Imperial College London
Portrait Picture

ProfessorJulieMcCann

Faculty of Engineering, Department of Computing

Vice-Dean (Research) for the Faculty of Engineering
 
 
 
//

Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
//

Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
//

Location

 

260ACE ExtensionSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Wang:2020:10.1109/TITS.2019.2912501,
author = {Wang, H and Zhou, G and Xue, R and Lu, Y and McCann, JA},
doi = {10.1109/TITS.2019.2912501},
journal = {IEEE Transactions on Intelligent Transportation Systems},
pages = {2090--2099},
title = {A driving-behavior-based SoC prediction method for light urban vehicles powered by supercapacitors},
url = {http://dx.doi.org/10.1109/TITS.2019.2912501},
volume = {21},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Range anxiety is one of the problems that hinder the large-scale application of electric vehicles (EVs). We propose a driving-behavior-based State-of-Charge (SoC) prediction (DBSP) algorithm to overcome this problem. This algorithm can determine whether drivers can reach their destinations while also predicting the SoC if drivers were to return the trip. First, two supercapacitor equivalent circuit models are established with one based on the historical average power and the other based on the equivalent current, which is proposed in this algorithm. Then, based on the equivalent transformation of the two models, an analytical expression relating the historical average power and the predicted SoC is derived by using the equivalent current as a “bridge.” Therefore, the predicted SoC can be dynamically adjusted in response to recorded historical data, including the output power, speed, and distance of EVs powered by supercapacitors. The simulation results demonstrate that the total prediction error is less than 0.5% of the real SoC at different initial SoC and temperature, which represents idealized behavior-based driving. In contrast, in actual driving experiments, the total prediction error is less than 3% of the real SoC at different initial SoC and temperature.
AU  - Wang,H
AU  - Zhou,G
AU  - Xue,R
AU  - Lu,Y
AU  - McCann,JA
DO  - 10.1109/TITS.2019.2912501
EP  - 2099
PY  - 2020///
SN  - 1524-9050
SP  - 2090
TI  - A driving-behavior-based SoC prediction method for light urban vehicles powered by supercapacitors
T2  - IEEE Transactions on Intelligent Transportation Systems
UR  - http://dx.doi.org/10.1109/TITS.2019.2912501
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000532285400022&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8700591
UR  - http://hdl.handle.net/10044/1/83262
VL  - 21
ER  -
Download