A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet
Abstract The rapid advancement of Artificial Intelligence (AI) has led to a profound transformation in the transportation industry, particularly in driving the shift toward carbon neutrality and electrification. AI has proven to be a key enabler in formulating innovative strategies for optimizing el...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-05953-7 |
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| _version_ | 1849238763271618560 |
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| author | Kaizhe Chen Jin Liu Wenjing Lyu Tianyuan Wang Jinxi Wen |
| author_facet | Kaizhe Chen Jin Liu Wenjing Lyu Tianyuan Wang Jinxi Wen |
| author_sort | Kaizhe Chen |
| collection | DOAJ |
| description | Abstract The rapid advancement of Artificial Intelligence (AI) has led to a profound transformation in the transportation industry, particularly in driving the shift toward carbon neutrality and electrification. AI has proven to be a key enabler in formulating innovative strategies for optimizing electric vehicle (EV) fleets, thus advancing transportation services. While extensive research has been conducted on AI’s role in transportation innovation, there remains a significant gap in empirical studies focusing on optimizing the charging behavior of operational EV fleets, particularly within ride-hailing services. The rise of ride-hailing services has revolutionized the transportation landscape, and their transition to EV fleets presents a major opportunity. The integration of AI to optimize the operations of these EV ride-hailing fleets could substantially help achieve the dual objectives of reducing charging costs and simultaneously lowering carbon emissions. Therefore, this research develops a Neural Network (NN) trained with the Adaptive Moment Estimation (Adam) algorithm, based on 2.14 million charging events. The goal is to analyze current charging behaviors and evaluate the impact of key variables on costs and emissions, providing data-driven insights for potential improvements, thus addressing a critical research gap. The novelty of this study lies in its novel combination of deep learning algorithms with large-scale real-world charging data, proposing a new method for optimizing EV ride-hailing charging behavior, and providing practical solutions for promoting electric vehicle adoption and achieving low-carbon transportation. |
| format | Article |
| id | doaj-art-4e9fda3a06c44af88d46120ec49d4f21 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4e9fda3a06c44af88d46120ec49d4f212025-08-20T04:01:25ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-05953-7A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleetKaizhe Chen0Jin Liu1Wenjing Lyu2Tianyuan Wang3Jinxi Wen4School of Education, Beijing Institute of TechnologySchool of Education, Beijing Institute of TechnologyComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology CambridgeSchool of Education, Beijing Institute of TechnologySchool of Education, Beijing Institute of TechnologyAbstract The rapid advancement of Artificial Intelligence (AI) has led to a profound transformation in the transportation industry, particularly in driving the shift toward carbon neutrality and electrification. AI has proven to be a key enabler in formulating innovative strategies for optimizing electric vehicle (EV) fleets, thus advancing transportation services. While extensive research has been conducted on AI’s role in transportation innovation, there remains a significant gap in empirical studies focusing on optimizing the charging behavior of operational EV fleets, particularly within ride-hailing services. The rise of ride-hailing services has revolutionized the transportation landscape, and their transition to EV fleets presents a major opportunity. The integration of AI to optimize the operations of these EV ride-hailing fleets could substantially help achieve the dual objectives of reducing charging costs and simultaneously lowering carbon emissions. Therefore, this research develops a Neural Network (NN) trained with the Adaptive Moment Estimation (Adam) algorithm, based on 2.14 million charging events. The goal is to analyze current charging behaviors and evaluate the impact of key variables on costs and emissions, providing data-driven insights for potential improvements, thus addressing a critical research gap. The novelty of this study lies in its novel combination of deep learning algorithms with large-scale real-world charging data, proposing a new method for optimizing EV ride-hailing charging behavior, and providing practical solutions for promoting electric vehicle adoption and achieving low-carbon transportation.https://doi.org/10.1038/s41598-025-05953-7 |
| spellingShingle | Kaizhe Chen Jin Liu Wenjing Lyu Tianyuan Wang Jinxi Wen A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet Scientific Reports |
| title | A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet |
| title_full | A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet |
| title_fullStr | A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet |
| title_full_unstemmed | A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet |
| title_short | A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet |
| title_sort | deep neural network approach for optimizing charging behavior for electric vehicle ride hailing fleet |
| url | https://doi.org/10.1038/s41598-025-05953-7 |
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