School of Mechanical Engineering, Yangzhou University, Yangzhou, Jiangsu, China.
Sci Rep. 2022 Mar 25;12(1):5209. doi: 10.1038/s41598-022-09156-2.
Autonomous electric vehicles promise to improve traffic safety, increase fuel efficiency and reduce congestion in future intelligent transportation systems. Ecological driving characteristics are first studied to concentrate on energy consumption, the ability to quickly pass its destination, etc. of autonomous electric vehicle plans (AEVPs) to maximize total energy efficiency benefits. To realize this goal, an optimal control model is developed to provide ecological driving suggestions to AEVPs. The Radau pseudospectral method (RPM) is adopted to put the optimal control model into nonlinear programs (NLP), and multiobjective optimization, including safety, economy and fast mobility, is considered, which conditions and constraints such as vehicle dynamics, traffic rules, and energy consumption. To enhance optimal model applicability, two ecological driving procedures are proposed. One procedure is that two-phase trajectory optimization and ecological driving states, such as velocity and acceleration, for the leading vehicle are developed according to RPM characteristics, while the other provides a set of targeted driving states to the following vehicles. The objective of the procedure is to minimize the total energy consumption of AEVPs, while travel comfort and safety are integrated into the schematization by optimization functions. Numerical experiments illustrate significance when ecological driving strategy for AEVPs considers energy consumption characteristics, thereby ensuring total energy consumption efficiency for AEVPs.
自动驾驶电动汽车有望在未来的智能交通系统中提高交通安全、提高燃油效率和减少拥堵。首先研究生态驾驶特性，集中于自动驾驶电动汽车计划 (AEVPs) 的能量消耗、快速到达目的地的能力等，以最大限度地提高总能源效率效益。为了实现这一目标，开发了一个最优控制模型，为 AEVPs 提供生态驾驶建议。采用 Radau 伪谱法 (RPM) 将最优控制模型转化为非线性规划 (NLP)，并考虑多目标优化，包括安全性、经济性和快速机动性，同时考虑车辆动力学、交通规则和能量消耗等条件和约束。为了增强最优模型的适用性，提出了两种生态驾驶程序。一种程序是根据 RPM 特性开发两阶段轨迹优化和生态驾驶状态，例如领先车辆的速度和加速度，而另一种程序为后续车辆提供一组有针对性的驾驶状态。该程序的目的是最小化 AEVPs 的总能量消耗，同时通过优化函数将行驶舒适性和安全性纳入到方案中。数值实验表明，当 AEVPs 的生态驾驶策略考虑能量消耗特性时，这对于确保 AEVPs 的总能量效率具有重要意义。
