Die untersuchten Ausgangsmaterialien α-Fe2U3, ɣ-Fe2O3, Fe3O4, CoO, Co3O4, NiO sowie CuO eignen sich durch ihre hohe theoretische spezifische Kapazität als Elektrodenmaterial für Lithium-Ionen-Batterien. Die zugrundeliegenden Mechanismen zur Speicherung der Li-Ionen konnten mit allen Phasenumwandlungen und der Bildung von Zwischenprodukten im ersten Reduktionsschritt beschrieben werden. In Abhängigkeit von der Kristallstruktur der Ausgangsmaterialien und den Reaktionsgeschwindigkeiten konnten der Gesamtreaktion die einzelnen Mechanismen Interkalation von Li-Ionen, Substitution von Kationen in der Kristallstruktur und Konversionsmechanismus zugeordnet werden. Auf Grund des gemeinsamen kubisch flächenzentrierten Sauerstoffuntergitters der Ausgangsmaterialien und Zwischenprodukte zeigen sich für die Materialsysteme Fe-O, Co-O und Ni-O Orientierungsbeziehungen zwischen den Kristalliten des Ausgangsoxids, des lithiierten Metalloxids und der Li2O-Matrix. Im Gegensatz dazu sind die auf der CuO-Phase basierenden Kristallite regellos orientiert und zeigen eine höhere Zyklenstabilität. Die Orientierungsbeziehung zwischen den lithiierten Metalloxiden und der Li2O-Matrix hindert dagegen den Austausch der Li-Ionen, beeinträchtigt die Zyklenstabilität und trägt so zu einer schnelleren Alterung der Elektrode bei.

Indiana University-Purdue University Indianapolis (IUPUI) / Rechargeable lithium ion batteries are widely used in portable consumer electronics such as cellphones, laptops, etc. These batteries are capable to provide high energy density with no memory effect and they have small self-discharge when they are not in use, which increases their potential for future electric vehicles. Investigators are attempting to improve the performance of these cells by focusing on the energy density, cost, safety, and durability. The energy density improves with high operation voltage and high capacity. Before any further development of high voltage materials, safe electrolytes with high ionic conductivity, wide electrochemical window, and high stability with both electrodes need to be developed. In this thesis a new strategy was investigated to develop electrolytes that can contribute to the further development of battery technology. The first study is focused on preparing a hybrid electrolyte, the combination of inorganic solid and organic liquid, for lithium based rechargeable batteries to illustrate the effect of electrode/electrolyte interfacing on electrochemical performance. This system behaves as a self-safety device at higher temperatures and provides better performance in comparison with the solid electrolyte cell, and it is also competitive with the pure liquid electrolyte cell. Then a multilayer electrolyte cell (MEC) was designed and developed as a new tool for investigating electrode/electrolyte interfacial reactions in a battery system. The MEC consists of two liquid electrolytes (L.E.) separated by a solid electrolyte (S.E.) which prevents electrolyte crossover while selectively transporting Li+ ions. The MEC successfully reproduced the performance of LiFePO4 comparable with that obtained from coin cells. In addition, the origin of capacity fading in LiNi0.5Mn1.5O4full-cell (with graphite negative electrode) was studied using the MEC. The performance of LiNi0.5Mn1.5O4 MEC full-cell was superior to that of coin full-cell by eliminating the Mn dissolution problem on graphite negative electrode as evidenced by transmission electron microscopy (TEM) analysis. The MEC can be a strong tool for identifying the electrochemical performances of future high voltage positive electrode materials and their electrode/electrolyte interfacial reactions. Finally, by employing the multilayer electrolyte concept, a new application will be introduced to recycle the lithium. This study demonstrates the feasibility of using water and the contents of waste Li-ion batteries for the electrodes in a Li-liquid battery system. Li metal was collected electrochemically from a waste Li-ion battery containing Li-ion source materials from the battery’s anode, cathode, and electrolyte, thereby recycling the Li contained in the waste battery at the room temperature.

