Different kinds of batteries suit different applications, and consequently several different chemistries exist. In order to better understand the limitations of low temperature performance, a Li-ion battery chemistry normally intended for room temperature use, graphite-Lithium Iron Phosphate, with 1 M LiPF6 ethylene carbonate:diethylene carbonate electrolyte, is here put under testing at -10°C and compared with room temperature cycling performance. Understanding the temperature limitations of this battery chemistry will give better understanding of the desired properties of a substitute using alternative materials. The experimental studies have comprised a combination of battery cycle testing, and surface analysis of the electrodes by Scanning Electron Microscopy and X-Ray Photoelectron Spectroscopy. Results showed that with low enough rate, temperature is less of a problem, but with increased charge rate, there are increasingly severe effects on performance at low temperatures. XPS measurements of low charge rate samples showed similar Solid Electrolyte Interface layers formed on the graphite anode for room- and low temperature batteries, but with indications of a thicker layer on the former. A section of the report handles specific low temperature battery chemistries. The conclusions- and outlook were made by comparing the results found in the study with earlier findings on low temperature Li-ion batteries and present possible approaches for modifying battery performance at lowered temperatures. / I detta projekt har litium-jon-batterier testats i avseende på sina lågtemperaturprestanda. Arbetet gjordes genom att testa och jämföra prestantda mellan prover vid -10°C och rumstemperaturprover. Med analytiska instrument studerades både den morfologiska och kemiska förändring som skett under användning. Vald batterikemi har varit av slaget grafit-litiumjärnfosfat med en typisk organisk elektrolyt. Denna batterikemi är inte på något sätt anpassad för lågtemperaturprestanda och med det hoppas kunna påvisas de effekter som en mer lämpligt lågtemperaturkemi åtgärdar, och förstå hur de gör det. Med låg temperatur uppkommer en större ’tröghet’ för de kemiska reaktioner som sker i ett batteri. Om designen inte är särskilt gjord för låg temperatur kan effekterna bli osäkra, rent av farliga. Risken ökar nämligen för plätering av metalliskt litium på den negativa elektroden, och skulle litiumdeponeringen växa i den riktning som kopplar samman batteriets poler så kortsluts systemet. Med den höga energidensitet som karaktäriserar litium-jon-batterier vore en kortslutning extra beklaglig då den organiska elektrolyten kan antändas, med en potentiell explosion som följd.Inom särskilda applikationer kan lågtemperaturmiljöer förväntas för ett batteri, till exempel för fordon. En elbil i skandinaviskt klimat skulle behöva fungera ohindrat även vintertid, då temperaturerna ofta når -10°C och lägre. Samtidigt får man påminnas om att litium-jon-batterierna är relativt moderna och ännu inte har fått något stort genomslag som framdrivningsmedel. Detta försätter bilindustrin i ett krafigt behov av omfattande forskning för att kunna ta strategiskt sunda beslut för att möjliggöra en ordentlig introducering av elbilar som trovärdig ersättare till de fossilt drivna bilarna. I linje med trenden att ständigt bygga säkrare bilar måste elbilarna kunna visa upp förutsägbarhet, och med detta pålitlighet och säkerhet. I detta arbetet erhölls resultat som visade på batterifunktion även vid den sänkta temperaturen, men med gränser för hur snabbt laddningöverföring kunde ske jämfört med i rumstemperatur. Bevis för bildande av skyddsfilm på anod efter 1.5 battericykler, snarlik komposition för -10°C - och rumstemperaturbatterier – men med vissa indikationer på ett tjockare bildat lager hos den senare. Därtill gjordes jämförelser med specifika lågtemperaturselektrolyter, där en skillnad i framförallt innehåll utav etylkarbonat (mindre andel vid lågtemperaturapplikationer) uppvisar stora förbättringar i kallare klimat. En sådan provblandning gjordes och uppvisade bättre prestanda vid -10°C än rumstemperaturbatterier med standardelektrolyt. Arbetet har utförts vid Institutionen för Kemi-Ångström vid Uppsala universitet.

