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The betavoltaic battery is a type of micro nuclear battery that harvests beta emitting radioactive decay energy using semiconductors. The literature results suggest that a better model is needed to design a betavoltaic battery. This dissertation creates a comprehensive model that includes all of the important factors that impact betavoltaic battery output and efficiency. Recent advancements in micro electro mechanical systems (MEMS) necessitate an onboard miniaturized power source. As these devices are highly functional, longevity of the power source is also preferred. Betavoltaic batteries are a very promising power source that can fulfill these requirements. They can be miniaturized to the size of a human hair. On the other hand, miniaturization of chemical batteries is restricted by low energy density. That is why betavoltaics are a viable option as a power source for sophisticated MEMS devices. They can also be used for implantable medical devices such as pacemakers; for remote applications such as spacecraft, undersea exploration, polar regions, mountains; military equipment; for sensor networks for environmental monitoring; and for sensors embedded in bridges due to their high energy density and long lifetime (up to 100 years). A betavoltaic battery simulation model was developed using Monte Carlo particle transport codes such as MCNP and PENELOPE whereas many researchers used simple empirical equations. These particle transport codes consider the comprehensive physics theory for electron transport in materials. They are used to estimate the energy deposition and the penetration depth of beta particles in the semiconductors. A full energy spectrum was used in the model to take into account the actual radioactive decay energy of the beta particles. These results were compared to the traditional betavoltaic battery design method of estimating energy deposition and penetration depth using monoenergetic beta average energy. Significant differences in results were observed that have a major impact on betavoltaic battery design. Furthermore, the angular distribution of the beta particles was incorporated in the model in order to take into account the effect of isotropic emission of beta decay. The backscattering of beta particles and loss of energy with angular dependence were analyzed. Then, the drift-diffusion semiconductor model was applied in order to estimate the power outputs for the battery, whereas many researchers used the simple collection probability model neglecting many design parameters. The results showed that an optimum junction depth can maximize the power output. The short circuit current and open circuit voltage of the battery varied with the semiconductor junction depth, angular distribution, and different activities. However, the analysis showed that the analytical results overpredicted the experimental results when self-absorption was not considered. Therefore, the percentage of self-absorption and the source thickness were estimated using a radioisotope source model. It was then validated with the thickness calculated from the specific activity of the radioisotope. As a result, the battery model was improved significantly. Furthermore, different tritiated metal sources were analyzed and the beta fluxes were compared. The optimum source thicknesses were designed to increase the source efficiencies. Both narrow and wide band gap semiconductors for beryllium tritide were analyzed. / PHD

Die Modellierung natürlicher und technischer Systeme spielt eine wichtige Rolle, um deren Verhalten zu simulieren und vorherzusagen. Die Impedanzspektroskopie ist in diesem Zusammenhang eine interessante Methode, da die Impedanz oft einfach messbar ist. Die herausfordernde Aufgabe ist die Interpretation gemessener Daten. Das Verständnis des Zusammenhanges zwischen realen Effekten und gemessener Impedanz anhand eines Impedanzmodells ist eine zentrale Problemstellung. Die Herleitung solcher Modelle wird in dieser Arbeit anhand drei verschiedenartiger Beispiele aus dem Gebiet der Messtechnik untersucht. Wirbelstromsensoren werden allgemein zur Messung von Abstand und Materialeigenschaften eingesetzt. Anhand eines Modells wird untersucht, wie diese Größen simultan bestimmbar sind. Die Messung der Zusammensetzung von Materialgemischen ist vielfach technisch relevant. Am Beispiel von Waschlaugen und Dispersionen mit Carbon Nano Tubes wird gezeigt, wie deren Zusammensetzung die Impedanz beeinflusst und welche Eigenschaften messbar sind. Batterien spielen eine wichtige Rolle zur Speicherung elektrischer Energie. Mit einem fraktionalen Differentialgleichungsmodell erfolgt eine Simulation der Batteriespannung unter wechselnden Einsatzbedingungen. Anhand der Anwendungen wird deutlich, dass es keinen Automatismus zur Modellerstellung und kein Modell für alles geben kann. Um so mehr liefert das vorgeschlagene Vorgehen einen Einstieg in die Modellerstellung. / Modeling natural and technical systems is important in order to simulate and predict their behavior. Impedance spectroscopy is an interesting method in the field of modeling because the impedance often is easily measurable. Nevertheless, interpretation of measured data is the challenging task in this field. The fundamental problem is understanding the relationship between real physical effects, measured impedance and impedance model. Fundamentals and advanced methods for deriving impedance models are investigated for three different problems in the field of measurement and sensor technology in this work. Eddy current sensors are commonly used to measure distance and material properties. Based on a model, it is investigated how these quantities can be determined simultaneously. Measuring the composition of material mixtures has many technical applications. Using the example of dispersions containing laundry detergents and dispersions with carbon nanotubes shows how their composition effects the impedance and measurable quantities. Batteries play an important role for storing electrical energy. Applying a fractional differential equation model allows a simulation of the battery voltage under varying operating conditions. Based on these applications it becomes clear, there can be no fully automated model creation method. A scientific analysis of the underlying problem is always required. The more the proposed approach provides an introduction to modeling.

