Dynamic Programming–Based Fuel-Saving Predictive Cruise Control

This article investigates the optimization problem of fuel economy for heavy-duty commercial vehicles. A Dynamic Programming–Based Fuel-Saving Predictive Cruise Control (DP-FSPCC) method is proposed, which is based on the Bellman optimality principle and uses the cost function to evaluate the optimal feedback control gain, thereby improving the fuel economy of heavy-duty commercial vehicles on complex roads with varying slopes. To address the issues of low accuracy in road feature representation and poor adaptability to different driving conditions in existing slope reconstruction algorithms, the road ahead is dynamically segmented for high-precision processing by integrating ADASIS (Advanced Driver Assistance Systems Interface Specifications) map information with significant turning point detection and dynamic sensitivity analysis. An engine fuel consumption mapping model based on local gradient information is established to provide an accurate cost function for dynamic programming. Furthermore, a feedforward optimization mechanism based on slope classification is proposed. This mechanism adopts a differentiated cost function weight design strategy for different road conditions, making the control strategy more in line with actual driving experience, effectively reducing the computational complexity of dynamic programming and improving the real-time performance and optimization efficiency of the algorithm. Finally, through numerical simulations and real-vehicle tests on highways, the effectiveness and superiority of the proposed method are verified.