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 multimode 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.

中文翻译：

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多模式插电式混合动力汽车的在线能源管理


本文提出了一种基于驾驶条件识别和遗传算法（GA）的插电式混合动力汽车（PHEV）在线能量管理控制器。所提出的控制器可用于实时应用。首先，对所研究的多模式插电式混合动力汽车进行建模，并详细介绍四种牵引操作模式。其次，利用主成分分析（PCA）算法对真实的历史驾驶条件数据进行分类。构建了四种类型的驾驶条件来描述代表性场景。然后，应用遗传算法离线搜索七个控制动作的最优值。这些针对不同驾驶条件的参数都会被保存并可以在线激活。最后在线识别行驶工况并加载并采取相应的控制动作。仿真结果表明，所提出的方法接近全局最优方法动态规划，并且优于电荷耗尽/电荷维持技术。此外，还建立了硬件在环实验来验证所提出策略的实时特性。