Sequential analysis of ageing on lithium-ion batteries
Final Report Abstract
For new fields of application such as the electrification of ships, trucks, airplanes, tractors, and construction machinery, a solid understanding of battery ageing and performance is indispensable. Only with sufficiently meaningful battery models that accurately reflect the physical, chemical, and electrical processes can we ensure that batteries meet the often enormous requirements for performance, energy density, and reliability. Design of Experiment (DoE) plays a crucial role in this context: by means of targeted test planning under various stress factors—such as current and temperature—data can be systematically collected to gain a deeper understanding of the complex ageing mechanisms. On this basis, it is possible to refine modelling approaches, create reliable lifetime predictions, and thus make the development of new battery systems more efficient. The project objectives focused on developing efficient and systematic testing strategies for large-scale ageing tests. Central to this was the development of a scalable concept for extensive test matrices that take into account a multitude of relevant stress factors, including current and temperature. A key outcome was the development of methods for test planning with more than 1000 cells, enabling both the quantification of the incremental benefit of individual measurements and better coverage of statistical variance. Furthermore, an automated tool for generating and continuously adapting an optimized test matrix based on Gaussian processes was developed. This tool leverages feedback loops from ongoing measurements to adjust the test procedure in real time, thereby maximizing the information gained. Through these methodological and technological advancements, it was possible to establish a solid foundation for data-driven and empirical models that can already be used in the short and medium term to evaluate the ageing of lithium-ion batteries. The project thus made essential contributions to closing the existing research gap and will enable even more precise lifetime predictions as well as more efficient use of available testing resources in the future.
Publications
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Demonstrating Linked Battery Data To Accelerate Knowledge Flow in Battery Science
P. Dechent, E. Barbers, S. Clark, S. Lehner, B. Planden, M. Adachi, D. A. Howey & S. Paarmann
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Short‐Term Tests, Long‐Term Predictions – Accelerating Ageing Characterisation of Lithium‐Ion Batteries. Batteries & Supercaps, 7(11).
Paarmann, Sabine; Schreiber, Markus; Chahbaz, Ahmed; Hildenbrand, Felix; Stahl, Gereon; Rogge, Marcel; Dechent, Philipp; Queisser, Oliver; Frankl, Sebastian Dominic; Morales Torricos, Pablo; Lu, Yao; Nikolov, Nikolay I.; Kateri, Maria; Sauer, Dirk Uwe; Danzer, Michael A.; Wetzel, Thomas; Endisch, Christian; Lienkamp, Markus; Jossen, Andreas & Lewerenz, Meinert
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Path signature-based life prognostics of Li-ion battery using pulse test data. Applied Energy, 378, 124820.
Ibraheem, Rasheed; Dechent, Philipp & dos Reis, Gonçalo
