Personalized Retiring and Assessing Methods for Lithium-ion Batteries within the Full Lifespan

doi:10.11985/2024.01.008

Abstract

Abstract:

Lithium-ion batteries(LIBs) have been widely used in energy storage system(ESS), electric vehicles(EVs) and other fields. However, the capacity will gradually decrease during the battery utilization until it is retired. 80% state of health(SOH) is usually taken as the retiring standard in traditional methods, which not considers the actual degradation rate. Not only can not make full use of the healthy batteries, but also make it difficult to efficiently ensure the safety of unhealthy batteries. Meanwhile, SOH is equal, but battery aging characteristics and rate are not necessarily the same. The SOH is used alone to evaluate to make the accurate reflection of aging differences impossible. Therefore, a personalized retiring standard and aging assessment methods are proposed in this paper, which aim to the LIBs within the full lifespan. The new index of decommissioning(IoD) is firstly defined, which is characterized by the capacity degradation gradient and current SOH. The distribution of IoD under 80% SOH is calculated to obtain the retired threshold, which is used as the standard to define the battery retirement. Meanwhile, the brand-new health assessment index - capacity terminal diving rate(TDR) is proposed to evaluate the nonlinear aging phenomena that happens during the battery use. Through the verification on the MIT public dataset, the proposed method has the advantages of simple calculation and strong robustness. It can realize the battery personalized retirement and more effective assessment in aging differences, leading to the higher utilization rate and safer use.

Key words: Lithium-ion battery, state of health, battery retiring indicators, capacity terminal diving rate

CLC Number:

TM912

ZHU Yuhao, WANG Teng, GU Xin, HOU Linfei, SHANG Yunlong. Personalized Retiring and Assessing Methods for Lithium-ion Batteries within the Full Lifespan[J]. Journal of Electrical Engineering, 2024, 19(1): 79-86.

