Permanent Magnet Synchronous Motors (PMSMs) are widely utilized in automotive and industrial applications due to their high efficiency, power, and torque density. Developing an accurate discrete-time model is crucial for implementing advanced control techniques, including direct torque control, model predictive control, and sensorless control. This paper presents the development of a discrete-time model for a PMSM, emphasizing numerical stability and dynamic accuracy. This model facilitates the creation of new current controllers, model-based observers, and parameter estimation techniques for motor drive systems operating at high fundamental frequencies and low sample rates. The model is derived using modified Euler discretization, extended to account for group delays and flux harmonics, and is compared with continuous-time simulations that include delta-sigma analog-to-digital converters. The switching inverter uses pulse-width modulation to generate the required constant voltages, with superimposed pseudo-random binary sequence (PRBS) signals, demonstrating the dynamic model’s accuracy. The same conditions are used for experimental validation. Comparison with simulations shows that the discrete-time model remains stable and accurate, while experimental verification demonstrates a significant increase in accuracy for motor drive systems with saturation.

Reducing the contact bouncing of electromechanical relays is a key ingredient to increase their switch reliability and overall lifetime. For this reason, this paper presents a control system as well as an optimization algorithm to reduce the kinetic energy of internal relay components by suspending acceleration during each switch cycle. A two-level control of the coil supply voltage is used in order to generate a control signal, which is defined by a certain start time as well as a specified duration. Both values are optimized via a computationally lightweight algorithm using a variant of a particle swarm optimization (PSO), which is a hybrid form of a bare-bone PSO and evolutionary PSO, augmented with dense estimation. Results are presented both qualitatively and quantitatively based on real-world experiments with various sample relays. Using the well-known run-to-run (R2R) algorithm as a baseline, the proposed bouncing optimization algorithm (BOA) reduces the switch on bouncing by 79 % ($+$24 % more than R2R) and the switching off bouncing by 59 % ($+$40 % more than R2R) compared to a conventional switch cycle without bouncing countermeasures. In addition, the proposed algorithm’s capability of self-adapting to changing environmental circumstances is validated experimentally.Note to Practitioners—Contact bounce increases relay wear. It is therefore desirable to reduce this. For this purpose, many mechanical optimizations have already been carried out. In this work, however, a software solution (BOA) is presented in which an online optimization algorithm is used to design the control signal of the relays in such a way that bouncing is reduced. The acceleration of the contacts is briefly suspended during the switching process, so that the contacts collide with less kinetic energy and, therefore, bounce less -as in the case of elastic impact. With BOA, it is possible to reduce the duration of bouncing by up to 79% during extensive tests. A transfer to various types of relays seems possible, but two points are critical for productization: First, a microcontroller is needed for implementation, which can significantly increase the manufacturing costs of a product. Secondly, a measurement signal of the load side of the relays is necessary, which can be technically challenging.

Predictive maintenance (PdM) offers enormous potential for improvement, both economically and ecologically. The extended use of components can improve sustainability, minimize the occurrence of component failures and associated safety risks for people and the environment, and ultimately increase the profitability of plants. PdM is therefore a future key technology that requires sensing, communication of collected data, and analysis of data to implement. In this work, the phoenix contact relay (PCR) dataset is provided and used to demonstrate that there is a need for research into new PdM techniques. The PCR dataset presented includes life data from 546 relays with a total of more than 106 million switching cycles collected over a period of several years at a sampling rate of 10 kHz. Manufacturers and loads were varied to ensure practical relevance. This sets PCR apart from previously published datasets in terms of number of measurements, complexity of measurements, scope of units tested, and practicality, providing the opportunity to design and evaluate new methods for PdM.

With electric power systems becoming more compact with higher power density, the relevance of thermal stress and precise real-time-capable model-based thermal monitoring increases. Previous work on thermal modeling by lumped-parameter thermal networks (LPTNs) suffers from mandatory expert knowledge for their design and from uncertainty regarding the required power loss model. In contrast, deep learning-based temperature models cannot be designed with the low amount of model parameters as in a LPTN at equal estimation accuracy. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based LPTNs, and data-driven nonlinear function approximation with supervised machine learning. The TNN approach overcomes the drawbacks of previous paradigms by having physically interpretable states through its state-space representation, is end-to-end differentiable through an automatic differentiation framework, and requires no material, geometry, nor expert knowledge for its design. Experiments on an electric motor data set show that a TNN achieves higher temperature estimation accuracies than previous white-/gray- or black-box models with a mean squared error of 3.18 K2 and a worst-case error of 5.84 K at 64 model parameters.

Besides precise motor control, a suitable operating strategy is required for a highly efficient operation of an induction motor. Especially due to the present extensive use of these motors in torque-controlled applications, e.g., electric vehicles, efficiency improvements by optimizing the operating strategy are of prime interest. Most model-based operating strategies in the literature use simplified motor models or offline optimized look up tables, so that they are generally not able to adequately consider all loss effects in the entire operating range of the drive. In contrast, this paper presents an adaptive operating strategy that takes into account all relevant losses and changing system conditions (e.g., temperature, DC-link voltage) at runtime. Using a precise electrical-thermal motor model identified offline, the changing system states are adapted online and the resulting nonlinear optimization problem for loss minimization is solved iteratively. Experimental validation demonstrates the efficiency improvement of the adaptive operating strategy – Over the entire constant torque range, the efficiency of an industrial motor can be increased by an average of 0.7 percentage points compared to a standard operating strategy.

Precise modeling and identification of induction motors is becoming increasingly important due to the extensive use of these motors in torque-controlled applications, e.g., electric vehicles. To achieve high precision, several nonideal motor characteristics including thermal effects have to be modeled and identified. Most thermal models in the literature utilize a loss model which is separated from the motor model considered in the control task leading to inconsistencies between these models. In this contribution, a combined electrical-thermal model is developed and its identification is addressed. Hence, the achieved universal drive model delivers flux, torque, loss and temperature estimations. Thus, the model provides information for three main drive tasks: general control, operating strategy and condition monitoring. With a comprehensive data set recorded at the test bench, the model parameters are optimally identified. On a separate test set, the proposed model is validated to estimate the torque generated by the motor with a root-mean-square error of 0.4 % related to nominal torque as well as the temperatures in the stator and rotor with root-mean-square errors of 1.0 K and 1.1 K, respectively.

For many permanent magnet synchronous motor (PMSM) drive applications (e.g., traction or automation), precise torque control is desired. Classically, this is based on extensive offline motor identification, e.g., by direct mapping of torque-flux-current look-up tables. In contrast, this article proposes a torque estimation method based on online differential inductances identification in combination with a data-driven finite-control-set (FCS) model predictive current control (MPCC). This scheme does not require offline identification or expert motor design knowledge. The required flux maps are determined by integrating the differential inductances in the left id-iqhalf-plane. By considering varying differential inductances, the proposed method is ideally suited for highly utilized PMSM with significant (cross-) saturation effects where estimation models with constant inductances fail. For the identification of the differential inductances, the system excitation, based on the FCS-MPCC working principle, is utilized. Consequently, no additional signal injection is required and the estimation scheme is applicable in the entire speed range. With this method, an open-loop torque control can be realized without knowledge of exact motor parameters except the permanent magnet flux linkage as a datasheet parameter. Extensive experimental investigations on a highly utilized PMSM in the entire speed range including standstill prove the performance of the proposed approach.

The main goal of an industrial microgrid during grid-connected operation is maximal cost saving for the microgrid owner. Many industrial companies do not only pay for the amount of electrical energy, but also for the maximum electrical power, which they have drawn from the distribution grid within the billing period. Under these conditions two basic options of cost saving exists utilizing the local energy storage systems inside the microgrid: reduction of the maximal power peak (peak shaving) and increase of self-consumption. For maximal cost saving, an operation strategy which combine both is desirable, but the combination requires information about the further residual power flow. A favorite option is the extrapolation of the residual power flow into the future. Unfortunately, it was found that errors in the extrapolation of the residual power lead to bad results in crucial situations. Therefore, this paper presents an additional artificial neural network (ANN) trained to predict residual power peaks, which will work in parallel to the extrapolation. This application-specific enhancement minimizes the effects of extrapolation errors and improves the original strategy in outcome and reliability. For an exemplary application, the self-consumption of the industrial microgrid is thereby increased by approx. 27 % compared to the original result without peak power prediction.

Monitoring critical temperatures in permanent magnet synchronous motors (PMSMs) is crucial to ensure safe operation and maximum device utilization as well. In this work, the application of recurrent neural networks featuring memory blocks (LSTMs/GRUs) are investigated upon their suitability to accurate temperature time series prediction inside PMSMs or similar motor types, which is the first time in literature to the author's best knowledge. Considered motor components are stator yoke, teeth and winding as well as the rotor's permanent magnets of a highly-utilized PMSM for electric vehicle applications. Having benchmark data available, numerous neural networks are trained and optimized with the aid of the Chainer framework and particle swarm optimization is conducted for finding suitable model hyper-parameters (e.g. number of hidden neurons or layers) on a computing cluster. It is found, that the Euclidean norm performance (in the range of 1-3 K) is similar but the worst-case predication errors (in the range of 9-14 K) are significantly higher compared to established modeling techniques like lumped-parameter thermal networks (LPTNs). This initial investigation motivates future research to increase ANN-based estimation accuracy by taken other ANN topologies, training methods or hyper-parameter optimization approaches into account.

In this paper, a commonly reported single-phase four-switch AC chopper topology is compared with a six-switch topology in terms of the total harmonic distortion (THD) of the input current, power factor (PF), commutation behavior, robustness and efficiency. Working principles of both topologies are presented along with simulation results. Based on a comparison of the findings, the four-switch topology is found to be better choice for the given application. Thus, the implementation of a four-switch topology is carried out at a power level of 10 kW. Experimental evaluation is carried out in order to verify the suitability of the circuit for the intended application. The experimental setup validates the findings of the simulations. Thus, the proposed approach is a practical way to improve input current THD and PF compared to thyristor choppers.

Accurate simulation of electric drives has always been desirable, as it allows the machine designer a deeper insight into the whole design. It is known that a co-simulation of Finite-Element-Analysis, circuit and control simulation can give much more accurate results than conventional simulation approaches. The effects of rotor segmentation, however, cannot be accurately modeled in two dimensions. Through introduction of a multi-slice approach to the co-simulation motors with segmented rotor structures can be analyzed even with a two-dimensional Finite-Element-Method approach. This publication presents a co-simulation of a permanent magnet synchronous drive, also taking the rotor segmentation into consideration with a two-dimensional multi-slice approach. The comparison of the simulation results with test bench measurements shows a good correspondence. Accuracy improvements of the results of the multi-slice approach in comparison to an unsegmented rotor structure as well as simulations with fundamental wave models are obvious.

The permanent magnet synchronous motor (PMSM) is widely used in highly utilised automotive traction drives and other industrial applications. With regards to the device life-time, safe operation and control performance, the magnet temperature is of great interest. Since a direct magnet temperature measurement is not feasible in most cases, this contribution gives a review on state-of-the-art model-based magnet temperature determination methods in PMSM. In this context, the existing publications can be classified into thermal models, flux observers and voltage signal injection approaches. Firstly, brief introductions of these methods are given, followed by a direct comparison regarding drawbacks and benefits. Finally, this contribution concludes with an outlook of potential further investigations in this research field.

Field-oriented control has become state-of-the-art in AC drives. Nevertheless it is undisputed that other control approaches like the direct torque control can outperform the field-oriented control in some operating conditions, e.g. in terms of torque dynamics or voltage utilization in the flux weakening region. Therefore, this paper's purpose is to demonstrate the potential of a dynamically reconfigurable control, which can, depending on the current operating situation, switch between different controllers depending on the particular situation in order to improve the control performance within the entire operating region. The possible reconfiguration approaches as well as the constraints which have to be considered while switching between the different controllers are presented and validated on an induction motor test bench. As a result, it is shown that by suitably combining of the very well known basic control approaches, an improved control behavior in the whole operating region can be achieved without the need of modifying them in order to enhance control approach specific deficiencies.

The LLC resonant converter has become quite popular in recent years and is more and more used for DC-DC converters within cost effective as well as high performance power supplies. Since in case of high output power and low output voltage it is typically beneficial to use paralleled converters, this paper investigates the load sharing issue of two LLC converters connected in parallel and operated with interleaved driving signals. Value deviations of all the three passive components of the LLC resonant circuit are considered and the worst case scenarios are highlighted. Moreover a simple and practical solution to the load sharing issue applicable to a high power server PSU is presented, including measurements on a prototype to support the theoretical analysis.

IPMSM for automotive traction applications exhibits a rather high magnetic saturation as a result of the maximization of power and torque densities. Additionally due to the high speed range high electrical frequencies in proportion to the converter switching frequencies occur. That results in a small number of samplings per electrical fundamental. Both effects have to be taken into account to design a suitable current controller. In this paper a discrete-time IPMSM model considering parameter variations due to magnetic saturation as well as the effect of a small number of sampling instants per electrical rotation is derived. Then the design of an adaptive current controller is presented and evaluated by experimental results.

This paper presents a completely modular power supply for high-power and high-current DC applications. The proposed power converter utilizes modular blocks consisting of a half bridge cell followed by an isolated DC-DC converter. The modules are connected in series at the input AC side in the fashion of a modular multilevel rectifier. At the output DC side, the modules are connected in parallel to supply a high-current load. The proposed configuration provides advantages like modularity, isolation, high efficiency, high input power factor, good input current quality and controlled output current over the full operating range. Circuit configuration, control and performance of the proposed system are discussed in detail. Simulation results are presented to verify the performance and effectiveness of the system.

In this contribution the frequency-domain modeling techniques for resonant converters under optimized modulation are reviewed. As a general modeling technique, the standard AC fundamental harmonic approach is suitable for all resonant converter topologies. However, it becomes inaccurate if the current or voltage of the resonant tank is distorted from sinusoidal waveform. The extended AC fundamental harmonic approach provides a much higher modeling accuracy, but it lacks generality. In order to overcome these drawbacks a general, systematic time-domain modeling procedure with high modeling accuracy is introduced and demonstrated by a series-parallel resonant converter with LC output-filter under optimized modulation. Verification with experimental results is given.

A high performance 55kW (peak) ferrite-based permanent magnet-assisted synchronous reluctance motor has been designed for an electric vehicle application. The design steps are outlined. Ferrite magnets have been chosen over conventional NdFeB due to cost issues. It is shown by finite element analysis that even under peak load conditions the ferrite magnets are safe from irreversible demagnetization. Key performance indicators for the motor are listed for the entire speed range. It is shown that a design of a high power density machine is possible even in the absence of NdFeB magnet.

Interior permanent magnet synchronous motors (IPMSM) are preferentially chosen as traction drives for electric vehicles due to their high power and torque density. In a field-oriented control scheme an operation point selection strategy is required to choose appropriate current set points to generate the requested torque with high precision and optimal efficiency. Although the currents are controlled in terms of a closed-loop control, the operation point selection is an open-loop torque control. Thus a precise motor model considering the impact of saturation effects and iron losses is required to estimate appropriate current set points. This paper proposes a lookup-table (LUT) based operation point selection. The lookup-tables are generated offline using the Maximum Efficiency (ME) strategy considering saturation effects and iron losses.