Body Load Identification for BEV Based on Power Spectrum Decomposition under Road Excitation

2014-01-2044

06/30/2014

Event
8th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference
Authors Abstract
Content
As motor assembly of Battery Electric Vehicle (BEV) replaces engine system of Internal Combustion Engine (ICE) vehicle, interior structure-borne noise induced by road random excitation becomes more prominent under middle and high speed.
The research is focused on central driving type BEV. In order to improve interior noise in middle and low frequency range, dynamic load of BEV body must be identified. Consequently the structural noise induced by road excitation is conducted. The limitations of common identification method for dynamic body load are analyzed. The applied several identification methods are proposed for deterministic dynamic load such as engine or motor. Random dynamic load generated by road excitation is different from deterministic dynamic load. The deterministic load identification method cannot be applied to the random load directly. An identification method of dynamic body load for BEV is presented based on power spectrum decomposition. The procedure of BEV body load identification is described. Finally the validation of the method is demonstrated by experiments.
From the experimental results, the identification accuracy satisfies the requirement of engineering application. Compared to traditional matrix inversion method, power spectrum decomposition method can effectively reduce the testing work while maintaining the better identification accuracy. The research results provide theoretical basis and experimental foundation for analysis and optimization control of BEV interior noise.
Meta TagsDetails
DOI
https://doi.org/10.4271/2014-01-2044
Pages
5
Citation
Che, Y., "Body Load Identification for BEV Based on Power Spectrum Decomposition under Road Excitation," SAE Technical Paper 2014-01-2044, 2014, https://doi.org/10.4271/2014-01-2044.
Additional Details
Publisher
Published
Jun 30, 2014
Product Code
2014-01-2044
Content Type
Technical Paper
Language
English