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The study proposes a parameter optimisation of the high-frequency (HF) signal injection sensorless algorithm applied to an interior permanent magnet synchronous machine (IPMSM). The latter is intended for electric vehicle (EV) traction applications with dedicated mission profile under specific constraints. This optimisation aims to minimise two conflicting objectives: (i) minimising the position estimation error which impacts the torque accuracy and (ii) minimising torque ripples responsible for additional stress on the motor shaft, causing unwanted noise in the vehicle powertrain. The main contribution of this study is to provide guidelines for the selection of the HF signal injection tuning parameters so as to satisfy these objectives. To do so, a genetic algorithm has been implemented and the optimisation process is based on a reliable simulation model. Using a simulation model is deliberately chosen in order to reflect the behaviour of a motor series. Besides, in order to integrate the specificities of the target application, the optimisation process is based on a typical EV mission profile. A set of non-dominated solutions satisfying the tradeoff between the position error and the torque ripples is obtained, discussed and finally tested with success on an experimental setup.

This paper presents a PV system featuring two grid modes; on and standalone (off) mode. The main purpose of this work is verifying the effectiveness and feasibility of the power flow management strategy of the proposed system to fulfill the connected load demand with best utilization of considered sources, analyze the PV system performance under different grid modes. In case of normal grid conditions (mode ON), the PV system is connected to a three-phase grid and AC load. It supplies the load and grid in case of excess of PV power, and shares the peak load demands between grid and PV system. In case of grid faults, PV switches to off-grid mode, which it supplies the connected load and battery bank. The latter can store excess PV power or supplies the load in case of low or zero irradiances. The considered system tested using MATLAB/ Simulink.

This paper studies the suitability of System-on-Chip (SoC) devices to perform Embedded Real-Time Simulators (eRTS) of electrical systems. These new devices combine powerful ARM processors, a Field-Programmable Gate Array (FPGA) and other peripherals which make them very convenient for controlling, monitoring and emulating a complete electrical system. There are two main categories of elements with their own respective characteristics and constraints: electromechanical/electromagnetic systems, and switching elements. Accordingly, the proposed investigation will focus on a Doubly-Fed Induction Generator (DFIG) on one hand, and on a Modular Multi-level Converter (MMC) on the other hand. In addition to Real-Time (RT) simulation results, SoC time/resources evaluations will be presented for different implementation strategies: full-software, full-hardware based on High-Level Synthesis (HLS) tools, and using 64/32-bit floating- and fixed-point formats. The validity of the MMC implementation will be tested experimentally.

Model-predictive control (MPC) is an advanced model-based control technique. It allows flexible control schemes with fast time responses. However, like most optimization-based control strategies, MPCs are usually computationally intensive and suffer from a lack of robustness toward parametric variations. The computational burden of these controllers is nowadays being relieved thanks to the progress of digital devices; however, the robustness is still a major issue that prevents the use of MPC controllers in some applications. Systems presenting slow and fast dynamics at the same time are usually controlled by cascaded control loops. MPC techniques are restricted to the control of inner loops, while outer loops use more robust control techniques. The aim of this paper is to provide MPC techniques for both the inner and outer loops of an active front-end rectifier. A first dual-MPC cascaded control shows the drawbacks of usual MPC techniques for outer loops. It is thereafter modified to achieve a robust cascaded dual-MPC that eliminates the variable resistive load parameter from the system dynamical model and formulates a new prediction model containing only known parameters and measurable quantities. Finally, a third method based on the previous one is also presented in order to reduce switching losses. Experimental results for the three methods are presented and compared with double precision offline software simulation results to validate the feasibility of the proposed techniques.

Comunicació presentada a IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society (October 21-23, 2018 Washington D.C., USA.) System-on-Chip (SoC) devices combine powerful general purpose processors, a Field-Programmable Gate Array (FPGA) and other peripherals which make them very convenient for Hardware-in-the-Loop (HIL) simulation. One of the limitations of these devices is that control engineers are not particularly familiarized with FPGA programming, which need extensive expertise in order to code these highly sophisticated algorithms using Hardware Description Languages (HDL). Notwithstanding, there exist High-Level Synthesis (HLS) tools which allow to program these devices using more generic programming languages such as C, C++ and SystemC. This paper evaluates SoC devices to implement a Modular Multi-Level Converter (MMC) model using HLS tools for being implemented in the FPGA fabric in order to perform HIL verification of control algorithms in a single low-cost device.

