论文标题
用于长时间远景的生态驱动的计算有效算法
Computationally efficient algorithm for eco-driving over long look-ahead horizons
论文作者
论文摘要
本文提出了一种在长期预测范围内用于生态驱动的计算有效算法。生态驱动问题被公正为双层程序，在该程序中，底层是离线求解的，将齿轮预先优化的齿轮作为纵向速度和加速度的函数。最高级别是在线解决的，以旅行时间，动能和加速度作为状态变量优化了非线性动态程序。为了进一步减少计算工作，旅行时间通过应用必要的Pontryagin最大原理条件与目标相邻，并且在模型预测控制框架中使用实时迭代顺序二次编程方案来解决非线性程序。与标准巡航控制相比，使用所提出的算法的节能高达15.71％。
This paper presents a computationally efficient algorithm for eco-driving over long prediction horizons. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimizing gear as a function of longitudinal velocity and acceleration. The top level is solved online, optimizing a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables. To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to standard cruise control, the energy savings of using the proposed algorithm is up to 15.71%.
