Yanzan Han1,*, Hang Zhou1, Zengfang Shi1, Shuang Liang2
Computer Systems Science and Engineering, Vol.40, No.3, pp. 1099-1108, 2022, DOI:10.32604/csse.2022.017741
- 24 September 2021
Abstract Urban rail trains have undergone rapid development in recent years due to their punctuality, high capacity and energy efficiency. Urban trains require frequent start/stop operations and are, therefore, prone to high energy losses. As trains have high inertia, the energy that can be recovered from braking comes in short bursts of high power. To effectively recover such braking energy, an onboard supercapacitor system based on a radial basis function neural network-based sliding mode control system is proposed, which provides robust adaptive performance. The supercapacitor energy storage system is connected to a bidirectional DC/DC converter to More >
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