A form of laboratory tests are performed to determine battery degradation due to charging and discharging of batteries (cycling). This is done as part of quality assurance in battery production since a certain amount of degradation corresponds to the end of the battery lifetime. Currently, this requires a significant amount of cycling. Thus, if it’s possible to decrease the number of cycles required, the time and costs for battery degradation testing can be reduced. The aim of this thesis is therefore to create a model for prediction of battery lifetime while using early cycle data. Further, to assist planning regarding scale of cycle testing this study aims to examine the impact of implementing such a prediction model in production. To examine which data driven model that should be used to predict the battery lifetime at the company, extensive feature engineering is performed where measurements from specific cycles are used, inspired by the previous work of Severson et al. (2019) and Fei et al. (2021). Two models are then examined: Linear Regression with Elastic net and Support Vector Regression. To investigate the extent to which an implementation of such a model can affect battery testing capacity, two scenarios are compared. The first scenario is that of the current cycle testing at the company and the second scenario involves implementing a prediction model. The comparison then examines the time required for battery testing and the number of machines to cycle the batteries (cyclers). Based on the results obtained, the data driven model that should be implemented is a Support Vector Regression model with features relating to different battery cycling phases or measurements, such as charge process, temperature and capacity. It can also be shown that if a battery lifetime prediction model is implemented, it can reduce the time and number of cyclers required for testing with approximately 93 %, compared to traditional testing.

No description available.

Continued emission of green house gases has energized research activity worldwide to develop efficient ways to harness renewal energy. The availability of large scale energy storage technologies is essential to make renewal energy a reliable source of energy. Redox flow batteries show potential in this direction. These batteries typically need expensive membranes which need replacement be-cause of fouling. The recently proposed soluble lead redox flow battery (SLRFB), in which lead ions deposit on electrodes in charge cycle and dissolve back in discharge cycle, can potentially cut down the cost of energy storage by eliminating membrane. A number of challenges need to be overcome though. Low cycleability, residue formation, and low efficiencies are foremost among these, all of which require an understanding of the underlying mechanisms. A model of laminar flow-through SLRFB is first developed to understand buildup of residue on electrodes with continued cycling. The model accounts for spatially and temporally growing concentration boundary layers on electrodes in a self consistent manner by permitting local deposition/dissolution rates to be controlled by local ion transport and reaction conditions. The model suggests controlling role for charge transfer reaction on electrodes (anode in particular) and movement of ions in the bulk and concentration boundary layers. The non-uniform current density on electrodes emerges as key to formation of bare patches, steep decrease in voltage marking the end of discharge cycle, and residue buildup with continuing cycles. The model captures the experimental observations very well, and points to improved operational efficiency and decreased residue build up with cylindrical electrodes and alternating flow direction of recirculation. The underlying mechanism for more than an order of magnitude increase in cycle life of a beaker cell battery with increase in stirrer speed is unraveled next. Our experiments show that charging with and without stirring occurs identically, which brings up the hitherto unknown but quite strong role of natural convection in SLRFB. The role of stirring is determined to be dislodgement/disintegration of residue building up on electrodes. The depletion of active material from electrolyte due to residue formation is offset by “internal regeneration mechanism”, unraveled in the present work. When the rate of residue formation, rate of dislodging/disintegration from electrode, and rate of regeneration of active material in bulk of the electrolyte becomes equal, perpetual operation of SLRFB is expected. The identification of strong role of free convection in battery is put to use to demonstrate a battery that requires stirring/mixing only intermittently, during open circuit stages between charge and discharge cycles when no current is drawn. Inspired by our experimental finding that the measured currents for apparently diffusion limited situations (no external flow) are far larger than the maxi-mum possible theoretical value, the earlier model is modified to account for natural convection driven by concentration gradient of lead ions in electrolyte. The model reveals the presence of strong natural convection in battery. The induced flow in the vicinity of the electrodes enhances mass transport rates substantially, to the extent that even in the absence of external flow, normal charge/discharge of battery is predicted. The model predicted electrochemical characteristics are verified quantitatively through voltage-time measurements. The formation of flow circulation loops driven by electrode processes is validated qualitatively through PIV measurements. Natural convection is predicted to play a significant role in the presence of external flow as well. The hitherto unexplained finding in the literature on insensitivity of charge-discharge characteristics to electrolyte flow rate is captured by the model when mixed mode of convection is invoked. Flow reversal and wavy flow are predicted when natural convection and forced convection act in opposite directions in the battery. The effect of the presence of non-conducting material (PbO on anode) on the performance of SLRFB is studied using a simplified approach in the model. The study reveals the presence of charge coup de fouet phenomenon in charge cycle. The phenomenon as well as the predicted effect of depth of discharge on the magnitude of charge coup de fouet are confirmed experimentally.

