Electricity providers has a growing interest in moving towards Renewable Energy Sources (RES) for power generation due to their attractive features. This has caused phasing out of coal, oil and nuclear power plants which use large synchronous generators for power production. These large rotational masses provide inertia to the electricity grid which compensate the sudden frequency instabilities of the grid. Therefore, lowering the system inertia opens up to frequency instabilities in the electricity grid.  As a solution for the lower system inertia, the concept of Fast Frequency Reserve (FFR) has been introduced. The timeframe of primary generation reserves can be too slow in case of a sudden frequency instability. Amongst the energy sources that can be used for FFR, this thesis work explores the possibility of a Battery Energy Storage System (BESS) to be used in FFR. To accomplish this objective, a total BESS system including power electronic converters for integration to the grid is designed in this work. The software of choice for simulation is Matlab/Simulink.  This work uses a hybrid battery model proposed by previous research which is a combination of runtime model and Thevenin model. A bidirectional Buck-Boost converter integrated with a current controller has been used as the DC-DC converter. An outer voltage control loop integrated with the inverter dq current controller has been used to connect the BESS to the gird. The function of each subsystem is observed to verify their functionality. The hybrid battery model is tested by comparing results with the battery model available in Simulink. Finally, power delivery to grid under FFR activation requirements is observed.  Results show that the hybrid battery model is a good approximation to represent a real battery cell in electrical grid applications. The simulation time can be reduced by replacing the series battery cell configuration used in this work with the Simulink battery model. The power delivery to the grid shows BESS is a reliable energy resource that can be used for FFR.

In this thesis, a physics-based single particle modeling is presented to analyze a proposed direct coupled hybrid energy storage modules using lithium-ion battery and ultracapacitor. Firstly, a state of the art for the energy storage system in the electrified vehicles are summarized. Several energy storage elements including lead-acid battery, nickel-metal hydride battery, lithium-ion battery, ultracapacitor, and lithium-ion capacitor are reviewed. Requirements of the energy storage systems in electric, hybrid electric, and plug-in hybrid electric vehicles are generalized. Typical hybrid energy storage system topologies are also reviewed. Moreover, these energy storage elements and hybrid energy storage system topologies are compared to the requirements of the energy storage systems in terms of specific power and specific energy. Secondly, the performance of different battery balancing topologies, including line shunting, ring shunting, synchronous flyback, multi-winding, and dissipative shunting are analyzed based on a linear programming methodology. As a traction battery in an electric or plug-in electric vehicle, high voltage lithium-ion packs are typically configured in a modular fashion, therefore, the analysis considers the balancing topologies at module level and cell level and focuses on minimum balancing time, minimum plug-in charge time, minimum energy loss, and component counts of every balancing topology for the entire battery pack. Thirdly, different modeling techniques for the lithium-ion battery and ultracapacitor are presented. One of the main contributions of this thesis is the development of a physics-based single particle modeling embedded with a solid-electrolyte interface growth model for a lithium-ion battery in battery management system. This development considers the numerical solution of diffusion equation, cell level quantities, parametrization method, effects of number of shells in a spherical particle, SOC-SOH estimation algorithms, and aging effects. The accuracy of the modeling is validated by experimental results of a Panasonic NCR18650A lithium-ion battery cell. Fourthly, the physics-based modeling is applied to analyze the performance of a proposed direct coupled hybrid energy storage module topology based on the Panasonic NCR18650A lithium-ion battery and Maxwell BCAP0350 ultracapacitor. There are many ways to directly connect battery cells and ultracapacitor cells in a module which would influence the performance of the module. The results show that a module has 9 cells in a battery string and 14 cells in an ultracapacitor string can obtain the highest power capability and utilize the most of the energy in an ultracapacitor. More ultracapacitor strings connected in parallel would increase the power density but reduce the energy density. Moreover, the simulation and experimental results indicate that the direct coupled hybrid modules can extend the operating range and slow the capacity fade of lithium-ion battery. An SOC-SOH estimation algorithm for the hybrid module is also developed based on the physics-based modeling. Finally, a pack design methodology is proposed to meet U.S. Advanced Battery Consortium LLC PHEV-40, power-assist, and 48V HEV performance targets for the battery packs or the proposed direct coupled topologies. In order to explore replacement tradeoffs between the battery and ultracapacitor, a case study of the direct coupled topologies is presented. From the case study, ultracapacitors enhance the power capability for short term pulse power and marginally reduce the cost of an entire energy storage system. Moreover, the hybrid module topologies can keep a relatively long all-electric range when the batteries degrade. / Dissertation / Doctor of Philosophy (PhD)

