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- Air conditioning;Electric potential;Biological system modeling;Production;Automobiles;Next generation networking;Engines (1)
- Couplings ; Switches; Predictive models ; Mathematical model ; Rotors ; Real-time systems ; Prediction algorithmso (1)
- DC power supply ; Design ; Converter circuit ; Measurement ; Power Supply (1)
- DC/DC converter ; efficiency ; tapped transformer ; wide input voltage range (1)
- Finite Element Analysis (FEA); Interior Permanent Magnet Synchronous Machine (IPMSM); saturation; cross- coupling; spatial harmonics (1)
- Model predictive control, FCS-MPC, VSP2CC, VV-MPC, multi-phase, PMS (1)
- electrical motors; sot filling factor; optimization algorithm; windings; magnetic wire; filling factor optimization (1)
Development of a Two In One Motor System as a new Vehicle Energy Concept for Next Generation Car
(2020)
The Interior Permanent Magnet Synchronous machine (IPMSM) conventional mathematical model is generally employed to investigate and simulate the IPMSM
control and drive system behaviour. However, magnetic nonlinearities and spatial harmonics have a substantial influence on the IPMSM electromagnetic behaviour and
performances. In order to simulate the IPMSM real electromagnetic behaviour, this paper describes an enhanced mathematical model that takes into account the
saturation, cross-coupling and spatial harmonics effects. This model has been implemented in Matlab®/Simulink environment where the electric and magnetic parameters are derived from FEA investigations and implemented by the use of lookup tables. The high fidelity of the proposed IPMSM Simulink model is validated by the results of the FEA investigations carried out.
Enhanced Flexible Algorithm for the Optimization of Slot Filling Factors in Electrical Machines
(2020)
The continuous development in the field of industrial automation and electric mobility has led to the need for more efficient electrical machines with a high power density. The improvement of electrical machines’ slot filling factors is one of the measures to satisfy these requirements. In recent years, this topic has aroused greater interest in the industrial sector, since the evolution of the winding technological manufacturing processes allows an economically sustainable realization of ordered winding arrangements, rather than random ones. Moreover, the manufacture of electrical machines’ windings must be preceded by an accurate design phase in which it is possible to evaluate the maximum slot filling factor obtainable for a given wire shape and for its dimensions. For this purpose, this paper presents an algorithmic approach for the evaluation of maximum slot filling factors in electrical machines under an ideal geometric premise. In particular, this algorithm has a greater degree of flexibility with respect to the algorithm approaches found in the literature, since the study has been extended to round, rectangular and hexagonal wire sections. Furthermore, the slot filling factor calculation was carried out both for standard and non-standard slots. The algorithmic approach proposed can be considered as an additional useful tool for the fast design of electrical machine windings.
This paper presents two rapid control prototyping (RCP) use cases facilitated by the open-source platform UltraZohm. The openly available UltraZohm development frame-
work eases the transition from simulation to the test bench. The framework offers the integration of automatic code generation for field-programmable gate arrays (FPGAs), either by using Simulink models based on the HDL Coder, or by synthesizing C++ code into VHDL via the Vivado high-level synthesis tool. The first use case focuses on the implementation details of an on-chip controller-in-the-loop setup, where a permanent magnetsynchronous machine is emulated in the FPGA with a sampling frequency of 2 MHz. The second use case presents an efficient real-time implemen-tation of the sphere decoding algorithm employed to solve the long-horizon finite control set model predictive control problem for a three-level neutral point clamped inverter driving an induction machine. Experimental results based on a small-scale prototype confirm that the algorithm can be executed in real time on the FPGA, with an execution time of a few tens of microseconds. Both use cases highlight the benefits of using a high-performance RCP platform for research in power electronics and their control.
