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Energies
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Modeling, Simulation and Control of Electric Drive Systems
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Modeling, Simulation and Control of Electric Drive Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (30 August 2021) | Viewed by 22560

Special Issue Editors

Prof. Dr. Paolo Mercorelli

E-Mail Website1 Website2
Guest Editor
Institute of Product and Process Innovation, Leuphana, University of Lueneburg, 21335 Lueneburg, Germany
Interests: control systems; mechatronics; actuators; engines control; signal processing; wavelets; Kalman filter; optimal energy control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Harald Aschemann

E-Mail Website
Guest Editor
Chair of Mechatronics, University of Rostock, Justus-von-Liebig-Weg 6, D-18059 Rostock, Germany
Interests: mechatronics; physical modelling; nonlinear control and estimation; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is common sense that an intelligent management of energy resources is mandatory to respect the environment and minimize the harmful side effects of energy usage. Hence, in future electric drive systems, model-based system design, intelligent control and optimization techniques, as well as learning algorithms will play a decisive role. In fact, energy optimization already represents a crucial issue in smart grid/micro-grid energy flow, renewable energy, electrical and hybrid vehicles, energy storage devices, electrical motors, and in any kind of actuators.

In addition to these aspects, also methods addressing the mechatronic design of energy-efficient drives and, moreover, their robust control in the presence of changing operating conditions, uncertainty, and disturbances are of great interest. In this sense, techniques based on concepts such as controllability and observability are welcome.

This upcoming Special Issue of Energies will address and collect papers successfully showing how the model-based control and optimization of electric drive systems can contribute to a sustainable use of energy in industrial and transport applications.

Prof. Dr. Paolo Mercorelli
Prof. Dr. Harald Aschemann
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • model-based optimization
  • combinations of model-based design techniques with machine learning
  • model-based system engineering in e-mobility
  • design of drives based on controllability and observability concepts

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Published Papers (8 papers)

