基于表面温度和增量容量的锂电池健康状态估计

doi:10.19562/j.chinasae.qcgc.2021.09.003

Abstract

Abstract:

In this paper， a state of health （SOH） estimation method based on the surface temperature and incremental capacity of battery is proposed. The differential temperature curves during constant charging are analyzed， from which three geometric features are extracted as health factors， and combined with the peak value of incremental capacity curve as the input of BP neural network to establish a model for SOH estimation. The results of the test verify the effectiveness of the method， and the average error of SOH estimation is less than 2%.

Key words: lithium?ion battery, state of health, differential temperature curse, neural network

Mingqiang Lin,Denggao Wu,Gengfeng Zheng,Ji Wu. Estimation Method of State of Health of Lithium Battery Based on Surface Temperature and Incremental Capacity[J].Automotive Engineering, 2021, 43(9): 1285-1290.

Figures/Tables 9

References 19

1	王震坡，袁昌贵，李晓宇. 新能源汽车动力电池安全管理技术挑战与发展趋势分析［J］. 汽车工程， 2020， 42（12）： 1606-1620.
	WANG Z， YUAN C， LI X. An analysis on challenge and development trend of safety management technologies for traction battery in new energy vehicles［J］. Automotive Engineering， 2020， 42（12）： 1606-1620.
2	DONG G， CHEN Z， WEI J， et al. Battery health prognosis using brownian motion modeling and particle filtering［J］. IEEE Transactions on Industrial Electronics， 2018， 65（11）： 8646-8655.
3	LIU K， LI K， PENG Q， et al. A brief review on key technologies in the battery management system of electric vehicles［J］. Front. Mech. Eng， 2019， 14（1）： 47-64.
4	WEI J， DONG G， CHEN Z. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression［J］. IEEE Transactions on Industrial Electronics， 2018， 65（11）： 5634-5643.
5	YU Q， XIONG R， YANG R， et al. Online capacity estimation for lithium-ion batteries through joint estimation method［J］. Applied Energy， 2019， 255： 113817.
6	ZHENG L， ZHANG L， ZHU J， et al. Coestimation of state-of-charge， capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model［J］. Applied Energy， 2016， 180： 424-434.
7	刘轶鑫，张頔，李雪，等. 基于SOC-OCV 曲线特征的SOH 估计方法研究［J］. 汽车工程， 2019， 41（10）： 1153-1168.
	LIU Y， ZHANG D， LI X， et al. A research on SOH estimation method based on SOC-OCV curve characteristics［J］. Automotive Engineering， 2019， 41（10）： 1153-1168.
8	WU J， WANG Y， ZHANG X， et al. A novel state of health estimation method of Li-ion battery using group method of data handling［J］. Journal of Power Sources， 2016， 327： 457–464.
9	YANG D， ZHANG X， PAN R， 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， 384： 387-395.
10	WENG C， FENG X， SUN J， et al. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking［J］. Applied Energy， 2016， 180， 360-368.
11	BIRKL C. Diagnosis and prognosis of degradation in lithium-ion batteries［D］. University of Oxford， 2017.
12	TIAN J， XIONG R， SHEN W. State-of-health estimation based on differential temperature for lithium ion batteries［J］. IEEE Transactions on Power Electronics， 2020， 35（10）： 10363-10373.
13	THOMPSON A H. Electrochemical potential spectroscopy： a new electrochemical measurement［J］. Journal of The Electrochemical Society， 1979， 126（4）： 608-616.
14	GAO Y， DAHN J R. Synthesis and characterization of LiMnO for Li-ion battery applications［J］. Journal of The Electrochemical Society， 1996， 143（1）： 100-114.
15	ANSEAN D， GARCIA V M， GONZALEZ M， et al. Lithium-ion battery degradation indicators via incremental capacity analysis［J］. IEEE Transactions on Industry Applications， 2019， 55： 2992–3002.
16	BIRKL C ， ROBERTS M R， McTURK E， et al. Degradation diagnostics for lithium ion cells［J］. Journal of Power Sources， 2017， 341： 373-386.
17	HAN X， LU L， ZHENG Y， et al. A review on the key issues of the lithium ion battery degradation among the whole life cycle［J］. eTransportation， 2019， 1： 100005.
18	PAN H， LU Z， WANG H， et al. Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine［J］. Energy， 2018， 160： 466-477.
19	周世杰，李顶根. 基于超声测量及神经网络的锂离子动力电池SOC 估算［J］. 汽车工程学报， 2021， 11 （1）： 19-24.
	ZHOU S， LI D. SOC estimation for lithium ion power batteries on ultrasonic measurement and neural networks［J］. Chinese Journal of Automotive Engineering， 2021， 11 （1）： 19-24.