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)

The electrification of the truck is crucial to meet the strategic vision of the European Union (EU) to contribute to net-zero greenhouse gas emissions for all sectors of the economy and society. The battery-electric truck is very efficient to reduce the emissions and has also a lower Total Cost of Ownership (TCO) compared to diesel trucks. Thus, the energy consumption of the battery-electric truck needs to be analysed in detail, and the differences in the conventional powertrain, recuperation by regenerative braking during driving and charging during standing, need to be considered. This master thesis aims to analyse the energy consumption of the battery-electric truck during driving and standing charging. For driving cycle simulation the Vehicle Energy Consumption calculation TOol (VECTO) and MATLAB are used. Different variations, such as payload, rolling resistance, air drag, and Power Take Off (PTO), are considered in the driving cycle simulation. The driving cycle simulation is verified by calculating the energy balance and compared with the on-road test results. For the standing charging simulation, MATLAB is used to analyse the charging loss with different battery packs and charging speeds. The results are shown with the Sankey diagram and other illustrative tools. Seen from the simulation results, the usable energy of the battery pack is enough for the truck to complete the designed driving cycle. The main loss in the powertrain is the Power Electronic Converter (PEC) and the electric machine. To increase the range and reduce energy loss, using a higher efficiency PEC and electric machine is an efficient method. For the charging simulation, the current Combined Charging System (CCS) standard charging station can charge the battery-electric truck with adequate voltage and reasonable charging time. The main loss during the charging comes from the charging station. / Elektrificering av lastbilen är avgörande för att uppfylla Europeiska Unionens (EUs) strategiska vision att bidra till nettonollutsläpp av växthusgaser för alla sektorer i samhället. Den batterielektriska lastbilen är väldigt effektiv för att reducera utsläppen och är också mer ekonomisk med en lägre Total Cost of Ownership (TCO) jämfört med diesel lastbilar. Således behöver energiförbrukningen för den batterielektriska lastbilen analyseras i detalj, och skillnaderna i den konventionella drivlinan, återhämtning genom regenerativ bromsning under körning och laddning, måste övervägas. Detta examensarbete syftar till att analysera energiförbrukningen för den batterielektriska lastbilen under körning och laddning. För körcykelsimuleringar används the Vehicle Energy Consumption calculation TOol (VECTO) och MATLAB. Olika variationer, såsom nyttolast, rullmotstånd, luftmotstånd och Power Take Off (PTO), beaktas i körcykelsimuleringen. Körcykelsimuleringen verifieras genom att beräkna energibalansen som jämförs med experimentella testresultat utförda på väg. För laddningssimuleringen används MATLAB för att analysera laddningsförlusten med olika batteripaket och laddningshastigheter. Resultaten visas med Sankey diagram och andra illustrativa verktyg. Simuleringsresultaten visar att batteripaketets användbara energi är tillräckligt för att lastbilen ska kunna slutföra den planerade körcykeln. Den största förlusten i drivlinan är kopplat till the Power Electronic Converter (PEC) och den elektriska maskinen. För att öka räckvidden och minska energiförlusten är det ett effektivt sätt att en använda PEC och en elektrisk maskin med högre effektivitet. För laddningssimuleringen kan den nuvarande stationen med Combined Charging System (CCS) standard ladda batteriladdaren med tillräcklig spänning och med rimlig laddningstid. Huvudförlusten under laddningen kommer från laddstationen.

