scholarly journals Battery optimal charging strategy based on a coupled thermoelectric model

Author(s):  
Kailong Liu ◽  
Kang Li ◽  
Zhile Yang ◽  
Cheng Zhang ◽  
Jing Deng
2017 ◽  
Vol 225 ◽  
pp. 330-344 ◽  
Author(s):  
Kailong Liu ◽  
Kang Li ◽  
Zhile Yang ◽  
Cheng Zhang ◽  
Jing Deng

2020 ◽  
Vol 53 (2) ◽  
pp. 13242-13247
Author(s):  
Wei Ji

Energy ◽  
2013 ◽  
Vol 60 ◽  
pp. 35-43 ◽  
Author(s):  
I.J. Fernández ◽  
C.F. Calvillo ◽  
A. Sánchez-Miralles ◽  
J. Boal

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3188 ◽  
Author(s):  
Jean-Michel Clairand ◽  
Javier Rodríguez-García ◽  
Carlos Álvarez-Bel

Inhabited islands depend primarily on fossil fuels for electricity generation and they also present frequently a vehicle fleet, which result in a significant environmental problem. To address this, several governments are investing in the integration of Renewable Energy Sources (RESs) and Electric Vehicles (EVs), but the combined integration of them creates challenges to the operation of these isolated grid systems. Thus, the aim of this paper is to propose an Electric Vehicle charging strategy considering high penetration of RES. The methodology proposes taxing CO2 emissions based on high pricing when the electricity is mostly generated by fossil fuels, and low pricing when there is a RES power excess. The Smart charging methodology for EV optimizes the total costs. Nine scenarios with different installed capacity of solar and wind power generation are evaluated and compared to cases of uncoordinated charging. The methodology was simulated in the Galapagos Islands, which is an archipelago of Ecuador, and recognized by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as both aWorld Heritage site and a biosphere reserve. Simulations results demonstrate that the EV aggregator could reduce costs: 7.9% for a case of 5 MW installed capacity (wind and PV each), and 7% for a case of 10 MW installed (wind and PV each). Moreover, the use of excess of RES power for EV charging will considerably reduce CO2 emissions


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 64193-64201 ◽  
Author(s):  
Min Ye ◽  
Haoran Gong ◽  
Rui Xiong ◽  
Hao Mu

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