This paper presents a comparative study of battery degradation cost models for battery management in residential PV-battery systems under day-ahead optimization. Three models are investigated: a State-of-Health (SoH)-based model, a cyclic (Full Equivalent Cycle, FEC-based) model, and a proposed model that incorporates the interest rate effect on replacement costs. The models are evaluated under two distinct scenarios: winter and summer, providing an estimate of the minimum and maximum battery lifetime. In addition, a linearized model for calendar aging and degradation cost is introduced to reduce computational time. The results show that the SoH-based model exhibits the highest sensitivity to degradation, whereas the proposed model offers a balanced trade-off between cost and lifetime while achieving the lowest computational time.

International audience

International audience

International audience

Modular Multilevel Converters (MMCs) are widely used in medium to high voltage/power applications such as in energy transformation and grid connected systems. MMCs benefit from being redundant and allowing transformer-less operations. Unfortunately, they suffer from having a high cost due to the high number of sub-modules (SMs). Moreover, they are hard to control. In order to operate the MMCs properly, the load current, the circulating current and the capacitor voltages should be controlled. The usual control methods are synthesized in order to control the average lower and upper arm capacitor voltage as well as the load current. However, in some special cases, the capacitor values could change due to the aging of the capacitors, or the SM may stop working completely. Thus, creating an imbalance in the capacitor voltages of each arm in the system. This paper presents a new control scheme that allows to control the voltage of the upper and lower arms separately while controlling the load current at the same time. The usual and the new proposed controllers are developed and compared through software simulation. Finally, the conclusion is drawn.

Squirrel cage induction motors are widely used in the industrial field as they present many advantages compared to other types of electric machines. They are self-starting, need little maintenance, can work in harsh environment and are relatively cheap. Many industrial applications requiring current or speed control of the machine need the knowledge of the machine parameters. Therefore, induction motor parameter identification algorithms are developed taking in consideration the accuracy of the identification process which impacts on the quality of the control and thereby the overall system. These algorithms are usually sensitive to measurement noise thus there usage in noisy industrial environment is not desirable. This paper presents three induction motor offline identification algorithms and test their accuracy and their ability to work in noisy environment. The first method subject to the study is a Least Mean Square (LMS) based identification method working at standstill, the second is a newly introduced method robust toward noises and the third is the classic no-load and blocked rotor identification method. Simulation results are presented and finally conclusions are drawn.

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.

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.

This paper proposes a novel hybrid position estimation strategy based on merging two self-sensing techniques according to the operating speed. High-frequency (HF) signal injection algorithm is deployed for zero and low-speeds, while the Machine Learning (ML) method is adopted for medium and high speeds. The proposed position estimator is intended for the fault-tolerant control of an interior permanent-magnet-synchronous motor (IPMSM) drive that propels an electrical vehicle (EV).

The way of implementing an adaptive maximum power point tracking algorithm for photovoltaic (PV) applications in a field programmable gate array (FPGA) is described in this paper. A dual Kalman filter allows estimating the settling time of the whole system, including the PV source and the dc/dc converter controlling the operating point thereof, so that the tracking algorithm self adapts its parameters to the actual weather conditions. The real-time identification need of this application requires an FPGA platform, so that the intrinsic algorithm parallelism is exploited and the execution time is reduced. The tradeoff solutions proposed in this paper, accounting for the algorithm complexity and the limited FPGA hardware, as well as some solutions for optimizing the implementation are described. The proposed adaptive algorithm is implemented in a low-cost Xilinx Spartan-6 FPGA and it is validated through experimental tests.

Dynamical reconfiguration is receiving attention from the scientific community because of the advantages it can ensure with respect to module-dedicated electronics when partial shadowing of the photovoltaic field occurs. Indeed, lower cost and conversion losses as well as improved diagnostic capabilities make this solution of high interest for industrialization in a near future. Some stochastic algorithms have been proposed recently for determining the best electrical connection among the panels that ensures the highest power production for the actual shadowing pattern. In this paper, no novel reconfiguration algorithm is proposed, but the optimized implementation of one of such recently proposed approaches is described. The advantages achieved by using a system-on-chip platform, through a suitable exploitation of the features it offers, are highlighted. The experimental results confirm that the use of a system-on-chip platform gives significant benefits for the proposed application. Indeed the parallelization of the algorithm allows to reduce the execution time greatly with respect to what can be achieved through a standard software implementation.

