Real road spectrum based electric automobile battery pack structure fatigue life prediction method

A fatigue life prediction, electric vehicle technology, applied in the testing of machine/structural components, vehicle testing, measuring devices, etc., can solve the problem that the real load spectrum of the battery pack structure cannot be obtained, to ensure accuracy and engineering practicability Strong, low-computation effects

Inactive Publication Date: 2018-05-04
开沃新能源汽车集团股份有限公司
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Problems solved by technology

[0003] The present invention provides a method for predicting the fatigue life of electric vehicle battery pack structure based on the real road spectrum in order to solve the problems existing in the above-mentio...

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  • Real road spectrum based electric automobile battery pack structure fatigue life prediction method
  • Real road spectrum based electric automobile battery pack structure fatigue life prediction method
  • Real road spectrum based electric automobile battery pack structure fatigue life prediction method

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Embodiment Construction

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] The structural failure of the battery pack is mostly caused by structural fatigue, that is, during the normal driving of the electric vehicle, the battery pack is subjected to cyclic alternating loads, and the stress level is low, usually lower than the structural yield limit, and eventually the structural fatigue fails due to the accumulation of fatigue damage. From the analysis of the excitation source, the battery pack mainly bears the random vibration load from the ground. For this type of random load, it is very beneficial to use the vibration fatigue analysis method to analyze and predict the fatigue life of the battery pack structure. Only the PSD (power spectral density) of the response is used to avoid the huge amount of calculation of the transient analysis. This method uses the transfer function between the input load signal and the dynamic stress of ...

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Abstract

The invention discloses a real road spectrum based electric automobile battery pack structure fatigue life prediction method. The method mainly includes electric automobile road load condition setting, road load spectrum acquisition and analysis, integral automobile multi-body dynamic model establishment, multi-body dynamic model transfer function acquisition and load spectrum iteration and fatigue life prediction. By adoption of a half-testing half-simulation iteration method, a real load spectrum of an electric automobile is obtained, battery pack structural load accuracy is guaranteed, highengineering practicality is realized, and expensive bench equipment input is avoided, so that low cost is realized. By battery pack structure fatigue life prediction according to a multi-axial fatigue theory and a broadband random fatigue life prediction method, low calculation amount and high prediction precision are realized.

Description

Technical field: [0001] The invention relates to a method for predicting the fatigue life of an electric vehicle battery pack structure based on a real road spectrum, which belongs to the field of power battery structure safety and reliability. Background technique: [0002] Electric vehicle battery packs are the energy source for electric vehicles. Due to the unevenness of the road surface, the battery pack installed on the electric vehicle will inevitably bear the vibration from the road surface. Although attenuated by tires and suspensions, the vibration acting on the battery pack cannot be ignored. Generally, the excitation from the road surface is random. This kind of random vibration is usually a long-term, low-stress continuous action, and the impact on the structure of the battery pack is also slow. If the battery structure design is unreasonable, the battery fatigue damage caused by vibration will continue to accumulate, which will also lead to fatigue failure of ...

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Application Information

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IPC IPC(8): G01M17/007
CPCG01M17/007
Inventor 张勇张蒙阳
Owner 开沃新能源汽车集团股份有限公司
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