This master’s thesis deals with lead acid battery in hybrid electric vehicles which operating at mode of partial state of charge (PSoC). In this mode, there is often a negative electrode degradation mostly due to irreversible mechanism of suphation. The idea of this work is detect ideal value of mechanical pressure exerted on active mass of negative electrode in combination with additives to prevent mechanism of sulphation and lead to increased cycle life of lead acid battery.

Continued emission of green house gases has energized research activity worldwide to develop efficient ways to harness renewal energy. The availability of large scale energy storage technologies is essential to make renewal energy a reliable source of energy. Redox flow batteries show potential in this direction. These batteries typically need expensive membranes which need replacement be-cause of fouling. The recently proposed soluble lead redox flow battery (SLRFB), in which lead ions deposit on electrodes in charge cycle and dissolve back in discharge cycle, can potentially cut down the cost of energy storage by eliminating membrane. A number of challenges need to be overcome though. Low cycleability, residue formation, and low efficiencies are foremost among these, all of which require an understanding of the underlying mechanisms. A model of laminar flow-through SLRFB is first developed to understand buildup of residue on electrodes with continued cycling. The model accounts for spatially and temporally growing concentration boundary layers on electrodes in a self consistent manner by permitting local deposition/dissolution rates to be controlled by local ion transport and reaction conditions. The model suggests controlling role for charge transfer reaction on electrodes (anode in particular) and movement of ions in the bulk and concentration boundary layers. The non-uniform current density on electrodes emerges as key to formation of bare patches, steep decrease in voltage marking the end of discharge cycle, and residue buildup with continuing cycles. The model captures the experimental observations very well, and points to improved operational efficiency and decreased residue build up with cylindrical electrodes and alternating flow direction of recirculation. The underlying mechanism for more than an order of magnitude increase in cycle life of a beaker cell battery with increase in stirrer speed is unraveled next. Our experiments show that charging with and without stirring occurs identically, which brings up the hitherto unknown but quite strong role of natural convection in SLRFB. The role of stirring is determined to be dislodgement/disintegration of residue building up on electrodes. The depletion of active material from electrolyte due to residue formation is offset by “internal regeneration mechanism”, unraveled in the present work. When the rate of residue formation, rate of dislodging/disintegration from electrode, and rate of regeneration of active material in bulk of the electrolyte becomes equal, perpetual operation of SLRFB is expected. The identification of strong role of free convection in battery is put to use to demonstrate a battery that requires stirring/mixing only intermittently, during open circuit stages between charge and discharge cycles when no current is drawn. Inspired by our experimental finding that the measured currents for apparently diffusion limited situations (no external flow) are far larger than the maxi-mum possible theoretical value, the earlier model is modified to account for natural convection driven by concentration gradient of lead ions in electrolyte. The model reveals the presence of strong natural convection in battery. The induced flow in the vicinity of the electrodes enhances mass transport rates substantially, to the extent that even in the absence of external flow, normal charge/discharge of battery is predicted. The model predicted electrochemical characteristics are verified quantitatively through voltage-time measurements. The formation of flow circulation loops driven by electrode processes is validated qualitatively through PIV measurements. Natural convection is predicted to play a significant role in the presence of external flow as well. The hitherto unexplained finding in the literature on insensitivity of charge-discharge characteristics to electrolyte flow rate is captured by the model when mixed mode of convection is invoked. Flow reversal and wavy flow are predicted when natural convection and forced convection act in opposite directions in the battery. The effect of the presence of non-conducting material (PbO on anode) on the performance of SLRFB is studied using a simplified approach in the model. The study reveals the presence of charge coup de fouet phenomenon in charge cycle. The phenomenon as well as the predicted effect of depth of discharge on the magnitude of charge coup de fouet are confirmed experimentally.

No description available.

The growing urgency of climate change has led to growth in the electrification technology field, where batteries have emerged as an essential role in the renewable energy transition, supporting the implementation of environmentally friendly technologies such as smart grids, energy storage systems, and electric vehicles. Battery cell degradation is a common occurrence indicating battery usage. Optimizing lithium-ion battery degradation during operation benefits the prediction of future degradation, minimizing the degradation mechanisms that result in power fade and capacity fade. This degree project aims to investigate battery degradation prediction based on capacity using deep learning methods. Through analysis of battery degradation and health prediction for lithium-ion cells using non-destructive techniques. Such as electrochemical impedance spectroscopy obtaining ECM and three different deep learning models using multi-channel data. Additionally, the AI models were designed and developed using multi-channel data and evaluated performance within MATLAB. The results reveal an increased resistance from EIS measurements as an indicator of ongoing battery aging processes such as loss o active materials, solid-electrolyte interphase thickening, and lithium plating. The AI models demonstrate accurate capacity estimation, with the LSTM model revealing exceptional performance based on the model evaluation with RMSE. These findings highlight the importance of carefully managing battery charging processes and considering factors contributing to degradation. Understanding degradation mechanisms enables the development of strategies to mitigate aging processes and extend battery lifespan, ultimately leading to improved performance.