This paper investigates the Field Programmable Gate Array (FPGA) implementation of a Space-Vector Delta Modulation Current Controller (SV-DMCC) for three-phase Grid connected Converters (GcCs). The proposed SV-DMCC is synthesized in the stationary reference frame (α,β) based on the sliding mode theory. The applied voltage vectors are accurately selected in order to reduce the current errors on α and β axis. The implementation of the SV-DMCC on FPGA was achieved with a very low execution time, which allowed a significant reduction of the current THD. Simulation and experimental results are presented and discussed in order to show the benefits of the investigated SV-DMCC compared to traditional delta modulation current controllers.

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.

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.

This paper presents the cascaded doubly fed induction generator as a potential candidate machine for electrical power generation in embedded aircraft applications. HVDC network is considered. The machine is connected to a diode bridge rectifier. The set operates as a stand alone DC generator connected to an isolated load. A new decoupled vector control is proposed to regulate the DC bus voltage. The controller aims to retain a constant output voltage regardless load and speed variations.

This paper is a comparative study of the effect of various water heating techniques on the sizing of a PV-battery backup system for an intermittent grid supply. The optimal water heating configuration is found based on the smallest sizing of the backup system. Four popular water heating techniques are modeled and compared; the electric water heater, hybrid solar electric water heater, boiler water heater and the hybrid solar boiler water heater. A high energy consumption residential application is considered, realistic constraints are applied, and a detailed economic study is conducted in order to accurately quantify the advantages and consequences of each installed water heating technique.

The paper proposes a new control strategy of a grid connected variable speed wind turbine (WT), based on a cascaded doubly fed induction generator (CDFIG). The generating plant is carried to extract the maximum power relative to the predicted mean wind speed, thus eliminating the power fluctuations associated to high frequencies component. Moreover the system attempts to adjust the power factor of the generated power according to grid code requirements. This can be achieved by means of a bidirectional back to back PWM converter. A virtual flux oriented vector control is applied for the decoupled regulation of active and reactive powers, leading to grid voltage sensorless operation. The suggested method ensures high efficiency and enhances the energy quality by feeding constant power into the grid, regardless wind fluctuations.

Current industrial electronic control systems have been integrating innovative reconfigurable technologies, as those available through Field Programmable Gate Arrays (FPGA) and embedded systems, supporting development of different types of solutions, ranging from system-on-chip (SoC) solutions to networked and highly distributed and heterogeneous solutions. This paper brings an updated view on usage of FPGA, embedded systems, and SoC solutions for industrial electronic control.

The proposed paper deals with Hardware-In-the-Loop (HIL) validation of a 3-phase PWM boost rectifier controller. This validation has been made using Field Programmable Gate Array (FPGA) target. The development of the Real-Time Emulator (RTE) of the power system is firstly discussed. All voltage sources from the grid, filters, power switches and the load real-time models are implemented in hardware using a Xilinx Spartan-6 FPGA device. As for the controller, a PI-based strategy has been chosen to control the DC-link voltage and a Hysteresis-based one for the control of 3-phase line currents. This controller has been implemented in software using the embedded Cortex-M3 processor of SmartFusion FPGA from MicroSemi. Real-time HIL simulation results, as well as offline simulation results are presented and compared.

One of the main functions any photovoltaic system is equipped with is the maximum power point tracking. The perturb and observe technique is one of the mainly used algorithms for tracking the optimal operating point of the photovoltaic source. Its performances depend on the values of the two design parameters, that are the amplitude and the frequency of the perturbations it applies for identifying the position of the maximum power point. In this paper, two methods for an on-line optimization of the perturbation period are presented. They are based on the identification of the dc/dc converter, that controls the photovoltaic source, by means of its impulse and frequency response, both evaluated by using the cross-correlation method. Such methods, as well as the tracking algorithm, are effectively implemented by using a Field Programmable Gate Array. The feasibility of the proposed methods has been confirmed by Matlab simulations.

This paper proposes a new control method for three-phase Pulse Width Modulation (PWM) boost rectifier using only one dc-link current sensor instead of two line current sensors. Single dc-link current sensor methods have critical problems with regard to the time spent for active voltage vectors application, which must be long enough to measure reliably the dc-link current during every switching interval. It is possible to solve this problem by the use of the proposed Direct Power Control (DPC) technique synthesized via sliding mode theory. The considered DPC control method computes the three phase grid line currents using the measured current from a single dc-link current sensor. The most important features of this method are simplicity and reliability. Also, it reduces the number of current sensors, weight and volume requirements. Simulation results demonstrate the feasibility and effectiveness of the proposed control method.