Model Predictive Control (MPC) algorithms are computationally intensive optimization based control techniques. Their complexity significantly increases when the cost function prediction horizon increases. This problem is more pronounced when applying this techniques to systems having fast dynamics, in this case the prediction horizon length is limited by the calculation power of the processor. The use of long prediction horizons should provide healthier results than single prediction horizon yet prediction horizon equal to one are widely used for the control of electrical system and are achieving beneficial results. This paper proves mathematically that in some special cases of MPC applied to first order systems the use of a prediction horizon equal to one is similar to the use of a prediction horizon greater than one. An MPC algorithm is then applied for the control of the current of an RL load driven by an H-Bridge, multiple prediction horizon are used and results are compared.

System-on-Chip (SoC) devices combine powerful general purpose processors, a Field-Programmable Gate Array (FPGA) and other peripherals which make them very convenient for controlling and monitoring a complete system. These characteristics make them very suitable to implement Embedded Real-Time Simulators (ERTS), which provide interesting and powerful functionalities (such as observers, parameter estimation, diagnostic, health monitoring, etc.) but increasing significantly the complexity of the controller. The main drawback of these devices is that control engineers are not particularly familiarized with FPGA programming, which need extensive expertise in order to code these highly sophisticated algorithms using Hardware Description Languages (HDL). Notwithstanding, there exist High-Level Synthesis (HLS) tools which allow to program these devices using more generic programming languages such as C, C++ and SystemC. This paper evaluates SoC devices to implement several versions of a Modular Multi-Level Converter (MMC) using HLS tools. An improved simplified model of the converter which includes the modulation, the voltage balancing algorithm, and the circulating current control keeping record of every sub-module (SM) capacitor voltage. The MMC model will be evaluated changing the number of SM from 6 to 700 per phase, and using three different formats for the variables and parameters: 64-bit floating-point, 32-bit floating-point, and 32-bit fixed-point variables. The different versions of the ERTS will be compared in terms of computational power, area utilization and precision.

Grid connected power converters are widely used to interface between grid – on one side- and loads or renewable energy sources – on the other side. This work presents an indirect sliding mode power control (ISMPC) for three phase grid connected power converter. This control is characterised by improved robustness with regard to external disturbances and parameters changes, while insuring at the same time low current total harmonic distortion factor. The development and implementation of the ISMPC algorithm on low cost Microsemi SmartFusion digital solution is also described. This digital solution includes in the same chip a Field Programmable Gate Array (FPGA) fabric and a Cortex-M3 processor associated to large number of peripheral cores. The developed ISMPC is compared with standard and commonly used controls for grid connected converters to show its interest and advantages. Moreover, an overview of the experimental set-up, the description of the algorithm modular partitioning between FPGA and processor, the resources optimisation as well as the execution time reduction are presented. Selected experimental results corresponding to different operation steps are presented to illustrate performances and effectiveness of the proposed ISMPC algorithm.

In this paper, a novel rotor position estimator for an interior permanent magnet synchronous motor is presented and evaluated. The proposed estimator lies on one of the most popular methods in the field of artificial intelligence: Machine learning-based neural networks algorithm. The main interest is to introduce a cost-efficient position estimator that is comparable to classic methods in terms of functional performances. The estimator model is built by learning from a dataset that associates phase currents and voltages to the rotor position. Learning signals are generated using a simulation model. This is primarily intended to save the resources invested in testbench trials. In this work, offline training steps and results are described and commented. The efficiency of the proposed position estimator is first verified by functional simulations. Second, real-time experiments are conducted on an actual scale testbench. The NN-based estimator covers a wide speed range and is implemented in the context of IPMSM-based EV traction drives. More broadly, these findings can also be applicable to the ac-based electric drives for the position estimation purpose.

Model predictive control (MPC) is a model-based control strategy that introduces many advantages but also some drawbacks. The biggest challenge that faces MPC controller is its sensitivity towards parametric variation. This study aims to robustify MPCs by adding a least mean square (LMS) identification algorithm within their structure. The feasibility of this method is tested on a simple example which is the MPC current control of a permanent magnet synchronous motor driven by a two-level power converter. Many tests are firstly performed without including inside the MPC algorithms the LMS online tuning procedure. These tests are later compared with the ones obtained with the same control technique but where the identification algorithm is turned on. Results clearly show all the benefits of using this approach on the controller performances.

DC link filter capacitors used in electric drives for automotive applications are most of the time aluminum electrolytic capacitors. However their high sensitivity to temperature results in very we...

