Construction and optimization of representative actual driving cycles based on the improved autoencoder

In this study, much work has been performed to accurately and efficiently develop representative actual driving cycles. Electric vehicle road tests were conducted and the associated data were gathered based on the manual driving method, and the Changsha Driving Cycle Construction (CS-DCC) method was...

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Bibliographic Details
Published in:Scientific reports 2024-01, Vol.14 (1), p.2351-2351, Article 2351
Main Authors: Zhao, Zhichao, Sun, Xilei, Wang, Xun, Wang, Yi, Fu, Jianqin, Liu, Jingping
Format: Article
Language:English
Subjects:
639/166/988
639/705/1046
639/705/117
Algorithms
Artificial intelligence
Automobile industry
Construction
Deep learning
Dynamic programming
Electric vehicles
Energy consumption
Energy efficiency
Humanities and Social Sciences
Markov analysis
multidisciplinary
Noise reduction
Optimization
Principal components analysis
Science
Science (multidisciplinary)
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Summary:In this study, much work has been performed to accurately and efficiently develop representative actual driving cycles. Electric vehicle road tests were conducted and the associated data were gathered based on the manual driving method, and the Changsha Driving Cycle Construction (CS-DCC) method was proposed to achieve systematical construction of a representative driving cycle from the original data. The results show that the refined data exhibit greater stability and a smoother pattern in contrast to the original data after noise reduction by five-scale wavelet analysis. The Gaussian Kernel Principal Component Analysis (KPCA) algorithm is chosen to reduce the dimensionality of the characteristic matrix, and the number of principal components is selected as 5 with a cumulative contribution rate of 85.99%. The average error of the characteristic parameters between the optimized drive cycle and the total data is further reduced from 13.6 to 6.1%, with a reduction ratio of 55.1%. Meanwhile, the constructed driving cycle has prominent local characteristics compared with four standard driving cycles, demonstrating the necessity of constructing an actual driving cycle that reflects localized driving patterns. The findings present a powerful application of artificial intelligence in advancing engineering technologies.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-52865-z