Cette thèse a pour objectif d’étudier l’influence de la recharge rapide sur le vieillissement des batteries lithium, et son impact sur les performances des véhicules électriques dans le cadre d’une utilisation postale. La première partie est consacrée à la modélisation de batteries lithium par un modèle à circuit électriques équivalent, dont les paramètres sont identifiables par des tests de caractérisation linéaires. La deuxième partie est dédiée à l’étude du vieillissement, et abouti sur un estimateur de vieillissement par l’exploitation des données des tests de vieillissement accélérés. Ensuite, l’utilisation postale est étudiée, et un profil de courant représentatif de la sollicitation réelle de la batterie est proposé. Ce dernier a permis de valider le modèle de la batterie dans le domaine de l’utilisation postale. Ensuite un modèle de véhicules électriques est présenté, il intègre le modèle de batterie, tout en le faisant vieillir en utilisant l’estimateur de vieillissement. Enfin, les différentes stratégies de recharges possibles sont énumérées et comparées. Pour conclure sur leurs influences sur le vieillissement des batteries, et donc les performances de véhicules. / This thesis aims to study the influence of fast charging on the aging of lithium batteries, and its impact on the performances of electric vehicles as part of a postal use. The first part is devoted to the modeling of lithium batteries with an equivalent electric circuit model, whose parameters could be identified using linear characterization tests. The second part is dedicated to the study of aging, and results in an aging estimator using data collected from accelerated aging tests programs. Then the postal usage is studied, and a power profile representative of the actual load on the battery is provided. The latter was used to validate the model of the battery in the field of postal use. Then the postal use is studied, and a current profile representative of the real behavior of the battery is provided. This profile was used to validate the model of the battery in the postal use domain. Then a model of electric vehicles is presented, it integrates the battery model, which can simulates the aging state of the battery using the ageing estimator. Finally, the various possible strategies of recharge are listed and compared, which leads to conclusions about their influences on aging of batteries, and the vehicles performance.

As an efficient energy storage technology, lithium-ion batteries play a key role in the ongoing electrification of the mobility sector. However, the required modelbased design process, including hardware in the loop solutions, demands precise battery models. In this work, an encoder-decoder model framework based on recurrent neural networks is developed and trained directly on unstructured battery data to replace time consuming characterisation tests and thus simplify the modelling process. A manifold pseudo-random bit stream dataset is used for model training and validation. A mean percentage error (MAPE) of 0.30% for the test dataset attests the proposed encoder-decoder model excellent generalisation capabilities. Instead of the recursive one-step prediction prevalent in the literature, the stage-wise trained encoder-decoder framework can instantaneously predict the battery voltage response for 2000 time steps and proves to be 120 times more time-efficient on the test dataset. Accuracy, generalisation capability and time efficiency of the developed battery model enable a potential online anomaly detection, power or range prediction. The fact that, apart from the initial voltage level, the battery model only relies on the current load as input and thus requires no estimated variables such as the state-of-charge (SOC) to predict the voltage response holds the potential of a battery ageing independent LIB modelling based on raw BMS signals. The intrinsically ageingindependent battery model is thus suitable to be used as a digital battery twin in virtual experiments to estimate the unknown battery SOH on purely BMS data basis.