论文标题
使用分支和界限优化的重型卡车的模型预测生态驾驶控制
Model predictive eco-driving control for heavy-duty trucks using Branch and Bound optimization
论文作者
论文摘要
生态驾驶（ED）可用于在现有车辆中节省燃油，只需要进行几个硬件修改。为了使该技术在动态环境中取得成功，ED需要在线实时实施政策。在这项工作中，提出了一个专用的分支和绑定模型预测控制（MPC）算法来解决ED最佳控制问题的优化部分。开发的用于ED的MPC解决方案基于以下成分。作为一个预测模型，速度动力学随距离的函数由有限数量的驾驶模式和齿轮位置建模。然后，我们制定了一个优化问题，该问题可以用两个术语最大程度地减少成本功能：一个惩罚燃油消耗，一项惩罚行程持续时间。我们利用上下文元素，并使用暖启动的解决方案实时运行BNB求解器。在以色列和法国的两条路线上以及车辆能量消耗计算工具（VECTO）的长途循环中，在数值模拟中评估了结果。与人类驾驶员和蓬松金的最低原理（PMP）解决方案相比，平均可节省25.8％和12.9％的燃料。
Eco-driving (ED) can be used for fuel savings in existing vehicles, requiring only a few hardware modifications. For this technology to be successful in a dynamic environment, ED requires an online real-time implementable policy. In this work, a dedicated Branch and Bound (BnB) model predictive control (MPC) algorithm is proposed to solve the optimization part of an ED optimal control problem. The developed MPC solution for ED is based on the following ingredients. As a prediction model, the velocity dynamics as a function of distance is modeled by a finite number of driving modes and gear positions. Then we formulate an optimization problem that minimizes a cost function with two terms: one penalizing the fuel consumption and one penalizing the trip duration. We exploit contextual elements and use a warm-started solution to make the BnB solver run in real-time. The results are evaluated in numerical simulations on two routes in Israel and France and the long haul cycle of the Vehicle Energy consumption Calculation Tool (VECTO). In comparison with a human driver and a Pontryagin's Minimum Principle (PMP) solution, 25.8% and 12.9% fuel savings, respectively, are achieved on average.
