|Table of Contents|

Prediction for state of health of lithium-ion batteries by genetic algorithm and support vector regression(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年03期
Page:
329-
Research Field:
Publishing date:

Info

Title:
Prediction for state of health of lithium-ion batteries by genetic algorithm and support vector regression
Author(s):
Liu Hao1Hu Mingxin2Zhu Yiheng2Yu Dongjun2
1.NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing 210003,China; 2.School of Computer Science and Engineering,Nanjing University of Science and Technology, Nanjing 210094,China
Keywords:
genetic algorithm support vector regression lithium-ion batteries state of health hyper-parameter optimization
PACS:
TM912.1
DOI:
10.14177/j.cnki.32-1397n.2018.42.03.011
Abstract:
A joint algorithm based on genetic algorithm(GA)and support vector regression(GA-SVR)is proposed to improve the prediction accuracy of state of health(SOH)for lithium-ion batteries. GA is used to optimize the hyper-parameters in SVR model. Several chromosomes are initialized randomly by GA-SVR,each includes the hyper-parameters of SVR. The fitness of each chromosome is calculated by a fitness function. The hyper-parameters information of chromosomes is updated by selection,crossover and mutation according to the fitness. A chromosome with the highest fitness is chosen after multiple iterations. The SVR is trained as a prediction model based on the hyper-parameters of the selected chromosome. The experimental results on batteries datasets of National Aeronautics and Space Administration(NASA)of the USA show that the proposed GA-SVR outperforms the four popular SOH predictors,including spectral mixture kernel-Gaussian process regression(SMK-GPR),periodic covariance function-multiscale Gaussian process regression(P-MGPR),squared exponential function-multiscale Gaussian process regression(SE-MGPR),improved particle swarm optimization-support vector regression(IPSO-SVR).

References:

[1] Zhang Jingliang,Lee J. A review on prognostics and health monitoring of li-ion battery[J]. Journal of Power Sources,2011,196(15):6007-6014. [2]Kim J G,Son B,Mukherjee S,et al. A review of lithium and non-lithium based solid state batteries[J]. Journal of Power Sources,2015,282(1):299-322. [3]Liu Datong,Pang Jingyue,Zhou Jianbao,et al. Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression[J]. Microelectronics Reliability,2013,53(6):832-839. [4]Biagetti T,Sciubba E. Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems[J]. Energy,2004,29(12-15):2553-2572. [5]Zheng Xiujuan,Fang Huajing. An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction[J]. Reliability Engineering & System Safety,2015,144(6):74-82. [6]Yu Jianbo. State-of-health monitoring and prediction of lithium-ion battery using probabilistic indication and state-space model[J]. IEEE Transactions on Instrumentation & Measurement,2015,64(11):2937-2949. [7]Mo Baohua,Yu Jingsong,Tang Diyin,et al. A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter[C]//Proceedings of the IEEE International Conference on Prognostics and Health Management. Ottawa,Canada:IEEE,2016:1-5. [8]赖少发,刘华军. 机动目标跟踪支持向量回归学习新方法[J]. 南京理工大学学报,2017,41(2):264-268. Lai Shaofa,Liu Huajun. Novel approach in maneuvering target tracking based on support vector regression[J]. Journal of Nanjing University of Science and Technology,2017,41(2):264-268. [9]Wang Shuai,Zhao Lingling,Su Xiaohong,et al. Prognostics of lithium-ion batteries based on battery performance analysis and flexible support vector regression[J]. Energies,2014,7(10):6492-6508. [10]Dong Hancheng,Jin Xiaoning,Lou Yangbing,et al. Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter[J]. Journal of Power Sources,2014,271(11):114-123. [11]Qin Taichun,Zeng Shengkui,Guo Jianbin. Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model[J]. Microelectronics Reliability,2015,55(9-10):1280-1284. [12]Ng S S Y,Xing Yinjiao,Tsui K L. A naive Bayes model for robust remaining useful life prediction of lithium-ion battery[J]. Applied Energy,2014,118(4):114-123. [13]王健峰,张磊,陈国兴,等. 基于改进的网格搜索法的SVM参数优化[J]. 应用科技,2012,39(3):28-31. Wang Jianfeng,Zhang Lei,Chen Guoxing,et al. A parameter optimization method for an SVM based on improved grid search algorithm[J]. Applied Science and Technology,2012,39(3):28-31. [14]张讲社,郭高. 加权稳健支撑向量回归方法[J]. 计算机学报,2005,28(7):1171-1177. Zhang Jiangshe,Guo Gao. Reweighted robust support vector regression method[J]. Chinese Journal of Computers,2005,28(7):1171-1177. [15]田盛丰. 基于核函数的学习算法[J]. 北方交通大学学报,2003,27(2):1-8. Tian Shengfeng. Kernal-based learning algorithms[J]. Journal of Northern Jiaotong University,2003,27(2):1-8. [16]边霞,米良. 遗传算法理论及其应用研究进展[J]. 计算机应用研究,2010,27(7):2425-2429. Bian Xia,Mi Liang. Development on genetic algorithm theory and its applications[J]. Computer Application Research,2010,27(7):2425-2429. [17]Saha B,Goebel K. Battery data set[R]. California,USA:NASA Ames Prognostics Data Repository,2007. [18]He Yijun,Shen Jiani,Shen Jifu,et al. State of health estimation of lithium-ion batteries:A multiscale Gaussian process regression modeling approach[J]. Aiche Journal,2015,61(5):1589-1600.

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Last Update: 2018-06-30