This work developed a novel experiment in order to determine the operational effects on a Lithium-ion battery (LIB) when a test resulting in non-catastrophic damage is performed. Accepted industry standards were used as a basis to develop a nail penetration test that would puncture a cell approximately halfway through during normal cycling at a rate of 1C, then allow the cell to continue cycling to determine how its operation was affected. The cells under test continued cycling after the punctures, showing that the experiment would be able to provide useful information on the topic. The experiment was found to be successful in simulating the operation of a cell in an abusive environment, such as those seen in electric vehicles and aerospace applications.<div><br></div><div>The results of these experiments showed that a sharp increase in temperature is observed immediately after the puncture, similar to cells that underwent tests with full penetrations. The temperatures then slowly decreased during the first few cycles after the puncture as the generated heat was dissipated through convection. The experiments also showed that it is possible for a LIB under test to continue operating for a short time after being punctured. However, the capacity and useful life of the cells were greatly reduced. The initial capacity of each cell decreased by approximately 11% after the initial impact, then continued decreasing at an accelerated rate during the ensuing cycling. The lifetime of the cells was also greatly reduced, with each cell reaching its end of life within approximately 15-75 cycles after the punctures. An analysis of the incremental capacity curves of the cells indicated that accelerated aging occurred due to both a loss of active material and a loss of lithium inventory. The information gained from the experiments gives insight into the operation of cells that experience abusive environments and will be useful in designing improved control systems, as well as promoting the development of more robust testing and safety standards for different types of cells.<br></div>

Battery Electric Vehicles (BEV) are gaining market share but still must overcome several engineering challenges related to the lithium-ion battery packs powering them. The batteries must be carefully managed to optimize safety and performance. The estimation of battery states, which cannot be measured directly, is an important part of battery management and remains an active area of research since small gains in estimation accuracy can help reduce cost and increase BEV range. This thesis presents several improvements to battery state estimation using different methods. Electrochemical Impedance Spectroscopy (EIS) is receiving increased attention from researchers as a method for state estimation and diagnostics for real-time applications. Due to battery relaxation behaviour, long rest times are commonly used before performing the EIS measurement. In this work, methods were developed to significantly shorten the required rest times, and a State of Health (SoH) estimation strategy was proposed by taking advantage of the relaxation effect as measured by EIS. This method was demonstrated to have an estimation error of below 1%. At low temperatures, the accuracy of the battery model becomes poor due to the non-linear battery response to current. By using an adaptive filter called the Interacting Multiple Model (IMM) filter, the next part of this work showed how to significantly improve low temperature State of Charge (SoC) estimation. Further reduction in estimation errors was achieved by pairing the IMM with the Smooth Variable Structure Filter (SVSF), for SoC estimation errors below 2%. The work presented in this thesis also includes the application of Deep Neural Networks (DNN) for SoC estimation from EIS data. Finally, an extensive aging study was conducted and an accelerated protocol was compared to a realistic drive cycle based protocol using EIS as a characterization tool. / Thesis / Doctor of Philosophy (PhD) / Replacing conventional gasoline/diesel powered cars with battery powered vehicles is part of a solution to the climate crisis. However, the initial costs paired with range anxiety stops many from switching to electric cars. Both cost and range are related to the battery pack. To achieve the best possible range for the lowest possible cost, battery packs must be carefully controlled by sophisticated algorithms. Unfortunately, battery range or health cannot be measured directly, but must be inferred through measurable indicators. This thesis explores battery behavior under different operating conditions and develops improved methods which can be used to determine battery health and/or range. A powerful method usually used only in laboratory settings is studied and improved to make it more suitable for implementation in electric cars. In this work it is used for accurate battery health determination. Furthermore, a strategy for improving battery range determination at low temperatures is also proposed.

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Remote hybrid power systems (RHPS) serve local off-grid loads or various island grids when no grid extension is possible. They combine renewable resources, conventional generators and energy storage systems in order to balance the load at any moment, while ensuring power quality and energy security similar to large centralized grids. Modelling such a complex system is crucial for carrying out proper simulations for predicting the system’s behavior and for optimal sizing of components. The model should include an estimation of the renewable resource availability over the period of the simulation, a prediction of the load consumption and time variation, the choice of technologies, a prior dimensioning approach, an energy dispatching strategy between the components and, finally, the behavioral model of all components. This study limits its scope to the simulation of a RHPS composed of solar PV panels, a diesel generator set, and a Li-ion battery bank supplying a dynamic isolated load with a daily demand variation between 10 kW and 80 kW. Methods for building load scenarii are explained first and then, rules of thumbs for selecting the technologies and pre-sizing the components are reviewed. Commonly used dispatching strategies are described before detailing the algorithm of a Matlab behavioral model for the system’s components with an emphasis on the proper prediction of performance and aging for the Li-ion battery model. Finally, a 10-year simulation is carried out over a case study and the results are analyzed.

