基于Transformer的纯电动汽车充电时间预测

doi:10.19562/j.chinasae.qcgc.2024.11.012

Abstract

Abstract:

The arrangement of charging time for pure electric vehicles is a crucial part of the daily life of car owners， directly affecting the convenience and comfortable experience of their travel. However， there are still challenges such as insufficient charging station resources and the need for advanced planning for charging. To solve the problem of car owners being unable to use the vehicle immediately due to insufficient battery， a charging time prediction solution based on the Transformer model is proposed to help car owners better plan their daily itinerary. In order to better understand the degree of battery performance degradation and capacity loss， the capacity method is used to evaluate the health status of batteries， and the charging behavior of drivers is analyzed to construct the characteristics of battery charging behavior. Savitzky Golay filter is used to smooth out the features representing battery attenuation and perform cumulative transformation， so that the features can more comprehensively represent battery information. Then the Pearson correlation coefficient and LASSO （Least Absolute Shrinkage and Selection Operator） regression algorithm are coupled to obtain the optimal feature set through secondary screening. Finally， using the Transformer model's strong attention mechanism， the charging time is predicted. Through experimental data verification， this scheme can accurately and quickly predict the charging time of pure electric vehicles， with a determination coefficient of 0.999 and a running speed of 156 ms.

Key words: electric vehicles, charging time, data driven, Transformer

Jie Hu,Lin Chen,Zhihong Wang,Haihua Qing,Haojie Wang. Transformer-Based Prediction of Charging Time for Pure Electric Vehicles[J].Automotive Engineering, 2024, 46(11): 2059-2067.

Figures/Tables 18

References 15

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数据字段名称	字段定义	有效值范围	含义与数值
车辆状态	车辆状态	01，02，03	启动，熄火，其它状态
累计里程	累计里程	0-9999999	0-99 999.9 km
总电压	总电压	0-10000	0-1 000 V
总电流	总电流	0-20000	-1 000-1 000 A，偏移量1 000 A
SOC	电池SOC	0-100	0-100%
电池单体电压最高值	单体电压最高值	0-15000	0-15 V
电池单体电压最低值	单体电压最低值	0-15000	0-15 V
最高温度值	电池单体最高温度	0-250	-40-210 ℃ 偏移量40 ℃
最低温度值	电池单体最低温度	0-250	-40-210 ℃ 偏移量40 ℃

数据字段名称	平均相对误差/%
电池单体电压最高值	0.520
电池单体电压最低值	0.521
最高温度值	0.518

SOC起始值/%	SOC终止值/%
SOC起始值/%	0-80	80-100
0-20	快速型	深度型
20-100	健康型	性能型

特征类型	特征字符
电池健康状态	SOH
充电行为特征	充电深度
	充电风格
	充电模式
电池特征	电压一致性评分
	温度一致性评分
	最低温度
	电池单体电压最高值
	总电压
	SOC
历史特征	充放电循环次数
历史特征	累计里程

模型	RMSE	R²	运行时间/ms
Transformer	1.627	0.999	156
SVR	54.221	0.989	2 710
XGBoost	10.874	0.996	182
LSTM	50.104	0.990	255

Transformer-Based Prediction of Charging Time for Pure Electric Vehicles

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