| 改进D-S证据理论在电动汽车锂电池故障诊断中的应用 |
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| 引用本文： | 夏飞,,,马茜,,张浩,,,彭道刚,,孙朋,,罗志疆,.改进D-S证据理论在电动汽车锂电池故障诊断中的应用[J].智能系统学报,2017,12(4):526-537. |
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| 作者姓名： | 夏飞 马茜 张浩 彭道刚 孙朋 罗志疆 |
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| 作者单位： | 1. 上海电力学院自动化工程学院, 上海 200090;2. 上海发电过程智能管控工程技术研究中心, 上海 200090;3. 同济大学电子与信息工程学院, 上海 201804 |
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| 摘 要： | 针对电动汽车电池系统的故障采用基于神经网络的改进D-S证据理论组合规则完成诊断过程。为了避免单一途径的诊断可能造成故障漏检误检的状况，决策层采用D-S证据理论组合规则来确定基于BP网络和RBF网络两种故障诊断算法结果。然而为了克服D-S证据理论处理高度冲突证据的缺陷，本文提出了一种基于神经网络改进的D-S证据理论组合规则。首先，采用神经网络对电池故障进行初步诊断，结合网络诊断准确率来分配不确定信息并构造证据体，又引入了证据间的支持矩阵来确定新的加权证据体。然后，把各个焦元的信任度融入D-S证据理论组合规则，从而融合神经网络证据体及新加权证据体。最后，依据决策准则确定锂电池系统的故障状态。通过仿真实验验证了本文提出的改进D-S证据理论融合诊断方法在电动汽车锂电池故障诊断中的有效性。
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| 关 键 词： | 故障诊断 电动汽车 锂电池 改进证据理论 信息融合 |
Application of improved D-S evidence theory in fault diagnosis of lithium batteries in electric vehicles |
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| XIA Fei,,,MA Xi,,ZHANG Hao,,,PENG Daogang,,SUN Peng,,LUO Zhijiang,.Application of improved D-S evidence theory in fault diagnosis of lithium batteries in electric vehicles[J].CAAL Transactions on Intelligent Systems,2017,12(4):526-537. |
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| Authors: | XIA Fei MA Xi ZHANG Hao PENG Daogang SUN Peng LUO Zhijiang |
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| Affiliation: | 1. College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;2. Shanghai Engineering Research Center of Intelligent Management and Control for Power Process, Shanghai 200090, China;3. College of Electronics a |
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| Abstract: | In this study,we used the improved Dempster-Shafer (D-S) evidence theory combination rules based on the neural network to construct a fault diagnosis process for an electric vehicle battery system.To avoid misdiagnoses and missed diagnoses caused by a single fault diagnosis method,we applied the D-S evidence theory combination principle to determine the result based on the back-propagation (BP) network and radial basis function (RBF) network fault diagnosis algorithm.However,to overcome the defects in the D-S evidence theory in dealing with highly conflicting evidence,we propose a D-S evidence theory combination principle based on an improved neural network.First,we apply a neural network to perform a preliminary diagnosis regarding battery failure and the accuracy of the network diagnosis.Then,we distribute indefinite information and construct a body of evidence.We also introduce a support matrix of this evidence to determine a new weighted body of evidence.We then integrate the credibility of every focal element into the D-S evidence theory combination rules to fuse the neural network body of evidence with the new weighted body of evidence.Lastly,based on the decision criterion,we determine the failure state of the lithium battery system.Our simulation results show that our proposed improved D-S evidence theory fusion diagnosis method is effective in the fault diagnosis of electric vehicles with lithium batteries. |
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| Keywords: | fault diagnosis electric vehicle lithium battery improved evidence theory information fusion |
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