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Research

16 pages, 2559 KiB  
Article
Comparison of Energy Prediction Algorithms for Differential and Skid-Steer Drive Mobile Robots on Different Ground Surfaces
by Krystian Góra, Mateusz Kujawinski, Damian Wroński and Grzegorz Granosik
Energies 2021, 14(20), 6722; https://doi.org/10.3390/en14206722 - 15 Oct 2021
Cited by 7 | Viewed by 3295
Abstract
A detailed literature analysis depicts that artificial neural networks are rarely used for the power consumption estimation in the mobile robotics field. Instead, researchers prefer to develop analytical models of investigated robots. This manuscript presents a comparison of mathematical models and non-complex artificial [...] Read more.
A detailed literature analysis depicts that artificial neural networks are rarely used for the power consumption estimation in the mobile robotics field. Instead, researchers prefer to develop analytical models of investigated robots. This manuscript presents a comparison of mathematical models and non-complex artificial neural networks in energy prediction tasks for differential and skid-steer drive robots which move over various types of surfaces. The results show that both methods could be used interchangeably but AI methods are more universal, do not depend on the kinematic structure of a robot and are tolerant for designers not having a complex knowledge about the system. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Electric Drive Systems)
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14 pages, 346 KiB  
Article
Nonlinear Modelling, Flatness-Based Current Control, and Torque Ripple Compensation for Interior Permanent Magnet Synchronous Machines
by Felix Veeser, Tristan Braun, Lothar Kiltz and Johannes Reuter
Energies 2021, 14(6), 1590; https://doi.org/10.3390/en14061590 - 12 Mar 2021
Cited by 7 | Viewed by 2267
Abstract
A nonlinear mathematical model for the dynamics of permanent magnet synchronous machines with interior magnets is discussed. The model of the current dynamics captures saturation and dependency on the rotor angle. Based on the model, a flatness-based field-oriented closed-loop controller and a feed-forward [...] Read more.
A nonlinear mathematical model for the dynamics of permanent magnet synchronous machines with interior magnets is discussed. The model of the current dynamics captures saturation and dependency on the rotor angle. Based on the model, a flatness-based field-oriented closed-loop controller and a feed-forward compensation of torque ripples are derived. Effectiveness and robustness of the proposed algorithms are demonstrated by simulation results. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Electric Drive Systems)
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12 pages, 1290 KiB  
Article
Adaptive Extremum Seeking Control of Urban Area Wind Turbines
by Felix Dietrich, Steffen Borchers-Tigasson, Till Naumann and Horst Schulte
Energies 2021, 14(5), 1356; https://doi.org/10.3390/en14051356 - 2 Mar 2021
Cited by 5 | Viewed by 2064
Abstract
Maximum-power point tracking of wind turbines is a challenging issue considering fast changing wind conditions of urban areas. For this purpose, an adaptive control approach that is fast and robust is required. Conventional approaches based on simple step perturbations and subsequent observation, however, [...] Read more.
Maximum-power point tracking of wind turbines is a challenging issue considering fast changing wind conditions of urban areas. For this purpose, an adaptive control approach that is fast and robust is required. Conventional approaches based on simple step perturbations and subsequent observation, however, are difficult to design and too slow for the demanding wind conditions of urban areas including gusts and turbulence. In this paper, an extremum seeking control scheme to the recently developed wind turbine MOWEA (Modulare Windenergieanlagen GmbH) is proposed and successfully applied. To this end, a comprehensive aero-electromechanical model of the wind turbine under study including basic control is formulated. Next, the extremum seeking control scheme is adapted to the system. Several aspects to increase adaptation speed are highlighted, including a novel phase compensation. Finally, a validation of the proposed approach is performed considering real wind data, thus demonstrating its fast and robust adaptability. The proposed control scheme is computationally efficient and can be easily implemented on the existing onboard electronics. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Electric Drive Systems)
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19 pages, 2395 KiB  
Article
Operating Performance of Pure Electric Loaders with Different Types of Motors Based on Simulation Analysis
by Xuefei Li, Chao Duan, Kun Bai and Zongwei Yao
Energies 2021, 14(3), 617; https://doi.org/10.3390/en14030617 - 26 Jan 2021
Cited by 12 | Viewed by 3257
Abstract
The electrification of loader designs can utilise several power motor types. Hence, this study investigates the operational performance of pure electric-powered loaders matched with three types of motors. Firstly, for the ZL08 loader, it is proposed that a pure electric-powered loader structure adopts [...] Read more.
The electrification of loader designs can utilise several power motor types. Hence, this study investigates the operational performance of pure electric-powered loaders matched with three types of motors. Firstly, for the ZL08 loader, it is proposed that a pure electric-powered loader structure adopts two motors to drive the walking and hydraulic systems separately. Secondly, the dynamic parameters of the two motors were matched, and then, a joint vehicle dynamics model of the control system, the Multi-Body Dynamics (MBD) module and the material Discrete Element Method (DEM) module, was established. Finally, the performance of the walking system with three motors was tested by inserting three materials and using accelerating and climbing methods. The operating performance of the hydraulic system was tested by shovelling and unloading three materials. Results show that when inserting difficult materials, the loader’s walking system with switched reluctance motors is 9.74–21.2% deeper than that with the other two motors and 11.7–56.2% faster at the same depth. The hydraulic system consumes 3–15.7% less energy when matched with a permanent magnet synchronous motor than the other two motors. Pure electric loaders have the best operating performance when the walking system is matched with a switched reluctance motor, and the hydraulic system is matched with a permanent magnet synchronous motor. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Electric Drive Systems)
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19 pages, 1843 KiB  
Article
The Influence of Operating Strategies regarding an Energy Optimized Driving Style for Electrically Driven Railway Vehicles
by Lukas Pröhl, Harald Aschemann and Roberto Palacin
Energies 2021, 14(3), 583; https://doi.org/10.3390/en14030583 - 23 Jan 2021
Cited by 6 | Viewed by 2016
Abstract
The aim of this paper is the optimization of velocity trajectories for electrical railway vehicles with the focus on total energy consumption. On the basis of four fundamental operating modes—acceleration, cruising, coasting, and braking—energy-optimal trajectories are determined by optimizing the sequence of the [...] Read more.
The aim of this paper is the optimization of velocity trajectories for electrical railway vehicles with the focus on total energy consumption. On the basis of four fundamental operating modes—acceleration, cruising, coasting, and braking—energy-optimal trajectories are determined by optimizing the sequence of the operating modes as well as the corresponding switching points. The optimization approach is carried out in two consecutive steps. The first step ensures compliance with the given timetable, regarding both time and position constraints. In the second step, the influence of different operating strategies, such as load distribution and the switch-off of traction components during low loads, are analyzed to investigate the characteristics of the energy-optimal velocity trajectory. A detailed simulation model has been developed to carry out the analysis, including an assessment of its capabilities and advantages. The results suggest that the application of load-distribution techniques, either by a switch-off of parallel traction units or by a load-distribution between active units, can affect the energy-optimal driving style. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Electric Drive Systems)
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18 pages, 13189 KiB  
Article
Open-Circuit Fault-Tolerant Control of Multi-Phase PM Machines by Compensating the d-q Axes Currents
by Ali Akay and Paul Lefley
Energies 2021, 14(1), 192; https://doi.org/10.3390/en14010192 - 1 Jan 2021
Cited by 4 | Viewed by 2445
Abstract
This paper presents a novel method to control sinusoidal distributed winding or sinusoidal back electromotive force (back-EMF) multi-phase permanent magnet (PM) machines under open-circuit fault conditions. In this study, five different fault conditions are considered: single-phase, adjacent double-phase, non-adjacent double-phase, adjacent three-phase, and [...] Read more.
This paper presents a novel method to control sinusoidal distributed winding or sinusoidal back electromotive force (back-EMF) multi-phase permanent magnet (PM) machines under open-circuit fault conditions. In this study, five different fault conditions are considered: single-phase, adjacent double-phase, non-adjacent double-phase, adjacent three-phase, and non-adjacent three-phase open circuit conditions. New current sets for the remaining healthy phase under open-circuit fault conditions are obtained by compensating the direct-quadrature (d-q) axes currents. For this purpose, an iterative method has been used to get the new set of currents. D-q axes currents, due to faulty phase/phases, are shared to the healthy phases to obtain the same d-q axes currents as in the healthy condition. Therefore, the same torque is produced as in the healthy condition. The developed method is simulated in MATLAB/Simulink by using a d-q modelled sinusoidal back-EMF five-phase machine. A vector control block diagram has been designed to run the machine under healthy and faulty conditions. The machine model has been run successfully under fault tolerant conditions. Additionally, a finite element analysis (FEA) has been undertaken to simulate the five-phase PM model machine by using MagNet software. Open-circuit fault-tolerant control currents are fed into the coils of the machine model. Satisfactory torque results have been obtained. Because the model five-phase PM machine includes higher order back-EMF harmonics, especially the third harmonic, torque has ripple due to interaction between the fault-tolerant control currents and the higher order back-EMF harmonics. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Electric Drive Systems)
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17 pages, 3249 KiB  
Article
Improvements on a Sensorless Scheme for a Surface-Mounted Permanent Magnet Synchronous Motor Using Very Low Voltage Injection
by Jaime Pando-Acedo, Enrique Romero-Cadaval, Maria Isabel Milanes-Montero and Fermin Barrero-Gonzalez
Energies 2020, 13(11), 2732; https://doi.org/10.3390/en13112732 - 29 May 2020
Cited by 10 | Viewed by 2596
Abstract
Sensorless control of electrical drives is still a difficult task, especially in the lower speed region. Moreover, when the machine has a low saliency such as in the case of Surface Mounted Permanent Magnet Synchronous Motor (SMPMSM), high frequency injection techniques are even [...] Read more.
Sensorless control of electrical drives is still a difficult task, especially in the lower speed region. Moreover, when the machine has a low saliency such as in the case of Surface Mounted Permanent Magnet Synchronous Motor (SMPMSM), high frequency injection techniques are even more challenging. In this paper, an enhanced demodulation algorithm for the sensorless control of a SMPMSM is proposed. The new scheme uses the high frequency injection in the Synchronous Reference Frame (SRF) and employs also the negative sequence content of the measured current for improved accuracy. This allows an improved performance with a lower amplitude of the injected signal, thus reducing the noise and additional losses in the motor. It is found that, by using both components with the algorithm developed the estimation ripple can be greatly reduced and the system can operate properly with a voltage injection of only 2.17% of the rated motor voltage, which is lower than most found in the literature, specially for low saliency machines. Simulations tests are carried out for the validation of the proposed method and experimental results in a 6.7 kW SMPMSM confirm its usefulness and correct operation with a reduced voltage injection, both in no-load and load conditions, for different low speeds, including start-up. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Electric Drive Systems)
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Graphical abstract

