Improving the fuel economy of semi-trailer tractors under complex terrain conditions is important for low-carbon road freight transportation. However, existing predictive control studies usually treat cruise control and thermal management separately. In DP-based predictive cruise control, fixed cost-function weights in the conventional fixed-weight DP method (FW-DP) cannot accommodate the terrain-dependent trade-off between fuel economy and travel efficiency. In thermal management, one-dimensional coolant temperature–fan speed lookup-table control (LT-TM) may cause unnecessary fan engagement, whereas detailed thermal-model-based methods are difficult to generalize in engineering practice. To address these issues, this study proposes an integrated predictive control framework combining cruise control and thermal management. First, an adaptive-weight dynamic programming method (AW-DP) is developed. Typical terrain scenarios are analyzed offline to identify the terrain-related feature most correlated with the fuel–time trade-off, and this feature is recalculated online over each preview horizon to update the cost-function weights in real time. Second, a fuzzy-logic-based predictive thermal management strategy (FL-PTM) is proposed. Using previewed road grade together with planned vehicle speed and acceleration, the method predicts future engine power and combines it with ambient temperature in a fuzzy controller to determine the target coolant temperature, thereby reducing unnecessary fan power consumption. Real-vehicle tests show that AW-DP improves fuel economy by 4.66% over FW-DP, while FL-PTM reduces fan power consumption by 61.1%, corresponding to an equivalent fuel-saving estimate of 0.12% over LT-TM. Overall, the proposed framework reduces fuel consumption by 4.78% relative to a baseline controller comprising FW-DP and LT-TM. These results demonstrate that the proposed framework can effectively coordinate predictive cruise control and thermal management while retaining engineering applicability for semi-trailer tractors.
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Yang et al. (2026) studied this question.
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