本文選題：混合動力履帶推土機 + 地面力學　； 參考：《北京理工大學》2015年博士論文

【摘要】：推土機的作業(yè)工況復雜,載荷變化頻繁,傳統(tǒng)機械與液壓傳動結構能量利用率低,排放性差,燃油消耗高。在現(xiàn)有的技術條件下,采用混合動力技術是改善推土機燃油經(jīng)濟性的最佳途徑。本課題依托國家科技支撐計劃“通用的商用車與工程機械模塊化混合動力總成”項目,主要研究混合動力推土機動力學建模及控制策略,旨在保證推土機動力性的前提下提高其燃油經(jīng)濟性。揭示履帶推土機地面力學特性是建立精確動力學模型的理論依據(jù)。在動力學建模的基礎上,設計混合動力推土機控制策略并采用自適應遺傳算法對控制策略參數(shù)進行優(yōu)化。論文主要研究內(nèi)容包括:1)進行履帶推土機地面力學特性的研究。主要討論推土機的行走裝置、工作部件與地面之間相互作用的力學問題,為履帶推土機行駛動力學分析提供理論依據(jù)。2)建立面向控制的行駛動力學模型。針對某履帶推土機進行直駛、轉(zhuǎn)向、作業(yè)工況的行駛動力學仿真并對仿真結果進行了分析,驗證模型的有效性。在此基礎上,進行了推土機關鍵部件的匹配與選型。3)建立混合動力推土機整車動力學模型。在地面力學特性分析與行駛動力學模型的基礎上,建立面向控制的混合動力推土機整車動力學仿真模型,模型包括發(fā)動機-發(fā)電機組模型、超級電容模型、驅(qū)動電機模型、駕駛員模型以及推土機動力學模型等。該模型能夠反映發(fā)動機-發(fā)電機組的工作點、超級電容的SOC以及燃油消耗等參數(shù)的變化情況,揭示發(fā)動機-發(fā)電機組與超級電容的能量控制與分配過程。通過仿真結果與實車試驗數(shù)據(jù)相比,驗證了模型的有效性。4)基于推土機的作業(yè)特點,研究了混合動力推土機的發(fā)動機控制方案以及能量管理策略。針對推土機的典型作業(yè)工況與綜合工況,進行控制策略離線仿真,驗證控制策略的有效性并分析不同控制策略燃油經(jīng)濟性的優(yōu)劣。進行臺架試驗,進一步驗證控制策略的合理性與可行性。5)為進一步提高燃油經(jīng)濟性,對混合動力推土機控制策略參數(shù)進行優(yōu)化。以最佳燃油經(jīng)濟性為優(yōu)化目標,以發(fā)動機的工作轉(zhuǎn)速點和超級電容的SOC為設計變量,建立系統(tǒng)優(yōu)化模型,采用自適應遺傳算法求得最優(yōu)解。計算結果表明本文的方法是有效的,將其用作控制策略參數(shù)優(yōu)化,能夠提高混合動力推土機的燃油經(jīng)濟性,并可以大大縮短控制參數(shù)的實車標定時間。通過基于實車試驗數(shù)據(jù)的仿真試驗與原型機相比,混合動力推土機采用負載功率跟隨的控制策略能有效改善推土機的燃油經(jīng)濟性。通過自適應遺傳算法對控制策略參數(shù)的優(yōu)化可以進一步降低推土機的油耗。
[Abstract]:The working conditions of bulldozer are complex, the load changes frequently, the energy utilization ratio of traditional mechanical and hydraulic transmission structure is low, the emission property is poor, and the fuel consumption is high. Under the existing technical conditions, hybrid power technology is the best way to improve the fuel economy of bulldozers. This topic relies on the national science and technology support plan "the general commercial vehicle and the construction machinery modularization hybrid power assembly" the project, mainly studies the hybrid electric bulldozer dynamics modeling and the control strategy, The purpose of this paper is to improve the fuel economy of bulldozer on the premise of ensuring its power performance. Revealing the mechanical properties of crawler bulldozer is the theoretical basis for the establishment of accurate dynamic model. On the basis of dynamic modeling, the control strategy of hybrid bulldozer is designed and the parameters of control strategy are optimized by adaptive genetic algorithm. The main contents of this paper include: 1) to study the mechanical properties of crawler bulldozer ground. This paper mainly discusses the mechanical problems of the walking device and the interaction between the working parts and the ground of the bulldozer, which provides a theoretical basis for the analysis of the driving dynamics of the crawler bulldozer. The driving dynamics of a crawler bulldozer is simulated and the simulation results are analyzed to verify the validity of the model. On this basis, the matching and selection of the key parts of the bulldozer are carried out. 3) the dynamic model of the hybrid bulldozer is established. Based on the analysis of ground mechanical characteristics and driving dynamics model, a control oriented dynamic simulation model of hybrid bulldozer is established. The models include engine generator unit model, super capacitor model, driving motor model, and so on. Driver model and bulldozer dynamics model. The model can reflect the change of the working point, SOC and fuel consumption of supercapacitor, and reveal the process of energy control and distribution between engine-generator unit and super capacitor. The simulation results show that the model is effective. 4) based on the operational characteristics of bulldozer, the engine control scheme and energy management strategy of hybrid bulldozer are studied. According to the typical and comprehensive working conditions of bulldozer, the off-line simulation of control strategy is carried out to verify the effectiveness of the control strategy and to analyze the advantages and disadvantages of different control strategies in fuel economy. In order to further improve fuel economy, the control strategy parameters of hybrid bulldozer were optimized by bench test to verify the rationality and feasibility of the control strategy. With the optimal fuel economy as the optimization objective, the system optimization model is established with the design variables of the engine operating speed point and the SOC of the super capacitor, and the optimal solution is obtained by using adaptive genetic algorithm (AGA). The results show that the proposed method is effective and can improve the fuel economy of the hybrid bulldozer and shorten the calibration time of the real vehicle. Compared with the prototype, the hybrid bulldozer adopts the load power following control strategy to improve the fuel economy of the bulldozer effectively through the simulation test based on the actual vehicle test data. The optimization of control strategy parameters by adaptive genetic algorithm can further reduce the fuel consumption of bulldozers.
【學位授予單位】：北京理工大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TU623.5

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