基于神经网络的液压混合动力装载机能量管理

IF 0.7 Q4 ENGINEERING, MECHANICAL
Henrique Raduenz, Liselott Ericson, K. Uebel, Kim Heybroek, P. Krus, V. Negri
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引用次数: 0

摘要

提出了一种液压混合动力轮式装载机的能量优化管理方法。能源效率是越野移动机械可持续性的一个关键方面。公路混合动力汽车的能量管理策略不能直接应用于越野混合动力汽车。一个重要的原因是,在不同功能(如动力传动系统或工作功能)中,如何回收、交换和再利用电力方面增加了自由度。这导致了更复杂的能源管理策略的产生。本文对一种液压混合动力轮式装载机在线能量管理优化方法进行了分析,并得出了初步结论。采用动态规划的方法,获得了一系列驱动循环下的最优离线能量管理策略。结果被用作训练神经网络的例子。然后,训练后的神经网络实现能量管理策略,并用于做出优化的控制决策。通过仿真,证明了神经网络学习动态规划决策过程的能力,使机器运行时的油耗与离线最优能量管理策略相似。讨论了为动态规划优化对这些机器建模的简单性、训练网络所需的数据、训练过程、用于学习动态规划决策过程的变量以及面对未知操作条件时网络的鲁棒性。本文演示了考虑影响控制决策的变量的方法的简单性,从而实现了优化的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Management Based on Neural Networks for a Hydraulic Hybrid Wheel Loader
This paper presents a method to derive optimised energy management strategies for a hydraulic hybrid wheel loader. Energy efficiency is a key aspect for the sustainability of off-road mobile machines. Energy management strategies for on-road hybrid vehicles cannot be directly applied to off-road hybrid machines. One significant reason is that there are added degrees of freedom with respect to how power can be recovered, exchanged and reused in the different functions, such as drivetrain or work functions. This results in more complex energy management strategies being derived. This paper presents an analysis and preliminary conclusions for a proposed method to derive optimised online energy management strategies for a hydraulic hybrid wheel loader. Dynamic programming is used to obtain optimal offline energy management strategies for a series of drive cycles. The results are used as examples to train a neural network. The trained neural network then implements the energy management strategy and is used to make optimised control decisions. Through simulation, the neural network’s ability to learn the dynamic programming decision-making process is shown, resulting in the machine operating with fuel consumption similar to that of the offline optimal energy management strategy. Aspects of simplicity to model these machines for dynamic programming optimisation, the data necessary to train the network, the training process, variables used to learn the dynamic programming decision-making process and the robustness of the network when facing unseen operational conditions are discussed. The paper demonstrates the simplicity of the method for taking into account variables that affect the control decisions, therefore achieving optimised solutions.
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来源期刊
International Journal of Fluid Power
International Journal of Fluid Power ENGINEERING, MECHANICAL-
CiteScore
1.60
自引率
0.00%
发文量
16
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