采用粒子群优化和高斯回归实现电池SOH估计

doi:10.19562/j.chinasae.qcgc.2021.10.008

Abstract

Abstract:

In order to accurately estimate the state of health （SOH） of lithium?ion battery in the process of nonlinear degradation， an AMPSO?GPR algorithm is proposed by the fusion of adaptive mutation particle swarm optimizer （AMPSO） with gaussian process regression （GPR）. Firstly， the increment of ohmic internal resistance and the sample entropy of voltage are extracted as degradation characterization indicators. Then AMPSO is introduced to optimize the hyperparameters of GPR kernel function， an SOH estimation framework based on AMPSO?GPR is constructed， and the degradation characterization indicators are extracted to perform SOH estimation. Finally， by comparing the results of SOH estimation using AMPSO?GPR with different kernel functions， the optimal kernel function is obtained. The results of experiment indicate that the AMPSO?GPR algorithm can effectively estimate the SOH of battery with a maximum absolute estimation error not more than 2.08%.

Key words: lithium?ion battery, state?of?health, gaussian process regression, adaptive mutation particle swarm optimizer, kernel functions

Lin Chen,Bohao Liu,Yunhui Ding,Shuxiao Wu,Zhe Feng,Haihong Pan. Estimation of Battery State⁃of⁃Health Using Particle Swarm Optimization with Gauss Process Regression[J].Automotive Engineering, 2021, 43(10): 1472-1478.

Figures/Tables 7

References 19

1	安富强，赵洪量，程志，等. 纯电动车用锂离子电池发展现状与研究进展［J］. 工程科学学报， 2019， 41（1）： 22-42.
	AN F Q， ZHAO H L， CHENG Z， et al. Development status and research progress of power battery for pure electric vehicles［J］. Chinese Journal of Engineering2019， 41（1）： 22-42.
2	LIPU M S H， HANNAN M A， HUSSAIN A， et al. A review of state of health and remaining useful life estimation methods for lithium⁃ion battery in electric vehicles： challenges and recommendations［J］. Journal of Cleaner Production， 2018（205）： 115-133.
3	ZHANG S， GUO X， DOU X， et al. A rapid online calculation method for state of health of lithium⁃ion battery based on coulomb counting method and differential voltage analysis［J］. Journal of Power Sources， 2020， 479（11）：228740.
4	MATTEO G， LUCIO C， CORRADO G， et al. Performance analysis and SOH （state of health） evaluation of lithium polymer batteries through electrochemical impedance spectroscopy［J］. Energy， 2015， 89： 678-686.
5	MAWONOU K S R， EDDAHECH A， DUMUR D， et al. State⁃of⁃health estimators coupled to a random forest approach forlithium⁃ion battery aging factor ranking［J］. Journal of Power Sources， 2021（484）： 229154.
6	CHEN Z， SUN M， SHU X， et al. Online state of health estimation for lithium⁃Ion batteries based on support vector machine［J］. Applied Sciences， 2018， 8（6）： 925.
7	YOU G， PARK S， OH D，et al. Real⁃time state⁃of⁃health estimation for electric vehicle batteries： a data⁃driven approach［J］. Applied Energy， 2016（176）： 92-103.
8	曾镖，徐位君，谈宸. 基于VMD⁃GPR的锂离子电池健康状态预测研究［J］. 南方农机， 2021， 52（5）：185-187.
	ZENG B， XU W J， TAN C. Battery health state prediction based on VMD⁃GPR［J］. China Southern Agricultural Machinery， 2021， 52（5）：185-187.
9	杨胜杰，罗冰洋，王菁，等. 基于容量增量曲线峰值区间特征参数的锂离子电池健康状态估算［J］. 电工技术学报， 2021， 36（11）： 2277-2287.
	YANG S J， LUO B Y， WANG J， et al. State of health estimation for lithium⁃ion batteries based on peak region feature parameters of incremental capacity curve［J］. Transactions of China Electrotechnical Society， 2021， 36（11）： 2277-2287.
10	郑雪莹，邓晓刚，曹玉苹. 基于能量加权高斯过程回归的锂离子电池健康状态预测［J］. 电子测量与仪器学报， 2020， 34（6）： 63-69.
	ZHENG X Y， DENG X G， CAO Y P. State of health prediction of lithium⁃ion batteries based on energy⁃weighted Gaussian process regression［J］. Journal of Electronic Measurement and Instrumentation， 2020， 34（6）： 63-69.
11	DAVID C F， PAU M M， SANDRA O C. Classification of fever patterns using a single extracted entropy feature： a feasibility study based on Sample Entropy［J］. Mathematical Biosciences and Engineering， 2019， 17（1）：235-249.
12	陈琳，王惠民，李熠婧，等. 用新陈代谢极限学习机实现电池健康状态估算［J］. 汽车工程， 2021， 43（1）： 10-18.
	CHEN L， WANG H M， LI Y J， et al. Battery state⁃of⁃health estimation based on metabolic extreme learning machine［J］. Automotive Engineering， 2021， 43（1）： 10-18.
13	CAO M D， ZHANG T， YU B， et al. A method for interval prediction of satellite battery state of health based on sample entropy［J］. IEEE Access， 2019（7）： 141549-141561.
14	CHEN L， WANG H M ，LIU B， et al. Battery state⁃of⁃health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation［J］. Energy， 2021，215： 119078.
15	谢文超，赵延明，方紫微，等. 带可变遗忘因子递推最小二乘法的超级电容模组等效模型参数辨识方法［J］. 电工技术学报， 2021， 36（5）： 996-1005.
	XIE W C， ZHAO Y M， FANG Z W， et al. Variable forgetting factor recursive least squales based parameter identification method for the equivalent circuit model of the supercapacitor cell module［J］. Transactions of China Electrotechnical Society， 2021， 36（5）： 996-1005.
16	PAN H H， LV Z Q， WANG H M， 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.
17	吕振肃，侯志荣. 自适应变异的粒子群优化算法［J］. 电子学报， 2004（3）： 416-420.
	LV Z S， HOU Z R. Particle swarm optimization with adaptive mutation［J］. Acta Electronica Sinica， 2004（3）： 416-420.
18	韦海燕，安晶晶，陈静，等. 基于改进粒子滤波算法实现锂离子电池RUL预测［J］. 汽车工程， 2019， 41（12）： 1377-1383.
	WEI H Y， AN J J， CHEN J， et al. RUL prediction of lithium⁃ion batteries based on the improved particle filter［J］. Automotive Engineering， 2019， 41（12）： 1377-1383.
19	李超然，肖飞，樊亚翔，等. 基于卷积神经网络的锂离子电池SOH估算［J］. 电工技术学报， 2020， 35（19）： 4106-4119.
	LI C R， XIAO F， FAN Y X， et al. An approach to lithium⁃ion battery SOH estimation based on convolutional neural network［J］. Transactions of China Electrotechnical Society， 2020， 35（19）： 4106-4119.