This article presents a flux linkage-based direct model predictive current control approach that achieves favorable performance both during steady-state and transient operation. The former is achieved by computing the optimal time instants at which a new switch position is applied to the converter. To this end, the future current behavior is not computed based on the machine inductances or inductance look-up tables; instead, flux linkage maps are utilized to predict the trajectory of the magnetic flux linkage, and subsequently of the current. This is advantageous for electric drives with noticeable magnetic nonlinearity in terms of saturation and/or cross-coupling effects. Hence, by using flux linkage maps in the prediction process, the evolution of the stator current can be calculated more accurately, enabling the controller to make better switching decisions. Moreover, the discussed predictive controller exhibits excellent dynamic performance owing to its direct control nature, i.e., the control and modulation tasks are performed in one computational stage rendering a dedicated modulation stage redundant. Three different drive systems based on permanent magnet synchronous motors are examined to demonstrate the effectiveness of the presented control approach.
Design Concept of a Wound Field Transverse Flux Machine using Soft Magnetic Composite Claw-Poles
(2020)
This paper presents the machine concept of a wound field transverse flux machine (WFTFM). In contrast to permanent magnet transverse flux machines (PMTFM), here the magneto motive force (mmf) of the rotor is generated by a current. The magnetic circuit of the WFTFM is made of soft magnetic composite (SMC) and is designed as claw-poles. Thus the magnetic circuit consists of only a few components. Together with the simple ring coils in stator and rotor, the WFTFM offers advantages in the production of electrical machines. The basic structure of the machine and a first design is described here. The results generated by numerical three-dimensional finite element analysis (FEA) are compared with those of conventional machines.
Design of a Wide Input Voltage Range Current-Fed DC/DC Converter Within a Reduced Duty-Cycle Range
(2020)
A design of a current-fed push-pull converter for wide input voltage ranges within a reduced duty-cycle range is presented. Based on the converter conversion ratios of three different clamping configurations, the potential of the input clamping is shown analytical, simulative and by measurements. A differentiation is made between the four possible operating modes, which can be distinguished in the control of the semiconductors and the magnetomotive force in the reactor. By optimizing the turn ratios of both transformers, the wide input voltage range capability of the input clamped current-fed push-pull converter was improved, which has been proved by measurements on a real prototype.
A transformer with variable primary windings for wide input voltage range DC/DC converters is presented. This transformer structure allows three states of the primary winding to be converted by only two individual windings. Based on the evaluations of a two-switch flyback converter, the potential of DC/DC converters with variable structures, the implementation of such a transformer and the advantages and disadvantages are shown and discussed. Real measurement results demonstrate the noticeable increase in efficiency and the reduction in component stress, having a positive influence on the operation of the converter and the component selection during the design process.
Machine learning algorithms are increasingly used in industrial applications for a multitude of use-cases. However, using them in control tasks is a challenge due to real-time requirements and limited resources. In this paper, an implementation scheme for real-time inference of multilayer perceptron (MLP) neural networks on FPGAs is proposed. Design constraints for using MLPs in reinforcement learning agents for motor control applications are derived and accounted for in the implementation. Two MLP architectures are evaluated on an FPGA, and the timing and resource-usage data are reported. The real-time capability of the implementation for motor control applications is investigated for standard control frequencies. It is shown by experimental validation that real-time interference with an area-efficient implementation for motor control applications is achievable. Therefore, the proposed implementation scheme can be applied to deep reinforcement learning controllers with hard real-time requirements.
In this paper, a flux linkage-based direct model predictive current control approach is presented for small permanent magnet synchronous motor (PMSM) drives. The method aims to minimize the current ripples at steady state by deciding on the optimal switching instant, while exhibiting fast dynamic behavior during transients. To this end, the future trajectory of the stator current is not computed based on the machine inductances or inductance look-up tables, but on the changes of the magnetic flux linkage by utilizing flux linkage maps. As shown, the proposed method can be particularly advantageous for electric drives with a noticeable nonlinearity in terms of saturation and/or cross-coupling effects since it allows for a significantly increased prediction accuracy, which leads to an improved steady-state performance as indicated by the reduced current distortions.