Influence of Different Data Interpolation Methods for Sparse Data on the Construction Accuracy of Electric Bus Driving Cycle

Abstract

Battery electric vehicles (BEVs) are one of the most promising new energy models for industrialization and marketization at this stage, which is an important way to solve the current urban haze air pollution, high fuel cost and sustainable development of the automobile industry. This paper takes pure electric buses as the research object and relies on the operation information management platform of new energy buses in Nantong city to propose an electric bus cycle construction method based on the mixed interpolation method to process sparse data. Three different interpolation methods, linear interpolation, step interpolation and mixed interpolation, were used to preprocess the collected data. The principal component analysis method and K-means clustering algorithm were used to reduce and classify the eigen parameter matrix. According to the clustering results, different categories of moving section and idle section libraries were established. According to the length of time and the correlation among various types, several moving sections and idle sections were selected to form a representative driving cycle of Nantong city buses. The results show that the mixed interpolation method, based on linear interpolation and cubic spline interpolation, has a good processing effect. The average relative error between the synthesized working conditions and the measured data are 15.71%, and the relative error of the seven characteristic parameters is less than 10%, which meets the development requirements. In addition, the comparison and analysis with the characteristic parameters of the world typical cycle conditions (NEDC, WLTC) show that the constructed cycle conditions of Nantong city are reasonable and reliable to represent the driving conditions of pure electric buses in Nantong city, which can provide a reference for the optimization of the bus energy control strategy.