Synthesis of Representative Driving Cycles for Energy Storage Based Urban Rail Vehicles

Abstract

For energy storage based urban rail vehicles, development of representative driving cycles is of great significance in the performance test and optimal sizing of energy storage systems. This paper proposes station-based microtrip analysis method to synthesize driving cycles for urban rail vehicles. Firstly, the data are segmented into microtrips by stations and then categorized depending on running time. Subsequntly, seventeen kinematics-related characteristic parameters are picked to describe each microtrip followed by the calculation of square Euclidean distances (referred as distance hereinafter) between microtrips with two methods. The first method employs the selected characteristic parameters to compute the distances while the second method adopts the components extracted by principal component analysis for calculation. On the principle of minimizing the sum of distance to the other microtrips, the candidate microtrip of each station which has the closest match to the real-world driving conditions is selected. Later the candidate of each station is spliced station by station and representative driving cycles can be achieved. Finally, a comparative analysis is made between the generated cycles and raw data to evaluate the validity of synthesized driving cycles. The results demonstrate that the driving cycles synthesized by these two methods are highly consistent with the original data in terms of statistical parameters and velocity probability distribution. Furthermore, with respect to energy economy, the relative error of driving cycles generated by PCA is within 2%, less than that obtained by characteristic parameters which is within 9%.