摘 要
混合动力汽车作为一种高效节能的交通工具，近年来在全球范围内得到了广泛关注。然而，其复杂的能量管理系统仍面临诸多挑战，如能量分配效率低、电池寿命短等问题。本研究旨在通过优化混合动力汽车的能量管理策略，提升整车性能和经济性。研究采用了基于模型预测控制和深度强化学习相结合的方法，构建了一个综合性的能量管理优化框架。首先，通过建立精确的动力系统模型，模拟不同驾驶条件下的能量流动情况；其次，利用MPC进行实时优化控制，确保能量在发动机和电池之间的合理分配；最后，引入DRL算法，通过大量仿真数据训练智能体，使其能够自主学习并适应多变的驾驶环境。实验结果表明，优化后的能量管理策略显著提高了燃油经济性和电池使用寿命。此外，该方法在实际道路测试中也表现出色，验证了其在不同驾驶场景下的适用性和鲁棒性。本研究的创新点在于将传统控制理论与现代人工智能技术相结合，为混合动力汽车的能量管理提供了新的思路和解决方案。未来工作将进一步探索该方法在大规模生产和多样化车型中的应用潜力。
关键词：混合动力汽车；能量管理策略；模型预测控制；深度强化学习
Abstract
As a kind of efficient and energy-saving means of transportation, hybrid electric vehicle (HEV) has been widely concerned in the world in recent years. However, its complex energy management system still faces many challenges, such as low energy distribution efficiency and short battery life. The purpose of this study is to improve the performance and economy of hybrid electric vehicles by optimizing the energy management strategy. A comprehensive energy management optimization fr amework is constructed based on the combination of model predictive control and deep reinforcement learning. Firstly, an accurate dynamic system model is established to simulate the energy flow under different driving conditions. Secondly, the MPC is used for real-time optimization control to ensure the reasonable distribution of energy between the engine and the battery. Finally, the DRL algorithm is introduced to train the agent through a large amount of simulation data, so that it can learn independently and adapt to the changing driving environment. Experimental results show that the optimized energy management strategy significantly improves fuel economy and battery life. In addition, the method performs well in actual road tests, verifying its applicability and robustness in different driving scenarios. The innovation point of this research is to combine traditional control theory with modern artificial intelligence technology to provide new ideas and solutions for energy management of hybrid electric vehicles. Future work will further explore the potential application of this method in mass production and diversified vehicle models.
Key Words：Hybrid electric vehicle; Energy management strategy; Model predictive control; Deep reinforcement learning
目 录
摘 要 I
Abstract II
第1章 引言 1
第2章 混合动力汽车能量管理策略概述 2
2.1 混合动力汽车系统架构分析 2
2.2 现有能量管理策略分类与评价 2
2.3 能量管理策略优化的必要性探讨 3
第3章 基于模型的能量管理策略优化方法 5
3.1 动态规划在能量管理中的应用 5
3.2 模型预测控制在混合动力汽车中的实现 5
3.3 基于机器学习的能量管理策略优化 6
第4章 混合动力汽车能量管理策略的研究 8
4.1 中国典型城市交通环境下的能量管理策略测试 8
4.2 不同驾驶模式对能量管理策略的影响分析 8
4.3 能量管理策略优化效果的综合评估 9
结 论 11
参考文献 12
致 谢 13
