Abstract: With the rapid development of new-energy vehicles, extending driving range and improving energy efficiency have become critical challenges. Predictive cruise control (PCC) leverages upcoming road information to optimize longitudinal control, thereby reducing energy consumption while enhancing driving comfort. Unlike previous studies that mainly focus on algorithmic improvement in simulation, this paper presents an engineering-oriented design framework that explicitly targets the constraints of an on-board vehicle control unit. The framework consists of three modules: environmental perception, path prediction, and control decision. By integrating dynamic programming (DP) with road-segment reconstruction, the system generates an optimal speed trajectory while fully utilizing grade-preview data. Simulation results over a mountainous highway scenario show a 3.24% reduction in energy consumption compared with a baseline human-driving profile. The results provide new insights and an engineering-ready reference for the subsequent real-vehicle implementation of intelligent energy-saving control in new-energy vehicles.

Key words: new energy vehicle; pure electric vehicle; predictive cruise control; dynamic programming; energy consumption optimization; intelligent control

摘要： 随着新能源汽车技术的快速发展，提升续航能力与行驶效率已成为关键挑战。预见性 巡航控制（PCC）利用前方道路信息优化纵向控制，从而有效降低能耗并提升驾驶舒适性。 不同于既有研究多停留在仿真算法改进，文章面向车载控制器资源约束，提出了一套工程化 设计框架，该框架包含环境感知、路径预测与控制决策三大模块；通过动态规划（DP）与路 段重构技术，实现坡度信息的最优利用和速度轨迹在线优化。在山区高速工况仿真中，所提 策略较人工驾驶能耗平均降低 3.24%。研究结果为新能源汽车智能节能控制的后续实车落地 提供了新的思路与工程化参考。

关键词: 新能源汽车；纯电动汽车；预见性巡航控制；动态规划；能耗优化；智能控制