摘要
作为燃料电池汽车的核心单元,车用燃料电池系统由于结构复杂、运行环境恶劣以及动态响应能力较差等原因在实际应用中难免出现失效、故障甚至重大事故,因此,对其开展故障诊断研究对提高其自身以及燃料电池汽车的安全性、可靠性、可维护性以及市场接受能力具有重要意义。本文以自主研发的60kW车用燃料电池系统为对象,对整体系统安全性与可维护性、辅助系统和电堆的可靠性等涉及到的若干关键问题进行了研究。本文的主要研究成果如下:
较系统地提出了车用燃料电池系统故障特征与故障机理的共性问题,分别从耦合性、渐变性、随机性和不确定方面定性分析了其故障特征,从机械因素、电气因素、设备因素、人为因素和环境因素方面定性分析了其故障机理,对自主研发的车用燃料电池系统进行了故障分类并划分了其故障等级。
针对车用燃料电池系统的氢安全,建立了氢气泄漏的故障树模型并进行了定性分析,找出了系统氢安全的薄弱环节;为了进行系统氢气泄漏的安全性定量评价,考虑到各底事件的发生概率难以精确化,将模糊数学理论与传统故障树相结合定量分析了顶事件发生的模糊概率以及各个底事件的模糊重要度,并提出了系统氢气泄漏的故障诊断策略与整改措施。
为了进行辅助系统的多传感器故障检测与信号容错,提出并建立了一种主、从两级神经网络的联合故障诊断与容错模型,利用主网络和各个子网络的预测值与多传感器的实际输出值构建残差,当主网络的误差大于所设定的阈值并且持续的时间节拍大于一定值时判断传感器存在故障,此时利用误差超过设定阈值的子网络的预测值代替所对应的故障传感器输出值并进行在线刷新,针对不同数量下的传感器同时发生故障情况进行了仿真,仿真结果表明,所建立的模型可以实现这些存在冗余关系的多传感器的故障联合检测和信号冗余重构,能满足车用燃料电池系统的温度和风量容错控制要求。
考虑到电堆水淹和膜干两种故障难以准确识别,提出了二叉树支持向量机和D-S证据理论相结合的电堆健康诊断信息融合方法,从影响和反映电堆水平衡传输的实时参数及其变化参数中提取故障特征,将二叉树支持向量机进行初步诊断,利用D-S证据理论进行融合得出最终诊断结果。仿真结果表明,单独利用支持向量机比神经网络具有更高的诊断精度和识别速度,对于少数测试样本进行诊断时证据冲突所带来的不确定性,利用D-S证据理论进行融合后得出的电堆健康状态诊断结论具有较高的信任度,不确定性明显降低,可为电堆的湿度软测量与优化控制提供便利。
最后,结合车用燃料电池系统离线和在线故障诊断与实际维护的需要,开发了一种嵌入式的手持故障诊断仪,设计了实用的故障码并提出了故障诊断策略与流程,此外,基于VC++编程语言设计了可视化的状态监测与故障诊断专家系统,完成了原型机基本功能的开发,诊断实例和结果证明了其实用性。
综上所述,本文分别从氢气泄漏、冗余多传感器的故障诊断与容错、电堆健康状态诊断以及状态监测与故障诊断系统设计这4个关键问题出发,提出了它们的研究方法和思路,相关计算和仿真结果验证了它们的可行性和有效性,可为今后进一步开展车用燃料电池系统故障诊断与维护所涉及到的其它问题研究提供一些参考。
As the dominating part of Fuel Cell Electric Vehicle(FCEV), atuomotive fuel cell system can hardly avoid malfunction, faults or even serious accidents for its complicated structure, atrocious operational circumstance and poor dynamic permformance, so it is important to study on its fault diagonosis to enhance the safety, relability, maintainability and market acceptance of both itself and FCEV. Based on the 60kW automotive fuel cell system designed by our group, in this paper, some key problems related to the safety and maintainability of the whole system, and the relability of its accessorial subsystems and fuel cell stack are studied. The main research works are as follows:
The common problems of automotive fuel cell system's fault characteristic and fault mechanism are put forward systemically, the fault characteristic is qualitatively analyzed from the points of coupling, gradual change, randomicity and uncertainty, and the fault mechanism is qualitatively analyzed from the aspects of mechanism factor, electric fator, device factors, human factors and environmental factors, based on the automotive fuel cell system designed, its faults are classified and ranked.
According to the hydrogen safety of the whole system, the fault tree analysis model of hydrogen leaking is established and qualitatively analyzed, its weakness is figured out, to evaluate the hydrogen safety, considering the uncertain fault probability of each elementary event, the fuzzy maths theory is combined into the traditional fault tree analysis method to quantitatively analyze the probability of top event and the importance degree of each elementary event, then some improvement measures and advice on hydrogen leaking are brought forward accordingly.
As to the fault dianosis and signals fault tolerant of multisensors in the accessorial systems, the united fault diagnosis and fault tolerant model based on two layer main and sub neural networks is set up, the difference between the predictive output of the main neural network and subnetworks with the sensors's practical output is constructed, these sensors are diagnosed to be in fault when the difference of the main network exceeds the threshold setted and lasts for the threshold steps time, and the output of certain sub network is used to replace the practical output of its corresponding sensor. Finally, the simulation is presented, it proves that the model set up can be effectively used to the fault diagosis and signals restoration of those sensors which have redundancey relationship, and it can meet the needs of the both temperature and air flow fault tolerant control of automotive fuel cell system.
Considering flooding and drying of fuel cell stack are difficult to be inerrably distinguished, an information fusion fault dignosis method combining binary tree support vector machine(SVM) and D-S evident theory is put forward, the fault characteristic is extracted from the real time process variables and their variational parameters feature which affect and reflect the water transmission in fuel cell stack, the output of binary tree SVM is utilized as the initial healthy state diagosis results, then they are fused with D-S evident theory to get the final fault dignosis conclusion. The simulation results reveal that, compared with neural networks it has preponderant precision and speed with binary tree SVM, and the belief degree is high and the uncertainty of the conclusion is largely lowered down when the conflicting results of SVMs are fused with D-S evident theory.
Finally, according to the requirement of off-line and online fault diagosis and maintenance, an embedded handheld fault diagosis instrument is designed, the diagnostic trouble code(DTC) is designed and the fualt diagnosis strategy and flow are provided. Besides, the visual state monitoring and fault diagnosis expert ssystem is designed with VC++ languange, the specific interfaces are realized, and the fault diagnosis example and its results prove their practicability.
Above all, hydrogen leaking, multi sensor fault diagnosis and tolerant, fuel cell stack healthy state diagnosis, the design of monitoring and fault diagnosis system are studied in this paper, the calculation and simulation results show the methods and ideas adopted are effective and practical, which can provide some reference to some other problems on its fault dignosis and maintenance in future.
引文
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