Study on Evaluation Method of Drivability of Hybrid Electric Vehicle Based on Ensemble Empirical Mode Decomposition Noise Reduction Method Wuhan University of Technology

Format:

Book

Conference/Event

Author/Creator:

Cai, Qinxi, author.

Contributor:

Mo, Min Yi

Ou, Zhang

Conference Name:

Automotive Technical Papers (2023-01-01 : Warrendale, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2023

Summary:

During the drivability test process, a large amount of noise generated by a series of internal and external factors of the vehicle reduces the accuracy of the drivability evaluation. To solve this problem, this paper introduces the EEMD denoising method and compares the denoising effects of the EMD denoising method and EEMD denoising method on the original signal using the entropy weight evaluation index. In addition, the optimal parameter setting is obtained by comparing the denoising results of different parameter settings in the EEMD denoising method. The results show that when the white noise is integrated 3000 times and the standard deviation of white noise is 0.1, the EEMD noise reduction method is the best, and the comprehensive score of noise reduction is 0.732 points higher than that of EMD. The research results indicate that the EEMD noise reduction method has a good noise reduction effect, which can ensure the accuracy of subsequent calculation of subsequent drivability indexes. It can be applied to the processing process of driving acceleration signals in hybrid vehicle acceleration conditions

Notes:

Vendor supplied data

Publisher Number:

2023-01-5083

Access Restriction:

Restricted for use by site license