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The state of charge estimation of power lithium battery based on RBF neural network optimized by particle swarm optimization

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成果类型:
期刊论文
作者:
Hou, Zhixiang;Xie, Ping;Hou, Jiqiang
通讯作者:
Hou, Zhixiang(736688480@qq.com)
作者机构:
[Hou, Zhixiang; Xie, Ping; Hou, Jiqiang] College of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410004, China
通讯机构:
College of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan, China
语种:
英文
关键词:
Battery management systems;Charging (batteries);Electric batteries;Electric vehicles;Lithium batteries;Optimization;Particle swarm optimization (PSO);Radial basis function networks;Vehicles;Non-linear relationships;Nonlinear problems;Particle swarm optimization algorithm;Radial basis function neural networks;RBF Neural Network;Searching ability;Stable operation;State-of-charge estimation;Secondary batteries
期刊:
Journal of Applied Science and Engineering
ISSN:
2708-9967
年:
2017
卷:
20
期:
4
页码:
483-490
机构署名:
本校为第一且通讯机构
院系归属:
汽车与机械工程学院
摘要:
In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear relationship between SOC and its influencing factors, RBF neural network has obvious advantages in solving nonlinear problems, so in this paper, an SOC estimation method of power battery based on RBF neural network is proposed. In order to improve the accuracy of SOC estimation, we use particle swarm optimization (PSO) to optimize the RBF neural network model and identify the value of...

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