Prognostics of Lithium-Ion Batteries Based on Capacity Regeneration Analysis and Long Short-Term Memory Network

Cui, Yuxuan; Chen, Yunxia

doi:10.1109/tim.2022.3154003

Cited by 19 publications

(3 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A lower value for RMSE and MAE signifies the superior predictive capability of the model. ( 8) (9) In Equations ( 8) and ( 9), represents the actual values of the capacity sequence, while represents the predicted values.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Electronics 2024, 13, 2501 2 of 14 Huang et al [6] identified this phenomenon by analyzing resting time. Cui et al [9] proposed using Support Vector Regression (SVR) to predict regeneration amplitude. However, local fluctuations not only involve capacity regeneration but also encompass a significant number of random fluctuations, making it difficult to fit the fluctuation sequence by only capturing the former.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

Sun,

Gu,

Liu

2024

Electronics

View full text Add to dashboard Cite

Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

Sun,

Gu,

Liu

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…3,4 And in many other fields, such as computers, aerospace, and military equipment, Li-ion batteries are also frequently used as energy supply. 5,6 The working condition of Li-ion batteries directly affects the safe and reliable operation of EVs. The battery management system (BMS) monitors the operation indices of Li-ion batteries, and the accuracy and reliability of Li-ion battery parameters are essential to EV operation.…”

mentioning

confidence: 99%

State-of-Health Prediction for Lithium-Ion Batteries based on Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Neural Network Optimized by Slime Mould Algorithm

Sun,

Zhang

2023

J. Electrochem. Soc.

View full text Add to dashboard Cite

State-of-health prediction of lithium-ion batteries has been one of the popular research subjects in recent years. Accurate state-of-health prediction has an especially significant role for battery management systems. This study combines the empirical mode decomposition and bidirectional gated recurrent unit neural network optimized by slime mould optimization algorithm to develop the state-of-health prediction model. First, to deal with the short-term capacity regeneration characteristics and the long-term degradation trend in state-of-health curve, the original battery state-of-health sequence is decomposed into some intrinsic mode functions and one residual sequence by using the empirical mode decomposition. Then, slime mould algorithm is used to automatically find the best hyperparameters of the bidirectional gated recurrent unit model. Finally, the bidirectional gated recurrent unit model is established to predict the state-of-health of lithium-ion batteries. The experimental results show that the proposed state-of-health prediction method always exhibit great accuracy both for the LiCoO2 battery datasets from the Center for Advanced Life Cycle Engineering and for the LiNCM battery datasets in our own laboratory. Furthermore, for the same type of batteries, the offline established prediction model does not need to be retrained. All these indicate that this combined model has high robustness, excellent universality, and superb practicality.

show abstract

Artificial intelligence-based data-driven prognostics in industry: A survey

El-Brawany,

Adel Ibrahim,

Elminir

et al. 2023

Computers & Industrial Engineering

View full text Add to dashboard Cite

Prognostics of Lithium-Ion Batteries Based on Capacity Regeneration Analysis and Long Short-Term Memory Network

Cited by 19 publications

References 54 publications

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

State-of-Health Prediction for Lithium-Ion Batteries based on Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Neural Network Optimized by Slime Mould Algorithm

Artificial intelligence-based data-driven prognostics in industry: A survey

Contact Info

Product

Resources

About