Aluminum electrolytic capacitors are the most cost effective solution for DC-link decoupling design in comparison to other technologies such as tantalum ones. However they are also the weakest part of static power converters. Thus, a lot of studies have been made in order to estimate their aging particularly through the Equivalent Series Resistance value (ESR) i.e. the real part of the impedance. Nowadays, aluminum electrolytic capacitor manufacturers have successfully created more sustainable high temperature components especially for automotive applications. In this context, most of electrical models found in the literature are not accurate over a broad frequency range. Furthermore, some studies show that the ESR is not always representative of the capacitor lifetime. As a consequence, an improved impedance model with the add of a diffusive element is proposed in this paper. Its purpose is to bring an efficient tool to quantify the evolution during the aging of each impedance part and not only the global real part for automotive dedicated DC-link capacitors.

Most of automotive applications rely on Permanent Magnet Synchronous Machines (PMSMs) thanks to the afforded high efficiency and reduced cost and size. However, this trend is subject to severe safety requirements, particulary related to the reliability of the mechanical position sensor. In this context, the present paper deals with PMSM sensorless control, aimed at providing the position information redundancy. Yet, some side effects, as saturation and cross-coupling, may compromise the estimation quality. To achieve a deeper insight into the impact of these phenomena, authors highlight the way that sensorless estimation accuracy is predicted from the rotor geometry anisotropy. The objective is two-fold: 1) Provide a detailed approach for the characterization of the PMSM magnetic model under different load conditions, 2) Deduce the machine saliency behaviour and the induced estimation error due to saturation effects. To do so, an accurate flux linkage model is required. This model should allow to evaluate the arising cross-saturation terms. Experimental results are presented, referring to two test traction motors dedicated to the electric vehicle application.

New System-on-Chip (SoC) devices, which include powerful general purpose processors and FPGA in the same chip are particularly suitable for Real-Time (RT) simulation of electromechanical systems. This paper introduces a systematic analysis of the capabilities of one of these devices for RT electromagnetic simulation. Considering all the capabilities of these platforms, it is not straightforward to use them efficiently and port existing developments due to the SoC relative complexity and variety of programming models. In this paper, authors evaluate some of the benefits of moving to such platforms through the implementation of an example application on the Xilinx Zynq-7000 SoC. Simulation results concerning execution time, resources usage and precision of the calculations are presented, compared and validated using MATLAB/Simulink.

The paper deals with the sensorless control of salient-pole Interior-Permanent-Magnet-Synchronous-Machines (IPMSMs) adressed to electric vehicle applications, using a high frequency voltage injection technique. This technique is effective at zero and low motor speed, and takes advantage of the peculiarity of the rotor geometry. Indeed, the expected estimation performances heavily depend on the rotor saliency, so, on the geometry of the IPMSM rotor. The main focus of this work is first to experiment the magnetic saturation effect on the machine saliency for different operating conditions and consequently identify the feasible sensorless control region when taking into account the chosen current control strategy. Then, it is intended to evaluate the impact of saturation induced cross-coupling terms on the position estimation accuracy. This analysis is supported by experimental evidence obtained using a salient-pole interior PM synchronous machine, however, the findings may also be applicable to other AC salient machines.

This paper proposes a current Model Predictive continuous Real-Time Simulator (RTS) on the Zynq-7000 All Programmable (AP) System-on-Chip (SoC). This device is a new integrating an FPGA fabric along with a powerful processor. The RLE load dynamical model is implemented in the FPGA fabric of the Zynq-7000 and is run at very high frequencies (50 MHz) to the hardware and software capabilities of the chosen Zynq-7000 AP SoC device. Real-time simulation results are presented and MATLAB.

This paper analyses the capacity of a new FPGA-based System-on-Chip (SoC) platform for the control of electrical systems. These new SoC platforms integrate both an FPGA fabric and a powerful processor. Among these platforms the Xilinx Zynq-7000 All Programmable (AP) System on-Chip is evaluated. The chosen algorithmic benchmark consists on a realtime simulation of a Voltage Source Rectifier (VSR) connected to the grid and supplying a resistive load. This VSR is controlled via a Model Predictive Control technique. The VSR dynamical model is implemented in the FPGA fabric of the Zynq-7000 while the predictive controller is implemented in its processing part. Both software and hardware parts of the benchmark are challenging. The model predictive control technique is computationally intensive. Moreover, the VSR model should approximate a quasi continuous-time behavior which implies that its dynamical model has to be executed at very high frequencies (10 MHz). Realtime simulation results are presented and compared with double precision off-line software simulation results.