Optimization strategies for real-time energy management of electric vehicles based on LSTM network learning
The orderly control of electric vehicle load can improve the load characteristics of regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future, it is impossible to make a global optimal arrangem...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722022843 |
| _version_ | 1828061745101406208 |
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| author | Wenqi Zhu |
| author_facet | Wenqi Zhu |
| author_sort | Wenqi Zhu |
| collection | DOAJ |
| description | The orderly control of electric vehicle load can improve the load characteristics of regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future, it is impossible to make a global optimal arrangement for accessing the grid when electric vehicles are charging. Aiming at this problem, a real-time energy management system and optimization strategy of electric vehicle based on deep long-term and short-term memory neural network are proposed. Firstly, a three-tier electric vehicle management architecture including power grid layer, regional energy management system and charging station energy management system is constructed to manage large-scale electric vehicles in layers and regions; Then, a region station interaction strategy based on deep long-term and short-term memory neural network is proposed. The historical optimal solution solved by historical load information is used to train the learning network to guide the new real-time optimization; The proposed strategy can further reduce the charging cost and improve the peak valley characteristics of regional load on the premise of ensuring the charging demand of users. Finally, a simulation example is given to verify the effectiveness and superiority of the proposed layered architecture and management strategy. |
| first_indexed | 2024-04-10T22:19:48Z |
| format | Article |
| id | doaj.art-6abf129091bb4b5da969fa42c5d54143 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T22:19:48Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-6abf129091bb4b5da969fa42c5d541432023-01-18T04:31:57ZengElsevierEnergy Reports2352-48472022-11-01810091019Optimization strategies for real-time energy management of electric vehicles based on LSTM network learningWenqi Zhu0Henan Polytechnic Institute, Nanyang Henan, 473000, ChinaThe orderly control of electric vehicle load can improve the load characteristics of regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future, it is impossible to make a global optimal arrangement for accessing the grid when electric vehicles are charging. Aiming at this problem, a real-time energy management system and optimization strategy of electric vehicle based on deep long-term and short-term memory neural network are proposed. Firstly, a three-tier electric vehicle management architecture including power grid layer, regional energy management system and charging station energy management system is constructed to manage large-scale electric vehicles in layers and regions; Then, a region station interaction strategy based on deep long-term and short-term memory neural network is proposed. The historical optimal solution solved by historical load information is used to train the learning network to guide the new real-time optimization; The proposed strategy can further reduce the charging cost and improve the peak valley characteristics of regional load on the premise of ensuring the charging demand of users. Finally, a simulation example is given to verify the effectiveness and superiority of the proposed layered architecture and management strategy.http://www.sciencedirect.com/science/article/pii/S2352484722022843Electric vehicleLayered architectureShort and long term memory neural networkDeep learningOrdered scheduling strategy |
| spellingShingle | Wenqi Zhu Optimization strategies for real-time energy management of electric vehicles based on LSTM network learning Energy Reports Electric vehicle Layered architecture Short and long term memory neural network Deep learning Ordered scheduling strategy |
| title | Optimization strategies for real-time energy management of electric vehicles based on LSTM network learning |
| title_full | Optimization strategies for real-time energy management of electric vehicles based on LSTM network learning |
| title_fullStr | Optimization strategies for real-time energy management of electric vehicles based on LSTM network learning |
| title_full_unstemmed | Optimization strategies for real-time energy management of electric vehicles based on LSTM network learning |
| title_short | Optimization strategies for real-time energy management of electric vehicles based on LSTM network learning |
| title_sort | optimization strategies for real time energy management of electric vehicles based on lstm network learning |
| topic | Electric vehicle Layered architecture Short and long term memory neural network Deep learning Ordered scheduling strategy |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722022843 |
| work_keys_str_mv | AT wenqizhu optimizationstrategiesforrealtimeenergymanagementofelectricvehiclesbasedonlstmnetworklearning |