This study proposes a modified Damage Stress Model (DSM) for fatigue life prediction, addressing the variability of ultimate stress during damage transfer under multi-stage stress. The model incorporates Generalized Ultimate Stress (GUS), influenced by the ultimate stress, the sum of the previous-stage fatigue damage, and the ratio of adjacent stress. Comparative analysis with the Miner, DSM, and Manson models, using experimental data from multiple materials and stages, shows that the proposed model outperforms others, rectifying limitations of the original DSM and providing more accurate fatigue predictions.

Aiming at the drawbacks of the heavy workload and lengthy calibration cycle of transmission control unit (TCU) development, a fast optimizing and test method of clutch–clutch shift calibration parameters is developed based on the “V” development process. A controlled-object simulation model of a six-speed automatic transmission (6AT) is established, and the shift calibration parameters and shift quality evaluation system are studied. Therefore, a model-in-the-loop (MIL) calibration platform for key calibration parameters and evaluation indicators in the shift process is constructed. By setting the optimization target of evaluation indicators, the initial calibration parameters’ ranges are reduced. Finally, the TCU is flashed with the optimized calibration parameters, and a hardware-in-the-loop (HIL) test system is built to verify the shift quality. The same shift quality verification test is carried out in the vehicle. The test results show that the application of the MIL simulation platform can enable the batch simulation analysis of eight key shift calibration parameters, gradually refine the initial rough interval, and ensure shift quality. The shift calibration parameters are cross-validated in multiple development links, and the dependence on vehicle calibration is greatly reduced. The development cycle is effectively shortened to less than 6 months.

Fatigue failure under alternating loads represents a significant failure mode for automotive components. This study focuses on predicting the fatigue life of automotive parts subjected to multi-level stress loading. Current nonlinear models often require extensive test data or face challenges in selecting appropriate reference values, which imposes limitations on automotive reliability assessments. By analyzing the process of fatigue damage transformation between two levels of stress, the relationship, i.e., $S_i^2\times \mathrm{\Delta }{S}_i\text{=}{S}_{i+1}^2\times \mathrm{\Delta }{S}_{i+1}$ , considering the equivalent transformation of adjacent stresses based on the S-N characteristic curve of materials. The equivalent formula for cumulative fatigue damage and the expression for residual fatigue life between adjacent stresses acress two, three, and higher stress levels are derived. Then a fatigue life prediction model based on the stress equivalence relation is proposed. The calculation process of this model relies solely on the fatigue life of the materials subjected to at least two stress levels to determine the S-N characteristic curve. The experimental data utilized include existing test results for two-, three-, four-, and five-level stress loading, as reported by relevant scholars. Specifically, the two-level stress materials analyzed are AL-2024 and 30CrMnSiA, the three-level stress material is LY12CZ, and the four- and five-level stress material is an aluminum alloy. The average and maximum relative errors of the Miner model, Manson model, Subramanyan model, Hashin model, and the proposed model are all calculated and compared. Additionally, the statistical values of cumulative fatigue damage, cumulative fatigue life, and the discrepancies between cumulative fatigue damage and test cumulative fatigue damage predicted by each model are summarized. The results show that the proposed model, which is based on the equivalent transformation of adjacent loads, demonstrates superior overall performance in predicting fatigue life and damage compared to the Miner model, Manson model, Subramanyan model, and Hashin model, thereby offering a more accurate application for fatigue life and damage prediction under multi-level loading conditions.