【作者】 王红；

【导师】 孙逢春； 宋强；

【作者基本信息】 北京理工大学 ， 车辆工程， 2015， 博士

【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。更多还原

【Abstract】 Bulldozers with traditional mechanical structure have low energy utilization rate, poor emissions performance, and high fuel consumption because of its poor load adaptability when confronted with complex working conditions with load changing frequently. Hybrid technology is the best way to improve the bulldozer’s fuel economy under the existing technology conditions. Dynamic Modeling and control strategy for hybrid tracked bulldozer are researched in this paper to improve its fuel economy in the premise of ensuring its dynamic characteristics.The analysis of terra-mechanics of tracked bulldozer provides the theoretical basis for dynamic modeling. Control strategy and its optimization for the hybrid tracked bulldozer are researched based on the dynamic modeling. The main research work is as follows:1) Terra-mechanics of tracked bulldozer is researched by discussing mechanical problems of the interaction between the walk device, working parts and the ground. Meanwhile, Terra-mechanics can provide the theoretical basis for travel dynamics modeling.2) A control-oriented travel dynamics model is established. The dynamic simulation of straight driving, steering and working condition are performed to verify the validity of the travel dynamics model. Then, the parameters matching and type selection are performed for the key components of the hybrid tracked bulldozer.3) The entire power train dynamic model for the hybrid tracked bulldozer is established. Based on analyzing of the Terra-mechanics and travel dynamics model, a control-oriented simulation model of the hybrid tracked bulldozer is established. This model includes the engine-generator, ultracapacitor, drive motor system, driveline and bulldozer travel dynamics model. The model can not only reflect the changes of the control strategy parameters such as the engine-generator set operating speed points, ultracapacitor’s SOC and fuel consumption, but also reveal the energy control and distribution process between engine-generator set and ultracapacitor. Comparison of the simulation results and the real vehicle experimental data is performed to verify the accuracy of the simulation model of the hybrid tracked bulldozer.4) Engine control strategy and energy management strategies are researched based on the working characteristics of the bulldozer. Off-line simulation is performed based on the bulldozer typical and integrated working condition to verify the effectiveness of these control strategy. The bench test experiment for the hybrid tracked bulldozer is performed to verify the rationality and feasibility of the control strategy further more.5) In order to further improve the fuel economy, control strategy parameters for the hybrid tracked bulldozer are optimized. The optimization model is established with optimum fuel consumption as the objective function, and engine speed and ultracapacitor’s SOC as the design variables. Then parameters optimization of the control strategy is performed by using the adaptive genetic algorithm. The results show that this method is effective which is used for control strategy optimization can improve the fuel economy of the hybrid tracked bulldozer, and it also can greatly reduce the control parameters of the real vehicle calibration time.Hybrid tracked bulldozer using the load power following control strategy can improve the fuel economy effectively and the control strategy parameters optimization by using the self-adaptive genetic algorithm can reduce the fuel consumption further more.更多还原