Eco-driving, a timely and well-known subject, aims at reducing fuel consumption by appropriately maneuvering a vehicle with a human or automated driver. In this work, the eco-driving problem is cast in an optimal control framework. State equations reflect the simple vehicle kinematics for position and speed, with the acceleration acting as a control input. Initial and final states (position and speed) are fixed. For the fuel consumption estimation, a number of alternatives are employed. To start with, a realistic, but nonlinear and non-smooth formula from the literature is considered. Simple smoothing procedures are then applied to enable the application of powerful numerical algorithms for the efficient solution of the resulting nonlinear optimal control problem. Furthermore, simpler quadratic approximations of the nonlinear formula are also considered, which enable analytical problem solutions. A comprehensive comparison on the basis of various driving scenarios demonstrates that the often utilized, but sometimes strongly questioned, square-of-acceleration term delivers excellent approximations for fuel minimizing trajectories in the present setting. A GLOSA (Green Light Optimal Speed Advisory) approach, based on the analytical solution of an optimal control problem is also presented.

中文翻译：

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最大限度地减少车辆轨迹的燃料消耗

生态驾驶是一个及时且广为人知的主题，旨在通过适当地操纵人类或自动驾驶的车辆来减少燃料消耗。在这项工作中，生态驾驶问题被置于最优控制框架中。状态方程反映了位置和速度的简单车辆运动学，加速度作为控制输入。初始和最终状态（位置和速度）是固定的。对于燃料消耗估计，采用了许多替代方案。首先，考虑了文献中的一个现实的但非线性和非平滑的公式。然后应用简单的平滑程序来实现强大的数值算法的应用，以有效解决由此产生的非线性最优控制问题。此外，还考虑了非线性公式的更简单的二次近似，这可以实现解析问题的解决方案。基于各种驾驶场景的综合比较表明，经常使用但有时受到强烈质疑的加速度平方项为当前设置中的燃料最小化轨迹提供了极好的近似值。还提出了基于最优控制问题的解析解的 GLOSA（绿灯最优速度咨询）方法。