The increasing demand for electric vehicles (EVs) has led to technological advancementsin the field of battery technology. State of charge (SOC) estimation is a vital function ofthe battery management system - the heart of EVs, and Kalman filtering is a commonmethod for SOC estimation. Due to the non uniformities in tuning and testing scenarios,quantifying performance of SOC estimation algorithms is difficult. Gathering data fordifferent operational scenarios is also cumbersome. In this thesis, SOC estimation algorithmsare developed and tested for a variety of scenarios like varying sensor noise andbias properties, varying state and parameter initializations as well as different initial celltemperatures. A validated and open-source simulation plant model is used to enable easygathering of data for different operational scenarios.The simulation results show that unscented Kalman filter performs better than extendedKalman filter in presence of hard nonlinearities and high initial uncertainties. However,both filters gave similar performance under nominal conditions implying that the choiceof estimation algorithms must depend on operational scenarios. Observability analysisalso gave valuable information to aid in selection of algorithms. The simulation plantmodel facilitated easy data collection for initial development of algorithms, which werethen tested successfully using a real dataset. Further testing using real datasets is requiredto enhance validation. / Den ¨okande efterfr°agan p°a elfordon har lett till teknologiska framsteg inom omr°adet batteriteknik.Estimering av batteriets laddningstillst°and ¨ar en essentiell funktion i batteristyrsystemet,hj¨artat i ett elfordon, och g¨ors ofta genom att till¨ampa metoden Kalmanfiltrering.P°a grund av varierande implementations och testmetodik i litteraturen ¨ar detsv°art att kvantifiera estimeringsalgoritmer. I denna avhandling utvecklas algoritmer f¨oratt estimera ett batteris laddningstillst°and. Algoritmerna testas f¨or olika former av sensorfeloch initialtillst°and, samt f¨or en rad olika temperaturer. En validerad datormodell avbatteri, sensorer och omgivning nyttjas f¨or att generera representativa data f¨or de olikaf¨orh°allandena.Simuleringsresultat visar att den s°a kallade doftl¨osa varianten av Kalmanfiltret (UKF)presterade b¨attre ¨an det utvidgade Kalmanfiltret (EKF) i fall d¨ar systembeteendet ¨ar mycketolinj¨art och d°a initialtillst°andet ¨ar os¨akert. Under normala f¨orh°allanden presterardock de b°ada algoritmerna likv¨ardigt, vilket antyder att valet av algoritm b¨or g¨oras medavseende anv¨andningsscenario. En observerbarhetsanalys av de olika filtervarianterna gavytterligare v¨ardefull information f¨or valet av algoritm. Efter utveckling av filtreringsalgoritmernai simuleringsmilj¨o utf¨ordes tester p°a faktiska m¨atdata med goda resultat. F¨or attfullst¨andig validering av algoritmerna kr¨avs emellertid mer utt¨ommande tester.

Cette thèse se concentre sur la fiabilité des batteries lithium pour des applications véhicules à faible émission en CO2. Pour cela, des méthodologies de caractérisation électriques et thermiques, des protocoles et des tests de vieillissement de batteries lithium sous différents modes (cyclage actif, calendaire simple et cyclage/calendaire) ont été mis en œuvre.Une première partie de ces travaux de thèse s’attache à la modélisation et à l’estimation des états de charge et de santé de la batterie.La deuxième partie est consacrée à l’étude du vieillissement calendaire des batteries lithium utilisant la spectroscopie d’impédance comme méthode de caractérisation. Ensuite, une méthode originale pour l’évaluation de l’état santé de la batterie est proposée. Elle est basée sur l’exploitation de l’étape de charge à tension constante lors d’une recharge complète et est donc bien adaptée à une intégration au sein d’un système de gestion de batterie. L’approche introduite est validée sur des données réelles de vieillissement allant jusqu’à deux ans de test.Enfin, une étude du phénomène de régénération de la capacité suite à un vieillissement des batteries de type combiné cyclage/calendaire est menée. Cette dernière partie constitue une ouverture pour le développement de stratégies d’usage des batteries lithium en incluant leur comportement thermique. / In this thesis, we focus on the reliability of lithium batteries used for automotive applications. For this purpose, electric and thermal characterization methodologies as well as aging tests under several modes (calendar, power cycling, calendar/power cycling) are carried out.In a first part of the work, battery modeling and battery state estimation (state-of-charge and state-of-health) are considered.Then, based on periodic characterization from electrochemical impedance spectroscopy, calendar aging is investigated. Next, we proposed an original process for precise battery state-of-health determination that exploits a full recharge and mainly constant-voltage charge step which allows easily its integration within a battery management system. Our experimental results, up to two years real-life data, confirm effectiveness of our technique.Finally, we study the capacity recovery phenomenon occurring due to combined battery aging (calendar/power cycling). This final part is almost dedicated to introduce strategies for battery use presenting at the same time a thermal behavior study.

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.

<h4>A generalized physics-based modeling framework is presented to analyze: (a) the effects of temperature on identified degradation mechanisms, (b) interfacial debonding processes, including deterministic and stochastic mechanisms, and (c) establishing model performance benchmarks of electrochemical porous electrode theory models, as a necessary stepping stone to perform valid battery degradation analyses and designs. Specifically, the effects of temperature were incorporated into a physics-based, reduced-order model and extended for a LiCoO₂ -graphite 18650 cell. Three dimensionless driving forces were identified, controlling the temperature-dependent reversible charge capacity. The identified temperature-dependent irreversible mechanisms include homogeneous SEI, at moderate to high temperatures, and the chemomechanical degradation of the cathode at low temperatures. Also, debonding of a statistically representative electrochemically active particle from the surrounding binder-electrolyte matrix in a porous electrode was modeled analytically, for the first time. The proposed framework enables to determine the space of C-Rates and electrode particle radii that suppresses or enhances debonding and is graphically summarized into performance–microstructure maps where four debonding mechanisms were identified, and condensed into power-law relations with respect to the particle radius. Finally, in order to incorporate existing or emerging degradation models into porous electrode theory (PET) implementations, a set of benchmarks were proposed to establish a common basis to assess their physical reaches, limitations, and accuracy. Three open source models: dualfoil, MPET, and LIONSIMBA were compared, exhibiting significant qualitative differences, despite showing the same macroscopic voltage response, leading the user to different conclusions regarding the battery performance and possible degradation mechanisms of the analyzed system.</h4>