Daniel Ioan Stroe

Selection and Performance-Degradation Modeling of LiMO

Advances in the development of energy storage technologies are making them attractive for grid integration together with wind power plants. Thus, the new system, the virtual power plant, is able to emulate the characteristics of todays conventional power plants. However, at present, energy storage devices are expensive and proper selection of the energy storage technology that is to be grid integrated with wind power plants is necessary. In this paper, a methodology for selection of the most suitable energy storage technology for grid integration with wind power plants is proposed. The selection process is service-oriented and thus the energy storage technology is selected based on certain requirement criteria. The primary frequency regulation service was chosen, for example, due to its potential economic benefits. For this service, low cost per cycle at partial charge/discharge was found as the requirement criterion while Li-ion batteries where found as the devices which could best fulfil this requirement. Since accurate and fast battery performance models are indispensable for studying the virtual power plant behavior under different operating conditions, impedance-based performance-degradation models were developed for the two most suitable Li-ion chemistries for the primary frequency regulation service: LiMO2/Li4Ti5O12 and LiFePO4/C.

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The development of lifetime estimation models for Lithium-ion battery cells, which are working under highly variable mission profiles characteristic for wind power plant applications, requires a lot of expenditures and time resources. Therefore, batteries have to be tested under accelerated lifetime aging conditions. This paper presents a three-stage methodology used for accelerated lifetime testing of Lithium-ion batteries. The results obtained at the end of the accelerated aging process were used for the parameterization of a performance-degradation lifetime model, which is able to predict both the capacity fade and the power capability decrease of the selected Lithium-ion battery cells. In the proposed methodology both calendar and cycling lifetime tests were considered since both components are influencing the lifetime of Lithium-ion batteries. Furthermore, the proposed methodology was validated by running a verification stage of the lifetime model, where Lithium-ion battery cells were tested at normal operating conditions using an application specific mission profile.

/Li

Direct Torque Control (DTC) and Field Oriented Control (FOC) are the most dominant control strategies used in generators for wind turbines. In this paper both control methods were implemented on a Permanent Magnet Synchronous Generator (PMSG). The variable speed wind turbine with full scale power converter topology was chosen for design. Parameters from a 2 MW wind turbine were used for system modeling. All the components of the wind turbine system (WTS), except the DC-link and the grid site converter were implemented in MATLAB/Simulink. The pitch controller was used to limit the output power produced by the turbine. DTC and FOC strategies, using SVM were used to control the generator rotor speed. The performance of the two control strategies were compared after different tests have been carried out.

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There are currently many different lithium ion (Li-ion) chemistries available on the market, and several new players are in the research and development process; however, none of them is superior to the other chemistries in all aspects. Relatively low price, long cycle and calendar lifetime, and intrinsic safety of the nanophosphate LiFePO4/C Li-ion chemistry make it possible to consider this chemistry for electric vehicle (EV) applications. This paper investigates the lifetime of the nanophosphate LiFePO4/C battery chemistry when it is used for full electrical vehicles. The investigation is performed considering a semiempirical calendar and cycle lifetime model, which was developed based on extended accelerated lifetime tests. Both capacity and power capability degradations during calendar and cycle life aging are considered and quantified. Finally, the developed battery cell lifetime model is used to study the capacity and power capability degradation behavior of the tested nanophosphate LiFePO4/C battery for two EV operational scenarios.

Ti

Uninterruptible power supply (UPS) systems have incorporated in their structure an electrochemical battery which allows for smooth power supply when a power failure occurs. In general, UPS systems are based on lead acid batteries; mainly a valve regulated lead acid (VRLA) battery. Recently, lithium ion batteries are getting more and more attention for their use in the back-up power systems and UPSs, because of their superior characteristics, which include increased safety and higher gravimetric and volumetric energy densities. This fact allows them to be smaller in size and weight less than VRLA batteries, which are currently used in UPS applications. The main purpose of this paper is to analyze how Li-ion batteries can become a useful alternative to present VRLA. In this study, three different electrochemical battery technologies were investigated; two of the most appealing Li-ion chemistries, lithium iron phosphate (LFP) and lithium titanate oxide (LTO) were compared with lead acid batteries, in terms of their basics characteristics (e.g. capacity, internal resistance) and their dependence on the operating conditions.

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The penetration of wind power into the power system has been increasing in the recent years. Therefore, a lot of concerns related to the reliable operation of the power system have been addressed. An attractive solution to minimize the limitations faced by the wind power grid integration, and thus to increase the power system stability and the energy quality, is to integrate energy storage devices into wind power plants. This paper gives an overview of the state-of-the-art short-term energy storage devices and presents several applications which can be provided by the energy storage device - wind power plant combined system. Moreover, two methods for estimating the remaining useful lifetime of the energy storage devices are presented.

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Energy storage systems based on Lithium-ion batteries have been proposed as an environmental friendly alternative to traditional conventional generating units for providing grid frequency regulation. One major challenge regarding the use of Lithium-ion batteries in such applications is their cost competitiveness in comparison to other storage technologies or with the traditional frequency regulation methods. In order to surpass this challenge and to allow for optimal sizing and proper use of the battery, accurate knowledge about the lifetime of the Lithium-ion battery and its degradation behaviour is required. This paper aims to investigate, based on a laboratory developed lifetime model, the degradation behaviour of the performance parameters (i.e., capacity and power capability) of a Lithium-ion battery cell when it is subjected to a field measured mission profile, which is characteristic to the primary frequency regulation service.

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The penetration of wind power into the power system has been increasing in the recent years. Therefore, a lot of concerns related to the reliable operation of the power system have been addressed. An attractive solution to minimize the limitations faced by the wind power grid integration is to integrate lithium-ion batteries into virtual power plants; thus, the power system stability and the energy quality can be increased. The selection of the best lithium-ion battery candidate for integration with wind power plants is a key aspect for the economic feasibility of the virtual power plant investment. This paper presents a methodology for selection, between three candidates, of a Li-ion battery which offers long cycle lifetime at partial charge/discharge (required by many grid support applications) while providing a low cost per cycle also. For the selected Li-ion battery an impedance-based diagnostic tool for lifetime estimation was developed and verified. This diagnostic tool can be extended into an impedance-based lifetime model that will be able to predict the remaining useful lifetime of Li-ion batteries for specific grid support applications.

and LiFePO

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.

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The increased grid-penetration levels of energy produced by renewable sources, which have almost no inertia, might have a negative impact on the reliable and stable operation of the power system. Various solutions for mitigating the aforementioned problem were proposed in the literature. The aim of this paper is to evaluate the technical viability of utilizing energy storage systems based on Lithium-ion batteries for providing inertial response in grids with high penetration levels of wind power. In order to perform this evaluation, the 12-bus system grid model was used; the inertia of the grid was varied by decreasing the number of conventional power plants in the studied grid model while in the same time increasing the load and the wind power penetration levels. Moreover, in order to perform a realistic investigation, a dynamic model of the Lithium-ion battery was considered and parameterized. All the studies were performed in real time on a laboratory setup composed of RTDS and dSPACE platforms.