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

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