A novel hybrid entropy multi-criteria decision-making method for combined optimization of vehicle durability test specifications

To address the inefficiency and lack of direction in developing vehicle structural durability test specifications correlated with target user fatigue life, this study proposes a combined optimization method for durability test specifications based on a novel hybrid entropy multi-criteria decision-making algorithm (Entropy-CRITIC-TOPSIS). First, a mathematical model was established using user correlation theory, with “user-proving ground” damage equivalence as the objective function and test efficiency metrics as constraints. The NSGA-II algorithm was then employed to solve this model, generating 30 Pareto optimal solutions. The novel Entropy-CRITIC-TOPSIS algorithm was applied to identify the comprehensive optimal solution, which served as the basis for formulating the durability test specifications. Comparative analysis across three dimensions—pseudo-damage characteristics, rainflow counting distribution, and test efficiency—revealed significant improvements over conventional methods: a 30.63% reduction in average pseudo-damage ratio, 63.5% decrease in standard deviation, 45.3% reduction in coefficient of variation, 12.12% compression of test scenario mileage, 10.5% reduction in connecting road mileage, and 10.23% shortening of test cycle duration. These results demonstrate that the proposed method enables efficient development of durability test specifications while more accurately reflecting structural fatigue damage under real-world driving conditions, offering substantial engineering value for advancing vehicle durability testing technology.