Despite the current intense research on each of the subjects of electrification and autonomous driving, potential advantages as a result of the interaction of these two mainstreams in automotive have not been effectively studied yet. Autonomous vehicles generate an unprecedented amount of real-time data due to excessive use of perception sensors and processing units. In this article, we present a novel approach for improving the fuel economy of an autonomous hybrid electric vehicle by taking advantage of this qrydata. We introduce the term of autonomous-specific energy management strategy (ASEMS) and we present an example of such a strategy using model predictive control (MPC). Specifically, we show how a more fuel-optimal energy management strategy (EMS) can be achieved for the power-split powertrain of an autonomous hybrid electric vehicle using the motion planning data. We use an optimization-based motion planning approach and feed the resulting velocity profile up to the prediction horizon to the MPC-based EMS. The presented approach shows 2% to 12.81% less fuel consumption for the two extreme cases of 100 and 1000 meters as the prediction horizons, compared to a rule-based EMS. The presented EMS fuel-optimality for the 1000 meters is only 6.91% sub-optimal compared to the globally optimal results of dynamic programming.

中文翻译：

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基于MPC的混合动力汽车能源管理策略

尽管目前对电气化和自动驾驶的每个主题都进行了深入的研究，但由于这两种主流技术在汽车领域的相互作用而产生的潜在优势尚未得到有效研究。由于过度使用感知传感器和处理单元，无人驾驶车辆会产生前所未有的实时数据。在本文中，我们提出了一种利用此qrydata改善自主混合动力电动汽车燃油经济性的新颖方法。我们介绍了自治专用能源管理策略（ASEMS）的术语，并提供了使用模型预测控制（MPC）的此类策略的示例。特别，我们展示了如何使用运动计划数据为自动混合动力汽车的动力分配动力总成实现更省油的能源管理策略（EMS）。我们使用基于优化的运动计划方法，并将最终的速度分布图馈送到基于MPC的EMS的预测范围。与基于规则的EMS相比，在100米和1000米这两种极端情况下，所提出的方法将油耗降低了2％至12.81％。与动态规划的全局最优结果相比，目前针对1000米的EMS燃料最优性仅为6.91％次优。与基于规则的EMS相比，在100米和1000米这两种极端情况下，油耗降低了81％。与动态规划的全局最优结果相比，目前针对1000米的EMS燃料最优性仅为6.91％次优。与基于规则的EMS相比，在100米和1000米这两种极端情况下，油耗降低了81％。与动态规划的全局最优结果相比，目前针对1000米的EMS燃料最优性仅为6.91％次优。