scholarly journals Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3126
Author(s):  
Siyu Jin ◽  
Xin Sui ◽  
Xinrong Huang ◽  
Shunli Wang ◽  
Remus Teodorescu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Essential Feature ◽  
Lithium Ion ◽  
Independent Learning ◽  
Learning Ability ◽  
Superior Performance ◽  
Estimation Methods ◽  
Useful Lifetime

Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized.

A general approach towards carbonization of plastic waste into a well-designed 3D porous carbon framework for super lithium-ion batteries

2020 ◽  
Vol 56 (64) ◽  
pp. 9142-9145 ◽  
Author(s):  
Jiakang Min ◽  
Xin Wen ◽  
Tao Tang ◽  
Xuecheng Chen ◽  
Kaifu Huo ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Porous Carbon ◽  
Lithium Ion ◽  
Superior Performance ◽  
Template Method ◽  
Carbon Sphere ◽  
Carbon Framework ◽  
Plastic Wastes ◽  
Hollow Carbon

The 3D hollow carbon sphere/porous carbon flake hybrids are facilely prepared from the carbonization of both hydrocarbon and halogen-containing plastic wastes by a general template method, which exhibits superior performance in a lithium-ion battery.

scholarly journals A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

2021 ◽  
Vol 7 ◽  
Author(s):  
Shunli Wang ◽  
Siyu Jin ◽  
Dan Deng ◽  
Carlos Fernandez
Keyword(s):  
Machine Learning ◽  
Service Life ◽  
Lithium Ion Batteries ◽  
Adaptive Filtering ◽  
Lithium Ion ◽  
The Other ◽  
Daily Lives ◽  
Remaining Service Life ◽  
Average Accuracy ◽  
Useful Lifetime

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods.

scholarly journals A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 660 ◽  
Author(s):  
Phattara Khumprom ◽  
Nita Yodo
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Learning Algorithms ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector

Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the National Aeronautics and Space Administration (NASA) Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application.

scholarly journals Effect of dynamic loads and vibrations on lithium-ion batteries

2021 ◽  
pp. 146134842110081
Author(s):  
Xia Hua ◽  
Alan Thomas
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Pack ◽  
Dynamic Loads ◽  
Electrical Performance ◽  
Mechanical Degradation ◽  
Energy Storage Devices ◽  
Battery Cell

Lithium-ion batteries are being increasingly used as the main energy storage devices in modern mobile applications, including modern spacecrafts, satellites, and electric vehicles, in which consistent and severe vibrations exist. As the lithium-ion battery market share grows, so must our understanding of the effect of mechanical vibrations and shocks on the electrical performance and mechanical properties of such batteries. Only a few recent studies investigated the effect of vibrations on the degradation and fatigue of battery cell materials as well as the effect of vibrations on the battery pack structure. This review focused on the recent progress in determining the effect of dynamic loads and vibrations on lithium-ion batteries to advance the understanding of lithium-ion battery systems. Theoretical, computational, and experimental studies conducted in both academia and industry in the past few years are reviewed herein. Although the effect of dynamic loads and random vibrations on the mechanical behavior of battery pack structures has been investigated and the correlation between vibration and the battery cell electrical performance has been determined to support the development of more robust electrical systems, it is still necessary to clarify the mechanical degradation mechanisms that affect the electrical performance and safety of battery cells.

scholarly journals Characterization of the Compressive Load on a Lithium-Ion Battery for Electric Vehicle Application

Machines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 71
Author(s):  
Seyed Saeed Madani ◽  
Erik Schaltz ◽  
Søren Knudsen Kær
Keyword(s):  
Electrochemical Impedance Spectroscopy ◽  
Impedance Spectroscopy ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Large Scale ◽  
Electrochemical Impedance ◽  
Applied Pressure ◽  
Lithium Ion ◽  
Battery Cells

Lithium-ion batteries are being implemented in different large-scale applications, including aerospace and electric vehicles. For these utilizations, it is essential to improve battery cells with a great life cycle because a battery substitute is costly. For their implementation in real applications, lithium-ion battery cells undergo extension during the course of discharging and charging. To avoid disconnection among battery pack ingredients and deformity during cycling, compacting force is exerted to battery packs in electric vehicles. This research used a mechanical design feature that can address these issues. This investigation exhibits a comprehensive description of the experimental setup that can be used for battery testing under pressure to consider lithium-ion batteries’ safety, which could be employed in electrified transportation. Besides, this investigation strives to demonstrate how exterior force affects a lithium-ion battery cell’s performance and behavior corresponding to static exterior force by monitoring the applied pressure at the dissimilar state of charge. Electrochemical impedance spectroscopy was used as the primary technique for this research. It was concluded that the profiles of the achieved spectrums from the experiments seem entirely dissimilar in comparison with the cases without external pressure. By employing electrochemical impedance spectroscopy, it was noticed that the pure ohmic resistance, which is related to ion transport resistance of the separator, could substantially result in the corresponding resistance increase.

scholarly journals Lithium ion battery recycling using high-intensity ultrasonication

2021 ◽  
Author(s):  
chunhong lei ◽  
Iain M Aldous ◽  
Jennifer Hartley ◽  
Dana Thompson ◽  
Sean Scott ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
High Intensity ◽  
Lithium Ion ◽  
Battery Recycling

Decarbonisation of energy will rely heavily, at least initially, on the use of lithium ion batteries for automotive transportation. The projected volumes of batteries necessitate the development of fast and...

scholarly journals Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 723
Author(s):  
Saurabh Saxena ◽  
Darius Roman ◽  
Valentin Robu ◽  
David Flynn ◽  
Michael Pecht
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Consumer Electronics ◽  
Stress Factors ◽  
Test Time ◽  
Capacity Fade ◽  
Maximum Acceleration ◽  
Accelerated Degradation ◽  
Qualification Testing

Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.

Carbonized polyaniline coupled molybdenum disulfide/graphene nanosheets for high performance lithium ion battery anodes

RSC Advances ◽  
2015 ◽  
Vol 5 (117) ◽  
pp. 96660-96664 ◽  
Author(s):  
Sheng Han ◽  
Yani Ai ◽  
Yanping Tang ◽  
Jianzhong Jiang ◽  
Dongqing Wu
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Molybdenum Disulfide ◽  
Anode Material ◽  
High Performance ◽  
Graphene Nanosheets ◽  
Lithium Ion ◽  
Electrochemical Performances

Carbonized polyaniline coupled molybdenum disulfide and graphene show excellent electrochemical performances as an anode material for lithium ion batteries.

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