Efficiency Optimization Control of an IPMSM Drive System for Electric Vehicles (EVs)
Efficiency Optimization Control of an IPMSM Drive System for Electric Vehicles (EVs)
复制标题
电动汽车 (EV) IPMSM 驱动系统的效率优化控制
DOI：
10.1007/s12555-019-0723-z
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发表时间：
2021-06
影响因子：
3.2
通讯作者：
Cao Wen-Ping
中科院分区：
文献类型：
--
作者：
Wu Qin-Mu;Zhan Yu;Zhang Mei;Chen Xiang-Ping;Cao Wen-Ping
电动汽车是运输行业脱碳的关键技术，其中内置永磁同步电机（IPMSM）是电气驱动系统核心的最佳性能。为了优化其运行效率，广泛采用与参数识别相关联的基于模型的方法。然而，现有方法中的效率优化和参数辨识是以顺序执行的方式通过不同的策略独立地实现的，这不会产生优化的系统级解决方案。在本文中，这两种方法相结合，以处理在IPMSM驱动器的约束优化问题。首先，基于变分原理和投影动力学理论，将问题转化为变分问题。然后，统一的投影动力学方程（CIEE）被用来估计的参数，并确定的IPMSM的最佳电流（OC）的解决方案。此外，一个递归神经网络（RNN）对应的的的IPMSM驱动器的快速效率优化的IPMSM驱动器实现开发。仿真实验结果表明，该方法能够快速、准确地辨识电机参数，确定驱动系统的OC。因此，它可以快速实现IPMSM驱动系统的效率优化。由于所设计的RNN可以容易地在硬件中实现，例如现场可编程门阵列（FPGA）或专用神经网络芯片，因此该方法可以实现IPMSM驱动系统的瞬时效率优化，从而提高IPMSM在电动汽车中的广泛应用。
Electric vehicles are a key technology to decarbonize the transport sector where interior permanent magnet synchronous motors (IPMSMs) are the best performer at the heart of the electrical drive system. In order to optimize their operational efficiency, the model-based method associated with parameter identification is widely adopted. However, efficiency optimization and parameter identification in the existing methods are implemented independently by different strategies in a sequential execution manner, which does not produce an optimized system-level solution. In this paper, the two methods are combined to deal with a constrained optimization problem in an IPMSM drive. Firstly, the problem is converted into a variational problem based on the variational principle and projection dynamic theory. Then, a unified projection dynamic equation (UPDE) is used to estimate the parameters and determine the solution of optimal current (OC) of the IPMSM. Further, a recursive neural network (RNN) corresponding to the UPDE is developed to implement the developed fast efficiency optimization of the IPMSM drive. The results of simulation experiments show the proposed method is effective to identify motor parameters and determine the OC of the drive system rapidly and accurately. Thus, it can rapidly realize efficiency optimization of an IPMSM drive-system. Because the designed RNN can be easily implemented in the hardware, such as a field-programmable gate array (FPGA) or dedicated neural network chip, the method can achieve instantaneous efficiency optimization of the IPMSM drive system and therefore improve the widespread application of IPMSMs in EVs.
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发表时间：
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影响因子：
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