This paper aims to provide Hardware/Software (Hw/Sw) codesign guidelines for system-on-chip field-programmable gate array-based sensorless ac drive applications. Among these guidelines, an efficient Hw/Sw partitioning procedure is presented. This Hw/Sw partitioning is performed taking into account both the control requirements (bandwidth and stability margin) and the architectural constraints (e.g., available area, memory, and hardware multipliers). A nondominated sorting genetic algorithm (NSGA-II) is used to solve the corresponding multi-objective optimization problem. The proposed Hw/Sw partitioning approach is then validated on a sensorless control algorithm for a synchronous motor based on an extended Kalman filter. Among the nondominated implementation solutions supplied by the NSGA-II, those that are considered as the most interesting are synthesized. Their time/area performances after synthesis are compared with success to their predictions. In addition, one of these optimal solutions is also tested on an experimental setup.

This paper presents the concept of dynamic reconfiguration between two sliding mode current controllers for synchronous motor drives. The first one is a Direct Sliding Mode (DSM) current controller, which is based on a switching table synthesized via sliding mode theory. The second one is an Indirect Sliding Mode (ISM) current controller that computes a reference voltage vector via sliding mode theory. The computed reference voltage is then applied to the terminals of the synchronous machine through a Pulse Width Modulation (PWM) process. This item studies and discusses the reconfiguration criteria and presents the main interest of using FPGA digital solutions for dynamic reconfiguration process implementation between the two sliding mode current controllers. Numerous experimental results are presented in order to confirm the interest and performances of the proposed reconfigurable sliding mode current controller.

This paper presents an optimal current control for the hybrid excitation synchronous motor in electric vehicle application. The control aims to meet the torque and speed requirements while insuring minimal copper and iron losses. Extended Lagrange multipliers optimization method (Kuhn-Tucker conditions) is used to elaborate analytical expressions for the optimal reference armature currents as well as for the field current ifwith respect to armature current and voltage constraints. Simulation over the new European driving cycle proves that the proposed optimal control leads to the lowest losses compared to the results obtained by other synchronous motor control strategies.

This paper presents a generic thermal model of electric appliances as an auxiliary heat source in low energy consumption buildings. The electric appliances are firstly classified into four categories by their heat and mass transfers. The mathematical model and its electric equivalent circuit are proposed based on the first law of thermodynamics and the thermal-electric analogy. Then the correspondence between the proposed model and the ARX model is presented for a parametric identification. To illustrate this study, two electric appliances of the closed-heating category, a monitor and a refrigerator, are selected. Each model has been experimentally tested and identified in a well-insulated building. They are implemented in a simple RC-lumped building model and simulated using Matlab/Simulink. Both experimental data and simulation results are compared.

This paper presents the implementation of a Direct Power Control (DPC) for three-phase PWM boost rectifier using the SmartFusion digital solution from Actel/Microsemi Company. This digital solution is today the only device that includes an FPGA, an embedded hard-core processor “Cortex M3” from ARM and a programmable analog part that includes several analog functions such as Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). Compared to traditional digital solutions, it offers full customization and more flexibility without the excessive cost of soft processor cores on traditional FPGAs. Indeed, it allows full System on Programmable Chip (SoPC) implementation of control algorithms in order to realize more reliable and compact controllers. Experimental results carried out on a prototyping platform are presented in order to illustrate the benefits and effectiveness of the used fully SoPC solution.

On-line system identification is generally used to find the mathematical or frequency model of a system for control applications. In particular, the cross-correlation technique is nowadays used for nonparametric identification of linear or linearized systems such as switching power converters. This method is however limited to systems with close time constants, therefore, it cannot be applied to systems with different time constants in a large bandwidth, such as machine drives. This paper presents a modified cross-correlation method. It first provides an improved calculation technique for the parameters of the pseudo-random binary signal used as input signal for identification. It then modifies the initial cross-correlation technique allowing the identification of systems with different time constants in a large bandwidth. Simulation results using MATLAB/Simulink validate the feasibility of the proposed technique in order to implement it using FPGAs.

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.