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Prediction and optimization of fatigue life of electric drive axle housing for commercial vehicle under multiple conditions
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Zhiyang ZHANG1, Qingshan GONG1, 2, Min LIU2, Zhigang JIANG3, Guangguo ZHANG1
Journal of Mechanical Strength | 2025, 47(3) : 129 - 135
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Journal of Mechanical Strength | 2025, 47(3): 129-135
Optimization·Reliability
Prediction and optimization of fatigue life of electric drive axle housing for commercial vehicle under multiple conditions
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Zhiyang ZHANG1, Qingshan GONG1, 2, Min LIU2, Zhigang JIANG3, Guangguo ZHANG1
Affiliations
  • 1.School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
  • 2.School of Art and Design, Hubei University of Automotive Technology, Shiyan 442002, China
  • 3.School of Mechanical Automation, Wuhan University of Science and Technology, Wuhan 430065, China
Published: 2025-03-15 doi: 10.16579/j.issn.1001.9669.2025.03.016
Outline
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In the course of real operation, the drive axle housing is likely to undergo fatigue failure due to prolonged exposure to cyclic alternating loads.To determine whether the drive axle housing of a commercial vehicle’s wheel-side electric motor complying with fatigue life requirements at the design stage, a three-dimensional model and finite element model of the drive axle were established. Firstly, an initial inertia release analysis was performed,indicating that its static strength and stiffness met the requirements.Secondly, based on this foundation,utilizing nCode DesignLife software, using the nominal stress method in conjunction with the material’s SN curve and fatigue loading curve, a new automotive industry standard was employed to predict the fatigue life of the housing under multiple conditions,such as vertical bending fatigue,braking fatigue and lateral fatigue.The results revealed that the fatigue life of the drive axle housing under braking fatigue and lateral fatigue conditions did not meet the requirements set out in the standard, necessitating structural optimization. Finally,reinforcement optimization was performed on the drive axle housing. The results show that, through optimization,the maximum stress of the drive axle housing is reduced by 95.8 MPa and the maximum deformation is decreased by 1.064 mm under the maximum impact conditions.Additionally, the minimum fatigue life under three different fatigue conditions is improved respectively by 1.076 million cycles, 289 000 cycles, and 497 000 cycles, exceeding the requirements stated in the standards.This validates the feasibility of optimizing the drive axle housing structure, effectively shortening and reducing the research and development cycles and associated costs.

Drive axle housing  /  Inertial release  /  Fatigue life  /  Structural optimization
Zhiyang ZHANG, Qingshan GONG, Min LIU, Zhigang JIANG, Guangguo ZHANG. Prediction and optimization of fatigue life of electric drive axle housing for commercial vehicle under multiple conditions[J]. Journal of Mechanical Strength, 2025 , 47 (3) : 129 -135 . DOI: 10.16579/j.issn.1001.9669.2025.03.016
  • National Natural Science Foundation of China(52375508)
  • Key Project of Hubei Provincial Department of Education(D20211803)
  • Hubei University of Automotive Technology Doctoral Fund(BK202001)
Year 2025 volume 47 Issue 3
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Article Info
doi: 10.16579/j.issn.1001.9669.2025.03.016
  • Receive Date:2023-07-05
  • Online Date:2026-03-17
  • Published:2025-03-15
Article Data
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History
  • Received:2023-07-05
  • Revised:2023-08-22
Funding
National Natural Science Foundation of China(52375508)
Key Project of Hubei Provincial Department of Education(D20211803)
Hubei University of Automotive Technology Doctoral Fund(BK202001)
Affiliations
    1.School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
    2.School of Art and Design, Hubei University of Automotive Technology, Shiyan 442002, China
    3.School of Mechanical Automation, Wuhan University of Science and Technology, Wuhan 430065, China

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LIU Min, E-mail:
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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