Abstract
To contract a more representative vehicle driving cycle, real-world driving data of a motor vehicle in Fuzhou area for 20 days are obtained. A total of 14 characteristic parameters were selected to represent the kinematic fragment information, principal component analysis, and K-means clustering were applied for clustering the divided kinematic fragment, and the candidate fragments were selected according to the distance of the clustering center and randomly combined to construct the condition set. A total of 11 characteristic parameters were selected to calculate the error of the construction driving cycle, we selected the driving state with the slightest error in the set as the construction driving condition. The improved autoencoder is used to build the driving cycle optimization model, the parameter calibration methods is proposed, and finally the average error is reduced from 2.97 to 2.39%. The analysis result and validation show that the optimization strategy can avoid the error uncertainty caused by the random selection effectively. The rationality of the proposed driving cycle construction process is verified, the accuracy of the driving cycle prediction is improved, and the recommended values of the model parameters are obtained. In addition, the characteristics and application significance of the construction driving cycle in different periods, road types, and data acquisition methods are analyzed.
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Acknowledgements
This research is supported by National Natural Science Foundation of China (41771182), Science and Technology Program of the Ministry of Housing and Urban-Rural Development (2016-K2-034), Beijing Municipal Natural Science Foundation (8184066) and Beijing Social Science Foundation (21GLB034).
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Lin, J., Liu, B. & Zhang, L. Autoencoder-based optimization method for driving cycle construction: a case study in Fuzhou, China. J Ambient Intell Human Comput 14, 12635–12650 (2023). https://doi.org/10.1007/s12652-022-04317-7
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DOI: https://doi.org/10.1007/s12652-022-04317-7