永磁同步电机自适应热模型建立与验证

Adaptive Thermal Model for Permanent Magnet Synchronous Machines of Vehicles

随着车用内置式永磁同步电机功率密度的不断提升,增加了永磁同步电机出现局部过热的风险。因此,实现对电机绕组和永磁体温度的在线实时估计,对于实现内置式永磁同步电机的热管理和自适应控制具有重要意义。首先建立在线电机损耗计算模型和5节点集总参数热网络模型。根据台架实验得到不同工况下的温度数据,利用粒子群优化算法得到损耗模型和集总参数热网络参数,并探究参数随转速、负载和温度变化规律。为了消除温度和磁饱和变化对温度估计的影响,利用估计的温度实时更新永磁体磁链和绕组阻值,根据估计的电阻和测量的电压电流,在线估计定子电感。最后根据估计的电机参数和当前工况更新电机损耗,建立适应工况变化的电机热模型。

With the improvement of the power density of the interior permanent magnet synchronous machines(IPMSMs) for vehicles the risk of local overheating of the permanent magnet synchronous motor has increased. Therefore, it is of great significance to realize the online real-time estimation of the temperature of the motor winding and permanent magnets for the thermal management and adaptive control of IPMSMs. An online motor loss calculation model and a 5-node lumped-parameter thermal network(LPTN) are established first. According to the temperature data under different operating conditions obtained from the experiment, the parameters of loss model and the LPTN were obtained by using the particle swarm optimization(PSO) algorithm, and the variation law of the parameters with the speed, load and temperature was explored.In order to eliminate the influence of temperature and magnetic saturation variation on temperature estimation, uses the estimated temperature to update the permanent magnet flux linkage and winding resistance in real time, and estimates the stator inductance online according to the estimated resistance and the measured voltage and current. Finally, the motor loss is updated according to the estimated motor parameters and the current operating conditions, and a thermal model of IPMSMs that adapts to the changing operating conditions is established.