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Low-Cost Reconstruction of Typical Driving Cycles Based on Empirical Information and Low-Frequency Speed Data

A representative driving cycle is crucial to estimating emissions and energy consumptions of vehicles. However, their traditional data acquisition is subject to large sampling requirements and high costs, whether a chase-car method or an on-board measurement method is adopted. Presently, the public transportation department and many private companies collect low-frequency driving data to track and monitor their vehicles. And a large number of typical driving cycles have been generated in the automobile field, which have been limited by the traditional designed method and have not been fully utilized. Herein, we investigate a driving cycle reconstruction method based on empirical information and low-frequency driving data. First, characteristic parameters were selected, based on the ergodicity of a Markov Chain method and an interpolation analysis of the low-frequency data. Second, similar driving cycles were grouped by K-means clustering and principal component analysis, based on the existing typical driving cycles. These were considered as empirical information of the original transient driving cycles. Ultimately, a Markov Chain evolution method based on the empirical information was used to reconstruct driving cycles. The method reconstructs driving cycles using low-frequency data of different sampling intervals, keeping the relative deviations of the characteristic parameters within a threshold range. In comparison with the original driving cycles having a 1-s sampling interval, the reconstructed cycles exhibit a high similarity with the original driving cycles in terms of their velocity and acceleration probability distribution and power spectral density. Additionally, calculation deviation of fuel consumption per 100 km is small. Therefore, this method can estimate energy consumption of urban vehicles in a timely and effective manner.