基于神经网络的插电式混合动力汽车最优生态驾驶控制

Jie Li, Z. Lei, ZhiHang Chen, Zheng Chen, Yonggang Liu
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引用次数: 1

摘要

随着智能网联汽车的发展,一种新的节能途径即生态驾驶控制受到了相关研究人员的关注。生态驾驶控制和插电式混合动力汽车的结合为进一步实现交通节能提供了机会。提出了一种基于神经网络的插电式混合动力汽车最优生态驾驶控制策略。为了减轻速度优化和动力系统控制的巨大计算成本,提出了一种高效的分层最优控制策略。建立了一个人工神经网络,对最优能源成本进行建模。将该最优能源成本模型作为求解最优生态驾驶问题的目标函数。仿真结果表明,与传统的生态驾驶控制策略相比,该方法可将燃油经济性提高4.29 ~ 12.71%。基于神经网络的最优能量成本模型显著提高了计算效率,与最优基准相比,燃油经济性的牺牲很小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Eco-driving Control for Plug-in Hybrid Electric Vehicles Based on Neural Network
with the development of intelligent and connected vehicles, a novel approach for energy-saving, i.e. eco-driving control, has attracted much attention from relative researchers. The combination of eco-driving control and plug-in hybrid electric vehicles provide an opportunity to achieve further energy-saving for transportation. In this paper, an optimal eco-driving control strategy is proposed for plug-in hybrid electric vehicles based on the neural network. In order to mitigate the huge computational cost of velocity optimization and powertrain control, an efficient hierarchical optimal control strategy is proposed. An artificial neural network is constructed for the modeling of optimal energy cost. This optimal energy cost model is applied as objective function in the solving of the optimal eco-driving problem. The simulation results show that the proposed method can improve fuel economy by 4.29-12.71%, compared with conventional eco-driving control strategy. The neural network based optimal energy cost model significantly heightens the computational efficiency, with small sacrifice for fuel economy compared to optimal bench mark.
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