Indiana University-Purdue University Indianapolis (IUPUI) / The prevalent applications of energy storage devices have incited wide-spread efforts on production of thin, flexible, and light-weight lithium-ion batteries. In this work, lithium-ion batteries using novel flexible paper-based current collectors have been developed. The paper-based current collectors were fabricated from carbon nanotube (CNT)-coated wood microfibers (CNT-microfiber paper). This thesis presents the fabrication of the CNT-microfiber paper using wood microfibers, coating electrode materials, design and assemblies of battery, testing methodologies, and experimental results and analyses. Wood microfibers were coated with carbon nanotubes and poly(3,4-ethylenedioxythiophene) (PEDOT) through an electrostatic layer-by-layer nanoassembely process and formed into a sheet, CNT-microfiber paper. The CNT loading of the fabricated paper was measured 10.1 μg/cm2 subsequently considered. Electrode material solutions were spray-coated on the CNT-microfiber paper to produce electrodes for the half and full-cell devices. The CNT current collector consists of a network structure of cellulose microfibers at the micro-scale, with micro-pores filled with the applied conductive electrode materials reducing the overall internal resistance for the cell. A bending test revealed that the paper-based electrodes, compared to metal ones, incurred fewer damages after 20 bends at an angle of 300o. The surface fractures on the paper-based electrodes were shallow and contained than metallic-based electrodes. The micro-pores in CNT-microfiber paper structure provides better adherence to the active material layer to the substrate and inhibits detachment while bending. Half-cells and full-cells using lithium cobalt oxide (LCO), lithium titanium oxide (LTO), and lithium magnesium oxide (LMO) were fabricated and tested. Coin cell assembly and liquid electrolyte was used. The capacities of half-cells were measured 150 mAh/g with LCO, 158 mAh/g with LTO, and 130 mAh/g with LMO. The capacity of the LTO/LCO full-cell also was measured 126 mAh/g at C/5 rate. The columbic efficiency of the LTO/LCO full-cell was measured 84% for the first charging cycle that increased to 96% after second cycle. The self-discharge test of the full-cell after charging to 2.7 V at C/5 current rate is showed a stable 2 V after 90 hours. The capacities of the developed batteries at lower currents are comparable to the metallic electrode-based devices, however, the capacities were observed to drop at higher currents. This makes the developed paper-based batteries more suitable for low current applications, such as, RFID tags, flexible electronics, bioassays, and displays. The capacities of the batteries at higher current can be improved by enhancing the conductivity of the fibers, which is identified as the future work. Furthermore, fabrication of an all solid state battery using solid electrolyte is also identified as the future work of this project.

Indiana University-Purdue University Indianapolis (IUPUI) / A gradient free function optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized in parameter identification of the electrochemical model of a Lithium-Ion battery having a LiCoO2 chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24-hr over-discharged battery, and over-charged battery. It is important for a battery management system to have these parameters changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. In this work, PSO methodology has been used to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions. The identified battery models were validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. These identified conditions of the battery were then used to monitor condition of the battery that can aid the battery management system (BMS) in improving overall performance. An adaptive estimation technique, namely multiple model adaptive estimation (MMAE) method, was implemented for this purpose. In this estimation algorithm, all the identified models were simulated for a battery current input profile extracted from the hybrid pulse power characterization (HPPC) cycle simulation of a hybrid electric vehicle (HEV). A partial differential algebraic equation (PDAE) observer was utilized to obtain the estimated voltage, which was used to generate the residuals. Analysis of these residuals through MMAE provided the probability of matching the current battery operating condition to that of one of the identified models. Simulation results show that the proposed model based method offered an accurate and effective fault diagnosis of the battery conditions. This type of fault diagnosis, which is based on the models capturing true physics of the battery electrochemistry, can lead to a more accurate and robust battery fault diagnosis and help BMS take appropriate steps to prevent battery operation in any of the stated severe or abusive conditions.

