| 基于CMAC神经网络的电池荷电状态估计 |
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| 引用本文： | 汤哲,刘万臣,郑果.基于CMAC神经网络的电池荷电状态估计[J].计算机工程,2011,37(14):200-201. |
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| 作者姓名： | 汤哲 刘万臣 郑果 |
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| 作者单位： | 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 |
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| 基金项目： | 湖南省自然科学基金资助项目 |
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| 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。
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| 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 |
| 收稿时间： | 2011-02-28 |
Battery State of Charge Estimation Based on Cerebellar Model Articulation Controller Neural Network |
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| TANG Zhe,LIU Wan-chen,ZHENG Guo.Battery State of Charge Estimation Based on Cerebellar Model Articulation Controller Neural Network[J].Computer Engineering,2011,37(14):200-201. |
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| Authors: | TANG Zhe LIU Wan-chen ZHENG Guo |
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| Affiliation: | 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) |
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| 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 Prooagation（BP） neural network, it can meet the real time requirement in SOC, and the estimation error of the CMAC is acceptable. |
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| Keywords: | Cerebellar Model Articulation Controller（CMAC） neural network battery State of Charge（SOC） embedded system |
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