Within the headway distance constraints, the potential for reduction of energy consumption by hybrid electric vehicles (HEVs) with connectivity could be achieved by optimizing the ego vehicle motion. This paper proposes a look-ahead traffic information-based real-time model predictive control scheme to minimize total monetary cost of HEVs. A chain Gaussian process approach is employed to estimate the probability distribution of future increments of vehicle number over a look-ahead horizon from vehicle-to-vehicle and vehicle-to-infrastructure information. The future motion of preceding vehicles could be predicted by the evolution of the traffic density model and velocity tracking model. The above problem is formulated as a nonlinear optimal control problem with predicted disturbance input and dynamic constraints. Optimal solutions are derived through Pontryagin's maximum principle. The effectiveness of the proposed control scheme is evaluated on a traffic-in-the-loop powertrain simulation platform by integrating a commercial traffic platform and an enterprise-level powertrain simulator.

中文翻译：

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用于互联 HEV 的基于前瞻性预测的实时优化能源管理

在车头距离限制内，可以通过优化自我车辆运动来实现具有连接性的混合动力电动汽车 (HEV) 降低能耗的潜力。本文提出了一种基于前瞻交通信息的实时模型预测控制方案，以最小化 HEV 的总货币成本。采用链式高斯过程方法从车辆到车辆和车辆到基础设施信息的前瞻范围内估计车辆数量未来增量的概率分布。前方车辆的未来运动可以通过交通密度模型和速度跟踪模型的演变来预测。上述问题被表述为具有预测干扰输入和动态约束的非线性最优控制问题。最优解是通过庞特里亚金的最大值原理推导出来的。通过集成商业交通平台和企业级动力系统模拟器，在交通在环动力系统仿真平台上评估所提出的控制方案的有效性。