Linearizing Battery Degradation for Health-Aware Vehicle Energy Management

doi:10.1109/TPWRS.2022.3217981

	Linearizing Battery Degradation for Health-Aware Vehicle Energy Management
	Li, Shuangqi 1,2,3; Zhao, Pengfei 4,5; Gu, Chenghong 1; Huo, Da 6; Li, Jianwei1 ; Cheng, Shuang 1
发表期刊	IEEE TRANSACTIONS ON POWER SYSTEMS
ISSN	0885-8950
	2023-09-01
卷号	38 期号:5 页码:4890-4899
通讯作者	Gu, Chenghong(c.gu@bath.ac.uk)
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.
关键词	Electric vehicle battery energy storage system battery aging model-data-driven method energy management vehicle to grid
DOI	10.1109/TPWRS.2022.3217981
关键词[WOS]	EXTREME LEARNING-MACHINE ; STORAGE ; PERFORMANCE ; REGRESSION ; PAY
收录类别	SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:001054600200068
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54135
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.Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Peoples R China 3.Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 6.Cranfield Univ, Sch Water Energy & Environm, Cranfield MK43 0AL, England
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.