Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network

Han, Yunlong; Li, Conghui; Zheng, Linfeng; Lei, Gang; Li, Li

doi:10.3390/en16176328

Cited by 14 publications

(7 citation statements)

References 41 publications

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“…Although Lasso ignores datasets with similar relationships, Elastic Net continues to reach saturation in case of multiple data. It is an attractive method due to its high predictability and easy interpretation [26]. estimators.…”

Section: Elastic Netmentioning

confidence: 99%

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Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

Oyucu,

Dümen,

Duru

et al. 2024

Symmetry

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Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model’s utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments.

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Section: Elastic Netmentioning

confidence: 99%

“…If this value is suitable for the desired data, the model provides more accurate results. In this process, the learning ability is improved by increasing the number of iterations [26].…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

Oyucu,

Dümen,

Duru

et al. 2024

Symmetry

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“…The prediction of the ith weak learner is denoted as f i (x). The final prediction of the XGBoost ensemble is determined by Equation (13).…”

Section: Extreme Gradient Boostingmentioning

confidence: 99%

“…Predicting the RUL of a test sample involves matching patterns against these established ones. Another notable approach to prognosis employs neural networks, particularly in forecasting the RUL of batteries [13]. Both similarity-based prognostication and neural networks are categorized as data-based methods.…”

Section: Introductionmentioning

confidence: 99%

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

Hussain,

Zaidi

2024

Applied Sciences

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This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.

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“…Numerous NN methods, such as the gated recurrent unit (GRU)-recurrent neural network (RNN) [17], autoencoder [18], CNN-LSTM [19], Attention-LSTM [20], transformerbased NN [21], and Bayesian-based NN [22], have been utilized for predicting the RUL of Li-ion batteries. In [23], multiple input data, such as voltage, current, and charging temperature patterns, are fed into a multi-input LSTM model as features with a single output to predict the battery RUL.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Safavi,

Mohammadi Vaniar,

Bazmohammadi

et al. 2024

Information

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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 of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network

Cited by 14 publications

References 41 publications

Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

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

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