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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Optimal Power Management Based on Q-Learning and Neuro-Dynamic Programming for Plug-in Hybrid Electric Vehicles
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Optimal Power Management Based on Q-Learning and Neuro-Dynamic Programming for Plug-in Hybrid Electric Vehicles

机译：基于Q-Learning的最优电源管理和用于插入式混合动力电动车的神经动力学编程

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摘要

Energy optimization for plug-in hybrid electric vehicles (PHEVs) is a challenging problem due to the system complexity and many physical and operational constraints in PHEVs. In this paper, we present a Q-learning-based in-vehicle learning system that is free of physical models and can robustly converge to an optimal energy control solution. The proposed machine learning algorithms combine neuro-dynamic programming (NDP) with future trip information to effectively estimate the expected future energy cost (expected cost-to-go) for a given vehicle state and control actions. The convergences of these learning algorithms were demonstrated on both fixed and randomly selected drive cycles. Based on the characteristics of these learning algorithms, we propose a two-stage deployment solution for PHEV power management applications. Furthermore, we introduce a new initialization strategy, which combines the optimal learning with a properly selected penalty function. This initialization scheme can reduce the learning convergence time by 70%, which is a significant improvement for in-vehicle implementation efficiency. Finally, we develop a neural network (NN) for predicting battery state-of-charge (SoC), rendering the proposed power management controller completely free of physical models.
机译：由于系统复杂性和PHEV的许多物理和操作约束，插入式混合动力电动车（PHEV）的能量优化是一个具有挑战性的问题。在本文中，我们提出了一种基于Q-Learnal的车载学习系统，其没有物理模型，并且可以鲁棒地收敛到最佳的能量控制解决方案。所提出的机器学习算法与未来旅行信息结合了神经动态编程（NDP），以有效地估计给定车辆状态和控制动作的预期未来能源成本（预期成本到达）。在固定和随机选择的驱动循环中对这些学习算法的收敛进行了演示。基于这些学习算法的特征，我们为PHEV电源管理应用提出了一种两级部署解决方案。此外，我们介绍了一种新的初始化策略，它将最佳学习与正确选择的惩罚功能相结合。该初始化方案可以将学习收敛时间降低70％，这是车载实施效率的显着改进。最后，我们开发了一种用于预测电池的神经网络（NN），用于预测充电状态（SOC），呈现所提出的电源管理控制器完全没有物理模型。

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