Eco-Driving Strategy Optimization for High-Speed Railways Considering Dynamic Traction System Efficiency
Minling Feng, Yaoming Huang, Shaofeng Lu
Abstract
In modern high-speed railway (HSR) systems, some pivot components of the train traction system conduct energy conversion with a certain efficiency. With different train operational points (TOPs) corresponding to the different combinations of train effort and speed, the traction system efficiency is dynamically changing, which can be represented by a traction system efficiency map (TSEM). In this article, an eco-driving strategy optimization model based on convex optimization (CO) is proposed to optimize the train speed trajectory considering the dynamic traction system efficiency (DTSE) of HSR. The nonconvex TSEM is first modeled through data transformation and curve fitting in the preprocessing, and thus, the model complexity and the computational burden are reduced. The numerical experiments show that the driving strategy optimized by the proposed model is to distribute the TOPs to the more efficient area of the TSEM compared with the driving strategy considering the static traction system efficiency (STSE). Comparative studies indicate that the proposed model can reduce 18.1% energy loss and 7.3% electrical energy in comparison with the model considering the STSE in the realistic scenario with the parameters of CRH380AL train in China. Benefiting from the high-computational efficiency with the CPU time at the scale of milliseconds, the proposed model has great potential to be integrated into the automatic train operation (ATO) of HSR.