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计算机工程 ›› 2011, Vol. 37 ›› Issue (14): 200-201. doi: 10.3969/j.issn.1000-3428.2011.14.067

• 人工智能及识别技术 • 上一篇    下一篇

基于CMAC神经网络的电池荷电状态估计

汤 哲 1,刘万臣 1,郑 果 2   

  1. (1. 中南大学信息科学与工程学院,长沙 410083;2. 中国电子科技集团公司第三十二研究所,上海 200233)
  • 收稿日期:2011-02-28 出版日期:2011-07-20 发布日期:2011-07-20
  • 作者简介:汤 哲(1977-),男,副教授,主研方向:智能控制,自动化控制;刘万臣,硕士研究生;郑 果,工程师
  • 基金资助:

    湖南省自然科学基金资助项目(09JJ5039)

Battery State of Charge Estimation Based on Cerebellar Model Articulation Controller Neural Network

TANG Zhe 1, LIU Wan-chen 1, ZHENG Guo 2   

  1. (1. School of Information Science and Engineering, Central South University, Changsha 410083, China; 2. The 32nd Research Institute of China Electronic Technology Group Corporation, Shanghai 200233, China)
  • Received:2011-02-28 Online:2011-07-20 Published:2011-07-20

摘要:

现有电池荷电状态(SOC)估计方法所需训练和学习时间较长,很难满足动力电池的实时性要求。为解决该问题,利用小脑模型关节控制器(CMAC)神经网络对电池SOC进行评估,CMAC神经网络具有学习算法简单和逼近任意非线性函数的能力。对镍氢电池的模拟测试结果表明,与反向传播神经网络相比,CMAC神经网络的学习和收敛速度较快,能实时估计出电池SOC,并使估计误差在可接受范围内。

关键词: 小脑模型关节控制器, 神经网络, 电池荷电状态, 嵌入式系统

Abstract:

Existing battery State of Charge(SOC) estimation methods are time consuming for the training and learning process, and it restricts the application in electrical vehicles. In order to resolve the problem, this paper uses Cerebellar Model Articulation Controller(CMAC) neural network to estimate SOC. The CMAC neural network has simpler learning algorithms and it has the ability of approximating arbitrary nonlinear functions. Experiment using the data of nickel hydride batteries demonstrate the better learning speed and convergence of CMAC method compared with Back Propagation(BP) neural network, it can meet the real time requirement in SOC, and the estimation error of the CMAC is acceptable.

Key words: Cerebellar Model Articulation Controller(CMAC), neural network, battery State of Charge(SOC), embedded system

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