南京理工大学学报（自然科学版）2018，Vol.42Issue(2)：162-168,7.DOI:10.14177/j.cnki.32-1397n.2018.42.02.005
基于粒子群优化及高斯过程回归的铅酸电池荷电状态预测
Prediction for state of charge of lead-acid battery by particle swarm optimization with Gaussian process regression
徐彬泰 1孟祥鹿 1田安琪 1孙勇健 1曹立斌 1江颖洁1
作者信息
- 1. 国网山东省电力公司 信息通信公司,山东 济南250001
- 折叠
摘要
Abstract
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 hyperparame-ters;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.关键词
铅酸电池荷电状态/高斯过程回归/粒子群优化/超参数优化Key words
state of charge of lead-acid batteries/Gaussian process regression/particle swarm optimization/hyperparameter optimization分类
信息技术与安全科学引用本文复制引用
徐彬泰,孟祥鹿,田安琪,孙勇健,曹立斌,江颖洁..基于粒子群优化及高斯过程回归的铅酸电池荷电状态预测[J].南京理工大学学报（自然科学版）,2018,42(2):162-168,7.
