Adaptive Multiobjective Shift Strategy for Heavy-Duty Vehicle Based on Performance State Evaluation of the Transmission Mechanism

Heavy-tracked vehicles frequently operate in intricate and demanding driving conditions, where the powertrain, a core component, inevitably experiences performance degradation due to mileage accumulation. This degradation not only undermines vehicle reliability but also constrains the full utilization of its capabilities. To address these challenges, this study proposes a multiobjective optimization approach to developing an adaptive shift strategy that balances the vehicle’s power, fuel economy, and reliability performance. The optimization problem is tackled using the nondominated sorting genetic algorithm-II (NSGA-II), which generates the Pareto front of shift speeds by considering the vehicle’s power, economy, and reliability performance. The optimal shift speeds are adaptively selected based on the transmission mechanism performance states, which are calculated using the fuzzy comprehensive evaluation (FCE) model. During acceleration from 0 to 80 km/h, total friction work is reduced by 6.59% and 23% when the performance state scores are 70 and 40, respectively, compared to a score of 90. Additionally, the maximum jerk is reduced by 45.07% at a score of 40 compared to a score of 90. These reductions in friction work and jerk decrease wear on the transmission mechanism system, thus improving vehicle reliability.