Abstract
Accurate prediction of the state of health of lithium-ion batteries is essential to ensure safe and reliable operation with minimum maintenance cost. However, estimating the state of health of lithium-ion batteries is a challenge due to the complex and nonlinear characteristics of battery degradation during its lifetime. To this end, data-driven techniques are becoming a more attractive promising approach to provide accurate state of health estimation of the lithium-ion battery because of their simplicity and ability to cope with the nonlinearity dynamic behaviour of the lithium-ion battery. Therefore, this paper compares the performance of three data-driven algorithms: the nonlinear autoregressive neural network, convolutional neural network, and long short-term memory network in the state of health estimation. The performance of the proposed algorithms is analysed by using 16 lithium-ion cells cycled under various operating conditions from the NASA Ames Research center. The comparison results demonstrate that the long short-term memory network outperforms the other two methods with a maximum root mean square error of 0.50% and mean absolute error of 0.36%.
Original language | English |
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Title of host publication | 56th International Universities Power Engineering Conference |
Subtitle of host publication | Powering Net Zero Emissions, UPEC 2021 - Proceedings |
Publisher | IEEE |
ISBN (Electronic) | 9781665443890 |
ISBN (Print) | 9781665443890 |
DOIs | |
Publication status | Published - 31 Aug 2021 |
Event | 56th International Universities Power Engineering Conference - Middlesbrough, United Kingdom Duration: 31 Aug 2021 → 3 Sept 2021 https://www.ieee-pes.org/meetings-and-conferences/conference-calendar/monthly-view/166-technically-cosponsored-by-pes/883-upec-2021 |
Publication series
Name | 2021 56th International Universities Power Engineering Conference (UPEC) |
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Conference
Conference | 56th International Universities Power Engineering Conference |
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Abbreviated title | UPEC |
Country/Territory | United Kingdom |
City | Middlesbrough |
Period | 31/08/21 → 3/09/21 |
Internet address |
Bibliographical note
Publisher Copyright:© 2021 IEEE.