An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control
Purpose – This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach – In this paper, an enhanced eco-driving control strategy based on an advan...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-10-01
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| Series: | Journal of Intelligent and Connected Vehicles |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JICV-07-2022-0030/full/pdf?title=an-enhanced-eco-driving-strategy-based-on-reinforcement-learning-for-connected-electric-vehicles-cooperative-velocity-and-lane-changing-control |
| _version_ | 1827364535358455808 |
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| author | Haitao Ding Wei Li Nan Xu Jianwei Zhang |
| author_facet | Haitao Ding Wei Li Nan Xu Jianwei Zhang |
| author_sort | Haitao Ding |
| collection | DOAJ |
| description | Purpose – This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach – In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance. Findings – To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states. Originality/value – In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance. |
| first_indexed | 2024-03-08T08:12:37Z |
| format | Article |
| id | doaj.art-9dfe9238222b4db8b4bea91f0b44c060 |
| institution | Directory Open Access Journal |
| issn | 2399-9802 |
| language | English |
| last_indexed | 2024-03-08T08:12:37Z |
| publishDate | 2022-10-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Intelligent and Connected Vehicles |
| spelling | doaj.art-9dfe9238222b4db8b4bea91f0b44c0602024-02-02T08:27:45ZengTsinghua University PressJournal of Intelligent and Connected Vehicles2399-98022022-10-015331633210.1108/JICV-07-2022-0030690715An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing controlHaitao Ding0Wei Li1Nan Xu2Jianwei Zhang3State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaPurpose – This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach – In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance. Findings – To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states. Originality/value – In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.https://www.emerald.com/insight/content/doi/10.1108/JICV-07-2022-0030/full/pdf?title=an-enhanced-eco-driving-strategy-based-on-reinforcement-learning-for-connected-electric-vehicles-cooperative-velocity-and-lane-changing-controlecological drivingelectric vehiclesreinforcement learning in hybrid action spacevelocity and lane-changing controlreward function |
| spellingShingle | Haitao Ding Wei Li Nan Xu Jianwei Zhang An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control Journal of Intelligent and Connected Vehicles ecological driving electric vehicles reinforcement learning in hybrid action space velocity and lane-changing control reward function |
| title | An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control |
| title_full | An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control |
| title_fullStr | An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control |
| title_full_unstemmed | An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control |
| title_short | An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control |
| title_sort | enhanced eco driving strategy based on reinforcement learning for connected electric vehicles cooperative velocity and lane changing control |
| topic | ecological driving electric vehicles reinforcement learning in hybrid action space velocity and lane-changing control reward function |
| url | https://www.emerald.com/insight/content/doi/10.1108/JICV-07-2022-0030/full/pdf?title=an-enhanced-eco-driving-strategy-based-on-reinforcement-learning-for-connected-electric-vehicles-cooperative-velocity-and-lane-changing-control |
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