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


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Prediction for state of health of lithium-ion batteries by genetic algorithm and support vector regression
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
genetic algorithm support vector regression lithium-ion batteries state of health hyper-parameter optimization
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).


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Last Update: 2018-06-30