This thesis proposes, validates, and compares low-complexity algorithms for the fast-and-safe charge and balance of Li-ion batteries both for the single cell case and for the case of a serially-connected string of battery cells. The proposed algorithms are based on a reduced-order electrochemical model (Equivalent Hydraulic Model, EHM), and make use of constrained-control strategies to limit the main electrochemical degradation phenomena that may accelerate aging, namely: Lithium plating in the anode and solvent oxidation inthe cathode. To avoid the computational intensiveness of solving an online optimization as in the Model Predictive Control (MPC) framework, this thesis proposes the use of Reference Governor schemes. Variants of both the Scalar Reference Governors (SRG) and the Explicit Reference Governors (ERG) are developed to deal with the non-convex admissible region for the charge of a battery cell, while keeping a low computational burden. To evaluate the performance of the proposed techniques for the single cell case, they are experimentallyvalidated on commercial Turnigy LCO cells of 160 mAh at four different constant temperatures (10, 20, 30 and 40 °C). In the second part of this thesis, the proposed charging strategies are extended to take into account the balance of a serially-connected string of cells. To equalize possible mismatches, a centralized policy based on a shunting grid (active balance) connects or disconnects the cells during the charge. After a preliminary analysis, a simple mixed-integer algorithm was proposed. Since this method is computationally inefficient due to the high number of scenarios to be evaluated, this thesis proposes a ratio-based algorithm based on a Pulse-Width Modulation (PWM) approach. This approach can be used within both MPC and RG schemes. The numerical validations of the proposed algorithms for the case of a string of four battery cells are carried out using a simulator based on a full-order electrochemical model. Numerical validations show that the PWM-like approach charges in parallel all the cells within the pack, whereas the mixed-integer approach charges the battery cells sequentially from the battery cell with the lowest state of charge to the ones with the highest states of charge. On the basis of the simulations, an algorithm based on a mixed logic that allows to charge in a “sequential parallel” approach is proposed. Some conclusions and future directions of research are proposed at the end of the thesis. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Electric vehicles have received substantial attention in the past few years since they provide a more sustainable, efficient, and greener transportation alternative in comparison to conventional fossil-fuel powered vehicles. Lithium-Ion batteries represent the most important component in the electric vehicle powertrain and thus require accurate monitoring and control. Many challenges are still facing the mass market production of electric vehicles; these challenges include battery cost, range anxiety, safety, and reliability. These challenges can be significantly mitigated by incorporating an efficient battery management system. The battery management system is responsible for estimating, in real-time, the battery state of charge, state of health, and remaining useful life in addition to communicating with other vehicle components and subsystems. In order for the battery management system to effectively perform these tasks, a high-fidelity battery model along with an accurate, robust estimation strategy must work collaboratively at various power demands, temperatures, and states of life. Lithium ion batteries are considered in this research. For these batteries, electrochemical models represent an attractive approach since they are capable of modeling lithium diffusion processes and track changes in lithium concentrations and potentials inside the electrodes and the electrolyte. Therefore, electrochemical models provide a connection to the physical reactions that occur in the battery thus favoured in state of charge and state of health estimation in comparison to other modeling techniques. The research presented in this thesis focuses on advancing the development and implementation of battery models, state of charge, and state of health estimation strategies. Most electrochemical battery models have been verified using simulation data and have rarely been experimentally applied. This is because most electrochemical battery model parameters are considered proprietary information to their manufacturers. In addition, most battery models have not accounted for battery aging and degradation over the lifetime of the vehicle using real-world driving cycles. Therefore, the first major contribution of this research is the formulation of a new battery state of charge parameterization strategy. Using this strategy, a full-set of parameters for a reduced-order electrochemical model can be estimated using real-world driving cycles while accurately calculating the