Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries
Abstract Accurate prediction of the remaining useful life (RUL) in Lithium‐ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-04-01
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| Series: | IET Smart Grid |
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| Online Access: | https://doi.org/10.1049/stg2.12013 |
| _version_ | 1811318988356976640 |
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| author | Mo'ath El‐Dalahmeh Maher Al‐Greer Ma'd El‐Dalahmeh Michael Short |
| author_facet | Mo'ath El‐Dalahmeh Maher Al‐Greer Ma'd El‐Dalahmeh Michael Short |
| author_sort | Mo'ath El‐Dalahmeh |
| collection | DOAJ |
| description | Abstract Accurate prediction of the remaining useful life (RUL) in Lithium‐ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. The author introduces a new online RUL prediction for LiB using smooth particle filter (SPF)‐ based likelihood approximation method. The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimisation problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy. From the experimental datasets published by Prognostics Centre of Excellence (PCoE) of NASA, a second order degradation model was created to explore the degradation of LiB, utilising non‐linear characteristics and non‐Gaussian capacity degradation. RUL prediction was tested with various predicted starting points to assess whether the amount of data and parameters' uncertainty influenced the accuracy of the prediction. Results show that the proposed prediction approach gives improved prediction accuracy and improves the convergence rate in comparison with the particle filter (PF) and other methods such as unscented particle filter (UPF). Since the maximum error of the SPF predicting approach is relatively small, RUL prediction in the best case at the prediction starting point consisting of 80 cycles is 127 cycles. The prediction relative error was approximately 0.024, and the absolute error of the proposed algorithm is around 2 cycles, which is lower than the PF (around 16 cycles). RUL prediction is close to 108 cycles and relative error is around 0.136, while the absolute error prediction is approximately 16. |
| first_indexed | 2024-04-13T12:34:35Z |
| format | Article |
| id | doaj.art-6b49a4b513ef47c6a64391bbffc68406 |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-04-13T12:34:35Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-6b49a4b513ef47c6a64391bbffc684062022-12-22T02:46:42ZengWileyIET Smart Grid2515-29472021-04-014215116110.1049/stg2.12013Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteriesMo'ath El‐Dalahmeh0Maher Al‐Greer1Ma'd El‐Dalahmeh2Michael Short3School of Computing Engineering and Digital Technologies Teesside University Middlesbrough UKSchool of Computing Engineering and Digital Technologies Teesside University Middlesbrough UKSchool of Computing Engineering and Digital Technologies Teesside University Middlesbrough UKSchool of Computing Engineering and Digital Technologies Teesside University Middlesbrough UKAbstract Accurate prediction of the remaining useful life (RUL) in Lithium‐ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. The author introduces a new online RUL prediction for LiB using smooth particle filter (SPF)‐ based likelihood approximation method. The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimisation problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy. From the experimental datasets published by Prognostics Centre of Excellence (PCoE) of NASA, a second order degradation model was created to explore the degradation of LiB, utilising non‐linear characteristics and non‐Gaussian capacity degradation. RUL prediction was tested with various predicted starting points to assess whether the amount of data and parameters' uncertainty influenced the accuracy of the prediction. Results show that the proposed prediction approach gives improved prediction accuracy and improves the convergence rate in comparison with the particle filter (PF) and other methods such as unscented particle filter (UPF). Since the maximum error of the SPF predicting approach is relatively small, RUL prediction in the best case at the prediction starting point consisting of 80 cycles is 127 cycles. The prediction relative error was approximately 0.024, and the absolute error of the proposed algorithm is around 2 cycles, which is lower than the PF (around 16 cycles). RUL prediction is close to 108 cycles and relative error is around 0.136, while the absolute error prediction is approximately 16.https://doi.org/10.1049/stg2.12013condition monitoringestimation theoryiterative methodsparameter estimationparticle filtering (numerical methods)reliability |
| spellingShingle | Mo'ath El‐Dalahmeh Maher Al‐Greer Ma'd El‐Dalahmeh Michael Short Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries IET Smart Grid condition monitoring estimation theory iterative methods parameter estimation particle filtering (numerical methods) reliability |
| title | Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries |
| title_full | Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries |
| title_fullStr | Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries |
| title_full_unstemmed | Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries |
| title_short | Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries |
| title_sort | smooth particle filter based likelihood approximations for remaining useful life prediction of lithium ion batteries |
| topic | condition monitoring estimation theory iterative methods parameter estimation particle filtering (numerical methods) reliability |
| url | https://doi.org/10.1049/stg2.12013 |
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