Modeling and control of fuel cell-battery hybrid energy sources

Environmental, political, and availability concerns regarding fossil fuels in recent decades have garnered substantial research and development in the area of alternative energy systems. Among various alternative energy systems, fuel cells and batteries have attracted significant attention both in academia and industry considering their superior performances and numerous advantages. In this dissertation, the modeling and control of these two electrochemical sources as the main constituents of fuel cell-battery hybrid energy sources are studied with ultimate goals of improving their performance, reducing their development and operational costs and consequently, easing their widespread commercialization. More specifically, Paper I provides a comprehensive background and literature review about Li-ion battery and its Battery Management System (BMS). Furthermore, the development of an experimental BMS design testbench is introduced in this paper. Paper II discusses the design of a novel observer for Li-ion battery State of Charge (SOC) estimation, as one of the most important functionalities of BMSs. Paper III addresses the control-oriented modeling and analysis of open-cathode fuel cells in order to provide a comprehensive system-level understanding of their real-time operation and to establish a basis for control design. Finally, in Paper IV a feedback controller, combined with a novel output-injection observer, is designed and implemented for open-cathode fuel cell temperature control. It is shown that temperature control not only ensures the fuel cell temperature reference is properly maintained, but, along with an uncertainty estimator, can also be used to adaptively stabilize the output voltage --Abstract, page iv

Geology, Geochemistry and Some Genetic Discussion of The IIC Anomaly, Bafq Ditrict, Central Iran

Zaqia IIC anomaly is located in the east of Bafq city in Yazd province. Alteration associated with mineralization has appeared within volcanic, intrusive and sedimentary rocks. The set of alterations of this anomaly consist potassium, sericitic and silicate alterations, with less sodic-calcic alterations. Iron minerals are observed in the form of veins and masses with various compositions in this deposit. Iron ore in Zaqia IIC anomaly is related to magma and hydrothermal fluids. A collection of accumulated zones is composed of high-temperature minerals at depth to subvolcanic assemblages on the surface. REE patterns in iron ores in IIC anomalies indicate LREE enrichment and Eu negative anomaly. The negative Eu anomaly shows the reducing conditions of the mineralizing fluid. Isotopic studies have been conducted to examine the source of the fluid. The mineralizing fluid source in this anomaly is sedimentary-metamorphism. Field observations, mineralogy, alterations along with ore geochemical data show that a magmatic fluid is turned to an iron-rich brine fluid; moreover, an IIC anomaly is formed

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Robust Nonlinear Observer for State of Charge Estimation of Li-Ion Batteries

In this paper, a robust nonlinear observer is proposed to estimate the State of Charge (SOC) of a Li-ion battery, a problem which is critical in designing efficient Li-ion battery management systems and energy management systems in battery-powered applications. An equivalent circuit is used to model the battery behavior. The advantage of this model is that a straightforward identification process can be utilized for parameter identification. Although this model can capture battery dynamics very well for various operating conditions, modeling errors and also unknown disturbances will still be present; therefore, the battery management system should be able to take these uncertainties into consideration. To this end, the proposed estimation algorithm is designed to be robust against uncertainties. Furthermore, the observer does not impose any constraints on the battery current or the SOC relationship with Open Circuit Voltage (OCV). In other words, this algorithm does not require the battery current to be constant or the SOC-OCV relationship to be linear. Global asymptotic convergence of the estimated SOC to its true value is proved via the Lyapanov Stability Theorem. Simulation and experimental results demonstrate the effectiveness of the proposed method

Development of a Series Hybrid Electric Vehicle Laboratory Test Bench with Hardware-in-the-Loop Capabilities

This paper describes the design and development of a laboratory test bench with Hardware-in-the-Loop (HIL) capabilities for the emulation of a Series Hybrid Electric Vehicle (SHEV). The practical challenges of developing a HIL test bench for SHEV emulation are discussed. Furthermore, the overall architecture and system component layout as well as the real-time control algorithm for the test bench are presented. Finally, experimental implementation results on a standard drive cycle are presented to validate the overall operation of the test bench

Reduced-Order Electrochemical Model-Based SOC Observer with Output Model Uncertainty Estimation

