Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries
نویسندگان
چکیده
One of the most challenging tasks modern battery management systems is accurate state health estimation. While physico-chemical models are accurate, they have high computational cost. Neural networks lack physical interpretability but efficient. Physics-informed neural tackle aforementioned shortcomings by combining efficiency with accuracy models. A physics-informed network developed and evaluated against three different datasets: pseudo-two-dimensional Newman model generates data at various points. This dataset fused experimental from laboratory measurements vehicle field to train a in which it exploits correlation internal modeled states measurable health. The resulting performs best synthetic achieves root mean squared error below 2% estimating stays within 3% for test data, lowest observed constant current discharge samples. outperforms several other purely data-driven methods proves its advantage. inclusion information simulation increases further enables broader application ranges.
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ژورنال
عنوان ژورنال: Journal of The Electrochemical Society
سال: 2023
ISSN: ['0013-4651', '1945-7111']
DOI: https://doi.org/10.1149/1945-7111/acf0ef