|Efficiency Optimization Control of an IPMSM Drive System for Electric Vehicles (EVs)
Qin-Mu Wu, Yu Zhan, Mei Zhang, Xiang-Ping Chen*, and Wen-Ping Cao
International Journal of Control, Automation, and Systems, vol. 19, no. 8, pp.2716-2733, 2021
Abstract : 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 systemlevel 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.
Current optimization, electric vehicles (EVs), interior permanent magnet synchronous motors (IPMSMs), parameter identification, recursive neural network.