As an integral part of energy storage systems, Li-ion batteries require extensive management to guarantee their safe and efficient operation. Estimation of the remaining energy capability of the battery, usually expressed in terms of state of charge (SOC), plays an important role in any battery-powered application. Electrochemical model-based estimation techniques have proven very attractive for this purpose due to the additional information they provide regarding the internal battery operating conditions. A modified reduced-order model based on the single particle approximation of the electrochemical model, suitable for the real-time implementation of SOC estimation, is employed in this paper. This model, while maintaining some of the physical insights about the battery operation, provides a basis for an output-injection observer design to estimate the SOC. Output model uncertainties, originating primarily from the electrolyte-phase potential difference approximation and encountered mainly at higher discharge rates, are handled by incorporating an adaptation algorithm in the observer. Therefore, the proposed method, while being suitable for online implementation, provides an electrochemical model-based solution for battery SOC estimation over a wide range of operations. System stability and the robustness of the estimates given measurement noise are proved analytically using Lyapunov stability. Finally, accurate performance of the proposed SOC estimation technique is illustrated using simulation data obtained from a full-order electrochemical model of a lithium manganese oxide battery

Li-Ion Battery State of Health Estimation based on an Improved Single Particle Model

Health-conscious battery management is one of the main facilitators for widespread commercialization of Li-ion batteries as the primary power source in electrified transportation and portable electronics and as the backup source in stationary energy storage systems. The majority of the existing Battery Management Systems (BMSs) define battery State of Health (SOH) in terms of internal resistance increase or battery capacity decay and use various open-loop criteria based on the battery cycle number and/or operating conditions to determine its SOH. However, considering the wide range of operating conditions and current profiles for Li-ion batteries, the use of a closed-loop SOH estimation approach based on the measureable quantities of the battery along with a battery model is of great importance. In this work, the battery internal resistance increase which can be attributed to various chemical and mechanical degradation mechanisms is considered as the measure of the battery SOH. In order to estimate the SOH, a modified reduced-order electrochemical model based on the Single Particle (SP) Li-ion battery model is proposed to improve the traditional SP model accuracy. This model not only incorporates an analytical expression for the electrolyte-phase potential difference, it is also capable of accurately predicting the battery performance over a wide range of operating currents by considering the effects of the unmodeled dynamics. Finally, this model integrated with an adaptive output-injection observer to estimate the SP model states and the output model uncertainties, can be used to estimate the internal resistance increase during the battery lifetime. The modeling and estimation results are validated via a comparison to the full-order electrochemical model simulations

Development of an Educational Small-Scale Hybrid Electric Vehicle (HEV) Setup

In this paper, the development of an educational small-scale hybrid electric vehicle (HEV) system is discussed. It consists of all the key components of a real HEV that have been scaled down to provide an ideal test platform to evaluate and study hybrid powertrains. The overall configuration is designed to allow the scaled vehicle to emulate the behavior of a series, parallel, or series-parallel HEV, as well as all-electric operation with minor modifications to the powertrain. The developed test bench not only facilitates hands-on experience but can be considered a safe and affordable solution to providing practical aspects of education in the field of electric and hybrid vehicle technology. Considering the potential of such clean transportation alternatives and their continuous growth, the learning experience obtained using the developed test bench will prove invaluable in preparing next generation professionals for further advancement of this field

Electrochemical Model-Based Adaptive Estimation of Li-Ion Battery State of Charge

Electrochemical model-based estimation techniques have attracted increasing attention in the past decade due to their inherent insight about the internal battery operating conditions and limits while being able to monitor important li-ion battery states. The applicability of these methods is, however, limited due to the implementation complexity of their underlying models. In order to facilitate online implementation while maintaining the physical insight, a reduced-order electrochemical model is used in this work. This model, which is based on the commonly-used single particle model, is further improved by incorporating the electrolyte-phase potential. Furthermore, an output-injection observer, suitable for online implementation, is proposed to estimate SOC. The observer convergence is proved analytically using Lyapunov theory. Although the proposed observer shows great performance at low C rates, its accuracy deteriorates at high C-rates. To overcome this issue and achieve accurate SOC estimates for such charge/discharge rates, an adaptation algorithm is augmented to the observer. The adaptation algorithm, which can be implemented online, is used to compensate for model uncertainties, especially at higher C rates. Finally, simulation results based on a full-order electrochemical model are used to validate the observer performance and effectiveness

Development of an Educational Small Scale Hybrid Electric Vehicle (HEV) Setup

In this paper, development of an educational small scale hybrid electric vehicle (HEV) learning module is discussed. The module is comprised of all the key components of an actual HEV that have been scaled down to provide an ideal test platform to evaluate and study hybrid power trains as well as simulate both electric and HEV systems. The test platform consists of low cost off-the-shelf RC parts controlled by Arduino microcontroller boards. LabVIEW is used as an interface to interact with the user, allowing power flow and energy analysis while simulating different drive cycles