TY - GEN
T1 - Hazard based modelling of electric vehicles charging patterns
AU - Daina, Nicoló
AU - Polak, John W.
N1 - Funding Information:
We thank UK Engineering and Physical Sciences Research Council for supporting this work under awards EP/L001 03911 and EP/103883711 and UK Power Networks for providing the EV charging datasets used for the analysis.
Publisher Copyright: © 2016 IEEE.
PY - 2016/7/13
Y1 - 2016/7/13
N2 - Understanding and predicting charging behaviour of electric vehicles' users is essential for the appropriate design of charging services and for the implementation aggregator services mediating between electric vehicle drivers, electricity markets, distribution system operator, and transmission system operator. Research into actionable charging behaviour insights to be implemented in predictive models has so far been modest. The present paper intends to contribute at the development of predictive models of charging patterns for operational implementation. It proposes hazard-based analysis of the gap times between charging events model with time dependent covariates in order to investigate which set of potential covariates that will enable an eventual formulation of short-term predictive model of the timing of charging events. Empirical estimation of the hazard model shows that both monitored vehicle state variables (e.g. state of charge, cumulative average driving speed) and individual characteristics significantly affect the instantaneous rate of occurrence of charging events.
AB - Understanding and predicting charging behaviour of electric vehicles' users is essential for the appropriate design of charging services and for the implementation aggregator services mediating between electric vehicle drivers, electricity markets, distribution system operator, and transmission system operator. Research into actionable charging behaviour insights to be implemented in predictive models has so far been modest. The present paper intends to contribute at the development of predictive models of charging patterns for operational implementation. It proposes hazard-based analysis of the gap times between charging events model with time dependent covariates in order to investigate which set of potential covariates that will enable an eventual formulation of short-term predictive model of the timing of charging events. Empirical estimation of the hazard model shows that both monitored vehicle state variables (e.g. state of charge, cumulative average driving speed) and individual characteristics significantly affect the instantaneous rate of occurrence of charging events.
KW - charging behaviour prediction
KW - electric vehicles
KW - electric vehicles charging behaviour
KW - hazards models
KW - power distribution
UR - http://www.scopus.com/inward/record.url?scp=84983464745&partnerID=8YFLogxK
U2 - 10.1109/ITEC-AP.2016.7513002
DO - 10.1109/ITEC-AP.2016.7513002
M3 - Conference contribution book
AN - SCOPUS:84983464745
T3 - 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2016
SP - 479
EP - 484
BT - 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2016
T2 - 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2016
Y2 - 1 June 2016 through 4 June 2016
ER -