[1]	LI Huan, HUANG Ying, ZHANG Fu-Jun, ZHAO Yu, GE Yan-Wu. [J]. , 2016, 38(12): 1420 -1426 .
[2]	HAO Han, WANG Si-南, LI Xiao, LIU Zong-Wei, ZHAO Fu-Quan. [J]. , 2017, 39(1): 1 -8 .
[3]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[4]	JIANG Ting, DAI Bing. [J]. , 2016, 38(12): 1459 -1466 .
[5]	ZHANG Yi-Bing, 吕Jian-Jun , YUAN Guang-Hui. [J]. , 2016, 38(12): 1467 -1470 .
[6]	ZHANG Lei, ZHANG Jin-Qiu, LUO Tao, BI Zhan-Dong, YAO Jun. [J]. , 2016, 38(12): 1494 -1499 .
[7]	CUI An, LI Bin, WANG Xue-Liang, ZHANG Shi-Zhan. [J]. , 2016, 38(12): 1521 -1525 .
[8]	CHU Liang, ZHAO Di, LI Wen-Hui. [J]. , 2017, 39(1): 61 -65 .
[9]	FENG Chong, ZHANG Dong-Hao, LUO Yu-Gong, LI Ke-Qiang. [J]. , 2017, 39(1): 66 -72 .
[10]	HAN Xiao-Mei, LIN Xue-Dong, LI De-Gang. [J]. , 2017, 39(1): 23 -27 .

电池	极大值	极小值	电压差	PICC
Cell 1	0.958	-0.797	0.991	0.918
Cell 2	0.973	-0.671	0.956	0.882
Cell 3	0.943	-0.777	0.991	0.928
Cell 4	0.941	-0.917	0.993	0.953
Cell 5	0.901	-0.766	0.993	0.957
Cell 6	0.944	-0.701	0.991	0.949
Cell 7	0.940	-0.727	0.986	0.924
Cell 8	0.962	-0.763	0.991	0.908

电池	MAE			MAPE/%			RMSE
电池	IC特征	温度特征	联合特征	IC特征	温度特征	联合特征	IC特征	温度特征	联合特征
Cell 1	0.005 6	0.003 6	0.002 1	0.71	0.44	0.26	0.007 7	0.004 7	0.002 9
Cell 2	0.006 1	0.004 5	0.004 1	0.81	0.59	0.53	0.011 8	0.009 4	0.007 8
Cell 3	0.004 8	0.003 4	0.003 4	0.58	0.42	0.41	0.006 7	0.004 5	0.004 8
Cell 4	0.004 4	0.002 4	0.001 8	0.52	0.28	0.22	0.005 7	0.003 2	0.002 4
Cell 5	0.002 4	0.003 8	0.002 3	0.28	0.44	0.27	0.004 1	0.006 1	0.003 5
Cell 6	0.004 6	0.003 1	0.003 8	0.54	0.37	0.45	0.007 8	0.004 6	0.005 6
Cell 7	0.004 2	0.003 6	0.002 8	0.50	0.43	0.34	0.005 5	0.004 8	0.004 2
Cell 8	0.005 2	0.002 9	0.003 7	0.63	0.36	0.45	0.006 9	0.004 4	0.004 6

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Estimation Method of State of Health of Lithium Battery Based on Surface Temperature and Incremental Capacity

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