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.
DazianoR. Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range. Resource Energy Econ2013; 35(3): 429–450.
5.
Gaining traction: will consumers ride the electric vehicle wave? Report, Deloitte Touche Tohmatsu Limited, New York, USA, April2011.
6.
DimitropoulosAPRietveldPVan OmmerenJN. Consumer valuation of driving range: a meta-analysis. Tinbergen Institute discussion paper TI 2011-133/3, Tinbergen Institute, Amsterdam, The Netherlands, 2011, pp. 1–35.
7.
CocronPBuählerFNeumannI. Methods of evaluating electric vehicles from a user’s perspective – the MINI E field trial in Berlin. IET Intell Transport Systems2011; 5(2): 127–133.
8.
EverettABurgessMHarrisM. Initial findings from the ultra-low carbon vehicle demonstrator programme how quickly did users adapt? A review of preliminary usage and perception data from the first three months of vehicle trials. Report, Technology Strategy Board, London, UK, 2011.
9.
TurrentineTGarasDLentzAWoodjackJ. The UC Davis MINI E consumer study. Report, Institute of Transportation Studies, University of California, Davis, California, USA, 2011.
10.
WalshCCarrollAEastlakemABlytheP. Electric vehicle driving style and duty variation performance study. Report, Cenex, Loughborough, Leicestershire, UK, 2010.
11.
Budde-MeiwesHDrillkensJLunzB. A review of current automotive battery technology and future prospects. Proc IMechE Part D: J Automobile Engineering2013; 227(5): 761–776.
12.
GonderJEarleywineMSparksWFauriaK. Interim report on the fuel saving effectiveness of various driver feedback approaches. Technical Report for the US Department of Energy Vehicle Technologies Program, Washington, DC, USA, 2010.
13.
EvansL. Driver behavior effects on fuel consumption in urban driving. Hum Factors: J Hum Factors Ergonomics Soc1979; 21(4): 389–398.
14.
Van der VoortMDoughertyMVan MaarseveenM. A prototype fuel-efficiency support tool. Transpn Res Part C: Emerging Technol2001; 9(4): 279–296.
15.
van HaarenR. Assessment of electric cars’ range requirements and usage patterns based on driving bahavior recorded in the national household travel survey of 2009, Solar Journey USA Publications, http://www.solarjourneyusa.com/HowFarWeDrive_v1.2.pdf (2011). (15May2013)
16.
GonderJMarkelTSimpsonAThorntonM. Using GPS travel data to assess the real world driving energy use of plug-in hybrid electric vehicles PHEVs. In: Transportation Research Board (TRB) 86th Annual Meeting2007, Washington, DC, USA, Dearborn, Michigan, USA, 2009, pp. 655–660.
17.
CalvoJBorregónMLópezAÁlvarezR. A route planner simulator that shows the performance of the battery in electric vehicles. In: 27th international electric vehicle symposium and exhibition, Barcelona, Spain, 17–20 November 2013, pp. 1–5. New York: IEEE.
18.
ButlerKEhsaniMKamathP. A Matlab-based modeling and simulation package for electric and hybrid electric vehicle design. IEEE Trans Veh Technol1999; 48(6): 1770–1778.
19.
GanjiBKouzaniZTrinhHM. Drive cycle analysis of the performance of hybrid electric vehicles. In: Life system modeling and intelligent computing, Lecture Notes in Computer Science, Vol 6328, 2010, pp. 434–444. Berlin: Springer.
20.
AndréMHickmanAHasselDJoumardR. Driving cycles for emission measurements under European conditions. SAE paper 950926, 1995.
21.
KwonJKimJFallasEPageritS. Impact of drive cycles on PHEV component requirements. SAE paper 2008-01-1337, 2008.
22.
GonderJMarkelTSimpsonAThorntonM. Using GPS travel data to assess the real world driving energy use of plug-in hybrid electric vehicles PHEVs. In: 2007 Transportation Research Board 86th annual meeting, Washington, DC, USA, 21–25 January 2007, pp. 21–25. Washington, DC: TRB.
23.
AdornatoBPatilBFilipiZ. Characterizing naturalistic driving patterns for plug-in hybrid electric vehicle analysis. In: IEEE vehicle power and propulsion conference, Dearborn, Michigan, USA, 2009, pp. 655–660. New York: IEEE.
24.
United Nations, Economic Commission for Europe (UN/ECE). Uniform provisions concerning the approval of passenger cars powered by an internal combustion engine only, or powered by a hybrid electric power train with regard to the measurement of the emission of carbon dioxide and fuel consumption and/or the measurement of electric energy consumption and electric range, and of categories M1 and N1 vehicles powered by an electric power train only with regard to the measurement of electric energy consumption and electric range. Regulation No 101, E/ECE/324, E/ECE/TRANS/505, Rev.2/Add.100/Rev.2, http://www.unece.org/fileadmin/DAM/trans/main/wp29/wp29regs/r101r2e.pdf (10February2013).
25.
MiCMasrurAGaoW. Hybrid electric vehicles: principles and applications with practical perspectives. New York: John Wiley, 2011.
BazziAMKreinP.Comparative evaluation of machines for electric and hybrid vehicles based on dynamic operation and loss minimization. In: 2010 IEEE energy conversion congress and exposition, Atlanta, Georgia, USA, 12–16 September 2010, pp. 3345–3351. New York: IEEE.
RahmanZ. A study of design issues on electrically peaking hybrid electric vehicle for diverse urban peaking patterns a study of design issues on electrically peaking hybrid electric vehicle for diverse urban driving patterns. SAE paper 1999-01-1151, 1999.
PopV. Battery management systems: accurate state-of-charge indication for battery-powered applications, Philips Research Book Series, Vol. 9. Berlin: Springer, 2008.
