Minimum Copper Losses Per Torque Optimization on Electrically Excited Synchronous Motors for Electric Vehicles Applications

Abstract

Increased environmental and energy security concerns have made electric vehicles an attractive solution over the recent years. Therefore, improving their traction chain global efficiency, especially in high-speed region, became a major task in today's research. This paper investigates a novel optimization method applied on electric vehicles motorized by an Electrically Excited Synchronous Motor (EESM). The study proposes an algebraic method based on the Lagrange multiplier technique to control the motor torque efficiently while extending its speed limit using the additional degree of freedom offered by this type of machines. The reference currents are calculated offline and stored in lookup tables (LUT) using MATLAB/Simulink. The proposed method is compared to conventional Maximum Torque Per Ampere (MTPA) strategies applied on Internal Permanent Magnet Synchronous Machines (IPMSM) by maintaining the EESM excitation current constant and controlling the d-q axis currents for a likewise behavior. The optimization and speed limit extension benefits of the novel control scheme, especially in high-speed region, are emphasized via simulation results in the torque/speed domain.

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

“…OFTC of synchronous machine (SM) is currently an actively researched and widely investigated topic: in particular of permanent magnet synchronous machine (PMSM) and reluctance synchronous machine (RSM) (see e.g., [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]) and of electrically excited synchronous machine (EESM; see, e.g, [49][50][51][52][53][54][55][56][57][58]). But only [48] considers and allows for (i) arbitrary nonlinearities (e.g., saturation, (d, q)-crosscoupling and cross-coupling between stator and exciter), (ii) iron and copper losses, (iii) stator and exciter voltage and current limits and (iiii) an analytical computation for a realtime implementation.…”

Artificial neural network based optimal feedforward torque control of electrically excited synchronous machines

“…In [31], the MTPA problem for stator and exciter currents is solved by the use of a Lagrangian. However, electromagnetic cross-coupling and iron losses are not considered.…”