An intelligent charging navigation scheme for electric vehicles using a cloud computing platform

ABSTRACT

Due to reasons of short range, long charging time, and insufficient information exchange between charging infrastructures, more attention needs to be paid to the charging problems of EVs. This work mainly focuses on designing an intelligent charging navigation method for EVs, building the logic framework, and internal structure framework. Then, battery SOC and energy consumption of EVs were predicted for the range, and queueing theory was used to calculate the charging queueing time. Furthermore, the historical trend method is used to predict road travel time. Finally, combined with the above data, an EV charging navigation model based on the shortest time was established and related algorithms were written to solve the model by using Dijkstra algorithm and an example is given to verify the feasibility of the intelligent charging navigation for EVs. About 24 minutes’ driving time and a total of 16.63 minutes were saved for the owner from charging decision to charging completion on a 50 km drive.

KEYWORDS:

• Electric vehicle
• charging navigation
• routing
• cloud computing
• Dijkstra algorithm

Nomenclature

$SO{C}_t$	=	The charged state at time t.
$SO{C}_{t_0}$	=	The charged state obtained at time t0 according to the open circuit voltage.
$SO{C}_{t_0}{ }_{\hspace{0.17em}}^{\prime }$	=	The state of charge at the last time the battery stopped charging and discharging.
${\int }^I\left(t\right)dt$	=	The change in SOC calculated by the ampere time method.
$K_L$/$K_T$	=	The correction coefficient for $SO{C}_{t_0}$ to cycle life and temperature.
$K_I$	=	The correction coefficient of charge and discharge current to Ah measurement current.
$K_t$	=	The correction coefficient of the temperature factor to the Ah meter current.
$I\left(t\right)$	=	The change in current during charging and discharging.
h	=	THE DISTANCE TRAVELlED PER UNIT OF ENERGY.
$\mathrm{\Delta }{S}_t$	=	The distance traveled from t0 to t.
W	=	THE AMOUNT OF ENERGY A BATTERY STORES WHEN FULLY CHARGED.
$SO{C}_j$	=	The cut-off charge state of a battery when discharging, this paper is set at 10%.
$S_L$	=	The remaining range of an EV.
$S_r$	=	The driving distance of an EV to a charging station after receiving a charging warning.
$\mathrm{\Delta }SO{C}_r$	=	The change in the state of charge brought to the battery during charging.
$P_e$	=	The charging power of an EV when it is charged by a charging point.
$T_c$	=	The time it takes to charge an EV.
$P_n$	=	The probability that N EVs in the system receive charging service from charging points.
$P_0$	=	The probability that the charging station does not have an EV in service.
$W_q$	=	Charging waiting time.
$T_z$	=	Total time spent on charging of an EV.
$T_{AB}\left(t\right)$	=	Total road travel time.
$T_i\left(t+\mathrm{\Delta }{t}_{i-1}\right)$	=	A forecast of the road travel time when an EV passes section i leaving from time t.
$\mathrm{\Delta }{t}_{i-1}$	=	The time it takes an EV to reach section i.
$T_i\left(K+{\mathrm{\Delta }}_{i-1}\right)$	=	A forecast of the road travel time required for EVs to travel through section I during the $K+{\mathrm{\Delta }}_{i-1}$ time of day
${\mathrm{\Delta }}_{i-1}$	=	The time taken for an EV to pass through i-1.
${T_i}^H\left(K+{\mathrm{\Delta }}_{i-1}\right)$	=	The historical average road journey time of section i in the period $K+{\mathrm{\Delta }}_{i-1}$.
$\alpha \left({\mathrm{\Delta }}_{i-1}\right)$	=	The weight coefficient, calculated by gaussian function.
$T_{total}$	=	The total time it takes to travel by either route.

Additional information

Notes on contributors

Hanlin Shao

Shao Hanlin received the B.E degree from Wuhan University Of Technology, China in 2016. He is currently a teaching assistant and worked in Wuhan University Of Technology from 2016 to 2018. Since 2018, he has been studying at the School of Automotive Engineering, Wuhan University of Technology for his M.E degree.

Guofang Zhang

Zhang Guofang received the B.E degree from Wuhan Institute of Technology (It is now the Wuhan University of Technology) , China in 1985, the M.E degree from Wuhan Institute of Technology (It is now the Wuhan University of Technology), China in 1991. He has been a professor at the School of Automotive Engineering, Wuhan University of Technology since 2004. He is now the Vice Director of the SAE-C/ASC.

Miao Xia

Xia Miao received the B.E degree from Wuhan University Of Technology, China in 2016 the M.E degree from Wuhan University Of Technology, China in 2019. He has been working as a lecturer at the School of Automotive Engineering, Wuhan University Of Technology since 2019.