Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries

Zraibi, Brahim; Mansouri, Mohamed; Okar, Chafik

doi:10.1051/e3sconf/202129701043

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…The capacity variation for the number of discharge cycles was used as training data. Experimental results show that the model has better accuracy than others [17].…”

Section: Literature Reviewmentioning

confidence: 93%

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Andrioaia,

Gaitan,

Culea

et al. 2024

Computers

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Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems.

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“…The capacity variation for the number of discharge cycles was used as training data. Experimental results show that the model has better accuracy than others [17].…”

Section: Literature Reviewmentioning

confidence: 93%

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Andrioaia,

Gaitan,

Culea

et al. 2024

Computers

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“…Also, its robustness is better when estimating the SOC of different chemistry batteries. To achieve higher accuracy of state estimation, various intelligent algorithms based on Machine Learning (ML) and Deep Learning (DL) Artificial Intelligence (AI) models are applied to the SOC estimation and terminal voltage prediction, as those developed in [7,12,13,17,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] easily to be adapted to all types of batteries and chemistries. The neural networks (NNs) learning techniques have a wide range of applications and are suitable for all types of batteries chemistry.…”

Section: Introductionmentioning

confidence: 99%

Investigations of Different Approaches for Controlling the Speed of an Electric Motor with Nonlinear Dynamics Powered by a Li-ion Battery – Case Study

Tudoroiu¹,

Zaheeruddin²,

Tudoroiu³

et al. 2023

Electric Vehicles - Design, Modelling and Simulation

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This research investigated different nonlinear models, state estimation techniques and control strategies applied to rechargeable Li-ion batteries and electric motors powered and adapted to these batteries. The finality of these investigations was achieved by finding the most suitable design approach for the real-time implementation of the most advanced state estimators based on intelligent neural networks and neural control strategies. For performance comparison purposes, was chosen as case study an accurate and robust EKF state of charge (SOC) estimator built on a simple second-order RC equivalent circuit model (2RC ECM) accurate enough to accomplish the main goal. An intelligent nonlinear autoregressive with exogenous input (NARX) Shallow Neural Network (SSN) estimator was developed to estimate the battery SOC, predict the terminal voltage, and map the nonlinear open circuit voltage (OCV) battery characteristic curve as a function of SOC. Focusing on nonlinear modeling and linearization techniques, such as partial state feedback linearization, for “proof concept” and simulations purposes in the case study, a third order nonlinear model for a DC motor (DCM) drive was selected. It is a valuable research support suitable to analyze the performance of state feedback linearization, system singularities, internal and zero dynamics, and solving reference tracking problems.

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“…Recently, many researches have started to focus on parameters of the BMS battery to estimate each of them. Many factors, including the state of charge (SOC), SOH, RUL, the charge capacity, and the internal resistance, must be monitored to ensure that Li-ion batteries are used efficiently and safely [6] [7]. Throughout the life cycle of lithium batteries in electrified vehicles, SOH is an essential parameter for problem diagnostics and safety early warnings in addition to its capacity to precisely predict the remaining mileage of EVs [8].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network Method for Predicting the SOH and RUL of Lithium-Ion Batteries

Zraibi¹,

Mansouri²,

Loukili³

et al. 2022

Adv. sci. technol. eng. syst. j.

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The use of a battery to power an electrical or electronic system is accompanied by battery management, i.e. a set of measures intended to preserve it for preventative maintenance, thus the cost reduction. This management is generally based on two key parameters, the (remaining useful life) RUL and the (State-of-health) SOH, which relate respectively to the charge output and the aging of the Lithium-ion battery. The issue will be resolved and advances in production, battery utilization, and optimization will be made possible by accurate SOH determination and dependable RUL prediction. The CNN-BGRU-DNN hybrid strategy, which we suggest in this study, integrates Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BGRU), and Deep Neural Networks (DNN) to increase the precision of SOH and RUL estimates for Lithium-ion batteries. To that purpose, the performance of the prediction findings is assessed using the MAE, RMSE, AE, and RE as well as the NASA datasets of lithium-ion batteries for experimental validation. The verification tests' findings show that, in comparison to existing approaches in the literature, the suggested method may greatly reduce prediction error and achieve high estimation accuracy of the battery's state of health.

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Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries

Cited by 9 publications

References 29 publications

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Investigations of Different Approaches for Controlling the Speed of an Electric Motor with Nonlinear Dynamics Powered by a Li-ion Battery – Case Study

Hybrid Neural Network Method for Predicting the SOH and RUL of Lithium-Ion Batteries

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