摘要
针对车载综合电力系统因离网运行、发动机动态响应慢以及工况复杂从而易失稳的问题,开展了系统失稳预测研究。通过建立各微源、负载变换器的小信号模型,得到各微源输出阻抗与负载输入阻抗。基于改进的阻抗比判据提出了车载综合电力系统小信号失稳判据。利用波特图从发动机转速、车速以及驱动电机转矩3个方面进行系统失稳预测分析。最后通过硬件在环仿真以及样车试验对理论分析结果进行了验证。研究结果表明:该样车在高速工况下,发动机转速应控制在1 900~2 000 r/min,车速不得超过100 km/h;单个驱动电机转矩对系统稳定性影响最大,超过48 N·m会直接导致系统崩溃。该小信号失稳判据能够准确预估系统失稳状态,对优化系统控制结构以及辅助系统设计具有一定指导意义。
This paper focuses on the stability of the vehicular integrated power system in terms of its island operation, the slow dynamic response of engine, and the complex working conditions. Firstly, the small signal models of each source and loads converter are proposed, based on which the output impedance and loads input impedance are obtained. Then, the instability criterion of small signal of the vehicular integrated power system is established based on an improved impedance criterion. Furthermore, the instability prediction of the system is analyzed from three aspects, such as its engine speed, vehicle speed, and the torque of each driving motor, by using the Bode diagram. Finally, the theoretical analysis is certificated by the Hardware-in-Loop simulation and prototype vehicle tests. The research results indicate that the engine speed should be controlled between 1 900 r/min and 2 000 r/min, and the vehicle speed shall not exceed 100 km/h, when the vehicle is in high-speed mode. The stability of system is heavily influenced by the torque of each driving motor, and the system will be collapsed caused by the torque when it is more than 48 N·m. It is concluded that the proposed instability criterion can precisely predict the system state, and help optimize the control structure and design the assistant system.
作者
高强
廖自力
马晓军
魏曙光
GAO Qiang;LIAO Zili;MA Xiaojun;WEI Shuguang(Department of Arms and Control,Academy of Army Armored Force,Beijing 100072,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2021年第1期313-321,共9页
High Voltage Engineering
基金
国家自然科学基金(5150070296)。
关键词
车载综合电力系统
高速工况
小信号模型
阻抗比判据
失稳预测
vehicular integrated power system
high speed mode
small-signal model
forbidden region of impedance ratio
instability prediction