An EV Charging Navigation Scheduling Strategy Based on Charging Power Adjustment CATARC: China Automotive Technology and Research Center

Format:

Book

Conference/Event

Author/Creator:

Zhang, Guoyu, author.

Contributor:

Huang, Yunliang

Zhang, Xiao

Conference Name:

SAE 2021 Intelligent and Connected Vehicles Symposium Part I (2021-11-04 : Chongqing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

With the continuous development of the electrical vehicles (EVs), the electric power network and transportation network are interconnected by EVs which require a coordinated operation of the two networks. In view of these coupled networks, this paper proposes a charging navigation strategy for EVs based on charging power adjustment, which can not only provide the navigation path with the shortest total operational time for EVs from the origin node to the completion of charging, but also effectively reduce load fluctuations in the electric power system. In the electric power system, an innovative optimization strategy for adjusting the EV charging power distribution is proposed, which can adjust the charging power in a timely and effective manner according to the response of EV charging. The multi-objective particle swarm optimization (MOPSO) algorithm and the improved Dijkstra algorithm are used for solving the obtained the EV charging power adjustment plan and charging paths. A case study validates the effectiveness of the charging navigation strategy and the charging power adjustment model. The average traffic speed has been increased by 5.61% using this strategy. The Dijkstra algorithm in this paper has also been improved in both computational speed and accuracy

Notes:

Vendor supplied data

Publisher Number:

2021-01-7021

Access Restriction:

Restricted for use by site license