Lack of technical cleanliness and particle contaminations in Lithium-ion battery manufacturing affect the performance of batteries which are a risk for the safety and quality of the product. Therefore, part of the manufacturing process occurs inside the Clean and Dry room area to maintain technical cleanliness. This paper aims to provide a framework to control particle contamination inside the Clean and Dry room and strengthen the product's quality and safety. A literature study was conducted, which was completed by a field study at Northvolt Labs in Västerås to achieve the study's aims. The study contributes to existing theories by providing a framework to find root causes of particle contamination in the manufacturing process based on the existing literature and standards. The Six Sigma problem-solving methodology DMAIC was implemented to conduct the field study. A risk assessment was conducted to find the possible threats toward technical cleanliness in the cell assembly process. The risk sources were identified by implementing measurement methods from relevant standards. The results indicate a high risk for technical cleanliness are coming from the decontamination method, material, machines, and environment. Furthermore, several recommendations were given that are expected to decrease the amount of nonconformity in the process. / Brist på teknisk renhet och partikelföroreningar vid tillverkning av litiumjonbatterier påverkar dess prestanda och utgör en risk för produktens säkerhet och kvalitet. Därför sker en del av tillverkningsprocessen i ett Clean & Dry rum för att upprätthålla teknisk renhet. Denna uppsats syftar till att ge ett ramverk för att kontrollera partikelföroreningar och därmed stärka produktens kvalitet och säkerhet. För att uppnå syftet genomfördes först en litteraturstudie vilket vidare kompletterades med en fältstudie vid Northvolt Labs i Västerås. Studien bidrar till befintliga teorier genom att tillhandahålla ett ramverk för att hitta och åtgärda rotorsaker till partikelkontaminering i tillverkningsprocessen baserat på befintlig litteratur och standarder. Sex Sigma problemlösningsmetoden DMAIC implementerades för att genomföra fältstudien. En riskbedömning genomfördes för att hitta riskfyllda aktiviteter i processen. Vidare implementerades mätmetoder från relevanta standarder för att mäta kontamineringsnivån. Resultaten indikerar stor risk för tekniskrenhet från saneringsmetoder, material, maskiner och miljön. Vidare rekommenderas flera åtgärder för att underhålla tekniskrenhet vilka förväntas minska avvikelser i processen.

