Fuzzy-tuned model predictive control for dynamic eco-driving on hilly roads
Fuzzy-tuned model predictive control for dynamic eco-driving on hilly roads
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DOI：
10.1016/j.asoc.2020.106875
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发表时间：
2021-01-07
影响因子：
8.7
通讯作者：
Imura, Jun-ichi
中科院分区：
文献类型：
--
作者：
Bakibillah, A. S. M.;Kamal, M. A. S.;Imura, Jun-ichi
考虑道路坡度影响的现有车辆最优控制系统使用与速度偏差相关的固定权重的成本函数，而不考虑斜坡上的驾驶状态。因此，不能有效地利用重力势能，在下坡制动（浪费能量）是不可避免的。因此，仍然有很大的空间来改善在斜坡上的节油行为。为了解决这一问题，本文提出了一种基于模糊权重模型预测控制（MPC）的（主）车辆动态生态驾驶系统（EDS），该系统有助于有效利用重力势能。在该模型中，我们根据影响车辆油耗的因素，建立了一个具有适当预测范围和目标函数的非线性优化问题。利用车辆的瞬时速度和道路坡度信息，通过模糊推理技术调整目标函数的权重。通过考虑车辆纵向动力学、前车状态和道路坡度信息（从数字路线图获得），优化生成主车辆的速度轨迹，从而最大限度地减少油耗和二氧化碳排放。我们还研究了密集交通中跟随车辆（在主车辆后面）的交通流性能；在丘陵道路上的现有工程中没有考虑到这一点。通过对日本福冈市一段真实道路的微观交通模拟，对该系统的有效性进行了评估，结果表明，在相同的行驶时间内，与传统的人工驾驶系统（TDS）相比，模糊调整的MPC EDS系统显著降低了主车辆的燃油消耗和二氧化碳排放。在交通密集的情况下，跟随车辆的油耗和二氧化碳排放量明显降低。(C) 2020 Elsevier B.V.版权所有
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 CO2 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 CO2 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 CO2 emission of following vehicles are noticeably reduced. (C) 2020 Elsevier B.V. All rights reserved.