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Offer Description
Li-ion batteries play a central role in the ongoing transition from fossil fuels to renewable energy, as an efficient energy storage device. Our increasing dependence on batteries calls for an improvement with their cycle life and safety. It calls for more detailed analysis on certain parameters upon cell ageing, such as temperature, pressure, strain etc. In this note, battery diagnostics with inserted optical fibre Bragg grating (FBG) sensors has yielded exciting results as it can be used in large scale systems without affecting the cell performances. Huge developments have been reached in our laboratory in understanding different phenomenon such as SEI formation, electrolyte degradation, material’s property etc using these FBGs or tilted FBGs (TFBG). With all these analyses, the integration of optical sensing data is a key part in maximizing battery performance and sustainability, but at a prohibitively high cost. It is thus proposed to use the optical sensing expertise developed in our group to train a machine learning (ML) model that can extract the same information but with conventional electrical sensing, thus ‘bypassing’ the expensive optical instruments. The post-doc will thus engage in developing ML model to predict degradation mechanism of different Li-ion cells from coupled optical and electrical sensing.
The experiments will be carried out in cylindrical cells (18650) or “pouch” cells. Sensors will be integrated into cells to track temperature, pressure and chemical reactions. The electrochemical behavior will be monitored in parallel with the evolution of the sensors. The results obtained will be used to feed the machine learning algorithm which will be developed during the post-doctoral period.
Majority of the work will be carried out in Chimie du solide et énergie at Collège de France, Paris. Few travels to other laboratories in RS2E network is expected based on the requirement.
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Candidates should have strong background in optic together with knowledge in signal processing and treatment algorithms.
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STATUS: EXPIRED
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