Battery Electric Vehicles have a high potential in modern transportation, however, they are facing limited cruising range. The driving style, the road geometries including slopes, curves, the static and dynamic traffic conditions such as speed limits and preceding vehicles have their share of energy consumption in the host electric vehicle. Optimal energy management based on a semi-autonomous ecological advanced driver assistance system can improve the longitudinal velocity regulation in a safe and energy-efficient driving strategy. The main contribution of this paper is the design of a realtime risk-sensitive nonlinear model predictive controller to plan the online cost-effective cruising velocity in a stochastic traffic environment. The basic idea is to measure the relevant states of the electric vehicle at runtime, and account for the road slopes, the upcoming curves, and the speed limit zones, as well as uncertainty in the preceding vehicle behaviour to determine the energy-efficient velocity profile. Closed-loop Entropic Value-at-Risk as a coherent risk measure is introduced to quantify the risk involved in the system constraints violation. The obtained simulation and field experimental results demonstrate the effectiveness of the proposed method for a semi-autonomous electric vehicle in terms of safe and energy-efficient states regulation and constraints satisfaction.

中文翻译：

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用于优化电动汽车能源管理的生态高级驾驶辅助系统

电池电动汽车在现代交通中具有很大的潜力，但它们面临着有限的续航里程。驾驶风格、道路几何形状（包括坡度、弯道）、静态和动态交通条件（如限速和前车）在主电动汽车中占能源消耗的份额。基于半自动生态高级驾驶员辅助系统的优化能量管理可以在安全和节能的驾驶策略中改善纵向速度调节。本文的主要贡献是设计了一种实时风险敏感的非线性模型预测控制器，以在随机交通环境中规划在线经济高效的巡航速度。基本思想是测量电动汽车在运行时的相关状态，并考虑道路坡度、即将到来的弯道和限速区，以及前方车辆行为的不确定性，以确定节能速度曲线。引入闭环熵值在险作为一种连贯的风险度量，以量化涉及违反系统约束的风险。获得的仿真和现场实验结果证明了该方法在安全和节能状态调节和约束满足方面对半自动电动汽车的有效性。引入闭环熵值在险作为一种连贯的风险度量，以量化涉及违反系统约束的风险。获得的仿真和现场实验结果证明了该方法在安全和节能状态调节和约束满足方面对半自动电动汽车的有效性。引入闭环熵值在险作为一种连贯的风险度量，以量化涉及违反系统约束的风险。获得的仿真和现场实验结果证明了该方法在安全和节能状态调节和约束满足方面对半自动电动汽车的有效性。