33.
PangSFarrellJDuJBarthM. Battery state-of-charge estimation. In: American control conference, Arlington, Virginia, USA, 25–27 June 2001, Vol 2, pp. 1644–1649. New York: IEEE.
34.
RodriguesSMunichandraiahNShuklaA. A review of state-of-charge indication of batteries by means of A.C. impedance measurements. J Power Sources2000: 87(1–2): 12–20.
35.
JyunichiLHiroyaT. Battery state-of-charge indicator for electric vehicle. In: 13th international electric vehicle symposium, Osaka, Japan, 13–16 October 1996, Vol 2, pp. 229–234. Tokyo: Japan Electric Vehicle Association.
36.
ShenWChanCLoEChauKT. Estimation of battery available capacity under variable discharge currents. J Power Sources2002; 103(2): 180–187.
37.
ShepherdCM. Design of primary and secondary cells – Part 2. An equation describing battery discharge. J Electrochem Soc1965; 112: 657–664.
38.
HeZGaoMXuJ. EKF-Ah based state of charge online estimation for lithium-ion power battery. In: International conference on computational intelligence and security, Beijing, People’s Republic of China, 11–14 December 2009, Vol 1, pp. 142–145. New York: IEEE.
39.
SzumanowskiAChangY. Battery management system based on battery nonlinear dynamics modeling. IEEE Trans Veh Technol2008; 57(3): 1425–1432.
40.
TremblayODessaintLDekkicheA. A generic battery model for the dynamic simulation of hybrid electric vehicles. In: IEEE vehicle power and propulsion conference, Arlington, Texas, USA, 9–12 September 2007, pp. 284–289. New York: IEEE.
41.
DessaintLTremblayO. Experimental validation of a battery dynamic model for EV applications. World Electr Veh J2009; 3: 1–10.
42.
VerbruggeMFrischDKochB. Adaptive energy management of electric and hybrid electric vehicles. J Electrochem Soc2005; 152(2): A333–A342.
43.
WiegmanHLN. Battery state estimation and control for power buffering applications. PhD Thesis, The University of Wisconsin, Madison, Wisconsin, USA, 1999.
44.
MalkhandiSSinhaKMuthukumarK. Estimation of state of charge of lead acid battery using radial basis function. In: 27th annual conference of the IEEE Industrial Electronics Society, Denver, Colorado, USA, 29 November–2 December 2001, Vol 1, pp. 131–136. New York: IEEE.
45.
WeigertTTianQLianK. State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks. J Power Sources2011; 196(8): 4061–4066.
46.
ZhangJXiaC. State-of-charge estimation of valve regulated lead acid battery based on multi-state unscented Kalman filter. Int J Electl Power Energy Systems2011; 33(3): 472–476.
47.
PlettGL. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification. Power Sources2004; 134(2): 262–276.
48.
OmarMDaowdMVan den BosscheP. Rechargeable energy storage systems for plug-in hybrid electric vehicles – assessment of electrical characteristics. Energies2012; 5: 2952–2988.
49.
PeukertW. Über die Abhänigkeit der Kapazität von der Entladestromstärke bei Bleiakkumulatoren. Elektrotechnisch Z1897; 27: 287–288.
50.
ChenMRincon-MoraGA. Accurate electrical battery model capable of predicting runtime and I–V performance. IEEE Trans Energy Conversion2006; 21(2): 504–511.
51.
OmarNVan den BosschePCoosemansT. Revisited – critical appraisal and need for modification for lithium-ion batteries. Energies2013; 6: 5625–5641.
52.
De la TorreNLópezAÁlvarezR. Battery model for life-preserving conditions in an EREV. In: 2013 world electric vehicle symposium and exhibition, Barcelona, Spain, 17–20 November 2013, pp. 1–8. New York: IEEE.
53.
AneirosELoboDLópezAÁlvarezR. A proposed mathematical model for discharge curves of Li-ion batteries. In: SmartMile 2013 international conference on new concepts in smart cities, Gijón, Spain, 11–13 December 2013, pp. 1–6. New York: IEEE.
54.
NocedalJWrightSJ. Numerical optimization. 2nd edition. New York: Springer, 2006.
55.
MarquardtD. An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math1963; 11(2): 431–441.
FletcherR. Practical methods of optimization. Chichester, West Sussex: John Wiley, 1987.
58.
ByrdRH. Analysis of a symmetric rank-one trust region method. SIAM J Optimization1996; 6(4): 1025–1039.
59.
GayDM. Some convergence properties of Broyden’s method. SIAM J Numer Analysis1979; 16(4): 623–630.
60.
KvaalenE. A faster Broyden method. BIT Numer Math (SIAM)1991; 31(2): 369–372.
61.
SunWYYuanY. Optimization theory and methods: nonlinear programming. New York: Springer, 2006.
62.
NelderJAMeadR. A simplex method for function minimization. Comput J1965; 7: 308–313.
63.
LagariasJReedsJWrightMWrightPE. Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM J Optimization1998; 9(1): 112–147.
64.
PowellMJ. On search directions for minimization algorithms. Mathl Programming1973; 4(1): 193–201.
65.
McKinnonKIM. Convergence of the Nelder–Mead simplex method to a non-stationary point, (algorithm summary online). SIAM J Optimization1999; 9: 148–158.
66.
AlessandriniAFilippiFOrecchiniFOrtenziF. A new method to collect vehicle behaviour in daily use for energy and environmental analysis. Proc IMechE Part D: J Automobile Engineering2006; 220(11): 1527–1537.
67.
Budde-MeiwesHDrillkensJBenediktL. A review of current automotive battery technology and future prospects. Proc IMechE Part D: J Automobile Engineering2013; 227(5): 761–776.
