Environment-friendly BEV charging strategy based on vehicle-grid collaboration

Zheheng Dong; Wujie Jin; Mina Huang; Siyi Wu

doi:10.1117/12.2641072

2 December 2022 Environment-friendly BEV charging strategy based on vehicle-grid collaboration

Zheheng Dong, Wujie Jin, Mina Huang, Siyi Wu

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 1228826 (2022) https://doi.org/10.1117/12.2641072
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

In the context of Peak Emission & Carbon Neutrality, focus on carbon dioxide emissions of BEV, make use of All-Round-STN (Traffic flow prediction network) and vehicle energy consumption model, predicted road state on predicted traffic flow rate, improve ACO as E-ACO, set energy consumption as the selection strategy, dynamic programming the path with the least energy consumption. Focus on range anxiety, combining shared road data and charging station data, build charging path selection algorithm. The development system provides optimal charging planning and intelligent travel service, which is based on the date of current power, the estimated energy consumption, the charging station layout, and the future road flow. Measuring emission reduction benefits based on public data, for carbon trading reference.

Citation Download Citation

Zheheng Dong, Wujie Jin, Mina Huang, and Siyi Wu "Environment-friendly BEV charging strategy based on vehicle-grid collaboration", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 1228826 (2 December 2022); https://doi.org/10.1117/12.2641072

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