In-depth evaluations of an electric drive's behavior typically result from co-simulation, combining models of motor, power electronics and control software. This approach can be used to evaluate pulse patterns for a given operating point, e.g., for electric vehicle applications, preventing real-world experiments. Here, the co-simulation model is fed with offline calculated optimized pulse patterns (OPPs) that are used to increase, among others, the drive's efficiency. Due to the large motor time constant compared to the fundamental wave period, reaching steady state using a simple open-loop control requires unnecessary long simulation times. Hence, a model predictive closed-loop control implementation of the OPPs is proposed which reduced the overall computational effort significantly. However, it turns out that the OPP evaluation using a finite-element-method-based co-simulation for a permanent magnet synchronous motor remains largely uncertain in terms of the predicted power losses which led to the development of a semi-analytical model to further reduce the required computational time. As none of the approaches was able to deliver a suitable trade-off between model accuracy and calculation time, this investigation highlights the remaining challenges of evaluating OPPs based on drive co-simulations motivating further research towards the surrogate-assisted or direct experimental OPP optimization in the future.

Induction machines (IMs) are widely used in many applications, e.g., electric vehicles or industrial automation, which motivates efficiency-optimal operation for the sake of energy and cost savings. However, a loss-minimal IM control leads to reduced flux operation at partial load, i.e., achievable torque dynamics after load steps are physically limited due to the IMâ\euro™s large rotor time constant. Hence, designing the IM torque control for minimal settling time is of particular importance to allow both an efficient and sufficiently dynamic drive operation. Against this background, a model predictive control (MPC) framework is proposed, which utilizes a precise model of the IM covering magnetic saturation, iron losses, skin effect and thermal influences. As this model is highly nonlinear, it is iteratively linearized along the predicted control trajectory so that a computationally efficient quadratic program can be defined for the MPC problem. To enable sufficiently long prediction horizons, a hierarchical control structure with the stator currents as the actuation variables of the model predictive torque control is utilized. Thanks to this scalable control approach, the proposed framework can be easily extended to multi-machine drive systems covering higher-dimensional problem spaces. Empirical tests validate the feasibility of the proposed approach both for single as well as multi-machine drive applications.

Improving control dynamics and enabling maximum torque and power conversion for a given electrical drive are important target quantities of drive control algorithms. To utilize the electrical drive to its maximum extent during transient and steady-state operation, a time-optimal continuous-control-set model predictive flux control (CCS-MPFC) for permanent magnet synchronous motors (PMSM) is proposed. This scheme considers torque and current limits as softened state constraints in the CCS-MPFC’s optimization problem to prevent transient overcurrents as well as torque over- and undershoots during time-optimal operating point changes. Furthermore, the overmodulation range including six-step operation can be entered seamlessly to ensure maximum power conversion at high speeds. Fastest transients within the whole modulation range are enabled by a time-optimal harmonic reference generator (TO-HRG). Here, the flux reference of the CCS-MPFC is complemented with a harmonic content that enables operation in the overmodulation region. Further, the reference is pre-rotated during transient operation to attain time-optimal control performance. Extensive simulative as well as experimental investigations for linearly and nonlinearly magnetized PMSMs show that, compared to state-of-the-art methods, time-optimal control performance in the whole modulation range without transient overcurrents as well as torque over- and undershoots can be achieved by the proposed control method.

By applying rotor angle sensorless control methods the costs of the electrical drive can be decreased while its reliability is increased. Traditionally, the design of both the rotor angle estimator and the drive controller require a detailed motor model and manual tuning leading to significant effort by human experts. In this work, a rotor anisotropy-based sensorless control scheme for interior permanent magnet synchronous motors (IPMSM) is proposed that enables sensorless control with minimum tuning effort and a priori motor knowledge. Due to these characteristics the scheme is particularly suitable for self-commissioning or low-cost drive applications. The scheme contains a finite control set model predictive current controller (FCS-MPCC) with an additional inequality constraint that enables the identification of the motor model in the stator-fixed coordinate system by using the last three measurement samples. Utilizing an automatic system identification procedure, the motor model is determined online in a data-driven fashion. The identified motor model serves both as prediction model of the FCS-MPCC and as  baseline for the rotor angle estimation via an eigenvalue decomposition approach. Challenging experimental investigations at standstill up to the medium speed range prove the applicability of the proposed approach. Here,  highly dynamic speed and current transients can be handled by the proposed method.

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.

Advances in the field of reinforcement learning (RL)-based drive control allow formulation of holistic optimization goals for the data-driven training phase. The resulting controllers feature efficient drive operation without the necessity of an a priori known plant model but, so far, conduction of the corresponding training phase in real-world drive systems has been applied only sparsely due to safety concerns. This contribution targets the challenging problem of self-learning torque control for a permanent magnet synchronous motor assuming a finite control set (FCS), i.e., the direct selection of switching actions instead of a modulator-based setup. In order to allow a secure and effective online training with real-world drive systems, the RL controller is monitored by a safeguarding algorithm that prevents application of unsafe switching actions, e.g., such that result in overcurrent. The accruing amount of measurement data is handled with the use of an edge computing pipeline to outsource the RL training from the embedded control hardware. The inference of the utilized artificial neural network in hard real time is realized with the use of a reconfigurable FPGA architecture. The resulting RL-based algorithm is able to learn a torque control policy in just ten minutes which has been validated during comprehensive real-world experiments.

To increase torque and power conversion of permanent magnet synchronous motors (PMSMs) at high-speed operation, the DC-link voltage of the inverter must be fully utilized during steady-state and transient operations. This paper proposes the incorporation of a harmonic reference generator (HRG) with a pulse clipping (PC) scheme into a model predictive control framework to achieve highest power output. The HRG calculates flux references in the stator-fixed coordinate system for an underlying continuous control set model predictive flux control (CCS-MPFC). These reference trajectories contain the harmonic flux component induced by the voltage hexagon in the overmodulation range and specific switching states (e.g., six-step operation) of the inverter are ensured by the proposed PC scheme. This enables a seamless transition to the overmodulation range including six-step operation and increases the drive’s power conversion to its maximum extent. Since the flux differential equation in the stator-fixed coordinate system, used as motor model for the HRG and CCS-MPFC, is able to represent PMSMs with linear and nonlinear magnetization, the proposed approach is well suited for the control of PMSMs with nonlinear magnetization. Extensive transient and steady-state experimental investigations on a highly utilized PMSM in the entire modulation and speed range prove the performance of the proposed approach.

Since temperature rise in electric machines is mainly due to power losses during electro-mechanical power conversion, temperature estimation is highly attached to power loss modelling. In this contribution, an extended iron loss model is introduced with a direct identification methodology in the context of temperature estimation. The iron loss model is implemented as part of a fourth-order lumped-parameter thermal network (LPTN), which is parametrised using empirical measurements and global identification. Once parameters are identified using training data, the LPTN model is validated using three unseen profiles cross-validation. Satisfactory estimation is achieved with the average mean squared error of 2.1 K2 and the error bias close to zero.