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首页> 外文期刊>International Journal Precision Engineering Manufacturing-Green Technology >Adaptive Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Pontryagin's Minimum Principle Based on Daily Driving Patterns
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Adaptive Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Pontryagin's Minimum Principle Based on Daily Driving Patterns

机译：基于日常驾驶模式的庞特里亚金最小原理的插电式混合动力汽车自适应能量管理策略

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摘要

Optimal control ideas based on Pontryagin's Minimum Principle (PMP) have become mature techniques for maximizing the fuel efficiency of Hybrid Electric Vehicles (HEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). The outstanding performance of this control concept has already been verified in many studies, in which the PMP-based control produces optimal solutions that are very close to the global optimal solution obtained by Dynamic Programming (DP). However, the drawback of the control concept is that the PMP-based control will not guarantee optimality if no information about the future driving condition is given. This is not just a drawback of the PMP-based control, but it is an unavoidable limitation in most optimal control concepts. Therefore, previous studies have been focused on finding an optimal costate when the future driving conditions are given or predicted prior to driving. In this study, a methodology that analyzes the past driving pattern and updates the control parameters is proposed by assuming that vehicles are operated under repeated driving conditions. A control parameter, or a costate in the PMP-based control, can be estimated from two parameters that characterize the driving conditions, and the correlation between the costates and the energy consumption patterns is used to update the control parameter. Based on this control concept, the final State of Charge (SOC) at the end of each drive gets gradually closer to the desired value as the driving cycle is repeated. The methodology can be used for vehicles operated under repeated driving patterns, such as commuting buses, parcel delivery vehicles, or refuse collection trucks.
机译：基于庞特里亚金最小原理（PMP）的最优控制思想已经成为使混合动力汽车（HEV）和插电式混合动力汽车（PHEV）的燃油效率最大化的成熟技术。这种控制概念的出色性能已经在许多研究中得到了验证，其中基于PMP的控制产生的最优解与动态规划（DP）获得的全局最优解非常接近。但是，该控制概念的缺点在于，如果未给出有关未来驾驶条件的信息，则基于PMP的控制将无法保证最优性。这不仅是基于PMP的控件的缺点，而且在大多数最佳控件概念中也是不可避免的限制。因此，先前的研究集中于在驾驶之前给出或预测未来的驾驶条件时寻找最佳成本。在这项研究中，通过假设车辆在重复驾驶条件下运行，提出了一种分析过去驾驶模式并更新控制参数的方法。可以从表征驾驶条件的两个参数中估算控制参数或基于PMP的控制中的costate，并且使用costate和能耗模式之间的相关性来更新控制参数。基于此控制概念，随着重复驱动周期，每次驱动结束时的最终充电状态（SOC）逐渐接近所需值。该方法可以用于以重复驾驶模式操作的车辆，例如通勤公共汽车，包裹运送车辆或垃圾收集卡车。

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