析表明，该模型能够保留原始数据的特征分布和时间动态。这种数据增强框架为推进电动汽车技术、锂离子电池（LIB）的数字能源管理以及自动驾驶汽车舒适系统的开发提供了巨大潜力。
