Smart and efficient EV charging navigation scheme in vehicular edge computing networks
Abstract With the increasing number of electric fast charging stations (FCSs) deployed along roadsides of both urban roads and highways, the long-distance travel of electric vehicles (EVs) becomes possible. The EV charging navigation scheme is significant for the quality of user experience. However,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-12-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-023-00547-y |
| _version_ | 1797388045544062976 |
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| author | Haoyu Li Jihuang Chen Chao Yang Xin Chen Le Chang Jiabei Liu |
| author_facet | Haoyu Li Jihuang Chen Chao Yang Xin Chen Le Chang Jiabei Liu |
| author_sort | Haoyu Li |
| collection | DOAJ |
| description | Abstract With the increasing number of electric fast charging stations (FCSs) deployed along roadsides of both urban roads and highways, the long-distance travel of electric vehicles (EVs) becomes possible. The EV charging navigation scheme is significant for the quality of user experience. However, the variable conditions of both power grid and traffic networks make it a serious challenge. In this paper, we propose an efficient EV charging navigation scheme while considering both the electric and computation resource sharing. With the support of vehicular edge computing networks in intelligent transportation systems (ITSs), EVs perform both the flexible power load and edge computing nodes. When the traffic network in the established route starts to become congested, EVs can select to enter the nearest FCS. In addition to being supplemented by electric resources, EVs also benefit by sharing their own computing resource with FCSs. We formulate the EV charging navigation as a mixed integer programming problem, the EV moving route planning, FCS selection, and staying time in FCSs are optimized, to balance the relationships among the traveling time, traveling cost and reward. To address the influence caused by the randomness of traffic conditions and charging prices, a two-stage charging navigation algorithm combined with $$A^{*}$$ A ∗ algorithm and deep reinforcement learning (DRL) is proposed, with a novel designed reward function. Eventually, numerous experimental results show the effectiveness of the proposed schemes. |
| first_indexed | 2024-03-08T22:35:02Z |
| format | Article |
| id | doaj.art-20ab6ee38d8a493db56bb8fdf9fad91d |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-03-08T22:35:02Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-20ab6ee38d8a493db56bb8fdf9fad91d2023-12-17T12:30:02ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2023-12-0112111510.1186/s13677-023-00547-ySmart and efficient EV charging navigation scheme in vehicular edge computing networksHaoyu Li0Jihuang Chen1Chao Yang2Xin Chen3Le Chang4Jiabei Liu5Guangdong Key Laboratory of IoT Information Technology, school of Automation, Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information Technology, school of Automation, Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information Technology, school of Automation, Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information Technology, school of Automation, Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information Technology, school of Automation, Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information Technology, school of Automation, Guangdong University of TechnologyAbstract With the increasing number of electric fast charging stations (FCSs) deployed along roadsides of both urban roads and highways, the long-distance travel of electric vehicles (EVs) becomes possible. The EV charging navigation scheme is significant for the quality of user experience. However, the variable conditions of both power grid and traffic networks make it a serious challenge. In this paper, we propose an efficient EV charging navigation scheme while considering both the electric and computation resource sharing. With the support of vehicular edge computing networks in intelligent transportation systems (ITSs), EVs perform both the flexible power load and edge computing nodes. When the traffic network in the established route starts to become congested, EVs can select to enter the nearest FCS. In addition to being supplemented by electric resources, EVs also benefit by sharing their own computing resource with FCSs. We formulate the EV charging navigation as a mixed integer programming problem, the EV moving route planning, FCS selection, and staying time in FCSs are optimized, to balance the relationships among the traveling time, traveling cost and reward. To address the influence caused by the randomness of traffic conditions and charging prices, a two-stage charging navigation algorithm combined with $$A^{*}$$ A ∗ algorithm and deep reinforcement learning (DRL) is proposed, with a novel designed reward function. Eventually, numerous experimental results show the effectiveness of the proposed schemes.https://doi.org/10.1186/s13677-023-00547-yVehicular edge computing networksEV charging navigationRoute planningDeep reinforcement learning |
| spellingShingle | Haoyu Li Jihuang Chen Chao Yang Xin Chen Le Chang Jiabei Liu Smart and efficient EV charging navigation scheme in vehicular edge computing networks Journal of Cloud Computing: Advances, Systems and Applications Vehicular edge computing networks EV charging navigation Route planning Deep reinforcement learning |
| title | Smart and efficient EV charging navigation scheme in vehicular edge computing networks |
| title_full | Smart and efficient EV charging navigation scheme in vehicular edge computing networks |
| title_fullStr | Smart and efficient EV charging navigation scheme in vehicular edge computing networks |
| title_full_unstemmed | Smart and efficient EV charging navigation scheme in vehicular edge computing networks |
| title_short | Smart and efficient EV charging navigation scheme in vehicular edge computing networks |
| title_sort | smart and efficient ev charging navigation scheme in vehicular edge computing networks |
| topic | Vehicular edge computing networks EV charging navigation Route planning Deep reinforcement learning |
| url | https://doi.org/10.1186/s13677-023-00547-y |
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