(Invited) Application of High Throughput Experimentation to Machine Learning Algorithms for the Development of Lithium Ion Battery Materials

2019 ◽  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Battery ◽  
High Throughput ◽  
Learning Algorithms ◽  
Lithium Ion ◽  
Machine Learning Algorithms ◽  
Battery Materials ◽  
High Throughput Experimentation

scholarly journals State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 38
Author(s):  
Venkatesan Chandran ◽  
Chandrashekhar K. Patil ◽  
Alagar Karthick ◽  
Dharmaraj Ganeshaperumal ◽  
Robbi Rahim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Learning Algorithms ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Machine Learning Algorithms ◽  
State Of Charge ◽  
Support Vector ◽  
Soc Estimation

The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

Study of machine learning algorithms to state of health estimation of iron phosphate lithium-ion battery used in fully electric vehicles

2018 ◽  
Author(s):  
Sender Rocha dos Santos ◽  
Juliana C. M. S. Aranha ◽  
Thiago Chiachio do Nascimento ◽  
Daniel Vieira ◽  
Eloy M. O. Junior ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Learning Algorithms ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Machine Learning Algorithms ◽  
State Of Health

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.

PM-02 Analysis of Lithium Ion Battery Materials Using Statistical Machine Learning and Image Processing of Electron Microscopy Data

Microscopy ◽  
2019 ◽  
Vol 68 (Supplement_1) ◽  
pp. i35-i35
Author(s):  
Hiromochi Tanaka ◽  
Tetsushi Watari ◽  
Takahiro Tsubouchi ◽  
Hisao Yamashige ◽  
Takashi Kato ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Image Processing ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Statistical Machine Learning ◽  
Battery Materials ◽  
Electron Microscopy Data ◽  
Microscopy Data

Advancements within modern machine learning methodology: Impacts and prospects for biomarker discovery

2021 ◽  
Vol 28 ◽  
Author(s):  
Dakila Ledesma ◽  
Steven Symes ◽  
Sean Richards
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Biomarker Discovery ◽  
Sequence Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Protein Sequencing ◽  
Fields Of Study ◽  
The Impact

Background : The adoption of biomarkers as part of high-throughput, complex microarray or sequencing data has necessitated the discovery and validation of these data through machine learning. Machine learning has remained a fundamental and indispensable tool due to its efficacy and efficiency in both feature extraction of relevant biomarkers as well as the classification of samples as validation of the discovered biomarkers. Objectives : This review aims to present the impact and ability of various machine learning methodologies and models to process high-throughput, high-dimensionality data found within mass spectrometry, microarray, and DNA/RNA-sequence data; data that precluded biomarker discovery prior to the use of machine learning. Methods : A vast array of literature highlighting machine learning for biomarker discovery was reviewed, resulting in the eligibility of 21 machine learning algorithms/networks and 3 combinatory architectures, spanning 17 fields of study. This literature was screened to investigate the usage and development of machine learning within the framework of biomarker discovery. Results : Out of the 93 papers collected, a total of 62 biomarker studies were further reviewed across different subfields-49 of which employed machine learning algorithms, and 13 of which employed neural network-based models. Through application, innovation, and creation of tools in biomarker-related machine learning methodologies, its use allowed for the discovery, accumulation, validation, and interpretation of biomarkers within varied data formats, sources, as well as fields of study. Conclusion: The use of machine learning methodologies for biomarker discovery is critical to the analysis of various types of data used for biomarker discovery, such as mass spectrometry, nucleotide and protein sequencing, and image (e.g. CT-scan) data. Further studies containing more standardized techniques for evaluation, and the use of cutting-edge machine learning architectures may lead to more accurate and specific results.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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