state of charge. The developed electrochemical model-based state of charge parameterization strategy depends on a number of spherical shells (model states) in conjunction with the final value theorem. The final value theorem is applied in order to calculate the initial values of lithium concentrations at various shells of the electrode. Then, this value is used in setting up constraints for the optimizer in order to achieve accurate state of charge estimation. Developed battery models at various battery states of life can be utilized in a real-time battery management system. Based on the developed models, estimation of the battery critical surface charge using a relatively new estimation strategy known as the Smooth Variable Structure Filter has been effectively applied. The technique has been extended to estimate the state of charge for aged batteries in addition to healthy ones. In addition, the thesis introduces a new battery aging model based on electrochemistry. The model is capable of capturing battery degradation by varying the effective electrode volume, open circuit potential-state of charge relationship, diffusion coefficients, and solid-electrolyte interface resistance. Extensive experiments for a range of aging scenarios have been carried out over a period of 12 months to emulate the entire life of the battery. The applications of the proposed parameterization method combined with experimental aging results significantly improve the reduced-order electrochemical model to adapt to various battery states of life. Furthermore, online and offline battery model parameters identification and state of charge estimation at various states of life has been implemented. A technique for tracking changes in the battery OCV-R-RC model parameters as battery ages in addition to estimation of the battery SOC using the relatively new Smooth Variable Structure Filter is presented. The strategy has been validated at both healthy and aged battery states of life using driving scenarios of an average North-American driver. Furthermore, online estimation of the battery model parameters using square-root recursive least square (SR-RLS) with forgetting factor methodology is conducted. Based on the estimated model parameters, estimation of the battery state of charge using regressed-voltage-based estimation strategy at various states of life is applied. The developed models provide a mechanism for combining the standalone estimation strategy that provide terminal voltage, state of charge, and state of health estimates based on one model to incorporate these different aspects at various battery states of life. Accordingly, a new model-based estimation strategy known as the interacting multiple model (IMM) method has been applied by utilizing multiple models at various states of life. The method is able to improve the state of charge estimation accuracy and stability, when compared with the most commonly used strategy. This research results in a number of novel contributions, and significantly advances the development of robust strategies that can be effectively applied in real-time on-board of a battery management system. / Thesis / Doctor of Philosophy (PhD)

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium-ion battery is one kind of rechargeable battery, and also renewable, sustainable and portable. With the merits of high density, slow loss of charge when spare and no memory effect, lithium-ion battery is widely used in portable electronics and hybrid vehicles. Apart from its advantages, safety is a major concern for Lithium-ion batteries due to devastating incidents with laptop and cell phone batteries. Overcharge and over-discharge are two of the most common electrical abuses a lithium-ion battery suffers. In this thesis, a fuzzy-rule-based system is proposed to detect the over-charge and over-discharge failure in early time. The preliminary results for the failure signatures of overcharged and over-discharged lithium-ion are listed based on the experimental results under both room temperature and high temperature. A fuzzy-rule-based model utilizing these failure signatures is developed and validated. For over-charge case, the abnormal increase of the surface temperature and decrease of the voltage are captured. While for over discharge case, unusual temperature increase during overcharge phases and abnormal current decrease during overcharge phases are obtained. The inference engine for fuzzy-rule-based system is designed based on these failure signatures. An early warning signal will be given by this algorithm before the failure occurs. This failure detection and early warning system is verified to be effective through experimental validation. In the validation test, the proposed methods are successfully implemented in a real-time system for failure detection and early warning. The result of validation is compatible with the design expectation. Finally an accurate failure detection and early warning system is built and tested successfully.

No description available.