The rate of degradation of a lithium-ion battery depends on its use i.e. how it is charged and discharged. Physics-based models are used to represent the processes inside a cell as well as the degradation mechanisms. This thesis aimed to compare how the battery lifetime is affected when charging with different charging protocols using different battery models and degradation mechanisms. The investigated models are the Single Particle Model (SPM), the Single Particle Model with electrolyte (SPMe), and the Doyle-Fuller Newman model (DFN). The degradation mechanisms are solid electrolyte interphase (SEI), and lithium plating (LP). The used charging protocols are constant-current constant voltage(CCCV), positive pulsed current (PPC), and constant current (CC). Pulsed charging was included to investigate if the battery lifetime can be improved as in an experiment by Huang where pulsed charging increased the battery lifetime by 60%. To perform the simulations using the physics-based models, PyBaMM (PythonBattery Mathematical Modeling) was used. The simulations were performed for a lithium cobalt oxide (LCO) cell. Two types of SEI were implemented, solvent-diffusion limited and reaction limited. For the LP only irreversible LP was used.1200 cycles were simulated. Comparing the PPC and CC protocols, there were no significant changes between the degradation mechanisms for the different protocols. The results were the same for all the models, except for the results of the internal resistance. The conclusion is that for the PPC and CC protocols, the cell degrades the same although the PPC protocol used twice the C-rate. The PPC charging did not increase the battery lifetime. For the CCCV and CC protocols, there were some bigger differences between the protocols, but between the different models, there weren’t any significant differences. The CCCV degrades the cell faster for all degradation mechanisms and all models. Simulating one degradation submodel at a time resulted in a very small capacity fade for some submodels. Therefore, for future work, it is suggested to use several degradation submodels at the same time but also to try other degradation mechanisms or try PPC protocols with different frequencies and duty cycles. / Hur snabbt litiumjonbatterier degraderas beror på hur de används, laddas och laddas ur. Fysikbaserde modeller används för att representera processerna inuti cellen och även degraderingsmekanismerna. Denna studie har genomförts för att undersöka hur batteriets livslängd påverkas av olika laddningsprotokoll genom att använda olika batterimodeller och degraderingsmekanismer. Modellerna som användes är Singel-partikelmodellen (SPM), Singel-partikelmodellen med elektrolyt (SPMe) och Doyle-Fuller Newman-modellen (DFN). Degraderingsmekanismerna är fast elektrolytinterfas (SEI) och litiumplätering (LP). Laddningsprotokollen som användes är konstant ström konstant spänning (CCCV), positiv pulserande ström (PPC) och konstant ström konstant (CC). Protokollet för pulsad laddning inkluderades för att undersöka om batteriets livslängd kan förbättras som i ett experiment av Huang, där pulsad laddning ökade batteriets livslängdmed 60%. För att utföra simuleringar med fysikbaserade modeller användes PyBaMM(Pyhton Battery Mathematical Modeling). Simuleringarna utfördes för en lithiumkobaltoxid-cell (LCO). Två typer av SEI implementerades, lösningsmedelsdiffusion-begränsad och reaktions-begränsad SEI. För LP användes endast irreversibel LP.1200 cykler simulerades. Jämförande PPC- och CC-protokollen fanns det inga signifikanta förändringar mellan degraderingsmekanismerna för de olika protokollen. Resultaten vardesamma för alla modellerna, förutom resultaten av den interna resistansen. Slutsatsen är att för både PPC- och CC-protokollen så degraderades cellen på samma sätt, trots att PPC-protokollet använde dubbelt så hög C-faktor. PPC-protokollet ökade inte batteriets livslängd. För CCCV- och CC-protokollen fanns det några större skillnader mellan protokollen, men mellan de olika modellerna fanns det inga signifikanta skillnader. CCCV-protokollet försämrade cellen snabbare för alla degraderingsmekanismer och alla modeller. Att simulera en degraderingsmodell i taget resulterade i mycket små kapacitetsförluster. Därmed föreslås det att i framtida arbete använda flera degraderingsmodeller samtidigt men även testa andra degraderingsmekanismer eller PPC-protokoll med olika frekvenser och arbetscykler