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24108号 / 工博第5030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

Due to climate change, the energy system needs to change from traditional fossil fuels to be dominated by renewable energy sources. Not only the energy system, but the increasing number of vehicles and emissions from the transport sector are a problem for climate change and that need to be solved. Both can be solved with batteries, to handle climate change issue. The lithium-ion batteries (LIBs) have a high energy density which is important due to the less needed materials for the batteries. LIBs can be used in a battery energy storage system (BESS) to store the excess energy for later usage, and as an electric vehicle (EV) battery. For these high energy density batteries, there comes drawbacks such as safety issues by deviating temperatures which have effects on the capacity, lifetime, performance, and in worst case a thermal runaway can occur which may lead to fire and explosions. These temperature issues can be solved with a battery thermal management system (BTMS), which can manage temperature deviation. Cylindrical battery cells with the dimension 18650 with the cell chemistry Lithium-Nickel-Cobalt-Aluminum-Oxide (NCA) will be investigated with different discharge rates, how the heat generation increases, and how it can be handled by cooling systems. A battery pack will be built up in computational fluid dynamics (CFD) software called Ansys Fluent, to be simulated and see how the influence of cooling methods affect the energy density of the 18650 batteries. Air-cooling and liquid-cooling with fan as air-cooling and plate cooling as liquid cooling will be used in this work. 20 cells were investigated with air and liquid cooling, with two different cases with air-cooling. 100 cells with just liquid cooling during 0,5C was investigated on how the number of cells impacted on the energy density. It was seen that the different discharge rates (C-rate) had an impact on the amount of cooling, with air cooling being not as good as liquid cooling for cooling the battery pack and more flow was needed. The energy density in relation to weight showed that 20 cells with less spacing using air-cooling had the best energy density at 196,68 Wh/kg. It was also seen that the number of cells had an impact on the energy density in relation to volume. With the best energy density with 100 cells using liquid cooling at 279,96 Wh/L.

The increasing number of vehicles in Colombian cities have resulted in alarmingly low quality of air, further resulting in increasing health issues. One potential solution to this issue could be a shift from ICEVs (internal combustion engine vehicles) to EVs (electric vehicles). However, EVs in Colombia are still very expensive, an issue that needs to be addressed in order for the EV market to increase enough to be able address the issue of low air quality in cities. One way of overcoming these cost barriers could be through implementation of a market for SLB (second life batteries), meaning that a battery retired from usage in EVs would be remanufactured, resold and reused in another application. Through SLB, the owner cost of EVs could potentially be decreased. SLB could also help improve the case for nondispatchable renewable energy sources by providing low cost BESS (battery energy storage solutions). Thus, SLB has the potential to facilitate sustainable transition within both the transport and the energy sector. This thesis aims to assess the potential of SLB in Colombia. This is done through a literature review where the current state of SLB is investigated, several interviews with potential stakeholders for a SLB market in Colombia, and a techno-economic assessment of four potential BESS applications in Colombia. The literature review provides with current knowledge and state of SLB in general. The interviews provide important insight to potential stakeholders’ view on SLB for the specific case of Colombia. The techno-economic assessment includes a sensitivity analysis aiming to provide insights in which factors, such as e.g. battery purchasing price or charging cost, that that gives rise to the largest impact on feasibility of SLB. Findings from the interviews shows a strong collective commitment from the interviewees to working towards cleaner air, resulting in high engagement and collaborative efforts between stakeholders for the SLB case. The main issue highlighted by stakeholders regards technoeconomic uncertainties of SLB. Findings from the techno-economic assessment indicates that SLB is viable for larger applications such as BESS at solar farms, but not for smaller applications such as backup power in residential buildings. However, SLB is not deemed to be a game changer for either application, and there are still many uncertainties regarding both technological and economic aspects that needs to be further investigated. The sensitivity analysis shows that the factors resulting in the highest impact on feasibility of SLB is battery SOH (state of health) at the beginning of SLB usage, and battery and repurposing cost. It will be hard to address both of these factors simultaneously due to a higher SOH would render higher battery prices, and vice versa. The findings from the thesis shows that SLB can facilitate sustainable transition within both the transport and energy sectors but is not to be considered a game changer for these sectors. However, even though SLB’s contribution to sustainable transition is not revolutionary, it is still necessary from a sustainability perspective. Given the environmental footprint of EV batteries and the amount of hazardous waste retired EV batteries will give rise to, circular economy must be pursued. / Det ökande antalet fordon i colombianska städer har resulterat i oroväckande låg luftkvalitet, vilket ytterligare resulterat i ökande hälsoproblem. En potentiell lösning på det problemet kan vara en övergång från ICEVs (förbränningsmotorfordon) till EV (elfordon). EVs i Colombia är fortfarande väldigt dyra, en fråga som måste adresseras för att EV-marknaden ska kunna öka tillräckligt för att kunna ge en inverkan på problemet med låg luftkvalitet i städer. Ett sätt att övervinna dessa kostnadshinder skulle kunna vara genom att implementera en marknad för SLB (second life-batterier), vilket innebär att ett batteri som bedömts inte längre uppfylla kraven för användning i EVs, och därmed byts ut, skulle kunna byggas om, säljas vidare och återanvändas i andra applikationer. Genom SLB kan ägarkostnaderna för EVs potentiellt sänkas. SLB skulle också kunna användas för att tillhandahålla billigare BESS (batterilagringslösningar) hos icke-reglerbara förnyelsebara kraftverk, såsom solkraftverk. Således har SLB potentialen att underlätta för hållbara förändringar inom både transportsektorn och energisektorn. Den här uppsatsen ämnar att utvärdera SLBs potential i Colombia. Detta görs genom en litteraturöversikt där det nuvarande tillståndet av SLBs undersöks, flera intervjuer med potentiella intressenter för en SLB-marknad i Colombia, och en tekno-ekonomisk bedömning av fyra potentiella BESS-applikationer i Colombia. Litteraturöversikten samlar aktuell kunskap och status inom SLB i allmänhet. Intervjuerna ger viktig insikt om potentiella intressenters syn på SLB för det specifika fallet i Colombia. Den tekno-ekonomiska bedömningen inkluderar en känslighetsanalys som syftar till att ge insikter i vilka faktorer, som t.ex. batteriets inköpspris eller laddningskostnad, som ger upphov till den största effekten på SLBs genomförbarhet. Resultat från intervjuerna visar ett starkt kollektivt engagemang från de intervjuade att arbeta mot renare luft, vilket resulterar i högt engagemang och samarbete mellan intressenterna. Det största problemet med SLB från intressenternas synpunkt berör tekno-ekonomiska osäkerheter. Resultat från den tekno-ekonomiska bedömningen indikerar att SLB är ekonomiskt försvarbart för större applikationer som BESS vid solkraftverk, men inte för mindre applikationer som t.ex. för reservenergi i bostadshus. SLB anses dock inte vara ett genombrott för användning vid någon av applikationerna, och det finns fortfarande många osäkerheter när det gäller både tekniska och ekonomiska aspekter som måste undersökas ytterligare. Känslighetsanalysen visar att de faktorer som resulterar i den högsta påverkan på genomförbarheten av SLB är batteriets SOH (hälsotillstånd) i början av SLB-användning och kostnaden för batteri och ombyggnad av batterier. Det kommer dock att vara svårt att hantera båda dessa faktorer samtidigt på grund av att högre SOH skulle ge högre batteripriser, och vice versa. Resultaten från uppsatsen visar att SLB kan underlätta för hållbara förändringar inom både transport- och energisektorerna, men att det inte ska betraktas som något fantastiskt genombrott för dessa sektorer. Även fast SLBs bidrag till hållbara förändringar är inte revolutionerande, är det fortfarande en nödvändig faktor ur ett hållbarhetsperspektiv. Med tanke på miljöavtrycket för EV-batterier och mängden av farligt avfall som EV-batterier kommer att ge upphov till då de inte längre är önskvärda, måste cirkulär ekonomi bedrivas i största möjliga mån.

