|Datum||Fr 29. Januar 2021, 11:30 Uhr|
|Vorheriger Termin||Fr 8. Januar 2021|
|Nächster Termin||Fr 29. Januar 2021|
|Titel||Theory-Guided Data Science for Lithium-Ion Battery Modeling|
|Kurzfassung||Lithium-ion batteries are driving innovation in the evolution of electromobility and renewable energy. These complex, dynamic systems require reliable and accurate monitoring through Battery Management Systems to ensure the safety and longevity of battery cells. Therefore an accurate prediction of the battery voltage is essential which is currently realized by so-called Equivalent Circuit (EC) Models.
Although state-of-the-art approaches deliver good results, they are hard to train due to the high number of variables, lacking the ability to generalize, and need to make many simplifying assumptions. In contrast to theory-based models, purely data-driven approaches require large datasets and are often unable to produce physically consistent results. Theory-Guided Data Science (TGDS) aims at using scientific knowledge to improve the effectiveness of Data Science models in scientific discovery. This concept has been very successful in several domains including climate science and material research.
Our work is the first one to apply TGDS to battery systems by working together closely with domain experts. We compare the performance of different TGDS approaches against each other as well as against the two baselines using only theory-based EC-Models and black-box Machine Learning models.
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