Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles

To improve the accuracy of estimation of the energy consumption of electric vehicles (EVs) and to enable the alleviation of range anxiety through the introduction of EV charging stations at suitable locations for the near future, multilevel mixed-effects linear regression models were used in this study to estimate the actual energy efficiency of EVs. The impacts of the heterogeneity in driving behaviour among various road environments and traffic conditions on EV energy efficiency were extracted from long-term daily trip-based energy consumption data, which were collected over 12months from 68 in-use EVs in Aichi Prefecture in Japan. Considering the variations in energy efficiency associated with different types of EV ownership, different external environments, and different driving habits, a two-level random intercept model, three two-level mixed-effects models, and two three-level mixed-effects models were developed and compared. The most reasonable nesting structure was determined by comparing the models, which were designed with different nesting structures and different random variance component specifications, thereby revealing the potential correlations and non-constant variability of the energy consumption per kilometre (ECPK) and improving the estimation accuracy by 7.5%.

[1]  Gilbert W. Fellingham,et al.  Sensitivity of point and interval estimates to distributional assumptions in longitudinal data analysis of small samples , 1995 .

[2]  Thomas H. Bradley,et al.  Geographical and temporal differences in electric vehicle range due to cabin conditioning energy consumption , 2015 .

[3]  Warren S. Vaz,et al.  Electric vehicle range prediction for constant speed trip using multi-objective optimization , 2015 .

[4]  John G. Hayes,et al.  Simplified electric vehicle power train models and range estimation , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[5]  Guolin Wang,et al.  New evaluation methodology of regenerative braking contribution to energy efficiency improvement of electric vehicles , 2016 .

[6]  Celil Ozkurt,et al.  Integration of sampling based battery state of health estimation method in electric vehicles , 2016 .

[7]  Hesham A Rakha,et al.  Impact of Stops on Vehicle Fuel Consumption and Emissions , 2003 .

[8]  A. Roskilly,et al.  Novel technologies and strategies for clean transport systems , 2015 .

[9]  Roel Bosker,et al.  Modeled Variance in Two-Level Models , 1994 .

[10]  Margaret O'Mahony,et al.  Environmental impacts of varying electric vehicle user behaviours and comparisons to internal combustion engine vehicle usage – An Irish case study , 2016 .

[11]  Xinkai Wu,et al.  Electric vehicles’ energy consumption measurement and estimation , 2015 .

[12]  H. Bosma,et al.  Analysis of regenerative braking efficiency — A case study of two electric vehicles operating in the Rotterdam area , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[13]  Margaret O'Mahony,et al.  Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas , 2016 .

[14]  David Baglee,et al.  The effect of driving style on electric vehicle performance, economy and perception , 2012 .

[15]  I D Greenwood,et al.  Estimating the Effects of Traffic Congestion on Fuel Consumption and Vehicle Emissions Based on Acceleration Noise , 2007 .

[16]  Hyunsun Choi,et al.  A study on possibility of commuting trip using private motorized modes in cities around the world: Application of multilevel model , 2015 .

[17]  Hewu Wang,et al.  Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing , 2015 .

[18]  Michael W. Levin,et al.  The effect of road elevation on network wide vehicle energy consumption and eco-routing , 2013 .

[19]  Hesham Rakha,et al.  ESTIMATING VEHICLE FUEL CONSUMPTION AND EMISSIONS BASED ON INSTANTANEOUS SPEED AND ACCELERATION LEVELS , 2002 .

[20]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[21]  Ryan Smith,et al.  Characterization of urban commuter driving profiles to optimize battery size in light-duty plug-in electric vehicles , 2011 .

[22]  Yves Dube,et al.  A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures , 2016 .

[23]  Jianqiu Li,et al.  A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications , 2015 .

[24]  Gae-won You,et al.  Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach , 2016 .

[25]  Lili Li,et al.  Energy and environmental impact of battery electric vehicle range in China , 2015 .

[26]  Matthieu Dubarry,et al.  From driving cycle analysis to understanding battery performance in real-life electric hybrid vehicle operation , 2007 .

[27]  James Marco,et al.  Method for estimating the energy consumption of electric vehicles and plug-in hybrid electric vehicles under real-world driving conditions , 2013 .

[28]  Michael W. Levin,et al.  Effect of Road Grade on Networkwide Vehicle Energy Consumption and Ecorouting , 2014 .

[29]  Hai-Jun Huang,et al.  Influences of the driver’s bounded rationality on micro driving behavior, fuel consumption and emissions , 2015 .

[30]  Jeremy J. Michalek,et al.  Influence of driving patterns on life cycle cost and emissions of hybrid and plug-in electric vehicle powertrains , 2013 .

[31]  João L. Afonso,et al.  Mobile geographic range prediction for electric vehicles , 2011 .

[32]  Oriol Travesset-Baro,et al.  Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra , 2015 .

[33]  Chuan Ding,et al.  Impacts of SOC on car-following behavior and travel time in the heterogeneous traffic system , 2016 .

[34]  Toshiyuki Yamamoto,et al.  Feasibility of Using Taxi Dispatch System as Probes for Collecting Traffic Information , 2009, J. Intell. Transp. Syst..

[35]  R. Farrington,et al.  IMPACT OF VEHICLE AIR-CONDITIONING ON FUEL ECONOMY. TAILPIPE EMISSIONS, AND ELECTRIC VEHICLE RANGE: PREPRINT , 2000 .

[36]  Chao Hu,et al.  Online estimation of lithium-ion battery capacity using sparse Bayesian learning , 2015 .

[37]  Kai He,et al.  Analysis of downshift’s improvement to energy efficiency of an electric vehicle during regenerative braking , 2016 .

[38]  Takayuki Morikawa,et al.  Impact of road gradient on energy consumption of electric vehicles , 2017 .

[39]  Peng Hao,et al.  Trajectory-based vehicle energy/emissions estimation for signalized arterials using mobile sensing data , 2015 .

[40]  Thomas H. Bradley,et al.  Estimating the HVAC energy consumption of plug-in electric vehicles , 2014 .

[41]  Kang G. Shin,et al.  Real-time prediction of battery power requirements for electric vehicles , 2013, 2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[42]  Hesham Rakha,et al.  Power-based electric vehicle energy consumption model: Model development and validation , 2016 .