惯量自补偿的纯电动汽车动力系统模拟试验台研究  被引量:9

Research on self-compensated inertia test rig of pure electric vehicle dynamic system

在线阅读下载全文

作  者:刘和平[1] 战祥真[1] 李红新[1] 袁闪闪[1] 

机构地区:[1]重庆大学输配电装备及系统安全与新技术国家重点实验室,重庆400044

出  处:《电机与控制学报》2011年第10期55-62,共8页Electric Machines and Control

基  金:中央高校基本科研业务资助项目(CDJXS11151156)

摘  要:通过分析纯电动汽车动力学特性以及动力系统中各部分的运行效率,进行了纯电动汽车动力系统模拟的电气建模和运行效率的动力学建模。针对纯电动汽车动力系统惯量模拟试验台自身复杂的阻力特性、试验台不同轴产生的扰动,提出在试验台力矩加载单元采用转速闭环矢量控制策略,来实现对试验台自身阻力和惯量差值的自动补偿,并详细给出了模拟纯电动汽车运行时力矩加载单元转速给定的计算公式。针对纯电动汽车动力系统惯量模拟车辆不同工况时,系统状态和参数存在不确定性因素,采用模糊自整定PID方案提高了试验台转速的跟随性和系统鲁棒性。通过仿真表明,该方法能够实现考虑车辆路况和试验台阻力与惯量差值状况下对纯电动汽车动力系统的精确模拟,为纯电动汽车动力系统惯量模拟试验台的研发提供了仿真理论依据。Dynamics of pure electric vehicles and efficiency of automotive parts was analyzed, then the model of pure electric vehicle power system was established and the electrical efficiency of the dynamics was modeled. For the complex resistance characteristics of pure electric vehicle inertia simulation test rig and torque disturbance produced by axes not aligned on test rig, speed closed-loop vector control method applied on torque loading motor was proposed, which compensate the rig resistance and inertia deviation automatically. The formula on how to eulculate the speed providing for torque loading motor is detailed when simulating electric vehicle. There exist variable system parameters and uncertain state of factors un- der different conditions when simulating electric vehicle, the scheme employing fuzzy self-tuning PID algorithm follows the given speed accurately and improve system robustness. Taking into account of the actual road conditions of the pure electric vehicle and test rig resistance simulation results show that this method simulate actual vehicle precisely, it provides a theoretical foundation for the research and development of pure electric vehicle dynamic system test rig.

关 键 词:电动汽车 惯量模拟 自补偿 模糊控制 

分 类 号:TM351[电气工程—电机]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象