基于模糊神经网络的电动汽车电子差速控制系统的研究与开发

发布时间：2018-12-14 13:24

【摘要】：新能源汽车作为未来汽车发展的重要方向,越来越受到各国的重视。其中电动汽车作为新能源汽车的重要分支,一直是世界各国学者和汽车厂商研究的重点,并取得了许多可喜的成果。轮毂电机驱动的电动汽车因其结构简单、控制灵活等优点,其发展近年来备受瞩目。轮毂电机直接集成在汽车轮胎内部,不需要传统汽车的离合器、传动机构和减速箱等机械结构,因而大大简化了汽车的底盘结构,减轻整车质量。另一方面,每个轮毂电机通过一个控制器控制,可实现每个车轮的独立控制,大大增加了汽车行驶的灵活性,同时也给电机控制系统提出了更高的要求。本文主要研究双轮毂电机驱动电动汽车的电子差速控制系统及其控制策略。第一章,详细介绍了国内外电动汽车近年的发展状况,重点介绍了轮毂电机驱动的电动汽车及其控制技术。第二章,以四轮汽车的Ackerman-Jeantand转向模型为基础,推导出汽车在转向时每个车轮的转速与汽车速度和方向盘转向角的关系。第三章,详细介绍了模糊神经网络理论,包括模糊控制、径向基神经网络和Takagi-Sugeno型模糊神经网络,并研究了网络的结构和算法。第四章,根据模糊神经网络理论,研究了用于驱动双轮毂电机的电子差速控制系统,包括控制算法、系统硬件设计和系统软件设计。第五章,在MATLAB中构建了电子差速控制系统的Simulink仿真模型,包括轮毂电机仿真模型、模糊神经网络控制器和模糊PID控制器。最后给出两后轮毂驱动的电动汽车的道路试验。第六章,总结全文并展望电动汽车未来的发展。研究结果表明,基于模糊神经网络的电动汽车电子差速控制系统能够实现轮毂电机驱动的电动汽车在转向时两个驱动轮的差速旋转,两个轮毂电机的转速符合Ackerman-Jeantand转向模型对两个后轮转速的要求,误差控制在设计要求以内。
[Abstract]:As an important direction of automobile development in the future, new energy vehicles are paid more and more attention by many countries. As an important branch of new energy vehicles, electric vehicles have been the focus of research by scholars and automobile manufacturers all over the world, and many gratifying results have been achieved. The development of electric vehicle driven by hub motor has attracted much attention in recent years because of its simple structure and flexible control. The hub motor is directly integrated into the tire of the automobile without the need of the traditional mechanical structure such as clutch, transmission and deceleration box, which greatly simplifies the chassis structure of the vehicle and reduces the quality of the whole vehicle. On the other hand, each hub motor can be controlled by a controller, which can realize the independent control of each wheel, greatly increase the flexibility of the vehicle driving, and put forward higher requirements for the motor control system. This paper mainly studies the electronic differential control system and its control strategy of electric vehicle driven by double hub motor. In the first chapter, the development of electric vehicles at home and abroad is introduced in detail, with emphasis on the wheel motor driven electric vehicles and their control technology. In the second chapter, based on the Ackerman-Jeantand steering model of a four-wheel vehicle, the relationship between the rotational speed of each wheel and the speed and steering angle of the steering wheel is derived. In chapter 3, the theory of fuzzy neural network is introduced in detail, including fuzzy control, radial basis function neural network and Takagi-Sugeno type fuzzy neural network, and the structure and algorithm of the network are studied. In chapter 4, according to the theory of fuzzy neural network, the electronic differential control system for driving double hub motor is studied, including control algorithm, system hardware design and system software design. In chapter 5, the Simulink simulation model of electronic differential control system is constructed in MATLAB, including hub motor simulation model, fuzzy neural network controller and fuzzy PID controller. Finally, the road test of electric vehicle driven by two rear wheels is given. Chapter six summarizes the full text and looks forward to the future development of electric vehicles. The results show that the electronic differential speed control system of electric vehicle based on fuzzy neural network can realize the differential rotation of two driving wheels of electric vehicle driven by hub motor. The speed of the two hub motors meets the requirements of the Ackerman-Jeantand steering model for the speed of the two rear wheels, and the error is controlled within the design requirements.
【学位授予单位】：浙江农林大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U469.72

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