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1	Degradační mechanismy u olověných akumulátorů / Degradation mechanisms of lead-acid batteries Pavlů, Michal January 2012 (has links) This work deals with problems of degradation mechanisms of lead-acid batteries. For a better understanding of the diverse effects that cause the degradation are analyzed and the physical explanation of each of the degradation mechanisms. The paper describes the measuring method by which they examined the different manifestations of degradation mechanisms. At the conclusion of works are carried out measurements in which it is possible to trace the manifestations of the various degradation mechanisms in lead battery taking place mainly on the active surface electrodes.
2	Battery Capacity Prediction Using Deep Learning : Estimating battery capacity using cycling data and deep learning methods Rojas Vazquez, Josefin January 2023 (has links) 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. Battery management system Lithium-ion batteries battery degradation mechanisms battery cycle life Electrical impedance spectroscopy Capacity estimation Incremental capacity analysis Deep learning models Equivalent circuit model Long short-term memory Feedforward neural network Conventional neural network multi-channel data Incremental capacity analysis Energy Engineering Energiteknik Energy Systems Energisystem Infrastructure Engineering Infrastrukturteknik Software Engineering Programvaruteknik Computer Sciences Datavetenskap (datalogi)

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Degradační mechanismy u olověných akumulátorů / Degradation mechanisms of lead-acid batteries

Battery Capacity Prediction Using Deep Learning : Estimating battery capacity using cycling data and deep learning methods