车用PMSM驱动控制系统研究

发布时间：2018-06-28 08:29

本文选题：电动汽车 + 永磁同步电机　；参考：《天津理工大学》2017年硕士论文

【摘要】：传统燃油汽车消耗大量化石能源,汽车尾气排放污染城市环境。相比之下,新能源电动汽车绿色环保无污染,因此国际重要汽车制造商已将电动汽车作为汽车未来发展重要方向。而电动汽车在世界范围内最大的发展瓶颈为续航里程问题。本文据此,采用最小开关损耗SVPWM方式,通过减小逆变器开关损耗,节约车载电池能量。并将此调制方式运用到永磁同步电机MTPA控制、弱磁控制领域。同时运用模型预测控制,提高电动汽车在动态工况下的转矩抗扰及转速转矩跟随性能。本文以内置式永磁同步电机为研究对象,首先建立电机在旋转坐标系下的数学模型。按照定子电流不同分解组合方式,形成id=0控制、MTPA控制、弱磁控制等策略。其次,提出改进五段式SVPWM方式,解决常规七段式SVPWM方式开关损耗大的问题,及常规五段式SVPWM方式使同一桥臂上下开关次数不一致的问题。为解决五段式SVPWM输出电流谐波大问题,在改进五段式SVPWM方式基础上进行优化,得到最小开关损耗SVPWM方式。该方式可进一步减小逆变器开关损耗,同时限制电压电流谐波输出。搭建最小开关损耗SVPWM方式仿真模型,并在= 0矢量控制中,验证该优化调制模式,并对仿真结果展开分析。再次,将优化SVPWM方式拓展到电机全速度范围控制策略中,基速以下采用MTPA控制,基速以上采用弱磁升速控制方式。搭建基于优化SVPWM的MTPA、弱磁控制仿真模型,并对两种控制方式下的仿真结果进行分析。为进一步提高电动汽车驱动性能,改进电动汽车在动态工况下的转矩、转速跟随性能及转矩抗扰性能,将模型预测控制运用到速度环控制器中,搭建速度环模型预测控制器,并对模型预测控制器和PI控制器在id =0矢量控制方式中展开对比分析。最后,构建以TMS320F28335为控制核心的硬件平台,设计主电路、控制电路、检测电路、保护电路。对id=0矢量控制方式进行软件设计,完成软件各个模块的编程工作。完成相关测试实验,验证了所设计驱动控制系统合理。
[Abstract]:Traditional fuel vehicles consume a lot of fossil energy and exhaust emissions pollute the urban environment. In contrast, new energy electric vehicles are environmentally friendly and pollution-free, so major international automakers have taken electric vehicles as an important direction in the future. The biggest bottleneck in the development of electric vehicles in the world is the mileage problem. In this paper, the minimum switching loss SVPWM is adopted to reduce the switching loss of the inverter and to save the energy of the on-board battery. The modulation is applied to the field of permanent magnet synchronous motor (PMSM) MTPA control and weak magnetic field control. At the same time, the model predictive control is used to improve the torque immunity and speed torque following performance of electric vehicles under dynamic working conditions. In this paper, the built-in permanent magnet synchronous motor (PMSM) is taken as the research object. Firstly, the mathematical model of PMSM in rotating coordinate system is established. According to different decomposition and combination of stator current, id=0 control and weak magnetic control are formed. Secondly, an improved five-segment SVPWM mode is proposed to solve the problem of high switching loss in the conventional seven-segment SVPWM mode, and the problem that the conventional five-segment SVPWM mode makes the same bridge arm switch times inconsistent. In order to solve the problem of large harmonics in the output current of five-segment SVPWM, the minimum switching loss SVPWM is obtained by optimizing on the basis of improving the five-segment SVPWM. This method can further reduce the switching loss of the inverter and limit the harmonic output of voltage and current. The simulation model of minimum switching loss SVPWM is built, and the optimized modulation mode is verified in = 0 vector control, and the simulation results are analyzed. Thirdly, the optimized SVPWM method is extended to the full speed range control strategy of the motor. MTPA control is used below the base speed, and the weak magnetic acceleration control is used above the base speed. The simulation model of MTPA and weak magnetic field control based on optimized SVPWM is built, and the simulation results under two control modes are analyzed. In order to further improve the driving performance of electric vehicle and improve the torque, speed following performance and torque immunity performance of electric vehicle under dynamic conditions, the model predictive control is applied to the speed loop controller and the speed loop model predictive controller is built. The model predictive controller and Pi controller are compared in id zero vector control mode. Finally, a hardware platform with TMS320F28335 as the control core is constructed. The main circuit, control circuit, detection circuit and protection circuit are designed. The software design of id=0 vector control mode is carried out, and the programming work of each module of the software is completed. The test results show that the designed drive control system is reasonable.
【学位授予单位】：天津理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM341;U469.72

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