[ES] El transporte de personas, así como de carga ha evolucionado y crecido tremendamente en los últimos años. El desarrollo tecnológico debió ser adaptado a las diferentes medidas gubernamentales en términos de control de emisiones contaminantes. Desde el acuerdo de Paris en 2015 para mantener el crecimiento de la temperatura global por debajo de 1.5oC, se han impuesto también límites para las emisiones de CO2 por parte de vehículos de carretera. Para el sector del transporte pesado, se han impuesto límites de flota de 15% para 2025 y 30% para 2030 de reducción del CO2 con respecto a 2019. Por lo tanto, esta doble restricción de muy bajos niveles de emisiones contaminantes, así como de gases de efecto invernadero hacen que el sector del transporte este ante un gran desafío tecnológico. En 2022, el transporte de carga tiene un 99% de vehículos propulsados a motor de combustión interna con Diesel como combustible y sin ningún tipo de ayuda eléctrica en el sistema de propulsión. Los límites de emisiones contaminantes como Euro 6 son alcanzados con complejos sistemas de postratamiento que además agregan el consumo de Urea. Trabajos previos en la bibliografía, así como sistemas prototipo han demostrado que es posible alcanzar los objetivos de emisiones contaminantes con métodos avanzados de control de la combustión y así disminuyendo la complejidad del post tratamiento en la salida de gases. Con mayor éxito, el concepto de Reactivity Controlled Combustion Ignition puede alcanzar valores por debajo de Euro 6 con eficiencia similar a la combustión de Diesel. Sin embargo, no soluciona los problemas de emisiones de CO2. Por otro lado, en vehículos de pasajeros fue demostrado con suceso la aplicación de motores eléctricos en el sistema de propulsión para mejorar la eficiencia global del vehículo. El caso extremo son los vehículos puramente electicos donde se alcanza eficiencias por arriba del 70% contra 35% de los vehículos no electrificados. Sin embargo, limitaciones de autonomía, tiempo de carga y la no clara reducción global de la contaminación debido a las emisiones de la energía de la red eléctrica y la contaminación de las baterías de ion-litio hacen que este sistema de propulsión este bajo discusión. Para los vehículos con algún grado de electrificación, las emisiones de gases contaminantes siguen siendo un problema como para el caso no electrificado. Por lo tanto, esta tesis doctoral aborda el problema de emisiones contaminantes, así como de CO2 combinado modos avanzados de combustión con sistemas de propulsión electrificado. La aplicación de estas tecnologías se centra en el sector del transporte de carretera pesado. En particular, un camión de 18 toneladas de carga máxima que originalmente en 2022 equipa un motor seis cilindros de 8 litros con combustión convencional Diesel. El presente trabajo utiliza herramientas experimentales como son medidas en banco motor, así como en carretera para alimentar y validar modelos numéricos de motor, sistema de postratamiento, así como de vehículo. Este último es el punto central del trabajo ya que permite abordar sistemas como el mild hybrid, full hybrid y plug-in hybrid. Calibración de motor experimental dedicada a sistemas de propulsión hibrido es presentada con combustibles sintéticos y/o para llegar a los límites de Euro 7. / [CA] El transport de persones, així com de càrrega ha evolucionat i crescut tremendament en els últims anys. El desenvolupament tecnològic degué ser adaptat a les diferents mesures governamentals en termes de control d'emissions contaminants. Des de l'acord de Paris en 2015 per a mantindre el creixement de la temperatura global per davall de 1.5oC, s'han imposat també límits per a les emissions de CO¿ per part de vehicles de carretera. Per al sector del transport pesat, s'han imposat limites de flota de 15% per a 2025 i 30% per a 2030 de reducció del CO¿ respecte a 2019. Per tant, aquesta doble restricció de molt baixos nivells d'emissions contaminants, així com de gasos d'efecte d'hivernacle fan que el sector del transport aquest davant un gran desafiament tecnològic. En 2022, el transport de càrrega té un 99% de vehicles propulsats a motor de combustió interna amb Dièsel com a combustible i sense cap mena d'ajuda elèctrica en el sistema de propulsió. Els limites d'emissions contaminants com a Euro 6 són aconseguits amb complexos sistemes de posttractament que a més agreguen el consum d'Urea. Treballs previs en la bibliografia, així com sistemes prototip han demostrat que és possible aconseguir els objectius d'emissions contaminants amb mètodes avançats de control de la combustió i així disminuint la complexitat del post tractament en l'eixida de gasos. Amb major èxit, el concepte de Reactivity Controlled Combustion Ignition pot aconseguir valors per davall d'Euro 6 amb eficiència similar a la combustió de Dièsel. No obstant això, no soluciona els problemes d'emissions de CO¿. D'altra banda, en vehicles de passatgers va ser demostrat amb succés