A spatial-temporal estimation model of residual energy for pure electric buses based on traffic performance index

Original scientific paper The relationship between the energy consumption of buses and traffic conditions has gradually garnered research attention with the expansion of the green transportation concept and the promotion of new energy buses. In line with these developments, this study develops a spatial–temporal estimation model of residual energy for pure electric buses with fuzzy clustering and time-series algorithms. These algorithms are based on the traffic performance index of the road sections between the nearest bus stops. Furthermore, they are established according to the positions of floating vehicles and the bus routes in combination with the energy consumption data derived from the battery management system of pure electric buses. Test results show that these estimation algorithms can accurately describe the spatial–temporal relationship between traffic performance index and the residual energy in pure electric buses. Thus, they can be applied as significant references in the analysis of traffic conditions, energy conservation, and emission reduction for buses.

[1]  Chris Bingham,et al.  Impact of driving characteristics on electric vehicle energy consumption and range , 2012 .

[2]  Hongwen He,et al.  State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model , 2011, IEEE Transactions on Vehicular Technology.

[3]  Torsten Bertram,et al.  Model-based remaining driving range prediction in electric vehicles by using particle filtering and Markov chains , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

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

[5]  C. Sivapragasam,et al.  Forecasting Cultivated Areas And Production Of Wheat In India Using ARIMA Model , 2013 .

[6]  Zuhaimy Ismail,et al.  Modified Weighted for Enrollment Forecasting Based on Fuzzy Time Series , 2009 .

[7]  Long Xu,et al.  Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model , 2012 .

[8]  Wei Zhang,et al.  Heat Transfer Mechanism in Porous Copper Foam Wick Heat Pipes Using Nanofluids , 2015 .

[9]  Kun-Huang Huarng,et al.  A bivariate fuzzy time series model to forecast the TAIEX , 2008, Expert Syst. Appl..

[10]  C. C. Christianson,et al.  A METHODOLOGY TO ASSESS THE IMPACT OF DRIVING SCHEDULES AND DRIVE TRAIN CHARACTERISTICS ON ELECTRIC VEHICLE RANGE , 1986 .

[11]  Olja Čokorilo,et al.  Aircraft safety analysis using clustering algorithms , 2014 .

[12]  Salvatore Antonio Biancardo,et al.  Risk-type density diagrams by crash type on two-lane rural roads , 2013 .

[13]  Zuhaimy Ismail,et al.  Enrollment forecasting based on modified weight fuzzy time series , 2011 .

[14]  Guglielmina Mutani,et al.  The role of urban form and socio-economic variables for estimating the building energy savings potential at the urban scale , 2015 .

[15]  Hamid Soori,et al.  PREDICTION OF FATAL ROAD TRAFFIC CRASHES IN IRAN USING THE BOX-JENKINS TIME SERIES MODEL , 2013 .

[16]  Kun-Huang Huarng,et al.  A neural network-based fuzzy time series model to improve forecasting , 2010, Expert Syst. Appl..