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

Ardeshiri, Reza Rouhi; Ma, Chengbin

doi:10.1002/er.6910

Cited by 51 publications

(24 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some related LSTM models used in RUL prediction can be found in the literature. [23][24][25][26] The LSTM model, developed by Hochreiter and Schmidhuber, 27 comprises three gates: an input gate, a forget gate, and an output gate. The decision whether to update the LSTM state is made by the input gate i t using the current input.…”

Section: Bidirectional Long Short-term Memory With An Attention Mecha...mentioning

confidence: 99%

See 1 more Smart Citation

A hybrid method for online cycle life prediction of lithium‐ion batteries

Wang

Amogne

Tseng

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

Many industrial applications use lithium-ion batteries, but lack of maintenance, harsh use environments, and poor charging operations accelerate their degradation. Therefore, online remaining useful lifetime (RUL) prediction is a hot research topic. The RUL estimation analysis of a battery can be based on the normalized capacity as the state of health of its cycle life. We propose a hybrid method based on a bidirectional long short-term memory model with an attention mechanism (BiLSTM-AM) model and a support vector regression (SVR) model for online cycle life prediction. Once the sensor collects temperature readings, it uses SVR to update the initial data online to obtain a multistep advance prediction of the temperature and then uses BiLSTM-AM to predict cycle life.The proposed model is verified using 12 lithium-ion phosphate/graphite cells, and the results show that the average RUL estimation error is 3.72%.

show abstract

Section: Bidirectional Long Short-term Memory With An Attention Mecha...mentioning

confidence: 99%

“…The bidirectional LSTM with attention mechanism (BiLSTM‐AM) is one of those variants. Some related LSTM models used in RUL prediction can be found in the literature 23‐26 . The LSTM model, developed by Hochreiter and Schmidhuber, 27 comprises three gates: an input gate, a forget gate, and an output gate.…”

Section: Related Workmentioning

confidence: 99%

A hybrid method for online cycle life prediction of lithium‐ion batteries

Wang

Amogne

Tseng

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

“…In order to verify the accuracy of the BP neural network prediction model, 60-80% data sets are usually used as the training set [27]. Therefore, distribute 27 sets of data, use 18 sets of data for training, and 9 sets of data for verification, which is shown as follows.…”

Section: ) Normalization and Anti-normalization Processingmentioning

confidence: 99%

Prediction of Abrasive Belt Wear Based on BP Neural Network

Cao

Zhao

et al. 2021

Machines

View full text Add to dashboard Cite

Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency.

show abstract

“…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%

“…The algorithm effectively selected a significant feature subset, which results in high accuracy of RUL prediction using charge/discharge data. Ardeshiri et al [19] performed the gated recurrent unit (GRU)-recurrent neural network (RNN) to train the extracted features for the multivariate time-series data prediction, which is 1.34 times better than the LSTM model. Additionally, Li et al [20] constructed a convolutional neural network (CNN) model.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 51 publications

References 39 publications

A hybrid method for online cycle life prediction of lithium‐ion batteries

A hybrid method for online cycle life prediction of lithium‐ion batteries

Prediction of Abrasive Belt Wear Based on BP Neural Network

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

Contact Info

Product

Resources

About