Abstract:

For complex and uncertain situations related to the powertrain mounting system （PMS） of electric vehicle where some parameters are probabilistic variables， and some parameters are discrete data， a study on the reliability optimization design for the PMS of electric vehicles is conducted based on the probabilistic model and data-driven model. Firstly， based on the arbitrary polynomial chaos （APC） expansion and generalized maximum entropy principle， an efficient method is derived for solving the uncertainty and reliability of the PMS response under the aforementioned complex uncertain situation. Then， based on the Monte Carlo sampling， a reference method is proposed for performing the uncertainty and reliability analysis of PMS. Next， a sensitivity analysis method based on APC expansion method is proposed， and an optimization method of PMS is further put forward considering the uncertainty and reliability of responses. Finally， a numerical example is used to verify the effectiveness of the proposed method， and the sensitivity analysis and reliability optimization of the system are carried out. The results show that the proposed method can effectively handle the complex and uncertain situations where some parameters of the electric vehicle PMS are probability variables and some parameters are discrete data and can optimize the PMS inherent characteristics reliably with good computational accuracy and efficiency.

Key words: powertrain mounting system of electric vehicle, arbitrary polynomial chaos expansion, maximum entropy principal, data-driven, uncertainty