Intelligent Hybrid Vehicle Power Control—Part II: Online Intelligent Energy Management
Intelligent Hybrid Vehicle Power Control—Part II: Online Intelligent Energy Management
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DOI：
10.1109/tvt.2012.2217362
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发表时间：
2013
影响因子：
6.8
通讯作者：
Y. Murphey;Jungme Park;Leonidas Kiliaris;M. Kuang;A. Masrur;A. Phillips;Qing Wang
中科院分区：
文献类型：
--
作者：
Y. Murphey;Jungme Park;Leonidas Kiliaris;M. Kuang;A. Masrur;A. Phillips;Qing Wang
这是第二个系列的两个文件，描述了我们的研究在混合动力电动汽车（HEV）的智能能源管理。在第一篇论文中，我们提出了机器学习框架ML_EMO_HEV，该框架是为学习HEV中的能量优化知识而开发的。该框架由机器学习算法组成，用于预测驾驶环境并为给定的驾驶环境生成HEV系统的最佳功率分配。本文介绍了三种在线智能能量控制器：1）IEC_HEV_SISE; 2）IEC_HEV_MISE ; 3）IEC_HEV_MIME。所有三个在线智能能源控制器都在机器学习框架ML_EMO_HEV内进行了训练，以真实的时间生成发动机功率和电池功率的最佳组合，从而使整个驾驶循环的总燃料消耗最小化，同时仍满足驾驶员的需求和系统约束，包括发动机，电机，电池和发电机的操作限制。三个在线控制器集成到福特Escape混合动力汽车模型的在线性能评估。基于Powertrain Systems Analysis Toolkit库提供的10个试驾循环的性能，我们可以得出结论，道路类型和交通拥堵水平特定的最佳能量管理机器学习对于车载能量控制是有效的。最好的控制器IEC_HEV_MISE，用具有多个初始SOC点和单个终点的DP优化算法生成的最佳功率分配进行训练，可以提供5%至19%的燃料节省。这两篇论文共同涵盖了建模功率流的创新技术，能量管理优化的数学背景，以及用于生成智能能量控制器的机器学习算法，用于功率分流HEV中的准最优能量流。
This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). In the first paper, we presented the machine-learning framework ML_EMO_HEV, which was developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine-learning algorithms for predicting driving environments and generating the optimal power split of the HEV system for a given driving environment. In this paper, we present the following three online intelligent energy controllers: 1) IEC_HEV_SISE; 2) IEC_HEV_MISE ; and 3) IEC_HEV_MIME. All three online intelligent energy controllers were trained within the machine-learning framework ML_EMO_HEV to generate the best combination of engine power and battery power in real time such that the total fuel consumption over the whole driving cycle is minimized while still meeting the driver's demand and the system constraints, including engine, motor, battery, and generator operation limits. The three online controllers were integrated into the Ford Escape hybrid vehicle model for online performance evaluation. Based on their performances on ten test drive cycles provided by the Powertrain Systems Analysis Toolkit library, we can conclude that the roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy control. The best controller, IEC_HEV_MISE, trained with the optimal power split generated by the DP optimization algorithm with multiple initial SOC points and single ending point, can provide fuel savings ranging from 5% to 19%. Together, these two papers cover the innovative technologies for modeling power flow, mathematical background of optimization in energy management, and machine-learning algorithms for generating intelligent energy controllers for quasioptimal energy flow in a power-split HEV.