This paper develops an appropriate predictive current controller (PCC) for fast torque monitoring and high-performance operation of a permanent-magnet synchronous motor (PMSM) drive involving constant switching frequency. The proposed controller is based on a convenient combination of deadbeat and direct predictive control (DPC) techniques. Its response is compared to that of a typical proportional-integral (PI) current controller in field-oriented control (FOC) strategy. The analysis illustrated improved transient characteristics concerning faster speed changes as well as reduced current and torque ripple while it enables additional control criteria inclusion. The simulated results have been validated by measurements on a test bench.

This paper proposes a multicriteria design optimization methodology for permanent magnet (PM) motors used in electric vehicle (EV) applications. In the process, an adaptive-network-based fuzzy inference system (ANFIS) is utilized, coupled with a multiobjective optimization algorithm, as a surrogate model of the electric motor. This allows for the consideration of the full drive cycle and respective efficiency map for every motor design. The prediction error of the ANFIS is minimized by employing appropriate membership functions, initial training data, and an adaptive learning scheme via iterative training. The efficiency map is then implemented in a vehicle dynamic model to compute the total consumed energy over the driving cycle. The optimization profile accounts for total energy efficiency, torque density, and additionally considers complementary design criteria via an a posteriori selection procedure on the resulting Pareto set. The methodology developed is applied to optimize a surface PM motor with concentrated fractional slot winding, mounted on a light EV that competes in fuel economy races. The selected motor design has been validated through measurements on a prototype.

This paper introduces a particular permanent magnet motor (PMM) design methodology, considering advanced magnetic material characteristics, for aerospace actuator applications. In this class of problems, increased electromagnetic power density, fault tolerance, and high-temperature withstand properties are of major importance, and favored single-layer (SL) and double-layer (DL) fractional slot concentrated winding (FSCW) optimal topologies with different motor segmentation strategies have been compared. Under such strict nature of specifications, both operational and spatial, the implementation of advanced magnetic materials, particularly Vacoflux50 cobalt iron laminations and NMX-S43SH neodymium PMs, offer great services. The optimization methodology introduced is based on composite cost and penalty functions involving performance, efficiency, reliability, weight, and thermal criteria for multioperational behavior under normal and temporary overload conditions. An appropriate particle swarm optimization algorithm ensures fast convergence of the optimization variables. The resulting optimal SL and DL FSCW PMM configurations present certain complementary advantages, while the former one offering greater efficiency, thermal robustness, and physical segregation of the two motor parts is favored for this class of applications. Finally, the proposed motor configuration has been validated through measurements on a manufactured prototype.

The extreme environmental issues and the resulting need to save energy have turned attention to the electrification of energy applications. One of the key components involved in energy efficiency improvements is the appropriate conception and manufacturing of electric machines. This paper overviews the electromagnetic analysis governing the behavior of permanent magnets that enable substantial efficiency gains in recent electric machine developments. Particular emphasis is given to modeling the properties and losses developed in permanent magnets in emerging high speed applications. In addition, the investigation of properties and harmonic losses related to ferromagnetic materials constituting the machine magnetic circuits are equally analyzed and discussed. The experimental validation of the implemented methodologies and developed models with respect to the obtained precision is reported. The introduction of mixed numerical techniques based on the finite element method intended to appropriately represent the different physical phenomena encountered is outlined and discussed. Finally, fast and accurate simulation techniques including aggregated lumped parameter models considering harmonic losses associated with inverter supplies are discussed.

This paper proposes a multiobjective optimization methodology for speed and torque trajectories of a light electric vehicle (LEV) traveling on a predefined route. The procedure introduced accounts for speed constraints and various elevation profiles and involves determination of the overload operation tolerances. The proposed technique facilitates the detection of alternative tradeoffs between energy and time savings for a predefined racing circuit. The method is based on the segmentation of the itinerary and involves a two-step procedure. In a first step, speed optimization is performed by tolerating substantial overloading, while in a second step, the torque overload limits per segment are optimized as well. Simulations based on a LEV dynamic model and experiments on racing circuit illustrated significant savings for both trip duration and energy consumption with respect to the classic constant-speed control technique.

This paper proposes a robust optimization algorithm, enabling global optimum tracking and manufacturing uncertainty factors handling, for high speed permanent magnet motor design. A new robustness criterion is introduced considering effectively the impact of the construction tooling uncertainties on the design variables in all objectives. An Adaptive-Network-based Fuzzy Inference System (ANFIS) is adopted acting as a surrogate for the time consuming finite element analyses and results in computationally fast robust electric machine design procedure. The procedure has been validated through finite element high speed motor design.