Effect of Engine Dynamics on Optimal Power-Split Control Strategies in Hybrid Electric Vehicles

Anand Ganesan, S. Gros, Nikolce Murgovski, Chih Feng Lee, M. Sivertsson
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引用次数: 2

Abstract

This paper presents a model predictive control (MPC) based supervisory power-split control strategy, which optimises fuel and energy consumption in Hybrid Electric Vehicles (HEVs) by incorporating powertrain actuator dynamic models. In HEVs, while distributing the driver demand to the powertrain actuators, a standard approach is to approximate the actuator energy conversion dynamics with steady-state maps, which leads to sub-optimal control policy and increased fuel & energy consumption, especially for a driving mission with high transient demands. To address this shortfall, the control strategy proposed in this paper explicitly integrates an experimentally validated dynamic model of gasoline internal combustion engine (ICE) into an MPC based power-split controller. The proposed strategy is validated in a parallel HEV platform, where the sensitivity of the HEV energy consumption w.r.t. its actuator dynamics and the transients in its load demands, is also established. The results enable an understanding of the energy saving potential in HEVs that supports the inclusion of actuator dynamic models in optimal power-split controllers and it also establishes that the proposed control strategy realises higher energy and fuel savings in HEVs.
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发动机动力学对混合动力汽车最优功率分配控制策略的影响
提出了一种基于模型预测控制(MPC)的监督功率分割控制策略,结合动力传动系统执行器的动力学模型,对混合动力汽车的燃料和能量消耗进行优化。在混合动力汽车中,在将驾驶员需求分配给动力系统执行器时,标准方法是用稳态映射近似执行器的能量转换动力学,这导致控制策略不是最优,并且增加了燃料和能量消耗,特别是对于具有高瞬态需求的驾驶任务。为了解决这一不足,本文提出的控制策略明确地将汽油内燃机(ICE)的实验验证动态模型集成到基于MPC的功率分割控制器中。在并联HEV平台上验证了该策略,建立了HEV能耗随动器动力学和负载需求瞬态变化的灵敏度。研究结果有助于理解混合动力汽车的节能潜力,支持在最优功率分配控制器中包含执行器动态模型,并确定所提出的控制策略实现了混合动力汽车的更高能源和燃料节约。
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