18 pages, 5610 KiB  
Article
Parameter Equivalent Method of Stator Anisotropic Material Based on Modal Analysis
by Zeyu Zhang, Zhiyong Jiao, Hongbing Xia and Yuhan Yao
Energies 2019, 12(22), 4257; https://doi.org/10.3390/en12224257 - 8 Nov 2019
Cited by 2 | Viewed by 2741
Abstract
Accurate calculation of the vibration mode and natural frequency of a motor stator is the basis for reducing motor noise and vibration. However, the stator core and winding material parameters are difficult to determine, posing issues which result in modal calculation bias. To [...] Read more.
Accurate calculation of the vibration mode and natural frequency of a motor stator is the basis for reducing motor noise and vibration. However, the stator core and winding material parameters are difficult to determine, posing issues which result in modal calculation bias. To address the problem of calibrating the stator material parameters, we developed a parameter correction method based on modal frequency. First, the stator system was simplified to build a stator system finite element model. Secondly, the relationship between modal frequency and material parameters was analyzed by finite element software, the relationship between modal frequency and material parameters was derived, and the anisotropic material parameter correction method was summarized. Finally, a modal experiment was carried out by the hammering method, and the simulation and experimental errors were within 3%, which verified the accuracy of the finite element model. The proposed correction method of anisotropic material can quickly determine the stator material parameters, and the stator core and winding anisotropic material can ensure the accuracy of the modal analysis. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Electric Drive Systems)
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