Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism

Li, Lei; Li, Yuanjiang; Mao, Runze; Li, Li; Hua, Wenbo; Zhang, Jinglin

doi:10.1109/tte.2023.3247614

Cited by 25 publications

(6 citation statements)

References 42 publications

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“…The core of our forecasting model combines the strengths of TCN and GRU, augmented with a multi-head attention mechanism [56][57][58]. This design leverages the TCN's capability to extract local feature patterns within time series data and the GRU's proficiency in capturing long-term dependencies, addressing two critical aspects of time series forecasting.…”

Section: Discussionmentioning

confidence: 99%

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Fu,

Zhang,

Xie

2024

Electronics

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Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.

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Section: Discussionmentioning

confidence: 99%

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Fu,

Zhang,

Xie

2024

Electronics

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“…1, as the batteries age, the TIE-DVD of the batteries decreases, so it can be selected as a feasible indirect health factor. 17 Its calculation formula is…”

Section: Construction Of Indirect Health Factorsmentioning

confidence: 99%

A Remaining Useful Life Indirect Prediction Method for Lithium-Ion Batteries Based on SA-DBN

Sun,

Liu

2024

J. Electrochem. Soc.

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To solve the issue that the battery capacity cannot be directly measured in practice, a more practical indirect remaining useful life (RUL) prediction method is proposed. First, the battery state characteristic parameters are analyzed and the time interval of equal discharge voltage drop (TIE-DVD) is selected as the indirect health factor. Second, the degradation relationship model is established by using back propagation (BP) neural network. Then, the deep belief network (DBN) model is used to establish the indirect health factor prediction model. Finally, to verify the adaptability of the proposed method to different types of batteries with different aging levels, both datasets from our own laboratory and the datasets from NASA Ames Research Center are used for experimental validations. The comparative experiments demonstrate that the proposed RUL prediction method is simple, accurate, and practical.

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“…A feature extraction model that represents battery aging is developed in [15] and the results demonstrate an enhancement in predicting the RUL. In [16], a model that integrates temporal attention, NN, temporal convolutional network (TCN), and feature attention is proposed to improve the accuracy of battery RUL prediction. The outcomes indicate a 33 and 54 percent reduction in the root-mean-squared error (RMSE) of the RUL prediction on NASA and CALCE Li-ion battery datasets, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Battery Remaining Useful Life Prediction Using Machine Learning Models: A Comparative Study

Safavi,

Mohammadi Vaniar,

Bazmohammadi

et al. 2024

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Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks.

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Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism

Cited by 25 publications

References 42 publications

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

A Remaining Useful Life Indirect Prediction Method for Lithium-Ion Batteries Based on SA-DBN

Battery Remaining Useful Life Prediction Using Machine Learning Models: A Comparative Study

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