[1]刘 皓,胡明昕,朱一亨,等.基于遗传算法和支持向量回归的锂电池健康状态预测[J].南京理工大学学报(自然科学版),2018,42(03):329.[doi:10.14177/j.cnki.32-1397n.2018.42.03.011]
　Liu Hao,Hu Mingxin,Zhu Yiheng,et al.Prediction for state of health of lithium-ion batteries by geneticalgorithm and support vector regression[J].Journal of Nanjing University of Science and Technology,2018,42(03):329.[doi:10.14177/j.cnki.32-1397n.2018.42.03.011]
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基于遗传算法和支持向量回归的锂电池 健康状态预测()
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《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年03期
页码:
329
栏目:
出版日期:
2018-06-30

文章信息/Info

Title:
Prediction for state of health of lithium-ion batteries by genetic algorithm and support vector regression
文章编号:
1005-9830(2018)03-0329-06
作者:
刘 皓1; 胡明昕2; 朱一亨2; 於东军2
1.南瑞集团(国网电力科学院)有限公司,江苏 南京 210003; 2.南京理工大学 计算机科学与工程学院,江苏 南京 210094
Author(s):
Liu Hao1; Hu Mingxin2; Zhu Yiheng2; Yu 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
关键词:
遗传算法;  支持向量回归;  锂电池;  健康状态;  超参数优化
Keywords:
genetic algorithm;  support vector regression;  lithium-ion batteries;  state of health;  hyper-parameter optimization
分类号:
TM912.1
DOI:
10.14177/j.cnki.32-1397n.2018.42.03.011
摘要:
为了提高锂电池健康状态(SOH)的预测精度,该文提出了1种基于遗传算法和支持向量回归(GA-SVR)的联合算法。通过GA解决SVR模型中的超参数优化问题。GA-SVR随机生成1组染色体,每个染色体包含了相应的SVR超参数信息。利用适应度函数计算出每条染色体的适应度值。根据适应度值对染色体进行选择、基因重组和变异等遗传操作,从而更新染色体的超参数信息。经过多次迭代后,找到适应度最大的染色体。从该染色体中提取相应的超参数信息,并训练最终的SVR预测模型。在美国国家航空航天局(NASA)锂电池数据集上的实验结果表明,该文算法优于基于混合像元核函数的高斯过程回归(SMK-GPR)算法、基于多尺度周期协方差函数的高斯过程回归(P-MGPR)算法、基于多尺度平方指数函数的高斯过程回归(SE-MGPR)算法和改进的基于粒子群优化的支持向量回归(IPSO-SVR)算法。
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).

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-02-27 修回日期:2018-04-08 基金项目:国家自然科学基金(61772273; 61373062) 作者简介:刘皓(1979-),男,硕士,主要研究方向:计算机应用,E-mail:liuhao9@sgepri.sgcc.com.cn; 通讯作者:於东军(1975-),男,博士,教授,主要研究方向:模式识别与智能信息处理、计算机应用,E-mail:njyudj@ njust.edu.cn。 引文格式:刘皓,胡明昕,朱一亨,等. 基于遗传算法和支持向量回归的锂电池健康状态预测[J]. 南京理工大学学报,2018,42(3):329-334. 投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-06-30