Batteries play a pivotal role in the modern world, powering everything from portable electronics to electric vehicles, and are critical in the shift towards renewable energy sources by enabling efficient energy storage. This thesis presents new computational strategies to understand and predict surface degradation and stacking faults in battery cathodes, phenomena that have crucial impact on the battery lifetime. The starting point is a detailed first-principles analysis of LiNiO₂ surface degradation, assessing the thermodynamics of oxygen release and its impact on the surface integrity of this prospective cathode material. This research led to the development of a method for the automated enumeration of surface reconstructions and the development of a Python software package implementing the methodology, thereby greatly accelerating the computational surface characterization of electrode materials. The methodology made it feasible to extend the investigation to LiCoO₂ surfaces, comparing their oxygen retention and surface stability with LiNiO₂ and identifying the unique properties of the two transition metals that control their behavior during battery operation. In addition to surface phase changes, stacking faults are another important class of two-dimensional defects that can affect the properties of cathode materials. Combining information from first principles calculations with 17O nuclear magnetic resonance (NMR) spectroscopy provided by collaborators, we uncovered how stacking faults affect the capacity and cyclability of Li₂MnO₃ cathodes, a prototypical lithium-rich material with oxygen redox activity. Although automated first-principles calculations are, in principle, an ideal tool for understanding atomic-scale degradation phenomena in batteries, they are computationally demanding and, therefore, limited to materials with simple compositions. In the final chapter, we explore the application of machine learning for further accelerating computational battery degradation simulations by leveraging existing data first-principles calculations for predicting the stability of new surface reconstructions. This chapter points toward a new direction that should be further explored in the future. The research presented in this thesis not only advances the understanding of lithium-ion battery cathode materials but also introduces more-widely applicable computational methodologies that lay a foundation for the development of advanced materials for energy storage applications. This work demonstrates the benefits of integrating traditional computational methods with machine learning, contributing to ongoing progress in materials science and opening up new possibilities for advancements in energy technology and material engineering.

