Mattia Ricco

Optimization of Perturbative PV MPPT Methods Through Online System Identification

The maximum power point (MPP) tracking function allows one to extract the maximum power from the photovoltaic (PV) system in any operating condition. The perturb and observe technique is one of the mainly used algorithms aimed at this function. Its performances depend on the values of the two design parameters, which are the amplitude and the frequency of the perturbations that it applies in identifying the position of the MPP. In this paper, a method for the online optimization of the perturbation period is presented. It is based on the identification of the whole system, including the PV source and the dc/dc converter controlling it. The system impulse and frequency responses are evaluated by using the cross-correlation method. Such a technique, as well as the tracking algorithm, is effectively implemented by using a field-programmable gate array, and it is validated by means of simulation and experimental results.

An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery

With the popularity of electrical vehicles, the lithium-ion battery industry is developing rapidly. To ensure battery safe usage and to reduce its average lifecycle cost, accurate state of charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper classifies the recently proposed online SOC estimation methods into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model-based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.

FPGA-Based Implementation of Dual Kalman Filter for PV MPPT Applications

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.

Dual-Kalman-Filter-Based Identification and Real-Time Optimization of PV Systems

In this paper, the use of the Dual Kalman Filter for the identification of photovoltaic system parameters is presented. The system includes the photovoltaic source, the dc/dc converter and the battery/dc bus and both its states and parameters in the actual operating conditions are identified. In particular, the proposed approach gives the confidence interval for the system settling time, which is used for the real-time optimization of the perturbative maximum power point tracking algorithm. The proposed technique is implemented by using a Field-Programmable Gate Array and it is validated by means of both simulation and experimental results.

On-line optimization of the P&O MPPT method by means of the system identification

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.

New MMC capacitor voltage balancing using sorting-less strategy in nearest level control

This paper proposes a new strategy for balancing the Capacitor Voltages (CVs) for Modular Multilevel Converters (MMCs). The balancing is one of the main challenges in MMC applications and it is usually solved by adopting a global arm control approach. For performing such an approach, a sorted list of the SubModules (SMs) according to their capacitor voltages is required. A common way to accomplish this task is to implement a sorting algorithm in the same controller used for the modulation technique. However, the execution time and the computational efforts of these kinds of algorithms increase very rapidly when the number of SMs grows. A novel idea is presented in this paper by using a mapping strategy that directly stores in a ranked list the SMs according to the measured CVs. Avoiding the use of sorting algorithms leads to a considerable reduction of the execution time as well as the computational efforts.

FPGA-based implementation of an adaptive P&O MPPT controller for PV applications

The aim of this paper is to present a Field Programmable Gate Array (FPGA) implementation of an adaptive perturb and observe (P&O) maximum power point tracking controller for photovoltaic application. The implemented controller is able to adapt the perturbation period of the P&O algorithm in order to face the changing environment conditions as well the aging of the photovoltaic system. The proposed implementation allows significantly improving the timing performances and reducing the FPGA resources. A complete time/area performance analysis and the experimental results confirm the feasibility and the efficiency of the proposed controller implementation.

An overview of online implementable SOC estimation methods for Lithium-ion batteries

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.

System-on-chip implementation of embedded real-time simulator for modular multilevel converters

The aim of this paper is to present the implementation of an Embedded Real-Time Simulator (ERTS) for Modular Multilevel Converters (MMCs), using low-cost System-on-Chip (SoC) platform. In order to achieve new functionalities such as sensor-less control, monitoring, diagnostic and fault detection, the MMC plant model can be implemented along with the controller. In MMC applications, the implementation of the RTS is particularly challenging due to the complex structure of the MMC and its stringent timing constraints, especially when the number of Sub-Modules (SMs) increases. In addition to the previous requirements, in case of ERTS, the hardware resources are also limited in order to keep low the cost of the entire controller. Moreover, the chosen device must also provide enough modulators for driving all the SMs and sufficient ADC interfaces for acquiring the capacitor voltages. For these reasons, a Xilinx Zynq SoC platform is adopted; this device provides two hard-processors along with the programmable gate array. In this work, the MMC plant model and the MMC controller are implemented in the two available microcontrollers, whereas, all the modulators and interfaces can be implemented in the programmable gate array. The achieved implementation is evaluated in terms of execution time and maximum allowable number of SMs. In order to validate the proposed implementation, HIL results for a single-phase MMC simulator are also provided.

FPGA-based implementation of sorting networks in MMC applications

In this paper an implementation technique for Field Programmable Gate Array (FPGA) devices of two Sorting Networks (SNs) used for control of Modular Multilevel Converter (MMC) is presented. In such applications, the classical sorting algorithms are based on repetitive/recursive loops, and they are usually implemented in microcontrollers or DSPs. However, they are not convenient for hardware implementation due to their inherent sequential operation. Instead, the proposed SNs, are suitable for FPGA devices thanks to their fixed parallel structure that allows improving the timing performance of the capacitor voltage balancing algorithm. The advantages and the main challenges of the Bitonic SN and Even-Odd SN in MMC applications are discussed. Moreover, in order to pre-evaluate the required resources and the execution time, equations are derived for both the proposed SNs and then a comparison is performed between them. The proposed equations are validated by comparing the real required resources with the estimated ones by using the Xilinx Vivado Design Suite tool. Finally, the operation of the proposed Bitonic SN is also tested in Vivado Simulator, achieving the sorted list of 8 elements in 18 clock cycles as expected.