Remaining useful life prediction of electric vehicle lithium-ion battery based on particle filter method

Omariba, Zachary Bosire; Zhang, Lijun; Sun, Dongbai

doi:10.1109/icbda.2018.8367718

Cited by 8 publications

(1 citation statement)

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“…In general, PFs suffer from two main problems: (1) particle degeneracy and (2) particle impoverishments. The latter is due to the fact that a PF has a resampling phase that can reduce particle degeneracy, and this may also result in a loss of sample particles [23]. Accordingly, other types of PFs used to solve these problems have been considered.…”

Section: Introductionmentioning

confidence: 99%

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

et al. 2021

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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. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%