基于ARM9的嵌入式系统在电动汽车电源管理系统中的应用

发布时间：2019-04-13 18:06

【摘要】：随着信息技术的发展，嵌入式系统被广泛的应用于日常生活的方方面面，嵌入式系统在汽车电子、网络设备、航空航天、微控制、消费电子、工业控制、安全系统等领域方兴未艾，尤其是在汽车电子领域已成为汽车整车不可分割的一部分。由于我国国民经济的发展和人民生活水平的日益提高，汽车开始走进千家万户，与此同时带来的环境问题也日益突出，电动汽车以其零排放、低噪音的优点越来越受到人们的青睐。电源管理系统作为电动汽车的关键部分，也越来越受到各个国家的重视。本文重点研究了电动汽车电源管理系统的特点和未来的发展方向，设计了一种基于ARM9的嵌入式电源管理系统。由于传统的电源管理系统对电池组剩余电量测量时受外界影响因素很大，使我们对系统建立具体模型很困难，为此本文采用了神经网络算法对电池组剩余电量进行预测，神经网络算法无需建立复杂的电池模型，，但神经网络算法需要很多组测量值对网络进行训练，需要很长的训练时间，同时网络易陷入局部极值，为了克服这些缺点，采用了自适应遗传算法优化后的神经网络算法，通过仿真实验，证实此算法具有很好的稳定性和控制精度。使用三星公司的ARM9内核芯片S3C2410作为系统的主控制器，进行了最小系统设计，同时设计了数据采集电路和热管理电路，并进行了相应的软件开发。最后在实验室环境中进行运行调试，使系统具有了良好的稳定性。
[Abstract]:With the development of information technology, embedded systems are widely used in all aspects of daily life. Embedded systems are used in automotive electronics, network equipment, aerospace, micro-control, consumer electronics, industrial control, and so on. Safety systems and other fields are in the ascendant, especially in the field of automotive electronics has become an integral part of the vehicle. As a result of the development of our country's national economy and the increasing living standard of the people, cars begin to enter thousands of households, and at the same time, the environmental problems brought about by them are becoming more and more prominent. With the zero emission of electric vehicles, The advantages of low noise are getting more and more popular. As a key part of electric vehicle, power management system has been paid more and more attention by many countries. This paper focuses on the characteristics and future development of electric vehicle power management system, and designs an embedded power management system based on ARM9. Because the traditional power management system is greatly influenced by the external factors, it is very difficult for us to establish the specific model of the system. Therefore, the neural network algorithm is used to predict the residual power of the battery pack, which makes it very difficult for us to establish the specific model of the system. The neural network algorithm does not need to establish the complex battery model, but the neural network algorithm needs a lot of measured values to train the network, and needs a long training time. At the same time, the network is easy to fall into the local extremum, in order to overcome these shortcomings. The neural network algorithm optimized by adaptive genetic algorithm is adopted. The simulation results show that the algorithm has good stability and control precision. Using the ARM9 core chip S3C2410 of Samsung Company as the main controller of the system, the minimum system design is carried out. At the same time, the data acquisition circuit and the thermal management circuit are designed, and the corresponding software development is carried out. Finally, running and debugging in the laboratory environment, the system has a good stability.
【学位授予单位】：燕山大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP368.1;TP315

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