本文選題：能量管理 + 實時性��； 參考：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2017年11期

【摘要】：目的:混合動力挖掘機的能量管理策略直接影響著系統(tǒng)的燃油經(jīng)濟性。本文旨在通過研究混合動力挖掘機能量管理系統(tǒng),得到最優(yōu)能量管理策略,并開發(fā)實時能量管理控制器,降低系統(tǒng)的燃油消耗。創(chuàng)新點:1.通過強化學(xué)習算法,設(shè)計時間無關(guān)的實時能量管理控制器;2.通過極大值原理求得最優(yōu)能量管理問題的解析解,并用來輔助實時能量管理控制器設(shè)計。方法:1.建立負載的馬爾科夫模型,運用強化學(xué)習算法,得到實時能量管理控制器;2.運用極大值原理,求得最優(yōu)能量管理問題的解析解,并將其作為初始能量管理策略;3.通過仿真模擬和實驗研究,驗證所設(shè)計的實時能量控制器的性能。結(jié)論:1.基于強化學(xué)習的能量管理控制器是一個可以在線應(yīng)用的與時間無關(guān)的實時能量管理控制器;2.基于強化學(xué)習的能量管理控制器優(yōu)于廣泛使用的恒溫控制器和等效消耗最小化策略控制器;3.基于強化學(xué)習的能量管理控制器由于其閉環(huán)特性可適用于不同類型的作業(yè)工況。
[Abstract]:Objective: the energy management strategy of hybrid excavator directly affects the fuel economy of the system. The purpose of this paper is to obtain the optimal energy management strategy by studying the hybrid excavator energy management system, and to develop a real-time energy management controller to reduce the fuel consumption of the system. The innovation point is 1: 1. Through reinforcement learning algorithm, a time-independent real-time energy management controller is designed. The analytical solution of the optimal energy management problem is obtained by the maximum principle and is used to aid the design of the real time energy management controller. Method 1: 1. The Markov model of load is established and the real time energy management controller is obtained by using reinforcement learning algorithm. The analytical solution of the optimal energy management problem is obtained by using the maximum principle, which is regarded as the initial energy management strategy. The performance of the designed real-time energy controller is verified by simulation and experimental research. Conclusion 1. The energy management controller based on reinforcement learning is a time independent real-time energy management controller, which can be applied online. The energy management controller based on reinforcement learning is superior to the constant temperature controller and equivalent consumption minimization strategy controller. The energy management controller based on reinforcement learning can be used in different operating conditions because of its closed loop characteristics.
【作者單位】： State
【基金】：Project supported by the National Natural Science Foundation of China(No.51475414) the Science Fund for Creative Research Groups of National Natural Science Foundation of China(No.51521064)
【分類號】：TU621

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