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Edge computing for vehicle battery management: Cloud-based online state estimation

文献类型：期刊论文

作者Li, Shuangqi1,2; He, Hongwen1; Wei, Zhongbao1; Zhao, Pengfei3
刊名JOURNAL OF ENERGY STORAGE
出版日期2022-11-15
卷号55页码:9
ISSN号2352-152X
关键词Electric vehicle Battery management system Edge computing Deep learning Battery energy storage State estimation
DOI10.1016/j.est.2022.105502
通讯作者He, Hongwen(hwhebit@bit.edu.cn) ; Wei, Zhongbao(weizb@bit.edu.cn)
英文摘要The adoption of electric vehicles (EVs), including battery EVs and hybrid EVs, makes it possible to reduce fossil fuel consumption and greenhouse gas emission. However, an accurate battery model and an effective battery management system should be established to enable this benefit. This paper proposes a novel cloud-assisted online battery management method based on artificial intelligence and edge computing technologies. Integra-tion of cloud computation and big data resources into real-time vehicle battery management is realized by establishing a novel cloud-edge battery management system (CEBMS). A deep learning algorithm-based cloud data mining and battery modeling method is developed to estimate the voltage and energy state of the battery. The accuracy of the established cloud battery model outperforms the onboard battery management system by utilizing multi-sources information from different EVs. Meanwhile, a cloud-assisted battery management method is established at edge nodes in the onboard battery management unit to realize real-time state estimation locally. By using precise battery state estimation provided by the cloud platform, vehicle battery model accuracy can be significantly improved. The performance of the proposed battery management method is verified by a vehicle big data platform and battery pack experimental test bench. Experimental results justify the effectiveness of the proposed method in battery state estimation, which can help the EVs use and manage the battery more effectively.
WOS关键词LITHIUM-ION BATTERY ; CHARGE ESTIMATION ; ENERGY MANAGEMENT ; NEURAL-NETWORK ; REAL-TIME ; OF-CHARGE ; ALGORITHM ; VOLTAGE ; POWER
WOS研究方向Energy & Fuels
语种英语
出版者ELSEVIER
WOS记录号WOS:000859725000009
源URL[http://ir.ia.ac.cn/handle/173211/50430]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者He, Hongwen; Wei, Zhongbao
作者单位1.Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
2.Univ Bath, Dept Elect & Elect Engn, Bath, Avon, England
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Shuangqi,He, Hongwen,Wei, Zhongbao,et al. Edge computing for vehicle battery management: Cloud-based online state estimation[J]. JOURNAL OF ENERGY STORAGE,2022,55:9.
APA Li, Shuangqi,He, Hongwen,Wei, Zhongbao,&Zhao, Pengfei.(2022).Edge computing for vehicle battery management: Cloud-based online state estimation.JOURNAL OF ENERGY STORAGE,55,9.
MLA Li, Shuangqi,et al."Edge computing for vehicle battery management: Cloud-based online state estimation".JOURNAL OF ENERGY STORAGE 55(2022):9.

入库方式： OAI收割

来源：自动化研究所

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