Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning

Li, Yihuan; Li, Kang; Li, Xuan; Wang, Yanxia; Zhang, Li

doi:10.1016/j.apenergy.2020.116410

Cited by 166 publications

(54 citation statements)

References 39 publications

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“…It is also worth mentioning that a traditional feedforward neural network is sufficient to describe the relationship between the selected IC-based features and the battery's capacities in this work. But readers may also use more advanced recurrent neural network 39 or convolutional neural network 40 for enhanced performances.…”

Section: Methodsmentioning

confidence: 99%

Recovering large-scale battery aging dataset with machine learning

Tang

Liu

et al. 2021

Patterns

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Section: Methodsmentioning

confidence: 99%

Recovering large-scale battery aging dataset with machine learning

Tang

Liu

et al. 2021

Patterns

Self Cite

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“…Such approaches can avoid the establishment of empirical mathematical models. Moreover, the advancements in deep learning techniques have further broadened the ability in complex nonlinear data analysis [14]; thus, numerous artificial intelligence methods have been applied to the predictive field, such as long short term memory (LSTM), adaptive recurrent neural network (ARNN), Box-Cox transformation (BCT) and so on [15][16][17][18][19][20][21][22][23]. Yong et al [15] introduced the LSTM model with the Monte Carlo simulation to generate probability distribution, which improved the prediction accuracy of the RNN algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…By employing the orthogonal method to optimize the model parameters, the proposed method reduced the training time and reached 90.9% prediction accuracy. Further assimilating the concepts of transfer learning and network pruning, Li et al [21] built a compact CNN model on a comparatively small dataset, which outperformed other models in terms of accuracy and computational efficiency. Moreover, Wilbik et al [22] used a fuzzy logic method to derive linguistic summaries of time series.…”

Section: Introductionmentioning

confidence: 99%

Prediction for the Remaining Useful Life of Lithium–Ion Battery Based on RVM-GM with Dynamic Size of Moving Window

Jiang

Deng

Cao

et al. 2022

WEVJ

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Accurate prediction of the remaining useful life of a lithium–ion battery (LiB) is of paramount importance for ensuring its durable operation. To achieve more accurate prediction with limited data, this paper proposes an RVM-GM algorithm based on dynamic window size. The method combines the advantages of the relevance vector machine (RVM) algorithm and grey predictive model (GM). The RVM is applied to provide the relevance vectors of fitting function and output probability prediction, and the GM is used to obtain the trend prediction with limited data information. The algorithm is further verified by the NASA PCoE lithium–ion battery data repository. The experimental prediction results of different batteries data show that the proposed algorithm has less error while applying a dynamic window size compared with a fixed window size, while it has higher prediction accuracy than particle filter algorithm (PF) and convolutional neural network (CNN), which has verified the effectiveness of the proposed algorithm.

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“…On the other hand, the convolution neural network and recurrent neural network (RNN) were proposed to the RUL field in recent years. For example, Li et al built a novel framework using compact convolutional neural network models through the concepts of transfer learning to improve the battery health estimation accuracy 18 . Another disadvantage of these methods is that the chosen prediction model possesses more input parameters and need to have time consecutive 19 .…”

Section: Introductionmentioning

confidence: 99%

Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach

Ardeshiri

2021

Int J Energy Res

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Summary This paper proposes the gated recurrent unit (GRU)‐recurrent neural network (RNN), a deep learning approach to predict the remaining useful life (RUL) of lithium‐ion batteries (LIBs), accurately. The GRU‐RNN structure can self‐learn the network parameters utilizing adaptive gradient descent algorithms, leading to a reduced computational cost. Unlike the long short‐term memory (LSTM) model, GRU‐RNN allows time‐series dependencies to be tracked between degraded capacities without using any memory cell. This enables the method to predict non‐linear capacity degradations and build an explicitly capacity‐oriented RUL predictor. Additionally, feature selection based on the random forest technique was used to enhance the prediction precision. The analyses were conducted based on four separate cycling life testing datasets of a lithium‐ion battery. The experimental results indicate that the average percentage of root mean square error for the proposed method is about 2% which respectively is 1.34 times and 8.32 times superior to the LSTM and support vector machine methods. The outcome of this work can be used for managing the Li‐ion battery's improvement and optimization.

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Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning

Cited by 166 publications

References 39 publications

Recovering large-scale battery aging dataset with machine learning

Recovering large-scale battery aging dataset with machine learning

Prediction for the Remaining Useful Life of Lithium–Ion Battery Based on RVM-GM with Dynamic Size of Moving Window

Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach

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