The fast-growing electric vehicles (EVs) market demands huge efforts from car manufacturers to develop and improve their current products’ systems. A fast charge of the battery pack is one of the challenges encountered due to the battery limitations regarding behaviour and additional degradation when exposed to such a rough situation. In addition, the outcome of a study performed on a battery does not apply to others, especially if their chemistries are different. Hence, extensive testing is required to understand the influence of design decisions on the particular energy storage device to be implemented. Due to batteries’ nonlinear behaviour that is highly dependent on external variables such as temperature, the dynamic load and aging, another defying task is the widely studied state of charge (SOC) estimation, commonly considered one of the most significant functions in a battery management system (BMS). This thesis presents an extensive battery fast charging aging test study equipped with promising current charging profiles from published literature to minimize aging. Four charging protocols are carefully designed to charge the cell from 10 to 80% SOC within fifteen minutes and have their performances discussed. A dual state estimation algorithm is modelled to estimate the SOC with the assistance of a capacity state of health (SOHcap) estimation. Finally, the dual state estimation model is validated with the fast charging aging test data. / Thesis / Master of Science in Mechanical Engineering (MSME)

While the second part of the 20th century was dominated by combustion engine powered vehicles, climate change and limited oil resources has been forcing car manufacturers and other companies in the mobility sector to switch to renewable energy sources. Electric engines supplied by Li-ion battery cells are on the forefront of this revolution in the mobility sector. A challenging but very important task hereby is the precise forecasting of the degradation of battery state-of-health and state-of-charge. Hence, there is a high demand in models that can predict the SOH and SOC and consider the specifics of a certain kind of battery cell and the usage profile of the battery. While traditional physics-based and data-driven approaches are used to monitor the SOH and SOC, they both have limitations related to computational costs or that require engineers to continually update their prediction models as new battery cells are developed and put into use in battery-powered vehicle fleets. In this dissertation, we enhance a hybrid physics-informed machine learning version of a battery SOC model to predict voltage drop during discharge. The enhanced model captures the effect of wide variation of load levels, in the form of input current, which causes large thermal stress cycles. The cell temperature build-up during a discharge cycle is used to identify temperature-sensitive model parameters. Additionally, we enhance an aging model built upon cumulative energy drawn by introducing the effect of the load level. We then map cumulative energy and load level to battery capacity with a Gaussian process model. To validate our approach, we use a battery aging dataset collected on a self-developed testbed, where we used a wide current level range to age battery packs in accelerated fashion. Prediction results show that our model can be successfully calibrated and generalizes across all applied load levels.

The energy demand is steadily increasing, and the electricity sector is undergoing a severe change in this decade. The primary drivers, such as the need to decarbonize the power industry and megatrends for more distributed and renewable systems, are resulting in revolutionary changes in our lifestyle and industry. The power grid cannot be easily or quickly be upgraded, as investment decisions, construction approvals, and payback time are the main factors to consider. Therefore, new technology, energy storage, tariff reform, and new business models are rapidly changing and challenging the conventional industry. In recent times, industrial peak shaving application has sparked an increased interest in battery energy storage system (BESS).  This work investigated BESS’s potential from peak shaving and Time-of-use (TOU) applications for a Culture-house in Skellefteå. Available literature provides the knowledge of various BESS applications, tariff systems, and how battery degradation functions. The predicted electrical load demand of the culture-house for 2019 is obtained from a consultant company Incoord. The linear optimization was implemented in MATLAB using optimproblem function to perform peak shaving and time-of-use application for the Culture-hose BESS. A cost-optimal charging/discharging strategy was derived through an optimization algorithm by analyzing the culture-house electrical demand and Skellefteå Kraft billing system. The decisional variable decides when to charge/discharge the battery for minimum battery degradation and electricity purchase charges from the grid.   Techno-economic viability is analyzed from BESS investment cost, peak-power tariff, battery lifespan, and batter aging perspective. Results indicate that the current BESS price and peak-power tariff of Skellefteå Kraft are not suitable for peak shaving. Electricity bill saving is too low to consider TOU application due to high battery degradation. However, combining peak shaving &amp; TOU does generate more profit annually due to additional savings from the electricity bill. However, including TOU also leads to higher battery degradation, making it not currently a viable application. A future scenario suggests a decrease in investment cost, resulting in a shorter payback period.  The case study also analyses the potential in the second-life battery, where they are purchased at 80 % State of Health (SoH) for peak shaving application. Second-life batteries are assumed to last until 70 % or 60 % before End of Life (EOL). The benefit-cost ratio indicates that second-life batteries are an attractive investment if batteries can perform until 60% end of life, it would be an excellent investment from an economic and sustainability perspective. Future work suggests integrating more BESS applications into the model to make BESS an economically viable project.

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