Modeling Lithium-Ion Batteries Using Machine Learning Algorithms for Mild-Hybrid Vehicle Applications

2021 ◽  
Author(s):  
Daniel Jerouschek ◽  
Omer Tan ◽  
Ralph Kennel ◽  
Ahmet Taskiran
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Learning Algorithms ◽  
Lithium Ion ◽  
Hybrid Vehicle ◽  
Machine Learning Algorithms ◽  
Mild Hybrid

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 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

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.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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