Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction
Lithium battery applications in a variety of engineering sectors must be safe and reliable while maintaining a high level of energy efficiency. An accurate assessment of the battery's state of health (SOH) is critical in battery management systems (BMS). In recent years, it has been proved...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7421/ http://eprints.uthm.edu.my/7421/1/J14370_ce5861d65a5b4c8ef821baf243eae028.pdf |
| Summary: | Lithium battery applications in a variety of engineering sectors must be safe and reliable while maintaining a
high level of energy efficiency. An accurate assessment of the battery's state of health (SOH) is critical in battery
management systems (BMS). In recent years, it has been proved that machine learning is effective at estimating
SOH. This work proposes a novel approach of health indicator (HI) extraction based on the U-chord curvature
model, based on a complete analysis of battery aging data. In contrast to previous approaches for feature
extraction, our method splits the discharge process into various phases based on the curvature of the discharge
curve and extracts many HIs with a high correlation to battery SOH in the discharge platform stage of the
discharge curve. To demonstrate the superiority of the proposed model, several well-known machine learning
algorithms are employed to estimate SOH using extracted attributes. Long short-term memory (LSTM) and
artificial neural networks (ANNs) are examples of these techniques. Accuracy, reliability, and robustness of the
proposed model are evaluated using three publicly available data sets. According to the data, the model appears
to be capable of accurately calculating the battery's SOH, with a mean absolute error of less than 1.08% and a
root mean square error of less than 1.46% for various battery types. |
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