Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries

One of the most challenging tasks of modern battery management systems is the accurate state of health estimation. While physico-chemical models are accurate, they have high computational cost. Neural networks lack physical interpretability but are efficient. Physics-informed neural networks tackle...

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Veröffentlicht in:Journal of the Electrochemical Society 2023-09, Vol.170 (9), p.90524
Hauptverfasser: Hofmann, Tobias, Hamar, Jacob, Rogge, Marcel, Zoerr, Christoph, Erhard, Simon, Philipp Schmidt, Jan
Format: Artikel
Sprache:eng
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Zusammenfassung:One of the most challenging tasks of modern battery management systems is the accurate state of health estimation. While physico-chemical models are accurate, they have high computational cost. Neural networks lack physical interpretability but are efficient. Physics-informed neural networks tackle the aforementioned shortcomings by combining the efficiency of neural networks with the accuracy of physico-chemical models. A physics-informed neural network is developed and evaluated against three different datasets: A pseudo-two-dimensional Newman model generates data at various state of health points. This dataset is fused with experimental data from laboratory measurements and vehicle field data to train a neural network in which it exploits correlation from internal modeled states to the measurable state of health. The resulting physics-informed neural network performs best with the synthetic dataset and achieves a root mean squared error below 2% at estimating the state of health. The root mean squared error stays within 3% for laboratory test data, with the lowest error observed for constant current discharge samples. The physics-informed neural network outperforms several other purely data-driven methods and proves its advantage. The inclusion of physico-chemical information from simulation increases accuracy and further enables broader application ranges.
ISSN:0013-4651
1945-7111
DOI:10.1149/1945-7111/acf0ef