电动汽车驱动电机与负载模拟系统建模及电弧故障仿真研究

电弧故障是引发电动汽车电气火灾的重要原因之一。电动汽车行驶工况复杂，电机及其驱动系统电压高、电流大，且其电弧故障随机性强、隐蔽性高导致真车故障实验难以开展，因此提出一种借助小功率电机与负载系统模拟故障的方法，以便快速开展大量实验，研究电弧故障特性。首先，在电动汽车负载转矩计算和等效缩放的基础上搭建了模拟实验平台，采集三相永磁同步电机线路串联电弧故障电流。其次，运用MATLAB软件构建空间矢量脉宽调制控制的电动汽车驱动电机与负载模拟系统，引入Cassie电弧故障模型并进行改进，对电动汽车三相永磁同步电机线路串联电弧故障展开仿真分析。最后，采用基于平肩宽度占比和小波包分解能量占比的特征提取方法，将仿真数据与实测数据进行比较并定量评价。结果表明，所提出的高斯电弧故障复合模型的相对平均误差最小，仅为7.6%。所构建的仿真系统可有效模拟实际线路的电弧故障，对电动汽车电气火灾的防控具有重要意义。

Modeling of electric vehicle driving motor and load simulationsystem and arc fault simulation research

Arc fault is an important cause of electric vehicle fire. The complex driving conditions of electric vehicles, the high voltage and current in the motor and its drive system, and the great randomness and concealment of the arc fault, make it difficult to conduct the real vehicle experiment. Therefore, a method using a fractional horsepower motor and load system is proposed in order to simulate faults and carry out a large number of experiments to research arc fault characteristics. Firstly, based on the load torque calculation and equivalent scaling, a simulation experiment platform is built to collect the line series arc fault current of a three-phase permanent magnet synchronous motor. Secondly, a driving motor and load simulation system of electric vehicle controlled by space vector pulse width modulation is built via MATLAB software, and through introducing and improving Cassie arc fault models, the line series arc fault of the electric vehicle three phase permanent magnet synchronous motor is simulated and analyzed. Finally, a feature extraction method based on the proportion of flat shoulder width and the proportion of wavelet packet decomposition energy is used to quantitatively evaluate the results by comparing the simulation and the measured data. The results show that the relative average error of the proposed Gaussian arc fault composite model is only 7.6%, the smallest one among all models. The constructed simulation system can effectively simulate the actual line arc fault, which is of great significance for the prevention and control of electric fire in electric vehicles.