Regularization-Theory-Based Fast Torque Tracking Method for Interior Permanent Magnet Synchronous Machines

Abstract

With the rapid growth of interior permanent magnet synchronous machines in electric vehicle applications, there is a need to generate torque tracking look-up tables, that can both track the torque command and implement maximum torque per ampere (MTPA)/maximum torque per volt (MTPV). So far, most torque tracking methods require a large amount of test points, giving rise to long test time and workloads. This paper proposes a fast torque tracking MTPA/MTPV look-up table generating method to improve the efficiency. The proposed method is based on a machine learning regularization theory, using a L1/L2 regularization to establish a data-driven torque tracking model. Then a Lagrange dual principle is introduced to solve the unknown parameters, so that the look-up tables of optimal d-q axis currents are yielded by a global optimization solver. Experimental results show that the proposed method can generate the look-up tables with the same accuracy as classical methods, but requires less test points and testing time. As a result, the testing work loads are reduced, as the time cost is only 10-15% of the classical methods.