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Online Energy Management for Multimode Plug-In Hybrid Electric Vehicles

机译：多模式插电式混合动力汽车的在线能源管理

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摘要

An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time application. First, the studied mult-imode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corresponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the globally optimal method, dynamic programming, and is superior to the charge-depleting/charge-sustaining technique. Also, hardware-in-the-loop experiment is built to validate the real-time characteristic of the proposed strategy.
机译：本文提出了一种基于插电式混合动力汽车（PHEV）的在线能量管理控制器，该控制器基于驾驶条件识别和遗传算法（GA）。所提出的控制器可以在实时应用中使用。首先，对所研究的多模式PHEV进行建模，并详细介绍四种牵引操作模式。其次，利用主成分分析（PCA）算法对实际历史驾驶状况数据进行分类。构建了四种类型的驾驶条件来描述代表性的场景。然后，应用GA离线搜索七个控制操作的最优值。这些用于不同驾驶条件的参数将保留并可以在线激活。最后，在线识别驾驶状况，并加载并采用相应的控制动作。仿真结果表明，该方法与全局最优方法，动态规划相近，并且优于电荷消耗/维持电荷技术。此外，还建立了硬件在环实验，以验证所提出策略的实时性。

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