[1]	ZHOU Wei, ZHANG Cheng-Ning, LI Jun-Qiu. [J]. , 2016, 38(12): 1407 -1414 .
[2]	ZONG Yi-Qi, GU Zheng-Qi, LUO Ze-Min, JIANG Cai-Mao, ZHANG Qi-Dong, YANG Zhen-Dong. [J]. , 2016, 38(12): 1427 -1433 .
[3]	ZHANG Ying-Chao, ZHAN Da-Peng, ZHAO Jing, ZHANG Zhe, LI Jie. [J]. , 2016, 38(12): 1434 -1439 .
[4]	TANG You-Ming, YAN Ling-Bo, LUO Qian, CAO Li-Bo. [J]. , 2016, 38(12): 1452 -1458 .
[5]	ZHANG Lei, LU Jian-Wei, JIANG Jun-Zhao, YAN Pei-Lei, LI Lei. [J]. , 2016, 38(12): 1477 -1482 .
[6]	ZHAO Liang, ZHANG Qiang, GUO Kong-Hui. [J]. , 2017, 39(1): 86 -91 .
[7]	SHEN Deng-Feng, WANG Chen, YU Hai-Sheng, ZHANG Tong, YI Xian-Ke. [J]. , 2017, 39(1): 15 -22 .
[8]	ZOU Tie-Fang, HU Lin, LI Ping-Fan, YI Liang. [J]. , 2017, 39(1): 41 -46 .
[9]	ZHOU Kai, ZANG Jing-Lun. [J]. , 2017, 39(2): 127 -132 .
[10]	ZUO Wen-Jie, CHEN Ji-Shun, LI Yi-Wen, LIU Nian. [J]. , 2017, 39(2): 145 -149 .

电池编号	特性测试		老化循环测试
电池编号	工况	测试温度/ °C	工况	温度/ °C
S17	容量测试/NEDC	25	UDDS+脉冲放电	45
S30	容量测试/NEDC	35	脉冲放电	35
S42/43	容量测试 NEDC/UDDS/JP1015	10/25/40	NEDC+UDDS+J1015	45

核函数	最大绝对误差/%	平均绝对误差/%	均方根误差/%
SE核	3.23	0.70	0.979 3
Matern核	1.32	0.27	0.367 4
RQ核	2.20	0.52	0.712 5
LIN核	3.24	0.72	1.002 1

方法	最大绝对误差/%	平均绝对误差/%	均方根误差/%
AMPSO?GPR	1.32	0.27	0.367 4
GPR	2.14	0.36	0.508 5
BPNN	2.36	0.72	5.163 1
SVM	2.35	1.18	8.095 4

测试编号	训练数据源	测试数据源	最大绝对误差/%	平均绝对误差/%	均方根误差/%
第1组	S42@10 ℃ NEDC	S43@10 ℃ NEDC	1.34	0.41	0.523 7
第2组	S43@10 ℃ NEDC	S42@10 ℃ NEDC	1.38	0.39	0.475 1
第3组	S42@25 ℃ UDDS	S43@25 ℃ JP1015	1.32	0.27	0.367 4
第4组	S43@25 ℃ UDDS	S42@25 ℃ JP1015	1.65	0.46	0.638 1
第5组	S42@40 ℃ NEDC	S43@40 ℃ UDDS	1.79	0.40	0.564 0
第6组	S43@40 ℃ NEDC	S42@40 ℃ UDDS	1.54	0.35	0.496 8
第7组	S17@25 ℃ NEDC	S30@35 ℃ NEDC	2.08	0.47	0.640 9
第8组	S30@35 ℃ NEDC	S17@25 ℃ NEDC	1.87	0.53	0.696 9

Estimation of Battery State⁃of⁃Health Using Particle Swarm Optimization with Gauss Process Regression

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