1,721,749 research outputs found
New MMC capacitor voltage balancing using sorting-less strategy in nearest level control
No full textThis 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 sorting networks in MMC applications
No full textIn 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
A Simplified Model based State-of-Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model
Full text linkThe performance of model based State-of-Charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the Lithium-ion (Li-ion) battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, Partial Least Squares (PLS) regression is able to establish a series of piecewise linear battery models automatically. One element state space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the Extended Kalman Filter (EKF) with two Resistance and Capacitance (RC) Equivalent Circuit Model (ECM) and the Adaptive Unscented Kalman Filter (AUKF) with Least Squares Support Vector Machines (LSSVM)
System-on-chip implementation of embedded real-Time simulator for modular multilevel converters
No full textThe 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
Single-point reactive power control method on voltage rise mitigation in residential networks with high PV penetration
No full textVoltage rise (VR) due to reverse power flow is an important obstacle for high integration of Photovoltaic (PV) into residential networks. This paper introduces and elaborates a novel methodology of an index-based single-point-reactive power-control (SPRPC) methodology to mitigate voltage rise by absorbing adequate reactive power from one selected point. The proposed index utilizes short circuit analysis to select the best point to apply this Volt/Var control method. SPRPC is supported technically and financially by distribution network operator that makes it cost effective, simple and efficient to eliminate VR in the affected network. With SPRPC none of the previous PV inverters need to upgrade and can retain their unity power factor to not to conflict with current grid codes. Comprehensive 24-h simulation studies are done on a modified IEEE 69-bus Network emulating a traditional residential power system with high r/x ratio. Efficacy, effectiveness and cost study of SPRPC is compared to droop control to evaluate its advantages
A Novel Multiple Correction Approach for Fast Open Circuit Voltage Prediction of Lithium-ion Battery
No full textThis paper proposes a novel fast open circuit voltage prediction approach for Lithium-ion battery, which is potential to facilitate a convenient battery modeling and states estimation in the energy storage system. Open circuit voltage measurement suffers from a long relaxation time (several hours, even days) to reach the thermodynamic equilibrium of the battery. On the basis of the feedback control theory, the proposed multiple correction approach utilizes the constrained nonlinear optimization of the power function in each curve fitting step. The voltage measurement in a short period is divided into several segments to correct the voltage prediction multiple times with the feedback errors after each curve fitting. The similarity between the shape of the power function and the variation of the terminal voltage during the relaxation time is utilized. The proposed method can speed up the time-consuming open circuit voltage measurement and predict the open circuit voltage with high accuracy. Experimental tests on a LiFePO4 battery prove the validation and effectiveness of the proposed method in accurately predicting the open circuit voltage within a very short relaxation time (less than 15 min)
Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles
Full text linkAs a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time
Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles
Full text linkThis paper proposes a low-complexity online state of charge estimation method for LiFePO4 battery in electrical vehicles. The proposed method is able to achieve accurate state of charge with less computational efforts in comparison with the nonlinear Kalman filters, and also can provide state of health information for battery management system. According to the error analysis of equivalent circuit model with two resistance and capacitance, two proportional-integral filters are designed to compensate the errors from inaccurate state of charge and current measurements, respectively. An error dividing process is proposed to tune the contribution of each filter to the finial estimation results, which enhances the validation and accuracy of the proposed method. Recursive least squares filter can provide the state of health information and updates the parameters of battery model online to eliminate the errors caused by parameters uncertainty. The proposed method is compared with extend Kalman filter in regards to accuracy and execution time. The execution time of the proposed method is measured on Zynq board platform to validate its suitability for online implementation. In this paper, the proposed method is able to obtain less than 1% error for state of charge estimation
An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery
Full text linkWith 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
An overview of online implementable SOC estimation methods for Lithium-ion batteries
No full textWith 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 drawnWith 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
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