A supercapacitor (SC)-based interior permanent magnet synchronous motor (IPMSM) drive including the speed tracking of a specific velocity profile and the charging of the SC is developed in this study to emulate the operation of an urban light rail vehicle (LRV). In the SC-based IPMSM drive, the motoring mode to emulate the LRV speed tracking control and the charging mode for the charging of the SC are both designed. In the motoring mode, a field-oriented controlled (FOC) IPMSM drive system is developed to emulate the speed control of an LRV. In the charging mode, the constant current and constant voltage (CC–CV) charging strategy is developed for the charging of the SC. Moreover, the above two modes use the same inverter and coordinate transformations to reduce the design complexity. Furthermore, in order to test the performance of SC, the speed command of the emulated LRV is obtained using a specific testing driving cycle. The design objective is for fast charging of SC being able to provide enough energy for the emulated LRV to operate a full testing driving cycle. In addition, to improve the transient speed response of the emulated LRV, a Chebyshev fuzzy neural network (CheFNN) intelligent speed controller is proposed. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the developed CC–CV charging strategy for the SC and the proposed CheFNN speed controller for the emulated LRV.

在这项研究中，开发了一种基于超级电容器（SC）的内部永磁同步电动机（IPMSM）驱动器，该驱动器包括特定速度曲线的速度跟踪和SC的充电，以模拟城市轻轨车辆（LRV）的运行。在基于SC的IPMSM驱动器中，设计了用于模拟LRV速度跟踪控制的电动模式和用于SC充电的充电模式。在电动模式下，开发了场定向控制（FOC）IPMSM驱动系统来模拟LRV的速度控制。在充电模式下，为SC的充电开发了恒定电流和恒定电压（CC-CV）充电策略。此外，上述两种模式使用相同的逆变器和坐标变换以降低设计复杂度。此外，为了测试SC的性能，仿真LRV的速度命令是使用特定的测试行驶周期获得的。设计目标是为SC的快速充电提供足够的能量，以使模拟LRV能够运行整个测试行驶周期。另外，为改善仿真LRV的瞬时速度响应，提出了一种Chebyshev模糊神经网络（CheFNN）智能速度控制器。最后，仿真和实验结果证明了所开发的针对SC的CC–CV充电策略以及针对仿真的LRV所建议的CheFNN速度控制器的有效性。为了提高仿真LRV的瞬时速度响应，提出了一种Chebyshev模糊神经网络（CheFNN）智能速度控制器。最后，仿真和实验结果证明了所开发的针对SC的CC–CV充电策略以及针对仿真的LRV所建议的CheFNN速度控制器的有效性。为了提高仿真LRV的瞬时速度响应，提出了一种Chebyshev模糊神经网络（CheFNN）智能速度控制器。最后，仿真和实验结果证明了所开发的针对SC的CC–CV充电策略以及针对仿真的LRV所建议的CheFNN速度控制器的有效性。