Remaining Useful Life Prediction of Lithium-Ion Battery With Adaptive Noise Estimation and Capacity Regeneration Detection

Zhang, Jiusi; Jiang, Yuchen; Li, Xiang; Luo, Hao; Yin, Shen; Kaynak, Okyay

doi:10.1109/tmech.2022.3202642

Cited by 90 publications

(32 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, 18 650 LIBs are widely used in everyday life [34], so the dataset selected for this experiment is from NASA PCOE [35], which records the aging data of 18 650 LIBs at a room temperature of 24 • C. Four cells (B5, B6, B7, and B18) were chosen for the experiment. All four batteries were charged at a constant current of 1.5 A until the voltage reached 4.2 V, then switched to constant voltage charging until the charging current was down to 20 mA.…”

Section: Data Descriptionmentioning

confidence: 99%

A hybrid remaining useful life prediction method for lithium-ion batteries based on transfer learning with CDRSN-BiGRU-AM

Li,

Zhang

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs), which are increasingly used in a wide range of applications, has been an important study. Existing techniques are difficult to strike a balance between prediction accuracy and execution time. To realize highly accurate RUL prediction in a short time, a hybrid RUL prediction method for LIBs was developed. The method first adopts a channel-wise deep residual shrinkage network (CDRSN) for adaptive extraction of input data feature, enhances important information features, and suppresses ineffective features according to the importance of feature information. Then, a bidirectional gated recurrent unit (BiGRU) was utilized to extract bidirectional temporal features of the processed data, and an attention mechanism (AM) was introduced to maximize the extraction of the important temporal mutual information in the data. Finally, a fully connected layer transfer strategy was applied to deploy the model from offline training to online prediction, which could prevent the problem of unstable prediction due to random initialization of the model and greatly improve the computational efficiency of the model. The simulation results indicated that the RMSE of the proposed method did not exceed 1.77% and the MAE did not exceed 1.44%. Both were smaller than the results in the other literature compared. Therefore, the proposed method could obtain accurate RUL prediction accuracy for LIBs online.

show abstract

Section: Data Descriptionmentioning

confidence: 99%

A hybrid remaining useful life prediction method for lithium-ion batteries based on transfer learning with CDRSN-BiGRU-AM

Li,

Zhang

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Accurate estimation of the SOC can enable the power performance of the battery to achieve its full capacity. Furthermore, the safety of the battery can be improved, and over-charging and over-discharging can be prevented [8,9].…”

Section: Introductionmentioning

confidence: 99%

Estimation of state of charge integrating spatial and temporal characteristics with transfer learning optimization

Zhang

Liu

Chuang³

et al. 2023

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

State of charge (SOC) estimation of lithium-ion battery is of vital significance for the control strategy in the battery management system (BMS). To integrate the spatial and temporal characteristics of the data, and to accomplish the transfer of knowledge, a novel convolutional neural network-bidirectional long short-term memory network based on transfer learning optimization (CNN-BiLSTM-TF) is proposed to estimate SOC. In detail, the spatial and temporal features hidden in the data are learned through CNN and BiLSTM, respectively. Furthermore, the CNN-BiLSTM network is established under one working condition and transferred to other working conditions through transfer learning, from which the SOC can be estimated online. A lithium-ion battery dataset is applied to verify the proposed SOC estimation approach. The results of case study demonstrate that the estimation performance of CNN-BiLSTM-TF is better than some existing approaches.

show abstract

“…The data-driven approaches extract the degradation information from historical aging data and predict the future degradation evolution with artificial intelligence algorithms and stochastic processes. It should be noted that the data-driven RUL prediction model follows the assumption that the training data and the test data are similar in distribution [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

Deng

Gao

Chen

et al. 2023

Entropy

View full text Add to dashboard Cite

In this paper, an adaptive remaining useful life prediction model is proposed for electric vehicle lithium batteries. Capacity degradation of the electric car lithium batteries is modeled by the multi-fractal Weibull motion. The varying degree of long-range dependence and the 1/f characteristics in the frequency domain are also analyzed. The age and state-dependent degradation model is derived, with the associated adaptive drift and diffusion coefficients. The adaptive mechanism considers the quantitative relations between the drift and diffusion coefficients. The unit-to-unit variability is considered a random variable. To facilitate the application, the convergence of the RUL prediction model is proved. Replacement of the lithium battery in the electric car is recommended according to the remaining useful life prediction results. The effectiveness of the proposed model is shown in the case study.

show abstract

Remaining Useful Life Prediction of Lithium-Ion Battery With Adaptive Noise Estimation and Capacity Regeneration Detection

Cited by 90 publications

References 38 publications

A hybrid remaining useful life prediction method for lithium-ion batteries based on transfer learning with CDRSN-BiGRU-AM

A hybrid remaining useful life prediction method for lithium-ion batteries based on transfer learning with CDRSN-BiGRU-AM

Estimation of state of charge integrating spatial and temporal characteristics with transfer learning optimization

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

Contact Info

Product

Resources

About