Existing optimal control systems for vehicles that consider the effect of road slopes use a cost function with fixed weights related to speed deviation, regardless of driving states on slopes. As a result, gravitational potential energy is not efficiently exploited and braking at down-slopes (which wastes energy) becomes unavoidable. Thus, there is still significant scope to improve fuel saving behavior on slopes. To address this opportunity, in this paper, we present a dynamic eco-driving system (EDS) for a (host) vehicle based on model predictive control (MPC) with fuzzy-tuned weights, which helps efficiently utilize the gravitational potential energy. In the proposed EDS, we formulate a nonlinear optimization problem with an appropriate prediction horizon and an objective function based on the factors affecting vehicle fuel consumption. The objective function’s weight is tuned via fuzzy inference techniques using information of the vehicle’s instantaneous velocity and the road slope angle. By considering the vehicle longitudinal dynamics, preceding vehicle’s state, and road slope information (obtained from the digital road map), the optimization generates velocity trajectories for the host vehicle that minimizes fuel consumption and CO${}_2$ emission. We also investigate the traffic flow performance of following vehicles (behind the host vehicle) in dense traffic; this was not considered in existing works on hilly roads. The effectiveness of the proposed EDS is evaluated using microscopic traffic simulations on a real road stretch in Fukuoka City, Japan, and the results demonstrate that the fuzzy-tuned MPC EDS significantly reduces fuel consumption and CO${}_2$ emission of the host vehicle compared to the traditional driving (human-based) system (TDS) for the same travel time. In dense traffic, the fuel consumption and CO${}_2$ emission of following vehicles are noticeably reduced.

考虑到斜坡的影响，现有的用于车辆的最佳控制系统使用具有与速度偏差相关的固定权重的成本函数，而不管斜坡上的驾驶状态如何。结果，不能有效地利用重力势能，并且在下坡处的制动（浪费能量）变得不可避免。因此，在改善斜坡上的燃油节省性能方面仍有很大的余地。为了解决这个机会，在本文中，我们基于模糊预测权重的模型预测控制（MPC），提出了一种用于（宿主）车辆的动态生态驾驶系统（EDS），这有助于有效地利用重力势能。在提出的EDS中，我们基于影响车辆燃油消耗的因素，制定了具有适当预测范围和目标函数的非线性优化问题。目标函数的权重通过模糊推理技术使用车辆的瞬时速度和道路倾斜角度信息进行调整。通过考虑车辆纵向动力学，先前车辆的状态以及道路坡度信息（从数字道路地图获得），优化生成了本车的速度轨迹，从而使燃油消耗和CO最小化${}_2$排放。我们还研究了在拥挤的交通中后续车辆（在本车后面）的交通流性能；在丘陵公路的现有工程中未考虑到这一点。在日本福冈市的真实道路上使用微观交通模拟对所提出的EDS的有效性进行了评估，结果表明，经过模糊调整的MPC EDS可以显着降低燃油消耗和CO${}_2$与传统驾驶（基于人的）系统（TDS）相比，在相同的行驶时间内本车辆的排放量。在繁忙的交通中，油耗和二氧化碳${}_2$ 后续车辆的排放显着降低。