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An attention-based deep learning model considering data contamination for energy management system application of hybrid vehicle

文献类型：期刊论文

作者Huang, Wei1,2,3; Zhang, Yujun3; Qian, Duode1; He, Ying3; Hu, Biqian1; You, Kun3
刊名COMPUTERS & ELECTRICAL ENGINEERING
出版日期2024-08-01
卷号118
关键词Energy management system Deep learning Attention mechanism LSTM Noise reduction
ISSN号0045-7906
DOI10.1016/j.compeleceng.2024.109297
通讯作者Zhang, Yujun(yjzhang@aiofm.ac.cn)
英文摘要Hybrid vehicles are pivotal in transitioning to sustainable transportation, but effective energy management is challenged by data contamination. This paper proposes a novel approach, utilizing deep learning trained with diverse data from existing energy management systems, to assess its adaptability to varying contamination levels. The training dataset covers emission test cycles, loadings, and driving styles, with dimensionality and noise reduction applied to control parameters. A Long Short -Term Memory (LSTM) prediction model with attention mechanism forecasts power demand, outperforming five other deep learning models. The study demonstrates exceptional performance, achieving an average Root Mean Square Error (RMSE) of 4.9761, surpassing RMSE values from 7.9497 to 9.1933. Real driving conditions yield a Mean Absolute Error (MAE) of 3.965 and RMSE of 5.908, highlighting the model ' s adaptability and predictive accuracy despite data contamination. Such robustness makes it valuable for hybrid electric vehicle energy management systems research and development.
WOS关键词STRATEGY
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23010200] ; National Natural Science Foundation of China[62033012] ; Major Subject of Science and Technology of Anhui Province[202003a07020005]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001245325100001
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Major Subject of Science and Technology of Anhui Province
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/136188]  
专题中国科学院合肥物质科学研究院
通讯作者Zhang, Yujun
作者单位1.Anhui Jianghuai Automobile Grp Co Ltd, Hefei 230601, Anhui, Peoples R China
2.Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China
3.Chinese Acad Sci, Key Lab Environm Opt & Technol, Anhui Inst Opt & Fine Mech, Hefei 230031, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Wei,Zhang, Yujun,Qian, Duode,et al. An attention-based deep learning model considering data contamination for energy management system application of hybrid vehicle[J]. COMPUTERS & ELECTRICAL ENGINEERING,2024,118.
APA Huang, Wei,Zhang, Yujun,Qian, Duode,He, Ying,Hu, Biqian,&You, Kun.(2024).An attention-based deep learning model considering data contamination for energy management system application of hybrid vehicle.COMPUTERS & ELECTRICAL ENGINEERING,118.
MLA Huang, Wei,et al."An attention-based deep learning model considering data contamination for energy management system application of hybrid vehicle".COMPUTERS & ELECTRICAL ENGINEERING 118(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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