电动汽车中直流无刷电机驱动系统研究
发布时间：2018-12-15 21:36
【摘要】：电动汽车采用电力驱动系统,可实现高效节能、低污染,同时促进了非石油资源的利用和开发,促进了电动汽车未来的发展方向。由于直流无刷电机具有体积小、效率高、性能可靠、调速范围广、噪声低等特点,在电动汽车中得到了广泛应用和发展。 针对电动汽车驱动系统的设计要求,在分析了国内外电动汽车及其驱动系统的发展状况的基础上,使用无位置传感器直流无刷电机作为电动汽车的驱动电机。通过对直流无刷电机的结构特性、工作原理、控制策略进行了深入的探讨和研究,并查阅了相关国外论文著作,基于现有无位置传感器检测技术的优缺点和应用场合的限制,使用扩展卡尔曼滤波算法(EKF)对直流无刷电机位置和转速进行估算。在对转速的实际控制过程中,当被控对象特性发生变化需要调节参数作相应调整,基于传统PID控制算法没有参数“自适应”整定功能,所以在传统PID控制器基础上提出了一种基于RBF神经网络PID控制器的设计方法,并在MATLAB中建立直流无刷电机电动汽车驱动系统仿真模型,通过对电机转速、转矩、电流、估计转速和位置的仿真结果进行分析,从而验证控制算法的可行性和正确性。基于STM32F407完成了电动汽车电机驱动控制系统的设计方案,并对其软硬件部分的设计进行了详细的介绍。 综合整篇文章,主要针对无位置传感器的直流无刷电机作为电动汽车驱动电机的控制方法及转子位置估算进行了研究,并结合RBF神经网络算法和扩展卡尔曼滤波算法(EKF),实现了本课题的需求。
[Abstract]:The electric vehicle adopts electric drive system, which can realize high efficiency and energy saving, low pollution, promote the utilization and development of non-oil resources, and promote the future development direction of electric vehicle. DC brushless motor has been widely used and developed in electric vehicles because of its small size, high efficiency, reliable performance, wide speed range and low noise. Based on the analysis of the development of electric vehicle and its drive system at home and abroad, the sensorless DC brushless motor is used as the driving motor of electric vehicle. The structure characteristics, working principle and control strategy of brushless DC motor are discussed and studied deeply, and related foreign papers are consulted. Based on the advantages and disadvantages of the existing sensorless detection technology and the limitation of its application, The extended Kalman filter (EKF) algorithm is used to estimate the position and speed of brushless DC motor. In the actual speed control process, when the characteristics of the controlled object change, the parameters need to be adjusted accordingly. Based on the traditional PID control algorithm, there is no "adaptive" tuning function of the parameters. Based on the traditional PID controller, a design method of PID controller based on RBF neural network is put forward, and the simulation model of drive system of DC brushless motor electric vehicle is established in MATLAB. The simulation results of the estimated speed and position are analyzed to verify the feasibility and correctness of the control algorithm. The design scheme of electric vehicle motor drive control system based on STM32F407 is completed, and the design of its hardware and software is introduced in detail. In this paper, the control method and rotor position estimation of sensorless DC brushless motor as driving motor of electric vehicle are studied, and combined with RBF neural network algorithm and extended Kalman filter algorithm (EKF),. The requirement of this subject is realized.
【学位授予单位】：安徽理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM33
本文编号：2381329
[Abstract]:The electric vehicle adopts electric drive system, which can realize high efficiency and energy saving, low pollution, promote the utilization and development of non-oil resources, and promote the future development direction of electric vehicle. DC brushless motor has been widely used and developed in electric vehicles because of its small size, high efficiency, reliable performance, wide speed range and low noise. Based on the analysis of the development of electric vehicle and its drive system at home and abroad, the sensorless DC brushless motor is used as the driving motor of electric vehicle. The structure characteristics, working principle and control strategy of brushless DC motor are discussed and studied deeply, and related foreign papers are consulted. Based on the advantages and disadvantages of the existing sensorless detection technology and the limitation of its application, The extended Kalman filter (EKF) algorithm is used to estimate the position and speed of brushless DC motor. In the actual speed control process, when the characteristics of the controlled object change, the parameters need to be adjusted accordingly. Based on the traditional PID control algorithm, there is no "adaptive" tuning function of the parameters. Based on the traditional PID controller, a design method of PID controller based on RBF neural network is put forward, and the simulation model of drive system of DC brushless motor electric vehicle is established in MATLAB. The simulation results of the estimated speed and position are analyzed to verify the feasibility and correctness of the control algorithm. The design scheme of electric vehicle motor drive control system based on STM32F407 is completed, and the design of its hardware and software is introduced in detail. In this paper, the control method and rotor position estimation of sensorless DC brushless motor as driving motor of electric vehicle are studied, and combined with RBF neural network algorithm and extended Kalman filter algorithm (EKF),. The requirement of this subject is realized.
【学位授予单位】：安徽理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM33
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相关期刊论文 前2条
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2 吴宏鑫,解永春,李智斌,何英姿;基于对象特征模型描述的智能控制[J];自动化学报;1999年01期
，本文编号：2381329
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