A new study on the prediction of the effects of road gradient and coolant flow on electric vehicle battery power electronics components using machine learning approach

Çolak, Andaç Batur

doi:10.1016/j.est.2023.108101

Cited by 29 publications

(2 citation statements)

References 24 publications

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“…A. B. Çolak [18] examines the impacts of road gradient and coolant flow on electric vehicle battery-powered electronic components using a machine learning approach. The study emphasizes the pivotal role of data quantity in enhancing predictive accuracy for artificial neural networks (ANNs), suggesting that adequate data are paramount for optimal performance, while acknowledging the computational resources required for training larger datasets.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation

Karthick,

Ravivarman,

Priyanka

2024

WEVJ

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Electric vehicles (EVs) are becoming increasingly popular, due to their beneficial environmental effects and low operating costs. However, one of the main challenges with EVs is their short battery life. This study presents a comprehensive approach for predicting the Remaining Useful Life (RUL) of Nickel Manganese Cobalt-Lithium Cobalt Oxide (NMC-LCO) batteries. This research utilizes a dataset derived from the Hawaii Natural Energy Institute, encompassing 14 individual batteries subjected to over 1000 cycles under controlled conditions. A multi-step methodology is adopted, starting with data collection and preprocessing, followed by feature selection and outlier elimination. Machine learning models, including XGBoost, BaggingRegressor, LightGBM, CatBoost, and ExtraTreesRegressor, are employed to develop the RUL prediction model. Feature importance analysis aids in identifying critical parameters influencing battery health and lifespan. Statistical evaluations reveal no missing or duplicate data, and outlier removal enhances model accuracy. Notably, XGBoost emerged as the most effective algorithm, providing near-perfect predictions. This research underscores the significance of RUL prediction for enhancing battery lifecycle management, particularly in applications like electric vehicles, ensuring optimal resource utilization, cost efficiency, and environmental sustainability.

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Section: Related Workmentioning

confidence: 99%

Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation

Karthick,

Ravivarman,

Priyanka

2024

WEVJ

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“…Despite the availability of various ML algorithms, their suitability for different problem types is limited due to the unique characteristics of each data set, such as data density, noise levels, and contour lines. 24 Hence, this study aims to identify the most appropriate ML model for the CO 2 methanation process by establishing and comparing models such as RF, MLP, XGB, and LGBM.…”

Section: Development and Optimization Of The ML Modelmentioning

confidence: 99%

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures

Yang,

Bao,

Rong

et al. 2024

Ind. Eng. Chem. Res.

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CO 2 methanation represents a promising technological pathway for achieving efficient carbon dioxide resource utilization and mitigation of greenhouse gas emissions. However, the development of CO 2 methanation catalysts with high activity at low temperatures (<250 °C) remains a formidable challenge. To address the time-consuming and costly nature of traditional catalyst development methods, this study proposes an interpretable machine learning (ML)-assisted reverse design framework for CO 2 methanation catalysts. This framework integrates the advantages of the ML, interpretability analysis, and multiobjective optimization methods to elucidate the intricate interplay among catalyst compositions, preparation conditions, reaction parameters, and catalyst activity. A data set containing 2777 data points is established to construct various ML models. After fine-tuning the key hyperparameters of the four models, a comprehensive comparison is conducted to evaluate their predictive performance. The light gradient boosting machine (LGBM) model demonstrates superior predictive accuracy, attributed to its minimal toot mean squared error of less than 0.31 and the highest R 2 value surpassing 0.90. An interpretable analysis is conducted to ascertain the most significant features and their impact on the outputs of the optimal LGBM model using postvalidation interpretation methods. It indicates that appropriately reducing active component content, first support content, calcination temperature, and inert gas content are favorable for the reaction. Finally, the LGBM model is coupled with the NGSA-III algorithm to maximize the CO 2 conversion ratio and CH 4 selectivity in CO 2 methanation reactions. Three Ru-and three Ni-based new CO 2 methanation catalysts have been successfully predicted and are recommended to have high activity at low temperatures. In particular, the optimized Ru−Ba/Cr 2 O 3 −SrO catalysts have a high CO 2 conversion ratio higher than 97.04% and a CH 4 selectivity higher than 72.22% at low temperatures.

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Magnetic impacts on double diffusion of a non‐Newtonian NEPCM in a grooved cavity: ANN model with ISPH simulations

Alsedias,

Aly

2024

Heat Trans

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Employing phase change materials (PCMs) offers the advantage of storing and releasing thermal energy while ensuring temperature stability. This characteristic makes PCMs valuable for reducing energy usage across various industrial applications. To explore the magnetic effects on double diffusion of a non‐Newtonian nano‐encapsulated phase change material (NEPCM) in a grooved cavity, the present study combined the incompressible smoothed particle hydrodynamics (ISPH) approach with an artificial neural network (ANN) model. The grooved shape is made up of three constructed grooves: triangular, curved, and rectangular grooves. In the cavity's walls, three segments of boundaries are considered as , , and . The ANN model correctly predicted the mean Nusselt number and Sherwood number when merged with current ISPH simulations. The study's novelty lies in exploring three distinct thermal and mass scenarios regarding double diffusion of a non‐Newtonian NEPCM within an innovative grooved domain. The relevant parameters include the fractional‐time derivative , power‐law index , Rayleigh number , Hartmann number , Soret–Dufour numbers (Sr and Du), and Lewis number Le. The obtained simulations present the significance of distinct boundary conditions in changing the velocity field, heat capacity ratio, temperature, and concentration in a grooved cavity. The fractional parameter accelerates the shift from unstable to steady condition. The increase in from 1.1 to 1.5 results in a 44.5% drop in the velocity maximum. Because of the Lorentz effect of a magnetic field, increasing from 0 to 50 reduces the maximum velocity by 20.9%.

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A new study on the prediction of the effects of road gradient and coolant flow on electric vehicle battery power electronics components using machine learning approach

Cited by 29 publications

References 24 publications

Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation

Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures

Magnetic impacts on double diffusion of a non‐Newtonian NEPCM in a grooved cavity: ANN model with ISPH simulations

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A new study on the prediction of the effects of road gradient and coolant flow on electric vehicle battery power electronics components using machine learning approach

Cited by 29 publications

References 24 publications

Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation

Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures

Magnetic impacts on double diffusion of a non‐Newtonian NEPCM in a grooved cavity: ANN model with ISPH simulations

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Product

Resources

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Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures