混合动力履带推土机动力学建模及控制策略研究

发布时间：2018-06-13 10:56

本文选题：混合动力履带推土机 + 地面力学　；参考：《北京理工大学》2015年博士论文

【摘要】：推土机的作业工况复杂,载荷变化频繁,传统机械与液压传动结构能量利用率低,排放性差,燃油消耗高。在现有的技术条件下,采用混合动力技术是改善推土机燃油经济性的最佳途径。本课题依托国家科技支撑计划“通用的商用车与工程机械模块化混合动力总成”项目,主要研究混合动力推土机动力学建模及控制策略,旨在保证推土机动力性的前提下提高其燃油经济性。揭示履带推土机地面力学特性是建立精确动力学模型的理论依据。在动力学建模的基础上,设计混合动力推土机控制策略并采用自适应遗传算法对控制策略参数进行优化。论文主要研究内容包括:1)进行履带推土机地面力学特性的研究。主要讨论推土机的行走装置、工作部件与地面之间相互作用的力学问题,为履带推土机行驶动力学分析提供理论依据。2)建立面向控制的行驶动力学模型。针对某履带推土机进行直驶、转向、作业工况的行驶动力学仿真并对仿真结果进行了分析,验证模型的有效性。在此基础上,进行了推土机关键部件的匹配与选型。3)建立混合动力推土机整车动力学模型。在地面力学特性分析与行驶动力学模型的基础上,建立面向控制的混合动力推土机整车动力学仿真模型,模型包括发动机-发电机组模型、超级电容模型、驱动电机模型、驾驶员模型以及推土机动力学模型等。该模型能够反映发动机-发电机组的工作点、超级电容的SOC以及燃油消耗等参数的变化情况,揭示发动机-发电机组与超级电容的能量控制与分配过程。通过仿真结果与实车试验数据相比,验证了模型的有效性。4)基于推土机的作业特点,研究了混合动力推土机的发动机控制方案以及能量管理策略。针对推土机的典型作业工况与综合工况,进行控制策略离线仿真,验证控制策略的有效性并分析不同控制策略燃油经济性的优劣。进行台架试验,进一步验证控制策略的合理性与可行性。5)为进一步提高燃油经济性,对混合动力推土机控制策略参数进行优化。以最佳燃油经济性为优化目标,以发动机的工作转速点和超级电容的SOC为设计变量,建立系统优化模型,采用自适应遗传算法求得最优解。计算结果表明本文的方法是有效的,将其用作控制策略参数优化,能够提高混合动力推土机的燃油经济性,并可以大大缩短控制参数的实车标定时间。通过基于实车试验数据的仿真试验与原型机相比,混合动力推土机采用负载功率跟随的控制策略能有效改善推土机的燃油经济性。通过自适应遗传算法对控制策略参数的优化可以进一步降低推土机的油耗。
[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.
【学位授予单位】：北京理工大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：TU623.5

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