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 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.