Predictive cruise control for heavy trucks based on slope information under cloud control system

With the advantage of fast calculation and map resources on cloud control system (CCS), cloud-based predictive cruise (CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant achieve goal national carbon neutrality. However, most investigations focus on-board (PCC) system, lack research CPCC architecture under CCS. Besides, current PCC algorithms have problems a single target high computational complexity, hinders improvement effect. In this paper, layered based CCS proposed effectively address real-time computing deployment its algorithm vehicle-cloud. addition, dynamic programming principle road point segmentation method (RPSM), designed optimize speed gear with slope information. Simulation results show that can adaptively vehicle driving through prediction, fuel-saving rate 6.17% in comparison constant control. Also, compared other similar algorithms, make engine operate more efficient zone by cooperatively optimizing speed. Moreover, RPSM reconfigure advance, 91% roadpoint reduction rate, significantly reducing complexity. Therefore, study essential significance economic promotion system.