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The mining industry of the future has a strong focus on sustainability, which for example is reflected in the Swedish mining industry association Swemin´s goal of a mining industry free of carbon dioxide in 2035. To make this possible, a broad investment is now being made in the electrification of mining machines, which is believed to have great potential for reducing emissions. Epiroc Rock Drills AB is a driving force in the transition to battery-driven vehicles and has had electric machines in its product catalog for several years. From 2022, they have started to assemble batteries in-house that were previously assembled by the supplier. This thesis investigates the streamlining opportunities for the assembly process for the batteries ST14, MT42, ST1030 and MLE regarding lead time. First, a description and analysis of the current situation were carried out where five areas were identified to have an impact on the assembly process. This resulted in several proposed general improvements: developing a system for handling proposed improvement, more distinct communication of the quality philosophy, better resource efficiency and improvement of kitting and 5S.  In addition, three new layout proposals were developed which were analyzed and weighted to select the most suitable proposal under the current conditions. The result was a layout where ST14 and MT42 models are assembled in two mixed model assembly lines and ST1030 and MLE are assembled by fixed position. Continued research includes making data collection when the processes are more mature and to apply the data on the study´s suggested improvements. / Framtidens gruvindustri har ett stort fokus på hållbarhet vilket bl.a. speglas i den svenska branschorganisationen Svemins målsättning om en koldioxidfri gruvdrift år 2035. För att detta ska vara möjligt sker nu en bred satsning på elektrifiering av gruvmaskiner vilket bedöms ha en enorm potential för att minska utsläppen. Epiroc Rock Drills AB är drivande i omställningen till batteridrift och har haft elmaskiner i sitt sortiment ett antal år. Från och med 2022 har de även tagit över montering av tillhörande batterier som tidigare monterats av leverantör. För att klara av att möta framtidens efterfrågan undersöker detta examensarbete vilka möjligheter det finns till effektivisering av monteringsprocessen för batterierna ST14, MT42, ST1030 och MLE med avseende på ledtid. Först genomfördes en nulägesbeskrivning och nulägesanalys där fem områden identifierades ha inverkan på monteringsprocessen. Det mynnade sedan ut i ett antal generella förbättringsförslag: utvecklat system för hantering av förbättringsförslag, tydligare kommunikation av kvalitetsfilosofi, bättre resurseffektivitet samt förbättring av kittning och 5S.  Dessutom togs tre nya layoutförslag fram som analyserades och viktades för att välja ut det under förutsättningarna mest lämpade förslaget. Det resulterade i en layout där ST14 och MT42 monteras på två blandflödeslinor och ST1030 och MLE monteras genom fast position. Fortsatt arbete inkluderar att göra ny datainsamling när processerna är mer mogna och applicera det på studiens förbättringsförslag.

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Detta projekt syftar till att undersöka den termiska rusningsprocessen hos ett litiumjonbatteri med en fluorfri elektrolyt och jämföra den med en kommersiellt använd fluor-innehållande elektrolyt. Battericellerna innehöll silikon-grafit som anod och LiNi0.6Mn0.2Co0.2O2 (NMC622) som katod. Den fluorfria elektrolyten var baserad på litium bis(oxalato)borat (LiBOB) i organisk lösning med additivet vinylen karbonat(VC). Det jämfördes med en fluor-innehållande elektrolyt med LiPF6 i samma organiska lösning tillsammans med VC och fluoroetylene karbonat (FEC). De termiska stabilitetstesterna utfördes med Accelerating Rate Calorimetry (ARC) och Differentiell svepkalorimetri (DSC). Både knappceller och pouchceller har undersökts med hjälp av ARC. Trots flera försök med olika uppställning kunde den termiska rusningen inte bli detekterad för någon av celltyperna, med slutsatsen att en störremängd aktivt material behövs. Istället användes DSC för att undersöka de termiska reaktionerna hos batteri-komponenterna. Resultaten visade att anoden var mer termisk stabil med den fluorfria elektolyten, medan samma elektrolyt visade mindre termisk stabilitet på katoden. Vidare undersökningar behövs dock för bekräftelse av katoden. / This project aims to investigate the thermal runaway process of fluorine-free lithium ion battery cells and to compare this with a commercially used fluorinated electrolyte. The cells consisted of a silicon-graphite composite anode and a LiNi0.6Mn0.2Co0.2O2(NMC622) cathode. The non-fluorinated electrolyte used was based on lithiumbis(oxalato)borate (LiBOB) in organic solvents with the additive vinylene carbonate(VC). Moreover, the fluorinated electrolyte consisted of LiPF6 in the same organic solvents together with VC and fluoroethylene carbonate (FEC). The thermal stability measurements have included Accelerating Rate Calorimetry (ARC) and Differential Scanning Calorimetry (DSC). Moreover, both coin cells and pouch cells have been examined by ARC. However, thermal runaway could not be detected for either type of cells, concluding that a greater amount of active material was needed. In order to measure the thermal reactions of the battery components, DSC was used. These results concluded that the anode was more thermally stable with a non-fluorinated electrolyte. However, the thermal stability appeared to be lower for the cathode, therefore, further investigation is needed for confirmation of the cathode.

