School of Engineering, Monash University, Bandar Sunway 47500, Malaysia.
Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo 152-8552, Japan.
J Environ Manage. 2024 Sep;368:122245. doi: 10.1016/j.jenvman.2024.122245. Epub 2024 Aug 21.
Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin's minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.
电动汽车（EV）是汽油动力汽车的理想替代品，具有降低能耗和排放的潜力。然而，电动汽车的能耗高度依赖于道路环境和驾驶行为，尤其是在城市交叉口。本文提出了一种基于中等和密集交通条件下城市信号交叉口最优能耗的新型电动汽车生态（eco）驾驶策略（EDS）。首先，我们开发了一种考虑道路坡度、曲率、滚动阻力、轴承摩擦、空气动力学阻力、电机欧姆损耗和再生制动等关键因素的电动汽车能耗模型。为了在不同交通速度下更好地回收能量，我们使用了 sigmoid 函数来计算再生制动效率，而不是许多其他研究中采用的简单常数或线性函数。其次，我们制定了一个生态驾驶最优控制问题，该问题受状态约束的限制，通过使用庞特里亚金最小原理（PMP）在时间和距离范围内为车辆的加速/减速找到一个闭式解，从而最小化电动汽车的能耗。最后，我们使用微观交通模拟评估了所提出的 EDS 的效果，考虑了城市信号交叉口的真实交通行为，并将其性能与（基于人类的）传统驾驶策略（TDS）进行了比较。结果表明，在各种交通需求下，该策略在能源效率和等待时间方面都有显著的性能提升，同时确保了驾驶安全和乘坐舒适性。我们提出的策略具有较低的计算成本，可以作为实时高级驾驶辅助系统（ADAS）使用。
