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Fuel Saving Potential of Optimal Route-Based Control for Plug-in Hybrid Electric Vehicle

机译：插电式混合动力汽车基于最优路径控制的节油潜力

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In this paper, we evaluate the fuel savings of a plug-in hybrid electric vehicle (PHEV) that uses an optimal controller, itself based on the Pontryagin Minimum Principle (PMP). A process was developed to synthesize speed profiles through a combination of Markov chains and information from a digital map about the future route. In a potential real-world scenario, the future trip (speed, grade, stops, etc.) can be estimated, but not deterministically known. The stochastic trip prediction process models such uncertainty. A PMP strategy was implemented in a Simulink controller for a model of Prius-like PHEV and compared to a baseline strategy using Autonomie, an automotive modeling environment. Multiple real-world itineraries were defined in urban areas with various environments, and for each of them multiple speed profiles were synthesized so as to provide a statistically representative dataset, and finally fuel savings were evaluated with the optimal control.
机译：在本文中，我们评估了插电式混合动力汽车（PHEV）的节油效果，该插电式混合动力汽车本身基于庞特里亚金最小原理（PMP）使用最优控制器。通过结合马尔可夫链和来自数字地图的有关未来路线的信息，开发了一种合成速度曲线的过程。在潜在的现实世界场景中，可以估计未来的行程（速度，坡度，停靠点等），但不确定性未知。随机行程预测过程对这种不确定性进行建模。在Simulink控制器中针对类似Prius的PHEV模型实施了PMP策略，并将其与使用汽车建模环境Autonomie的基线策略进行了比较。在具有各种环境的市区中定义了多个现实世界的路线，并为每个路线合成了多个速度曲线，以提供具有统计意义的数据集，最后通过最佳控制对燃油节省进行了评估。

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