State of health estimation and remaining useful life assessment of lithium-ion batteries: A comparative study

Toughzaoui, Yassine; Toosi, Safieh Bamati; Chaoui, Hicham; Louahlia-Gualous, H.; Petrone, Raffaele; Masson, Stéphane Le; Gualous, Hamid

doi:10.1016/j.est.2022.104520

Cited by 62 publications

(11 citation statements)

References 21 publications

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“…Recurrent neural network (RNN) 201 has been extensively utilized to diagnose and prognosis LiBs, as it has demonstrated superior performance. It is also applied to the recognition of driving patterns and applied to energy management optimization.…”

Section: Intelligent Analysis By Machine Learningmentioning

confidence: 99%

Towards interactional management for power batteries of electric vehicles

Xie

et al. 2023

RSC Adv.

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Section: Intelligent Analysis By Machine Learningmentioning

confidence: 99%

Towards interactional management for power batteries of electric vehicles

Xie

et al. 2023

RSC Adv.

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“…Ren et al applied a hybrid CNN-LSTM model for battery RUL prediction [11]. The method performed well using a limited amount of data in the learning phase, and the prediction errors were satisfactorily small [12]. In addition to all these methods, our research team discussed enhancements to the LSTM (long short-term memory) and Bi-LSTM (Bidirectional Long Short-Term Memory) methods.…”

Section: Introductionmentioning

confidence: 99%

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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“…This approach facilitates the prediction of RUL without necessitating an intricate comprehension of precise degradation mechanisms. Noteworthy techniques encompass artificial neural networks (ANN) [10,11], convolution neural networks (CNN) [12,13], and long-short-term memory (LSTM) networks [14,15].…”

Section: Introductionmentioning

confidence: 99%

An Indirect Prediction Method based on Feature Extraction of Lithium-ion Batteries Remaining Useful Life

Deng,

Wang

2023

FCIS

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Predicting the remaining useful life (RUL) of lithium-ion batteries is of paramount significance for the lifespan management of electric vehicles. This study delves into a feature extraction-based approach for indirectly predicting RUL in lithium-ion batteries, circumventing the limitations posed by the challenge of measuring capacity during battery operation. Initially, the health feature (HF) exhibiting strong correlations with capacity is extracted from localized charging voltage intervals, and capacity estimates are obtained through polynomial fitting. Subsequently, a dynamic long short-term memory (D-LSTM) neural network is established, iteratively incorporating prediction outcomes into training data to continuously enhance the model and ultimately accomplish RUL prediction. Experimental results conducted on batteries with varying initial states from the NASA dataset highlight the substantial accuracy and robustness of the proposed D-LSTM method. It outperforms other commonly utilized models, offering the capability for online RUL prediction.

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State of health estimation and remaining useful life assessment of lithium-ion batteries: A comparative study

Cited by 62 publications

References 21 publications

Towards interactional management for power batteries of electric vehicles

Towards interactional management for power batteries of electric vehicles

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

An Indirect Prediction Method based on Feature Extraction of Lithium-ion Batteries Remaining Useful Life

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