<p>Lithium-ion (Li-ion) cells are increasingly used in many applications affecting our</p> <p>daily life, such as laptops computers, cell phones, digital cameras, and other portable</p> <p>electronic devices. Lithium-ion batteries are increasingly being considered for their use in Electric Vehicles (EV), Hybrid Electric Vehicles (HEV) and Plug-in Hybrid Electric Vehicles (PHEV) due to their high energy density, slow loss of charge when not in use, and for lack of hysteresis effect. New application domains for these batteries has placed greater emphasis on their energy management, monitoring and control strategies.</p> <p>In this thesis, a comparative study between different models and state of charge (SOC) estimation strategies is performed. Battery models range from black-box representation to detailed electrochemical reaction models that consider the underlying physics. The state of charge is estimated using the Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and SOC estimation strategies are applied to experimental results from BMW Electrical and Hybrid Research and Development center and validated using a simulation model from AVL CRUISE software.</p> <p>Overall, different models and SOC estimation scenarios were studied. An average improvement of 30% in the estimation accuracy was shown by the SVSF SOC method when compared with the EKF SOC strategy. In general, the SVSF SOC estimation technique demonstrates excellent capability and a fast speed of convergence.</p> / Master of Applied Science (MASc)

The accurate and reliable estimation of the State-of-Charge (SOC) and State-of-Health (SOH) of Li-ion batteries is paramount to the safe and reliable operation of any electrified vehicle. Not only is accuracy and reliability necessary, but these estimation techniques must also be practical and intelligent since their use in real world applications can include noisy input signals, varying ambient conditions and incomplete or partial sequences of measured battery data. To that end, a novel framework, utilizing deep learning techniques, is considered whereby battery modelling and state estimation are performed in a single unified step. For SOC estimation, two different deep learning techniques are used with experimental data. These include a Recurrent Neural Network with Long Short-Term Memory (LSTM-RNN) and a Deep Feedforward Neural Network (DNN); each one possessing its own set of advantages. The LSTM-RNN achieves a Mean Absolute Error (MAE) of 0.57% over a fixed ambient temperature and a MAE of 1.61% over a dataset with ambient temperatures increasing from 10°C to 25°C. The DNN algorithm, on the other hand, achieves a MAE of 1.10% over a 25°C dataset while, at -20°C, a MAE of 2.17% is obtained. A Convolutional Neural Network (CNN), which has the advantage of shared weights, is used with randomized battery usage data to map raw battery measurements directly to an estimated SOH value. Using this strategy, average errors of below 1% are obtained when using fixed reference charge profiles. To further increase the practicality of this algorithm, the CNN is trained and validated over partial reference charge curves. SOH is estimated with a partial reference profile with the SOC ranging from 60% to 95% and achieves a MAE of 0.81%. A smaller SOC range is then used where the partial charge profile spans a SOC of 85% to 95% and a MAE of 1.60% is obtained. Finally, a fused convolutional recurrent neural network (CNN-RNN) is used to perform combined SOC and SOH estimation over constant charge profiles. This is performed by feeding the estimated SOH from the CNN into a LSTM-RNN, which, in turn, estimates SOC with a MAE of less than 0.5% over the lifetime of the battery. / Thesis / Doctor of Philosophy (PhD)

Ultra-fast charging of electric vehicles will soon be available to charge the batteries in less than 15 minutes to 80% state of charge. However, very few studies of batteries under these conditions exist. To design a battery pack with ultra-fast charging in mind, more information about batteries is needed, both electrically and thermally. In this thesis, the performance of three specific commercial lithium-ion batteries during ultra-fast charging is investigated and their thermal behaviour is simulated for use in the battery pack design process. The cells are charged at 1C to 6C current rates, or as high as 10C, and the surface temperature of each cell is measured. The loss calculated from the charging tests are used in a thermal analysis of the three batteries using finite element analysis. The batteries are modeled in a simple cooling apparatus to determine their thermal management requirements in a pack, i.e., how effectively must the heat be removed from the cells to obtain a specific temperature in a pack. Test results show that ultra-fast charging is possible with very little loss; but, it is dependent on the battery. The analysis illustrates important trade-offs between the battery type, charge rate, and the thermal management system. This thesis presents a holistic view to the study of the batteries for eventual use in the design of a battery pack. The thermal performance of the batteries is equally important as their electrical (charge) performance. It also attempts to justify the observed behaviour of the batteries by their underlying chemical behaviour. The work here can be used as a jumping-off point for further work on the ultra-fast charging of batteries or the design of a battery pack. / Thesis / Master of Applied Science (MASc) / Ultra-fast charging of electric vehicles, i.e., fully charging the vehicle in less than 15 minutes, will soon be more available. However, literature on the ultra-fast charging of the batteries used in these vehicles is limited. It is not widely known whether the batteries can effectively achieve ultra-fast charging or how the batteries behave under these conditions. Charging batteries this fast means that the battery cells will heat up. The temperature of the cell greatly impacts its longevity and safety. The thesis attempts to address these questions by studying three commercial lithium-ion batteries, selected for specific characteristics, that show potential for ultra-fast charging. The batteries are charged at different rates to ultra-fast charging levels and the charge performance at each rate is determined. The temperature of the batteries is simulated with different cooling systems to determine how effectively must heat be removed from the batteries to maintain the cells at a specific temperature.