The optimum condition for electric vehicles’ battery powering factors to travel distance: A model-based approach
The development of electric vehicles (EVs) and their power source systems (PSS) is a rapidly growing field of technology. However, the EV's travel distance (range between charging stations) depends on the agility of the PSS, or battery capacity system. EV driving range and battery capacity are...
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| Language: | English |
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Elsevier
2024-11-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024157507 |
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| author | MD Shouquat Hossain Audrius Senulis Laura Saltyte-Vaisiauske Mohammad Jakir Hossain Khan |
| author_facet | MD Shouquat Hossain Audrius Senulis Laura Saltyte-Vaisiauske Mohammad Jakir Hossain Khan |
| author_sort | MD Shouquat Hossain |
| collection | DOAJ |
| description | The development of electric vehicles (EVs) and their power source systems (PSS) is a rapidly growing field of technology. However, the EV's travel distance (range between charging stations) depends on the agility of the PSS, or battery capacity system. EV driving range and battery capacity are the two most significant technical challenges in commercializing EVs. This study aims to propose an integrated model that identifies the optimal energy factor orientation, enabling EVs to cover the maximum travel distance and reach the charging station for their next trip. Additionally, the artificial intelligence (AI) and statistical models were integrated and applied to predict, validate, and explain how energy factors affect the driving range of EVs. The developed models and validations revealed that maintaining precise assimilation of battery power factors can vary the EV's travel distance from 60 to 610 km. In this case, we have identified 77.5 kWh battery capacity and 14.5 kW charging capacity as the optimum power source factors. After 5.5 h of charging, various adjustments to power source factors allow for optimum battery performance. We have also proposed the central composite factorial design (CCFD) to compute the impact of energy factors on travel distance. The study used the response surface methodology (RSM) and an in-house-developed AI-based algorithm to achieve the research results. The alignment percentage between model-predicted data and real-time outputs showed an extremely high precision of over 95 % and confidence in the findings' reliability. |
| format | Article |
| id | doaj-art-48e2d3308b0a4d1585ee5ca5ffed7a5c |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-48e2d3308b0a4d1585ee5ca5ffed7a5c2024-11-15T06:13:19ZengElsevierHeliyon2405-84402024-11-011021e39719The optimum condition for electric vehicles’ battery powering factors to travel distance: A model-based approachMD Shouquat Hossain0Audrius Senulis1Laura Saltyte-Vaisiauske2Mohammad Jakir Hossain Khan3Department of Electrical and Electronic Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka, 1230, BangladeshEngineering Department, Faculty of Marine Technology and Natural Sciences, Klaipeda University, H. Manto 84, 92294, Klaipeda, LithuaniaEngineering Department, Faculty of Marine Technology and Natural Sciences, Klaipeda University, H. Manto 84, 92294, Klaipeda, LithuaniaEngineering Department, Faculty of Marine Technology and Natural Sciences, Klaipeda University, H. Manto 84, 92294, Klaipeda, Lithuania; Corresponding author.The development of electric vehicles (EVs) and their power source systems (PSS) is a rapidly growing field of technology. However, the EV's travel distance (range between charging stations) depends on the agility of the PSS, or battery capacity system. EV driving range and battery capacity are the two most significant technical challenges in commercializing EVs. This study aims to propose an integrated model that identifies the optimal energy factor orientation, enabling EVs to cover the maximum travel distance and reach the charging station for their next trip. Additionally, the artificial intelligence (AI) and statistical models were integrated and applied to predict, validate, and explain how energy factors affect the driving range of EVs. The developed models and validations revealed that maintaining precise assimilation of battery power factors can vary the EV's travel distance from 60 to 610 km. In this case, we have identified 77.5 kWh battery capacity and 14.5 kW charging capacity as the optimum power source factors. After 5.5 h of charging, various adjustments to power source factors allow for optimum battery performance. We have also proposed the central composite factorial design (CCFD) to compute the impact of energy factors on travel distance. The study used the response surface methodology (RSM) and an in-house-developed AI-based algorithm to achieve the research results. The alignment percentage between model-predicted data and real-time outputs showed an extremely high precision of over 95 % and confidence in the findings' reliability.http://www.sciencedirect.com/science/article/pii/S2405844024157507Electric vehiclesBatteriesIntegrated modelModeling and validationPower sources performance |
| spellingShingle | MD Shouquat Hossain Audrius Senulis Laura Saltyte-Vaisiauske Mohammad Jakir Hossain Khan The optimum condition for electric vehicles’ battery powering factors to travel distance: A model-based approach Heliyon Electric vehicles Batteries Integrated model Modeling and validation Power sources performance |
| title | The optimum condition for electric vehicles’ battery powering factors to travel distance: A model-based approach |
| title_full | The optimum condition for electric vehicles’ battery powering factors to travel distance: A model-based approach |
| title_fullStr | The optimum condition for electric vehicles’ battery powering factors to travel distance: A model-based approach |
| title_full_unstemmed | The optimum condition for electric vehicles’ battery powering factors to travel distance: A model-based approach |
| title_short | The optimum condition for electric vehicles’ battery powering factors to travel distance: A model-based approach |
| title_sort | optimum condition for electric vehicles battery powering factors to travel distance a model based approach |
| topic | Electric vehicles Batteries Integrated model Modeling and validation Power sources performance |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024157507 |
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