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Multiphase machines have recently gained interest in the research community for their use in applications where high power density, wide speed range and fault-tolerant capabilities are required. The optimal control of such drives requires the consideration of voltage and current limits imposed by the power converter and the machine. While conventional three-phase drives have been extensively analyzed taking into account such limits, the same cannot be said in the multiphase drives’ case. This paper deals with this issue, where a novel two-stage Model Predictive optimal Control (2S-MPC) technique is presented, and a five-phase permanent magnet synchronous multiphase machine (PMSM) is used as a case example. The proposed method first applies a Continuous-Control-Set Model Predictive Control (CCS-MPC) stage to obtain the optimal real-time stator current reference for given DC-link voltage and stator current limits, exploiting the maximum performance characteristics of the multiphase drive. Then, a Finite-Control-Set Model Predictive Control (FCS-MPC) stage is utilized to generate the switching state in the power converter and force the stator current tracking. An experimental validation of the proposed controller is finally provided using a real-time simulation environment based on OPAL-RT technologies.

The performance of a dual energy storage electric vehicle system mainly depends on the quality of its power and energy managements. A real-time management strategy supported by a Model Predictive Control using the non-uniform sampling time concept is developed and fully addressed in this paper. First, the overall multiple energy storage powertrain model including its inner control layer is represented with the Energetic Macroscopic Representation and used to introduce the energy strategy level. The model of the system with its inner control layer is translated into the state space domain in order to develop a Model Predictive Control approach. The management algorithm based on mixed short- and long-term predictions is compared to rule-based and constant sampling time Model Predictive Control strategies in order to assess its performance and its ability to be used in a real vehicle. The real-time simulation results indicate that, compared to other strategies, the proposed Model Predictive Control strategy can balance the power and the energy of the dual energy storage system more effectively, and reduce the stress on batteries. Moreover, battery and supercapacitor key variables are kept within safety limits, increasing the lifetime of the overall system.

Multiphase machines have recently gained importance in the research community for their use in applications where high power density, wide speed range and fault-tolerant capabilities are needed. The optimal control of such drives requires to consider voltage and current constraints imposed by the power converter and the machine itself. If classical three-phase drives have been optimally controlled under such limits for a long time, the same cannot be said in the case of multiphase drives. This paper deals with this issue, where an optimal control technique based on Cascaded Model Predictive Controls (MPC) is presented for a five-phase permanent magnet synchronous machine (PMSM). A Continuous-Control-Set MPC (CCS-MPC) numerically computes optimal current references in real-time in order to exploit the maximum performance for given DC bus voltage and current limits. Then, a Finite-Control-Set MPC (FCS-MPC) is used to carry out the current control in the machine, directly applying the switching state that minimizes a cost function related to the current tracking. Obtained mixed microprocessor-based and FPGA-based real-time simulations prove the interest of the proposal, which ensures the optimal control of the multiphase drive operating under current and voltage constraints.

The optimal control of electrical drives necessitates to take into account current and voltage limits that are imposed by the power electronics and the electrical machines. Let’s cite for example the flux-weakening operation of electrical drives or propulsion. If the control of classical three-phase drives under voltage and current limits are known for a long time, the specific characteristics of multiphase drives open the way to researches on their control under such constraints. This paper aims to explain what are the main differences between three-phase and multiphase drives when they run under voltage and current constraints and try to show what are the scientific and technical problems to be solved. Some first results are given in order to show that Model Predictive Control (MPC) is expected to be a good candidate to answer the proposed challenge.