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium-ion batteries are widely used for portable electronics due to high energy density, mature processing technology and reduced cost. However, their applications are somewhat limited by safety concerns. The lithium-ion battery users will take risks in burn or explosion which results from some internal components failure. So, a practical method is required urgently to find out the failures in early time. In this thesis, a new method based on temperature difference between internal point and surface (TDIS) of the battery is developed to detect the thermal failure especially the thermal runaway in early time. A lumped simple thermal model of a lithium-ion battery is developed based on TDIS. Heat transfer coefficients and heat capacity are determined from simultaneous measurements of the surface temperature and the internal temperature in cyclic constant current charging/discharging test. A look-up table of heating power in lithium ion battery is developed based on the lumped model and cyclic charging/discharging experimental results in normal operating condition. A failure detector is also built based on TDIS and reference heating power curve from the look-up table to detect aberrant heating power and bad parameters in transfer function of the lumped model. The TDIS method and TDIS detector is validated to be effective in thermal runaway detection in a thermal runway experiment. In the validation of thermal runway test, the system can find the abnormal heat generation before thermal runaway happens by detecting both abnormal heating power generation and parameter change in transfer function of thermal model of lithium ion batteries. The result of validation is compatible with the expectation of detector design. A simple and applicable detector is developed for lithium ion battery catastrophic failure detection.

Silicon (Si) has been considered as a promising negative electrode material for lithium ion batteries (LIBs) because of its high theoretical capacity, low discharge voltage, and low cost. However, the utilization of Si electrode has been hampered by problems such as slow ionic transport, large stress/strain generation, and unstable solid electrolyte interphase (SEI). These problems severely influence the performance and cycle life of Si electrodes. In general, ionic conduction determines the rate performance of the electrode, while electron leakage through the SEI causes electrolyte decomposition and, thus, causes capacity loss. The goal of this thesis research is to design Si electrodes with high current efficiency and durability through a fundamental understanding of the ionic and electronic conduction in Si and its SEI. Multi-scale physical and chemical processes occur in the electrode during charging and discharging. This thesis, thus, focuses on multi-scale modeling, including developing new methods, to help understand these coupled physical and chemical processes. For example, we developed a new method based on ab initio molecular dynamics to study the effects of stress/strain on Li ion transport in amorphous lithiated Si electrodes. This method not only quantitatively shows the effect of stress on ionic transport in amorphous materials, but also uncovers the underlying atomistic mechanisms. However, the origin of ionic conduction in the inorganic components in SEI is different from that in the amorphous Si electrode. To tackle this problem, we developed a model by separating the problem into two scales: 1) atomistic scale: defect physics and transport in individual SEI components with consideration of the environment, e.g., LiF in equilibrium with Si electrode; 2) mesoscopic scale: defect distribution near the heterogeneous interface based on a space charge model. In addition, to help design better artificial SEI, we further demonstrated a theoretical design of multicomponent SEIs by utilizing the synergetic effect found in the natural SEI. We show that the electrical conduction can be optimized by varying the grain size and volume fraction of two phases in the artificial multicomponent SEI.

There is an intense, worldwide effort to develop durable lithium ion batteries with high energy and power densities for a wide range of applications, including electric and hybrid electric vehicles. For improvement of battery technology understanding the capacity fading mechanism in batteries is of utmost importance. Novel electrode material and improved electrode designs are needed for high energy- high power batteries with less capacity fading. Furthermore, for applications such as automotive applications, precise cycle-life prediction of batteries is necessary. One of the critical challenges in advancing lithium ion battery technologies is fracture and decrepitation of the electrodes as a result of lithium diffusion during charging and discharging operations. When lithium is inserted in either the positive or negative electrode, there is a volume change associated with insertion or de-insertion. Diffusion-induced stresses (DISs) can therefore cause the nucleation and growth of cracks, leading to mechanical degradation of the batteries. With different mathematical models we studied the behavior of diffusion induces stresses and effects of electrode shape, size, concentration dependent material properties, pre-existing cracks, phase transformations, operating conditions etc. on the diffusion induced stresses. Thus we develop tools to guide the design of the electrode material with better mechanical stability for durable batteries. Along with mechanical degradation, chemical degradation of batteries also plays an important role in deciding battery cycle life. The instability of commonly employed electrolytes results in solid electrolyte interphase (SEI) formation. Although SEI formation contributes to irreversible capacity loss, the SEI layer is necessary, as it passivates the electrode-electrolyte interface from further solvent decomposition. SEI layer and diffusion induced stresses are inter-dependent and affect each-other. We study coupled chemical-mechanical degradation of electrode materials to understand the capacity fading of the battery with cycling. With the understanding of chemical and mechanical degradation, we develop a simple phenomenological model to predict battery life. On the experimental part we come up with a novel concept of using liquid metal alloy as a self-healing battery electrode. We develop a method to prepare thin film liquid gallium electrode on a conductive substrate. This enabled us to perform a series of electrochemical and characterization experiments which certify that liquid electrode undergo liquid-solid-liquid transition and thus self-heals the cracks formed during de-insertion. Thus the mechanical degradation can be avoided. We also perform ab-initio calculations to understand the equilibrium potential of various lithium-gallium phases.