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.
Highlights
Electric storage devices, for example in electric vehicles (EV) and grid balancing applications, are heavily reliant on Lithium‐ion Batteries (LiBs)
The authors have presented an innovative online remaining useful life (RUL) prediction of LiBs known as smooth particle filter (SPF) algorithm
Experimental datasets published by Prognostics Centre of Excellence (PCoE) of NASA, were used and a second‐order exponential degradation model to validate the effectiveness and stability of the proposed method was developed
Summary
Electric storage devices, for example in electric vehicles (EV) and grid balancing applications, are heavily reliant on Lithium‐ion Batteries (LiBs). The authors in [10] proposed a framework aimed at estimating battery capacity based on multi‐channel ML methods using an FNN, convolutional neural network (CNN) and long short‐term memory (LSTM), to improve prediction accuracy based on the diversity of possible data from current, voltage and temperature In all these techniques, the training needs to be extensive, inclusive, unbiased, and good quality. The proposed SPF algorithm improves the accuracy of RUL prediction by choosing the proposal distribution and the resampling weights, depending on certain current parameter estimates, overcoming the problem of particle impoverishment and uncertainty in the degradation model parameters. When using the PF algorithm to estimate maximum probability (likelihood) parameters in the non‐linear state‐space model, the PF removes the light weights and copies the heavy weights in a resample phase, which results in a loss of diversity in the particle distribution [26]. Function particle filter ðθk−1Þ x n 0 particles are first taken from the initial distribution p(x0)
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