Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4600
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dc.contributor.authorRanjan, Amit-
dc.contributor.authorGuha, Arijit-
dc.contributor.authorRouth, Bikky-
dc.contributor.authorPradhan, Jatin Kumar-
dc.date.accessioned2024-07-02T12:11:22Z-
dc.date.available2024-07-02T12:11:22Z-
dc.date.issued2024-06-
dc.identifier.citationIEEE International Conference on Prognostics and Health Management (ICPHM), Spokane, Washington, 17-19 June 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4600-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractNowadays, Li-ion batteries (LiBs) serves as the key power source in various applications such as electronic devices, smart grid systems, unmanned aerial vehicles (UAVs), electric vehicles (EVs), etc. Therefore, precise state-of-health (SOH) and end-of-life (EOL) prediction of LiBs are very much essential for their dependable and secure functioning, which can be achieved either by using a data-driven or a model-based approach. For model based approaches, the prerequisite is a precise battery model without which there can be a possibility of inaccuracies in the predictions, unlike data-driven approaches which are model free. This paper proposes a data-driven approach for the SOH prediction of LiBs based on both the capacity fading and internal resistance growth information. Accordingly, three different machine learning (ML) methods i.e., Gaussian process regression (GPR), Long short-term memory (LSTM), and Random forest (RF) have been implemented for the SOH predictions. Thereafter, a comparative analysis has been also carried out among the aforementioned methods in order to quantify the prediction accuracies. Based on the comparisons it has been observed that in the case of RF, the percentage relative error (RE) in the prediction is within 1.36% for capacity degradation and within 0.68% for internal resistance growth information which is the lowest among all the three methods corresponding to the 85th cycle with 50% of the data considered for training and the remaining data for testing purpose.en_US
dc.subjectLi-ion batteries (LiBs)en_US
dc.subjectState-of-Health (SOH)en_US
dc.subjectMachine learning (ML)en_US
dc.subjectGaussian process regression (GPR)en_US
dc.subjectLong short-term memory (LSTM)en_US
dc.subjectRandom forest (RF)en_US
dc.subjectCapacity fadeen_US
dc.subjectInternal resistance growthen_US
dc.titleMachine Learning Based Battery Health Prediction Using Capacity Fade and Resistance Growthen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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