Journal
JOURNAL OF ENERGY STORAGE
Volume 23, Issue -, Pages 320-328Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.est.2019.03.022
Keywords
Gaussian process regression; Lithium-ion; Battery; Degradation; Prognostics; Health
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Funding
- Continental AG
- RCUK Engineering and Physical Sciences Research Council [EP/K002252/1]
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Accurately predicting the future health of batteries is necessary to ensure reliable operation, minimise maintenance costs, and calculate the value of energy storage investments. The complex nature of degradation renders data-driven approaches a promising alternative to mechanistic modelling. Here we show that a Bayesian non-parametric approach, using Gaussian process regression, can predict capacity fade in a variety of usage scenarios, forming a generalised health model. Our results are demonstrated on the open-source NASA Randomised Battery Usage Dataset, with data of 26 cells aged under widely varying operational conditions. Using half of the cells for training, and half for validation, we can accurately predict long term capacity fade, with a best case normalised root mean square error of 4.3%, including accurate estimation of the uncertainty of the prediction.
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