Figures/Tables 13

References 20

[1]	张茜. 乘用车和商用车场景下电动汽车与燃油车技术路线对比分析[J]. 石油石化绿色低碳, 2022, 7(4):6-9,53.
	ZHANG Xi. Comparative analysis of EV and ICE vehicles in passenger and commercial vehicle scenarios[J]. Green Petroleum & Petrochemicals, 2022, 7(4):6-9,53.
[2]	来鑫, 陈权威, 顾黄辉, 等. 面向“双碳”战略目标的锂离子电池生命周期评价:框架、方法与进展[J]. 机械工程学报, 2022, 58(22):3-18. doi: 10.3901/JME.2022.22.003
	LAI Xin, CHEN Quanwei, GU Huanghui, et al. Life cycle assessment of lithium-ion batteries for carbon-peaking and carbon-neutrality:Framework,methods,and progress[J]. Journal of Mechanical Engineering, 2022, 58(22):3-18. doi: 10.3901/JME.2022.22.003
[3]	肖曦, 田培根, 于璐, 等. 动力电池梯次利用储能系统电热安全研究现状及展望[J]. 电气工程学报, 2022, 17(1):206-224.
	XIAO Xi, TIAN Peigen, YU Lu, et al. Status and prospect of safety studies of cascade power battery energy storage system[J]. Journal of Electrical Engineering, 2022, 17(1):206-224.
[4]	李放, 闵永军, 张涌. 基于大数据的动力锂电池可靠性关键技术研究综述[J]. 储能科学与技术, 2023, 12(6):1981-1994. doi: 10.19799/j.cnki.2095-4239.2023.0316
	LI Fang, MIN Yongjun, ZHANG Yong. Review of key technology research on the reliability of power lithium batteries based on big data[J]. Energy Storage Science and Technology, 2023, 12(6):1981-1994. doi: 10.19799/j.cnki.2095-4239.2023.0316
[5]	蔡涛, 张钊诚, 袁奥特, 等. 锂离子电池储能安全管理中的机器学习方法综述[J]. 电力系统保护与控制, 2022, 50(24):178-187.
	CAI Tao, ZHANG Zhaocheng, YUAN Aote, et al. Review of machine learning for safety management of li-ion battery energy storage[J]. Power System Protection and Control, 2022, 50(24):178-187.
[6]	徐彬翔, 郑林锋, 黄乙恒, 等. 基于改进最小二乘支持向量机的锂离子电池健康状态快速估计方法[J]. 电气工程学报, 2022, 17(4):11-19.
	XU Binxiang, ZHENG Linfeng, HUANG Yiheng, et al. Fast estimating the state of health of lithium-ion batteries based on improved least squares support vector machine[J]. Journal of Electrical Engineering, 2022, 17(4):11-19.
[7]	胡晓亚, 郭永芳, 张若可. 锂离子电池健康状态估计方法研究综述[J]. 电源学报, 2022, 20(1):126-133.
	HU Xiaoya, GUO Yongfang, ZHANG Ruoke. Review of state-of-health estimation methods for lithium-ion battery[J]. Journal of Power Supply, 2022, 20(1):126-133. doi: 10.13234/j.issn.2095-2805.2022.1.126
[8]	LIU Kailong, SHANG Yunlong, OUYANG Quan, et al. A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4):3170-3180. doi: 10.1109/TIE.41
[9]	孙丙香, 任鹏博, 陈育哲, 等. 锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测[J]. 电工技术学报, 2021, 36(3):666-674.
	SUN Bingxiang, REN Pengbo, CHEN Yuzhe, et al. Analysis of influencing factors of degradation under different interval stress and prediction of aging trend in any interval for lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2021, 36(3):666-674.
[10]	方德宇, 楚潇, 刘涛, 等. 基于数据-模型驱动的锂离子电池健康状态估计[J]. 电气工程学报, 2022, 17(4):20-31.
	FANG Deyu, CHU Xiao, LIU Tao, et al. Research on health assessment method of lithium-ion battery based on bata-model hybrid drive[J]. Journal of Electrical Engineering, 2022, 17(4):20-31.
[11]	李欣. 车用锂离子电池全生命周期寿命预测与健康管理方法研究[D]. 长春: 吉林大学, 2022.
	LI Xin. Research on life prognostics and health management of electric vehicles lithium-ion battery in the whole life cycle[D]. Changchun: Jilin University, 2022.
[12]	ZHANG Lijun, MU Zhongqiang, SUN Changyan. Remaining useful life prediction for lithium-ion batteries based on exponential model and particle filter[J]. IEEE Access, 2018,6:17729-17740.
[13]	GUHA A, PATRA A. State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models[J]. IEEE Transactions on Transportation Electrification, 2018, 4(1):135-146. doi: 10.1109/TTE.2017.2776558
[14]	石海鹏, 来文青, 王永红, 等. 一种用于辨识预判三元电池容量“跳水”故障的方法:中国,CN111175653A[P]. 2020-05-19.
	SHI Haipeng, LAI Wenqing, WANG Yonghong, et al. A method for recognizing and predicting ternary battery capacity “diving” faults:China,CN111175653A[P]. 2020-05-19.
[15]	YANG Duo, ZHANG Xu, PAN Rui, et al. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve[J]. Journal of Power Sources, 2018, 15(3):387-395.
[16]	ZHOU Ziyou, LIU Yonggang, YOU Mingxing, et al. Two-stage aging trajectory prediction of LFP lithium-ion battery based on transfer learning with the cycle life prediction[J]. Green Energy and Intelligent Transportation, 2022, 1(1):100008. doi: 10.1016/j.geits.2022.100008
[17]	YOU Hezhe, ZHU Jiangong, WANG Xueyuan, et al. Nonlinear health evaluation for lithium-ion battery within full-lifespan[J]. Journal of Energy Chemistry, 2022,72:333-341.
[18]	马剑, 马梁, 宋登巍, 等. 一种锂电池容量跳水识别方法及装置:中国,CN112327194B[P]. 2021-09-24.
	MA Jian, MA Liang, SONG Dengwei, et al. A lithium battery capacity diving identification method and device:China, CN112327194B[P]. 2021-09-24.
[19]	马剑, 宋登巍, 马梁, 等. 一种电池容量跳水风险评估方法及系统:中国,CN112327167B[P]. 2022-01-28.
	MA Jian, SONG Dengwei, MA Liang, et al. A battery capacity diving risk assessment method and system:China, CN112327167B[P]. 2022-01-28.
[20]	戴海峰, 尤贺泽, 魏学哲, 等. 基于几何特征融合决策的锂电池容量跳水转折点识别方法:中国,CN113777494A[P]. 2021-12-10.
	DAI Haifeng, YOU Heze, WEI Xuezhe, et al. An identification method for lithium battery capacity diving turning point based on geometric feature fusion decision:China, CN113777494A[P]. 2021-12-10.