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>