Lithium-ion (Li-ion) batteries are amongst the most commonly used types in Electric (EVs) and Hybrid Electric (HEVs) Vehicles due to their high energy and power densities, as well as long lifetime. A battery is one of the most important components of an EV and hence it needs to be monitored and controlled accurately. The safety, and reliability of battery packs must then, be ensured by accurate management, control, and monitoring functions by using a Battery Management System (BMS). A BMS is also responsible for accurate real-time estimation of the State of Charge (SoC), State of Health (SoH) and State of Power (SoP) of the battery. The battery SoC provides information on the amount of energy left in the battery. The SoH determines the remaining capacity and health of a pack, and the SoP represents the maximum available power. These critical battery states cannot be directly measured. Therefore, they have to be inferred from measurable parameters such as the current delivered by the battery as well as its terminal voltage. Consequently, in order to offer accurate monitoring of SoC, SoH and SoP, advanced numerical estimation methods need to be deployed. In the estimation process, the states and parameters of a system are extracted from measurements. The objective is to reduce the estimation errors in the presence of uncertainties and noise under different operating conditions. This thesis uses and provides different enhancements to a robust estimation strategy referred to as the Smooth Variable Structure Filter (SVSF) for condition monitoring of batteries. The SVSF is a predictor-corrector method based on sliding mode control that enhances the robustness in the presence of noise and uncertainties. The methods are proposed to provide accurate estimates of the battery states of operation and can be implemented in real-time in BMS. To improve the performance of battery condition monitoring, a measurement-based SoC estimation method called coulomb counting is paired with model-based state estimation strategy. Important considerations in parameter and state estimation are model formulation and observability. In this research, a new model formulation that treats coulomb counting as an added measurement is proposed. It is shown that this formulation enhanced information extraction, leading to a more accurate state estimation, as well as an increase in the number of parameters and variables that can be estimated while maintaining observability. This model formulation is used for characterizing the battery in a range of operating conditions. In turn, the models are integral to a proposed adaptive filter that is a combination of the Interacting Multiple Model (IMM) concept and the SVSF. It is shown that this combined strategy is an efficient estimation approach that can effectively deal with battery aging. The proposed method provides accurate estimation for various SoH of a battery. Further to battery aging adaptation, measurement errors such as sensor noise, drift, and bias that affect estimation performance, are considered. To improve the accuracy of battery state estimation, a noise covariance adaptation scheme is developed for the SVSF method. This strategy further improves the robustness of the SVSF in the presence of unknown physical disturbances, noise, and initial conditions. The proposed estimation strategies are also considered for their implementation on battery packs. An important consideration in pack level battery management is cell-to-cell variations that impact battery safety. This study considers online battery parametrization to update the pack’s model over time and to detect cell-to-cell variability in parallel-connected battery cells configurations. Experimental data are used to validate and test the efficacy of the proposed methods in this thesis. / Thesis / Doctor of Philosophy (PhD) / To address the critical issue of climate change, it is necessary to replace fossil-fuel vehicles with battery-powered electric vehicles. Despite the benefits of electric vehicles, their popularity is still limited by the range anxiety and the cost determined by the battery pack. The range of an electric vehicle is determined by the amount of charge in its battery pack. This is comparable to the amount of gasoline in a gasoline vehicle’s tank. In consideration of the need for methods to address range anxiety, it is necessary to develop advanced algorithms for continuous monitoring and control of a battery pack to maximize its performance. However, the amount of charge and health of a battery pack cannot be measured directly and must be inferred from measurable variables including current, voltage and temperature. This research presents several algorithms for detecting the range and health of a battery pack under a variety of operating conditions. With a more accurate algorithm, a battery pack can be monitored closely, resulting in lower long-term costs. Adaptive methods for determining a battery’s state of charge and health in uncertain and noisy conditions have been developed to provide an accurate measure of available charge and capacity. Methods are then extended to improve the determination of state of charge and health for a battery module.

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京都大学 / 新制・課程博士 / 博士(工学) / 甲第24107号 / 工博第5029号 / 新制||工||1785(附属図書館) / 京都大学大学院工学研究科物質エネルギー化学専攻 / (主査)教授 安部 武志, 教授 作花 哲夫, 教授 阿部 竜 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

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To reach the Swedish goal of reaching a completely fossil-free electricity sector by the year 2040, there is a need for an increased rate of installed renewable electricity sources. Companies have the opportunity to work towards this goal by investing in solar power technologies, which results in a lowered electricity bill, and an additional revenue when electricity is sold to the grid. As a result, the investment usually pays back within a reasonable timeframe. GEHAB is a company located that is located in Alvesta, Sweden, and they are interested in investing in rooftop solar power. This thesis investigates the potential and effects of such an investment at the company through energy simulations. This is done through four different scenarios, which aim at finding the largest possible installation, the most cost-optimal installation, according to the Levelized Cost Of Energy (LCOE), the impact of an added battery installation and finding the current issues with becoming a net-zero consumer of electricity. Finally, a sensitivity analysis was made to investigate how different factors impacted the LCOE. The results showed that the most cost-optimal size for the company to invest in was a 215 kWp installation, which is smaller than the maximum possible size of 335 kWp that can be installed on the rooftop. Such an installation would have an LCOE of -366 SEK/MWh when the avoided costs are included. The discounted payback time of that investment was 11.3 years. The involvement of batteries showed that they would lead to a higher LCOE and for the largest possible solar installation size, including a battery, means that it would not pay back within the lifetime of the PVs. Finally, the net-zero electricity consumption scenario found that currently, the largest issue to reach this scenario is that there is a regulation that limits solar installations to 500 kWp to avoid an energy tax.