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Prediction for state of charge of lead-acid battery by particleswarm optimization with Gaussian process regression(PDF)


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Prediction for state of charge of lead-acid battery by particleswarm optimization with Gaussian process regression
Xu BintaiMeng XiangluTian AnqiSun YongjianCao LibinJiang Yingjie
Information & Telecommunication Company,State Grid Shandong ElectricPower Company,Jinan 250001,China
state of charge of lead-acid batteries Gaussian process regression particle swarm optimization hyperparameter optimization
To improve the prediction accuracy of state of charge(SOC)for lead-acid batteries,a particle swarm optimization with Gaussian process regression(PSO-GPR)is designed here. The basic idea of PSO-GPR is using the particle swarm optimization(PSO)to optimize the hyperparameters of the Gaussian process regression(GPR). Firstly,the PSO-GPR randomly initializes several particles,and every parlicle contains the hyperparameters of GPR. Then the following iterations are executed:for each particle,the corresponding GPR is trained and evaluated by its information of hyperparameters; the fitness function is combined with the evaluation result of GPR to calculate the fitness of each particle,and then the hyperparameter information of all particles is updated. After multiple iterations,the particle sharing the lowest fitness is chosen,and the corresponding hyperparameters are extracted to train the final GPR. The experiment results in lead-acid battery datasets demonstrat that the proposed PSO-GPR outperforms other comparison models and shares the broad prospects.


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Last Update: 2018-04-30