A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

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.

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Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

Forschung im Ingenieurwesen ◽

10.1007/s10010-020-00415-0 ◽

2020 ◽

Vol 84 (4) ◽

pp. 305-314

Author(s):

Daniel Vietze ◽

Michael Hein ◽

Karsten Stahl

Keyword(s):

Machine Learning ◽

Big Data ◽

Service Life ◽

Operating Time ◽

The Other ◽

Learning Approaches ◽

State Of Health ◽

Remaining Service Life ◽

Other Hand ◽

The One

AbstractMost vehicle-gearboxes operating today are designed for a limited service-life. On the one hand, this creates significant potential for decreasing cost and mass as well as reduction of the carbon-footprint. On the other hand, this causes a rising risk of failure with increasing operating time of the machine. Especially if a failure can result in a high economic loss, this fact creates a conflict of goals. On the one hand, the machine should only be maintained or replaced when necessary and, on the other hand, the probability of a failure increases with longer operating times. Therefore, a method is desirable, making it possible to predict the remaining service-life and state of health with as little effort as possible.Centerpiece of gearboxes are the gears. A failure of these components usually causes the whole gearbox to fail. The fatigue life analysis deals with the dimensioning of gears according to the expected loads and the required service-life. Unfortunately, there is very little possibility to validate the technical design during operation, today. Hence, the goal of this paper is to present a method, enabling the prediction of the remaining-service-life and state-of-health of gears during operation. Within this method big-data and machine-learning approaches are used. The method is designed in a way, enabling an easy transfer to other machine elements and kinds of machinery.

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

Electronics ◽

10.3390/electronics10243126 ◽

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.

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Predicting the new carbon nanocages, fullerynes: a DFT study

Scientific Reports ◽

10.1038/s41598-021-82142-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mohammad Qasemnazhand ◽

Farhad Khoeini ◽

Farah Marsusi

Keyword(s):

Density Functional Theory ◽

Lithium Ion Batteries ◽

Density Functional ◽

Lithium Ion ◽

The Other ◽

Electron Affinities ◽

Chemical Formula ◽

Functional Theory ◽

Triple Bonds ◽

Structural And Electronic Properties

AbstractIn this study, based on density functional theory, we propose a new branch of pseudo-fullerenes which contain triple bonds with sp hybridization. We call these new nanostructures fullerynes, according to IUPAC. We present four samples with the chemical formula of C4nHn, and the structures derived from fulleranes. We compare the structural and electronic properties of these structures with those of two common fullerenes and fulleranes systems. The calculated electron affinities of the sampled fullerynes are negative, and much smaller than those of fullerenes, so they should be chemically more stable than fullerenes. Although fulleranes also exhibit higher chemical stability than fullerynes, but pentagon or hexagon of the fullerane structures cannot pass ions and molecules. Applications of fullerynes can be included in the storage of ions and gases at the nanoscale. On the other hand, they can also be used as cathode/anode electrodes in lithium-ion batteries.

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

Energies ◽

10.3390/en14030723 ◽

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.

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