Evaluating the effect of a driver’s behaviour on the range of a battery electric vehicle

A battery electric vehicle uses the electric energy stored in a battery to supply power to the electric motor. The estimation of an accurate electric range is an important matter because of the consumer’s fear of running out of electricity while driving, often referred to as ‘range anxiety’. This article both analyses and quantifies the influence of driving aggressiveness on the battery life. In order to achieve this, a modification of the New European Driving Cycle is performed, simulating progressively more aggressive behaviours. The calculation of the energy consumption and, accordingly, of the vehicle range is performed through a complete vehicle model implemented in MATLAB/Simulink, where an improvement in the battery model is presented, allowing faster processing. The methodology presented can contribute to more efficient management of the power consumed by the vehicle and a decrease in range anxiety.

[1]  Thomas S Turrentine,et al.  The UC Davis MINI E Consumer Study , 2011 .

[2]  Francesco Filippi,et al.  A new method for collecting vehicle behaviour in daily use for energy and environmental analysis , 2006 .

[3]  L.-A. Dessaint,et al.  A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[4]  R. Fletcher Practical Methods of Optimization , 1988 .

[5]  Zoran Filipi,et al.  Characterizing naturalistic driving patterns for Plug-in Hybrid Electric Vehicle analysis , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[6]  Piet Rietveld,et al.  Consumer Valuation of Driving Range: A Meta-Analysis , 2011 .

[7]  Ya-Xiang Yuan,et al.  Optimization Theory and Methods: Nonlinear Programming , 2010 .

[8]  Henk Jan Bergveld,et al.  Battery Management Systems: Accurate State-of-Charge Indication for Battery-Powered Applications , 2008 .

[9]  Antoni Szumanowski,et al.  Battery Management System Based on Battery Nonlinear Dynamics Modeling , 2008, IEEE Transactions on Vehicular Technology.

[10]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[11]  John Krumm,et al.  How People Use Their Vehicles: Statistics from the 2009 National Household Travel Survey , 2012 .

[12]  Jie Xu,et al.  EKF-Ah Based State of Charge Online Estimation for Lithium-ion Power Battery , 2009, 2009 International Conference on Computational Intelligence and Security.

[13]  D. Gay,et al.  Some Convergence Properties of Broyden&Apos;S Method , 1977 .

[14]  Souradip Malkhandi,et al.  Estimation of state of charge of lead acid battery using radial basis function , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[15]  Mehrdad Ehsani,et al.  A Study of Design Issues on Electrically Peaking Hybrid Electric Vehicle for Diverse Urban Driving Patterns , 1999 .

[16]  K. T. Chau,et al.  Estimation of battery available capacity under variable discharge currents , 2002 .

[17]  Mehrdad Ehsani,et al.  A Matlab-based modeling and simulation package for electric and hybrid electric vehicle design , 1999 .

[18]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[19]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[20]  N. Omar,et al.  Rechargeable Energy Storage Systems for Plug-in Hybrid Electric Vehicles—Assessment of Electrical Characteristics , 2012 .

[21]  Dirk Uwe Sauer,et al.  A review of current automotive battery technology and future prospects , 2013 .

[22]  Philip T. Krein,et al.  Comparative evaluation of machines for electric and hybrid vehicles based on dynamic operation and loss minimization , 2010, 2010 IEEE Energy Conversion Congress and Exposition.

[23]  Leonard Evans Driver Behavior Effects on Fuel Consumption in Urban Driving , 1979 .

[24]  Chris Mi,et al.  Hybrid Electric Vehicles: Principles and Applications with Practical Perspectives , 2011 .

[25]  Jinlong Zhang,et al.  State-of-charge estimation of valve regulated lead acid battery based on multi-state Unscented Kalman Filter , 2011 .

[26]  Tony Markel,et al.  Using GPS Travel Data to Assess the Real World Driving Energy Use of Plug-In Hybrid Electric Vehicles (PHEVs) , 2007 .

[27]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[28]  Joeri Van Mierlo,et al.  Peukert Revisited—Critical Appraisal and Need for Modification for Lithium-Ion Batteries , 2013 .

[29]  Mark W. Verbrugge,et al.  Adaptive Energy Management of Electric and Hybrid Electric Vehicles , 2005 .

[30]  Jeffrey Gonder,et al.  Final Report on the Fuel Saving Effectiveness of Various Driver Feedback Approaches , 2011 .

[31]  Shuo Pang,et al.  Battery state-of-charge estimation , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[32]  M. J. D. Powell,et al.  On search directions for minimization algorithms , 1973, Math. Program..

[33]  Robert Joumard,et al.  Driving Cycles for Emission Measurements Under European Conditions , 1995 .

[34]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[35]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[36]  Maximilian Schwalm,et al.  Methods of evaluating electric vehicles from a user's perspective - The MINI E field trial in Berlin , 2011 .

[37]  Aymeric Rousseau,et al.  Impact of Drive Cycles on PHEV Component Requirements , 2008 .

[38]  N. de la Torre,et al.  Battery model for life-preserving conditions , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[39]  Rob van Haaren,et al.  Assessment of Electric Cars ‟ Range Requirements and Usage Patterns based on Driving Behavior recorded in the National Household Travel Survey of 2009 , 2012 .

[40]  Hieu Minh Trinh,et al.  Drive Cycle Analysis of the Performance of Hybrid Electric Vehicles , 2010, LSMS/ICSEE.

[41]  Ricardo A. Daziano,et al.  Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range , 2013 .

[42]  Eva Aneiros,et al.  A proposed mathematical model for discharge curves of Li-Ion batteries , 2013, 2013 International Conference on New Concepts in Smart Cities: Fostering Public and Private Alliances (SmartMILE).

[43]  William C. Davidon,et al.  Variable Metric Method for Minimization , 1959, SIAM J. Optim..

[44]  P. Van den Bossche,et al.  The Cell versus the System: Standardization challenges for electricity storage devices , 2009 .

[45]  T. Weigert,et al.  State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks , 2011 .

[46]  Olivier Tremblay,et al.  Experimental validation of a battery dynamic model for EV applications , 2009 .

[47]  Mascha C. van der Voort,et al.  A prototype fuel-efficiency support tool , 2001 .

[48]  S. Rodrigues,et al.  A review of state-of-charge indication of batteries by means of a.c. impedance measurements , 2000 .

[49]  C. M. Shepherd Design of Primary and Secondary Cells II . An Equation Describing Battery Discharge , 1965 .

[50]  Eric Kvaalen A faster Broyden method , 1991 .

[51]  Richard H. Byrd,et al.  Analysis of a Symmetric Rank-One Trust Region Method , 1996, SIAM J. Optim..

[52]  Phil Blythe,et al.  Electric Vehicle Driving Style and Duty Variation Performance Study , 2010 .

[53]  K. I. M. McKinnon,et al.  Convergence of the Nelder-Mead Simplex Method to a Nonstationary Point , 1998, SIAM J. Optim..