收藏切换
Transformer-Based Prediction of Charging Time for Pure Electric Vehicles
收藏切换
PDF
Jie Hu1, 2, 3, Lin Chen1, 2, 3, Zhihong Wang1, 2, 3, Haihua Qing1, 2, 3, Haojie Wang1, 2, 3
Automotive Engineering | 2024, 46(11) : 2059 - 2067
Less
收藏切换
Automotive Engineering | 2024, 46(11): 2059-2067
Selected Papers
Transformer-Based Prediction of Charging Time for Pure Electric Vehicles
Full
Jie Hu1, 2, 3, Lin Chen1, 2, 3, Zhihong Wang1, 2, 3, Haihua Qing1, 2, 3, Haojie Wang1, 2, 3
Affiliations
  • 1. Wuhan University of Technology,Hubei Key Laboratory of Modern Auto Parts Technology,Wuhan 430070
  • 2. Wuhan University of Technology,Auto Parts Technology Hubei Collaborative Innovation Center,Wuhan 430070
  • 3. Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering,Wuhan 430070
Published: 2024-11-25 doi: 10.19562/j.chinasae.qcgc.2024.11.012
Outline
收藏切换

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.

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 . DOI: 10.19562/j.chinasae.qcgc.2024.11.012
Year 2024 volume 46 Issue 11
PDF
357
150
Cite this Article
BibTeX
Article Info
doi: 10.19562/j.chinasae.qcgc.2024.11.012
  • Receive Date:2024-03-30
  • Online Date:2025-07-21
  • Published:2024-11-25
Article Data
Affiliations
History
  • Received:2024-03-30
  • Revised:2024-05-16
Funding
Affiliations
    1. Wuhan University of Technology,Hubei Key Laboratory of Modern Auto Parts Technology,Wuhan 430070
    2. Wuhan University of Technology,Auto Parts Technology Hubei Collaborative Innovation Center,Wuhan 430070
    3. Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering,Wuhan 430070
References
Share
https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2024.11.012
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT