Search

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 $i_{\mathrm{d}}$ – $i_{\mathrm{q}}$ half-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.

In various permanent magnet synchronous motor (PMSM) drive applications the torque dynamics are an important performance criterion. Here, time-optimal control (TOC) methods can be utilized to achieve highest control dynamics. Applying state-of-the-art TOC methods leads to unintended overcurrents and torque over- and undershoots during transient operation. To prevent these unintended control characteristics while still achieving TOC performance the time-optimal model predictive control (TO-MPC) is proposed in this work. The TO-MPC contains a reference pre-rotation (RPR) and a continuous control set model predictive flux control (CCS-MPFC). By applying Pontryagin's maximum principle, the TOC solution trajectories for states and inputs of the PMSM are determined neglecting current and torque limits. With the TOC solution, a flux linkage reference for the CCS-MPFC is calculated that corresponds to a pre-rotation of the operating point in the stator-fixed coordinate system. This pre-rotated flux linkage reference is reached in minimum time without overcurrents and torque over- as well as undershoots by incorporating current and torque limits as time-varying softened state constraints into the CCS-MPFC. Simulative and experimental investigations for linearly and nonlinearly magnetized PMSMs in the whole speed and torque range show that, compared to state-of-the-art TOC methods, overcurrents and torque over- as well as undershoots are prevented by the proposed TO-MPC.

High-precision torque estimation and control of induction motor drives is an important research field due to the extensive use of these motors in torque controlled applications, for example, in electric vehicles. The open-loop torque control performance depends strongly on the accuracy of both the motor model and the flux estimation. To address this appropriately, an adaptive Kalman filter with offline parameter and observer design optimization is proposed in this article. Thereby, the basic induction motor model is extended by magnetic saturation, iron losses, and skin effect influences. All uncertain motor model and observer configuration parameters are offline identified by a global optimization technique, namely particle swarm optimization. The identification utilizes a comprehensive data set consisting of test bench measurements and leads to an optimized observer enabling precise torque estimation and control. In experimental validation, for both torque estimation and control, the root-mean-square error is below 1 % of the nominal torque over the entire operating range. With the help of an accurate gray-box inverter model for phase voltage estimation and a speed-adaptive Kalman filter tuning scheme, the proposed observer is able to operate also at slow speeds including standstill.

Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or sensor-based methods still prove unfeasible in a commercial context. Overheating results in severe motor deterioration and is thus of high concern for the machine's control strategy and its design. Lack of precise temperature estimations leads to lesser device utilization and higher material cost. In this work, several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles, specifically, ordinary least squares, support vector regression, $k$ -nearest neighbors, randomized trees, and neural networks. Having test bench data available, it is shown that ML approaches relying merely on collected data meet the estimation performance of classical thermal models built on thermodynamic theory. Through benchmarking, this work reveals the potential of simpler ML models in terms of regression accuracy, model size, and their data demand in comparison to parameter-heavy deep neural networks, which were investigated in the literature before. Especially linear regression and simple feed-forward neural networks with optimized hyperparameters mark strong predictive quality at low to moderate model sizes.

Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this article, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors. Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multihead architecture, lean models are presented that exhibit a strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented.

Model predictive control (MPC) of power electronic converters has obtained much attention in many applications and especially in electric motor control. As the control loop is closed by predicting the future plant behavior by means of a mathematical model, disturbances and uncertainties are important aspects when using any MPC strategy. The plant model may be inaccurate due to plenty of reasons, such as parameter mismatches or the inverter nonlinearity. If these disturbances are not properly addressed during the MPC design process, the control performance is deteriorated. Hence, a suitable disturbance observer (DOB) is required to compensate for model inaccuracies. This contribution is comparing different lumped-DOB designs in the context of a continuous-control set MPC for induction motor current control. As a baseline for comparison, a field-oriented proportional-integral (PI)-type regulator is utilized which does not require a DOB due to its integral feedback.Comprehensive experimental results prove the necessity of a proper DOB; however, it is also shown that the overall transient and steady-state control improvement due to MPC has to be bought at a high price since the computational burden is at least doubled compared to the PI-baseline.

This article presents an approach to the controller design for electrical drives, which makes use of methods of deep reinforcement learning. Conventional control methods dominated the field for a long time, since they usually lead to control solutions with very robust and steady results. Yet, it often can be found that the overall control performance heavily correlates with the experience and education of the developing engineer. Moreover, conventional methods strongly depend on the available knowledge of the control system (e.g., plant model accuracy), which often causes the necessity for thorough identification methods. Real-time capability issues are also a present problem of sophisticated control approaches, such as model-predictive methods. Especially, in the domain of electrical drive train control, solving elaborate online optimization problems may be critical when very small plant time constants have to be considered. The methods of deep reinforcement learning will not only enable to acquire a suitable controller structure, but, moreover, the procedure will tune itself, which will allow for a more abstract level of investigation. This article presents a first proof of concept by means of controlling the phase currents of a permanent magnet synchronous motor in a field-oriented framework. The results found are promising and motivate further research in this field.

In this paper, a new temperature observer topology is presented which overcomes the shortcomings of previous ones and achieves a higher accuracy, and a more robust disturbance rejection. It makes use of the Gopinath-style flux observer and combines a lumped-parameter thermal network operating at low speeds and a flux-based permanent magnet temperature observer operating at medium and high speeds. Simulation and experimental results on a 50 kW permanent magnet motor show a performance enhancement over standard topologies; particularly, a superior disturbance rejection to voltage estimation errors. A detailed analysis of the optimal controller tuning is also presented. Furthermore, a Kalman filter is incorporated to account for sensor noise and model uncertainties. Experimental results show an effective fusion of independent temperature estimation methods leading to a superior accuracy compared to the previously investigated approaches. Moreover, the Kalman filter-based fusion offers the capability of detecting temperature-related system failures, e.g., cooling circuit malfunctions.

With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly. Whenever critical temperatures cannot be measured economically on a sensor base, a thermal model lends itself to estimate those unknown quantities. Thermal models for electric power systems are usually required to be both, real-time capable and of high estimation accuracy. Moreover, ease of implementation and time to production play an increasingly important role. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based lumped-parameter models, and data-driven nonlinear function approximation with supervised machine learning. A quasi-linear parameter-varying system is identified solely from empirical data, where relationships between scheduling variables and system matrices are inferred statistically and automatically. At the same time, a TNN has physically interpretable states through its state-space representation, is end-to-end trainable -- similar to deep learning models -- with automatic differentiation, 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-/grey- or black-box models with a mean squared error of $3.18~\text{K}^2$ and a worst-case error of $5.84~\text{K}$ at 64 model parameters., Comment: Preprint; Fix typos, streamline math. notation; 10 pages

Monitoring critical temperatures in permanent magnet synchronous motors (PMSM) is essential to prevent device failures or excessive motor life-time reduction due to thermal stress. A lumped-parameter thermal network (LPTN) consisting of four nodes is designed to model the most important motor parts, i.e., the stator yoke, stator winding, stator teeth, and the permanent magnets. An empirical approach based on the comprehensive experimental training data and a particle swarm optimization are used to identify the LPTN parameters of a $\mbox{60}$ -kW automotive traction PMSM. Varying parameters and physically motivated constraints are taken into account to extend the model scope beyond the training data domain. Here, a so-called global identification technique for linear parameter-varying systems is innovatively applied to a thermal motor model for the first time. The model accuracy is cross-validated with independent load profiles, and a maximum estimation error (worst-case) of $\mbox{8}\,^\circ$ C regarding all considered motor temperatures is achieved. Also, a comprehensive residual statistical analysis proves suitable estimation results in terms of model robustness and accuracy.

The performance of model predictive controllers (MPC) strongly depends on the quality of their models. In the field of electric drive control, white-box (WB) modeling approaches derived from first-order physical principles are most common. This procedure typically does not cover parasitic effects and often comes with parameter deviations. These issues are particularly crucial in the domain of self-commissioning drives where a hand-tailored, accurate WB plant model is not available. In order to compensate for such modeling errors and, consequently, to improve the control performance during transients and steady state, this article proposes a data-driven, real-time capable recursive least squares estimation method for the current control of a permanent magnet synchronous motor. Following this machine learning approach, the effect of the flux linkage and voltage harmonics due to the winding scheme can also be taken into account through suitable feature engineering. Moreover, a compensating scheme for the interlocking time of the inverter is proposed. The resulting algorithm is investigated using the well-known finite-control-set MPC (FCS-MPC) in the rotor-oriented coordinate system. The extensive experimental results show the superior performance of the presented scheme compared to a FCS-MPC-based on a state-of-the-art WB motor model using look-up tables for adressing (cross-)saturation.

The permanent magnet synchronous motor (PMSM) is widely applied in highly utilised automotive traction drives and other industrial applications. With regards to the device life-time, thermal utilisation, safe operation and control performance, the permanent magnet temperature is of great interest. Since a direct magnet temperature measurement is not feasible due to constructional and cost issues in most applications, this contribution gives a review on state-of-the-art model-based magnet temperature determination techniques regarding 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. This evaluation exhibits the individual constraints of each approach motivating their fusion as an outlook of further investigations in this research field.