基于RBF网络的车载超级电容滑模控制系统

Sliding Mode Control System of Vehicle Supercapacitor Based on RBF Neural-network

针对地铁在频繁的加速和制动过程中导致电能浪费、牵引网电压的剧烈波动和电网运行不稳定等问题,提出一种基于RBF网络的车载超级电容滑模控制系统。超级电容储能装置通过与双向DC/DC变换器连接,给列车提供牵引或者吸收列车产生的再生制动能量。在Boost模式和Buck模式下,分别设计RBF神经滑模控制器。仿真结果表明,与传统的PI控制相比,神经滑模控制下的车载超级电容储能装置提高了再生制动能量吸收效果,抑制了牵引网电压波动。

Aiming at the problem such as electric energy waste,the violent fluctuation of the traction network voltage,unstable operation of power grid and other issues during frequent acceleration and braking of subway,a sliding mode control system of vehicle supercapacitor based on RBF neural-network was proposed.The supercapacitor energy storage device was connected to a bi-directional DC/DC converter to provide traction for train or absorb the regenerative braking energy produced by the train.In the Boost mode and the Buck mode,the RBF neural sliding mode controller was designed respectively.The simulation results show that,the vehicle supercapacitor energy storage device with neural sliding mode control improves the energy absorption effect of regenerative braking and restrains the voltage fluctuation of traction network compared with the traditional PI control.