l'aplicació de motors elèctrics en el sistema de propulsió per a millorar l'eficiència global del vehicle. El cas extrem són els vehicles purament electicos on s'aconsegueix eficiències per dalt del 70% contra 35% dels vehicles no electrificats. No obstant això, limitacions d'autonomia, temps de càrrega i la no clara reducció global de la contaminació a causa de les emissions de l'energia de la xarxa elèctrica i la contaminació de les bateries d'ió-liti fan que aquest sistema de propulsió aquest baix discussió. Per als vehicles amb algun grau d'electrificació, les emissions de gasos contaminants continuen sent un problema com per al cas no electrificat. Per tant, aquesta tesi doctoral aborda el problema d'emissions contaminants, així com de CO¿ combinat maneres avançades de combustió amb sistemes de propulsió electrificat. L'aplicació d'aquestes tecnologies se centra en el sector del transport de carretera pesat. En particular, un camió de 18 tones de càrrega màxima que originalment en 2022 equipa un motor sis cilindres de 8 litres amb combustió convencional Dièsel. El present treball utilitza eines experimentals com són mesures en banc motor, així com en carretera per a alimentar i validar models numèrics de motor, sistema de posttractament, així com de vehicle. Est ultime és el punt central del treball ja que permet abordar sistemes com el mild hybrid, full *hybrid i plug-in hybrid. Calibratge de motor experimental dedicada a sistemes de propulsió hibride és presentada amb combustibles sintètics i/o per a arribar als límits d'Euro 7. / [EN] The transport of people, as well as cargo, has evolved and grown tremendously over the recent years. Technological development had to be adapted to the different government measures for controlling polluting emissions. Since the Paris agreement in 2015 limits have also been imposed on the CO2 emissions from road vehicles to keep global temperature growth below 1.5oC. For the heavy transport sector, fleet limits of 15% for 2025 and 30% for 2030 CO2 reduction have been introduced with respect to the limits of 2019. Therefore, the current restriction of very low levels of polluting emissions, as well as greenhouse gases, makes the transport sector face a great technological challenge. In 2021, 99% of freight transport was powered by an internal combustion engine with Diesel as fuel and without any type of electrical assistance in the propulsion system. Moreover, polluting emission limits such as the Euro 6 are achieved with complex post-treatment systems that also add to the consumption of Urea. Previous research and prototype systems have shown that it is possible to achieve polluting emission targets with advanced combustion control methods, thus reducing the complexity of post-treatment in the exhaust gas. With greater success, the concept of Reactivity Controlled Combustion Ignition can reach values below the Euro 6 with similar efficiency to Diesel combustion. Unfortunately, it does not solve the CO2 emission problems. On the other hand, in passenger vehicles, the application of electric motors in the propulsion system has been shown to successfully improve the overall efficiency of the vehicle. The extreme case is the purely electric vehicles, where efficiencies above 70% are achieved against 35% of the non-electrified vehicles. However, limitations of vehicle range, charging time, payload reduction and an unclear overall reduction in greenhouse emissions bring this propulsion system under discussion. For vehicles with some degree of electrification, polluting gas emissions continue to be a problem as for the non-electrified case. Therefore, this doctoral Thesis addresses the problem of polluting emissions and CO2 combined with advanced modes of combustion with electrified propulsion systems. The application of these technologies focuses on the heavy road transport sector. In particular, an 18-ton maximum load truck that originally was equipped with an 8-liter six-cylinder engine with conventional Diesel combustion. The present work uses experimental tools such as measurements on the engine bench as well as on the road to feed and validate numerical models of the engine, after-treatment system, and the vehicle. The latter is the central point of the work since it allows addressing systems such as mild hybrid, full hybrid, and plug-in hybrid. Experimental engine calibration dedicated to hybrid propulsion systems is presented with synthetic fuels in order to reach the limits of the Euro 7. / This Doctoral Thesis has been partially supported by the Universitat Politècnica de València through the predoctoral contract of the author (Subprograma 2), which is included within the framework of Programa de Apoyo para la Investigación y Desarrollo (PAID) / Martínez Boggio, SD. (2022). Study of the Potential of Electrified Powertrains with Dual-Fuel Combustion to Achieve the 2025 Emissions Targets in Heavy-Duty Applications [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/188835