论文标题
适应高速公路地形的自动驾驶和电动卡车的最佳生态驾驶控制：能量最小化和电池寿命扩展
Optimal Eco-driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension
论文作者
论文摘要
在本文中，我们开发了一个模型，以考虑到车辆前的地形和交通信息，以实时计划电动卡车的速度轨迹。在这个实时控制模型中，首先开发了一种新型的状态空间模型，以捕获车速，加速度和充电状态。然后，我们制定了一个能量最小化问题，并通过利用问题结构的乘数（ADMM）方法的交替方向方法（ADMM）来解决。然后，使用模型预测控制框架来实时处理地形和交通不确定性。进行了一项关于拟议的生态驱动算法及其对电池降解的影响的实证研究。实验结果表明，使用开发方法的能源消耗降低了5.05％，与基准测定溶液相比，电池寿命延长了高达35.35％。
In this paper, we develop a model to plan energy-efficient speed trajectories of electric trucks in real-time by taking into account the information of topography and traffic ahead of the vehicle. In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge. We then formulate an energy minimization problem and solve it by an alternating direction method of multipliers (ADMM) method that exploits the structure of the problem. A model predictive control framework is then employed to deal with topographic and traffic uncertainties in real-time. An empirical study is conducted on the performance of the proposed eco-driving algorithm and its impact on battery degradation. The experimental results show that the energy consumption by using the developed method is reduced by up to 5.05%, and the battery life extended by as high as 35.35% compared to benchmarking solutions.
