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Linearizing Battery Degradation for Health-Aware Vehicle Energy Management

文献类型：期刊论文

作者Li, Shuangqi1,3,4; Zhao, Pengfei5,6; Gu, Chenghong1; Huo, Da2; Li, Jianwei1; Cheng, Shuang1
刊名IEEE TRANSACTIONS ON POWER SYSTEMS
出版日期2023-09-01
卷号38期号:5页码:4890-4899
ISSN号0885-8950
关键词Electric vehicle battery energy storage system battery aging model-data-driven method energy management vehicle to grid
DOI10.1109/TPWRS.2022.3217981
通讯作者Gu, Chenghong(c.gu@bath.ac.uk)
英文摘要The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.
WOS关键词EXTREME LEARNING-MACHINE ; STORAGE ; PERFORMANCE ; REGRESSION ; PAY
WOS研究方向Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001054600200068
源URL[http://ir.ia.ac.cn/handle/173211/54134]  
专题舆论大数据科学与技术应用联合实验室
通讯作者Gu, Chenghong
作者单位1.Univ Bath, Dept Elect & Elect Engn, Bath BA2 7AY, England
2.Cranfield Univ, Sch Water Energy & Environm, Cranfield MK43 0AL, England
3.Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Peoples R China
4.Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Shuangqi,Zhao, Pengfei,Gu, Chenghong,et al. Linearizing Battery Degradation for Health-Aware Vehicle Energy Management[J]. IEEE TRANSACTIONS ON POWER SYSTEMS,2023,38(5):4890-4899.
APA Li, Shuangqi,Zhao, Pengfei,Gu, Chenghong,Huo, Da,Li, Jianwei,&Cheng, Shuang.(2023).Linearizing Battery Degradation for Health-Aware Vehicle Energy Management.IEEE TRANSACTIONS ON POWER SYSTEMS,38(5),4890-4899.
MLA Li, Shuangqi,et al."Linearizing Battery Degradation for Health-Aware Vehicle Energy Management".IEEE TRANSACTIONS ON POWER SYSTEMS 38.5(2023):4890-4899.

入库方式： OAI收割

来源：自动化研究所

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