<p>This work focuses on the collection and analysis of Lithium-ion battery operational and temperature data during nail penetration testing through two different experimental approaches. Raman spectroscopy, machine learning, and internal temperature sensors are used to collect and analyze data to further investigate the effects on cell operation during and after nail penetrations, and the feasibility of using this data to predict future performance.</p> <p><br></p> <p>The first section of this work analyzes the effects on continued operation of a small Lithium-ion prismatic cell after nail penetration. Raman spectroscopy is used to examine the effects on the anode and cathode materials of cells that are cycled for different amounts of time after a nail puncture. Incremental capacity analysis is then used to corroborate the findings from the Raman analysis. The study finds that the operational capacity and lifetime of cells is greatly reduced due to the accelerated degradation caused by loss of material, uneven current distribution, and exposure to atmosphere. This leads into the study of using the magnitude and corresponding voltage of incremental capacity peaks after nail puncture to forecast the operation of damaged cells. A Gaussian process regression is used to predict discharge capacity of different cells that experience the same type of nail puncture. The results from this study show that the method is capable of making accurate predictions of cell discharge capacity even with the higher rate of variance in operation after nail puncture, showing the method of prediction has the potential to be implemented in devices such as battery management systems.</p> <p><br></p> <p>The second section of this work proposes a method of inserting temperature sensors into commercially-available cylindrical cells to directly obtain internal temperature readings. Characterization tests are used to determine the effect on the operability of the modified cells after the sensors are inserted, and lifetime cycle testing is implemented to determine the long-term effects on cell performance. The results show the sensor insertion causes a small reduction in operational performance, and lifetime cycle testing shows the cells can operate near their optimal output for approximately 100-150 cycles. Modified cells are then used to monitor internal temperatures during nail penetration tests and how the amount of aging affects the temperature response. The results show that more aging in a cell causes higher temperatures during nail puncture, as well as a larger difference between internal and external temperatures, due mostly to the larger contribution of Joule heating caused by increased internal resistance.</p>

The increasing adoption of electric vehicles (EVs) brings forth the challenge of effectively managing the second-life and end-of-life cycles for lithium-ion batteries. Augmented Reality (AR) offers a promising solution to sustainably and efficiently dismantle these batteries. This thesis explores the development and evaluation of an AR mobile app specifically designed for guiding the dismantling process of a Volkswagen (VW) ID.4 lithium-ion EV battery. Subsequently, a detailed end-to-end development pipeline is presented, spanning from identifying the correct dismantling steps and building complete 3D reconstructions of the ID.4 battery using photogrammetry and CAD or 3D modelling, to creating an AR mobile application in Unity with the help of Vuforia allowing users to visualize the disassembly steps through an interactive guide. Tracking recognition testing results for each model indicates that simpler models exhibit a higher chance of producing false positives, while composite models have a greater minimum recognition distance compared to the faithfulto-real-life one-piece counterparts. User testing is conducted using a hybrid approach, combining a Figma prototype with video recordings to replicate the app’s behavior in a safe environment, without the physical presence of a high voltage battery. Results show positive user feedback, demonstrating the app’s usability and effectiveness in guiding the dismantling process. Furthermore, the thesis evaluates the app’s performance through the System Usability Scale (SUS) and the Technology Acceptance Model. The obtained SUS score of 80 (Grade B - Good) indicates favorable usability, while the Technology Acceptance Model provides insights into potential users’ perceptions. / Den ökande användningen av elektriska fordon (EV) frambringar utmaningen att effektivt hantera andra livscykler och slutlivscykler för litiumjonbatterier. För att hållbart och effektivt demontera dessa batterier erbjuder Augmented Reality (AR) en lovande lösning. Denna uppsats utforskar utvecklingen och utvärderingen av en AR-mobilapplikation som specifikt är utformad för att guida demonteringsprocessen av ett Volkswagen (VW) ID.4 litiumjon EVbatteri. Därefter presenteras en detaljerad genomgående utvecklingsprocess, som sträcker sig från att identifiera korrekta demonteringssteg och skapa kompletta 3D-rekonstruktioner av ID.4-batteriet med hjälp av fotogrammetri och CAD eller 3D-modellering, till att skapa en AR-mobilapplikation i Unity med hjälp av Vuforia, som tillåter användare att visualisera demonteringsstegen genom en interaktiv guide. Resultaten bättre identifieringstester för varje modell indikerar att enklare modeller har större chans att producera falska positiva resultat, medan komplexa modeller har större minsta igenkänningsavstånd jämfört med helhetsmodeller som är trogna verkligheten. Användartester genomförs med hjälp av en hybridmetod som kombinerar en Figma-prototyp med videoinspelningar för att återskapa appens beteende i en säker miljö, utan att behöva ha ett högspänningsbatteri fysiskt närvarande. Resultaten visar positivt användarfeedback och bekräftar appens användarvänlighet och effektivitet vid guidning av demonteringsprocessen. Uppsatsen utvärderar också appens prestanda genom System Usability Scale (SUS) och Technology Acceptance Model. Den erhållna SUS-poängen på 80 (Betyg B - Bra) indikerar en god användbarhet, medan Technology Acceptance Model ger insikter om potentiella användares uppfattningar.

The purpose of this diploma thesis is to describe the impact of compaction pressure on the electrochemical parameters of lithium-sulfur batteries. Theoretical part of this thesis contains briefly described terminology and general issues of batteries and their division. Every kind of battery is provided with a closer description of a specific battery type. A separate chapter is dedicated to lithium cells, mainly lithium-ion batteries. Considering various composition of lithium-ion batteries, this chapter deeply analyzes mostly used active materials of electrodes, used electrolytes and separators. Considering that the electrochemical principle of Li-S and Li-O batteries is different to Li-ion batteries, these accumulators of new generation are included in individual subhead. In the experimental part of this thesis are described methods used to measure electrochemical parameters of Li-S batteries. Next chapter contains description of preparing individual electrodes and their composition. Rest of the experimental part of my thesis is dedicated to the description of individual experiments and achieved results.

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.

Indiana University-Purdue University Indianapolis (IUPUI) / There is a strong urge for advanced diagnosis method, especially in high power battery packs and high energy density cell design applications, such as electric vehicle (EV) and hybrid electric vehicle segment, due to safety concerns. Accurate and robust diagnosis methods are required in order to optimize battery charge utilization and improve EV range. Battery faults cause significant model parameter variation affecting battery internal states and output. This work is focused on developing diagnosis method to reliably detect various faults inside lithium-ion cell using electrochemical model based observer and fuzzy logic algorithm, which is implementable in real-time. The internal states and outputs from battery plant model were compared against those from the electrochemical model based observer to generate the residuals. These residuals and states were further used in a fuzzy logic based residual evaluation algorithm in order to detect the battery faults. Simulation results show that the proposed methodology is able to detect various fault types including overcharge, over-discharge and aged battery quickly and reliably, thus providing an effective and accurate way of diagnosing li-ion battery faults.