SCIENCE
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`Computer modelling of the automotive
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`energy requirements for internal
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`combustion engine and battery
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`electric-powered vehicles
`J.R. Bumby, B.Sc., Ph.D., C.Eng., M.I.E.E., P.H. Clarke, B.Sc., Ph.D., C.Eng.,
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`F.inst.E., and |. Forster, B.Sc.
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`Indexing terms: Measurement and measuring, Instrumentation and measuring science, Computer simulation
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`Abstract: In the paper the road vehicle simulation package Janus, developed in the Engineering Departmentat
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`Durham University, is described. Janus is a flexible simulation package that allows internal combustion engine
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`vehicles, electric vehicles and hybrid vehicles to be simulated, and their performance and energy consumption
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`evaluated over standard driving cycles. The simulation techniques used in these programs are described and the
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`simulation program shownto produce results comparable with experimental data.
`©
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`List of symbols
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`a
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`vehicle acceleration, m/s?
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`A
`= vehicle projected frontal area, m?
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`BMEP
`= brake meaneffective pressure, kPa
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`cc
`= engine cubic capacity, cm?
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`Cy
`= aerodynamic drag coefficient
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`Cp
`= discharged ampere-hours. A h
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`CG
`= coefficient of rolling resistance
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`Cr
`= engine compression ratio
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`C,, C,, C; = motorloss constants
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`= distance travelled, km
`d
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`= EMF, V
`E
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`= battery energy density, kJ/kg
`Ey
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`= battery open-circuit
`terminal voltage when
`E,
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`fully charged, V
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`= battery terminal voltage, V
`E,
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`= cold engine fuel flow factor
`Sr
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`= acceleration due to gravity, 9.81 m/s”
`g
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`= armature current, A
`I,
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`= battery current, A
`I,
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`= field current
`I;
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`= battery discharge current
`1,
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`= wheelinertia, kg m?
`ly
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`= DC motor EMFconstant
`ky
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`= DC motor torque constant
`ky
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`= polarisation resistance, Q
`K
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`= rotational speed, rev/min
`n
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`= Peukert index
`n
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`= DC machine armature conduction loss, W
`Por
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`= DC machine brush loss, W
`Pur
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`= DC machinecore loss, W
`Poy
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`= DC machinefield resistance loss, W
`Pr
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`= input power, W
`P;
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`= DC machine mechanical loss, W
`Pat
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`= DC machinestray loss, W
`Poy
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`= battery power density, W/kg
`Py:
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`= gearbox losses, W
`Pear
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`= battery ampere-hour capacity, Ah
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`rp
`= wheel rolling radius, m
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`R
`= ohmicresistance, 2
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`Paper 4048A (SI), first received 1st November 1984 and in revised form Ist May
`1985
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`The authors are with the Department of Engineering, University of Durham,
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`Science Laboratorics, South Road, Durham DH] 3LE, United Kingdom
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`1EE PROCEEDINGS,Vol. 132, Pt. A, No.5, SEPTEMBER 1985
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`= battery state of charge
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`= time
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`= ambient temperature, °C
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`= airgap torque, Nm
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`= engine compression braking torque, N m
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`= tractive effort at the road wheels, N
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`= vehicle velocity, m/s
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`= armature voltage, V
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`= battery voltage, V
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`= head(ortail wind) velocity m/s
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`= vehicle mass, kg
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`= modified vehicle accelerative mass, kg
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`= time interval
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`= chopperefficiency
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`= battery charge efficiency
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`= gearbox partialefficiency
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`= battery discharge time at constant current
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`I,,h
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`= battery discharge time at constant power
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`density Pp;,h
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`= air density, kg/m*
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`= hill severity, degrees
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`= field flux, Wb
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`Soc
`t
`T
`T,
`Tes
`Ty
`V
`Vi
`v,
`Vv,
`w
`Ww’
`At
`He
`Nee
`gb
`Tei
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`Tpi
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`p
`d
`br
`Suffixes
`max
`a
`f
`i
`5
`k
`pu
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`= maximum
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`= armature
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`= field
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`= discharge rate
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`= 5 hourdischarge rate
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`= step number
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`= per unit
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`NBcc denotes engine cubic capacity, 1 cc = 1 cm?
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`1
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`Introduction
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`In 1976 the Department of Industry commissioned The
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`International Research and Development Co. Lid., New-
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`castle upon Tyne, to produce a worldwide survey of hybrid
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`electric vehicles
`[1-3]. This report, prepared by A.J.
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`Mitcham and J.R. Bumby, described numerous types of
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`hybrid vehicles and their operating philosophies. It was
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`evident that hybrid vehicles, and hybrid electric vehicles in
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`particular, could be designed to meet a numberofdifferent
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`265
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`operating objectives. However, as to which was the best
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`design to use to meet a particular operating objective was
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`muchless obvious. Consequently, the report recommended
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`that initially the performance potential of different hybrid
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`vehicle designs should be examined using computer simu-
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`lation.
`this
`this
`by
`paper
`recommendation,
`Stimulated
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`describes a general road vehicle simulation program for
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`evaluating the performance and energy efficiency of inter-
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`nal combustion engined vehicles, battery electric vehicles
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`or hybrid electric vehicles. This simulation package was
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`conceived as a user-friendly interactive program, capable
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`of evaluating the performance of a vehicle that is charac-
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`teristic of a particular vehicle group, e.g. small car, medium
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`car, light commercial van ete. The program was given the
`name Janus. However, during the development of Janus,it
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`became evident that additional software options must be
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`included to allow the user to define precisely the vehicle to
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`be simulated, thereby extending the program to specific
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`vehicle studies. Indeed,
`is this option that
`is used in
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`program verification.
`Since the publication of the Mitcham and Bumby
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`the use of computer simulation has played an
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`increasingly important role in evaluating the potential of
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`new vehicle technologies, and in the design of specific vehi-
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`cles [4-6]. A number of simulations have been written spe-
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`cifically for one type of vehicle [7-10], while others, such
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`as ELVEC [11-12] and HEAVY[13-14], are general road
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`this
`vehicle
`simulation packages. The majority of
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`published work has been in, and from, the USA,andthisis
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`particularly true of the two general packages ELVEC and
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`HEAVY,both of which are powerful design tools. One of
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`these, HEAVY, has been mounted on the SERC comput-
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`ing network, but, at the present time, cannot be run inter-
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`actively. In its conception, Janus was designed to be an
`interactive simulation package directly applicable to Euro-
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`pean vehicles,
`In this paper, the characteristics of Janus are described
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`in detail and used to demonstrate how a particular vehicle
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`can be quickly and easily assembled from a standard sub-
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`routine
`library.
`Initially the
`fundamental
`equations
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`describing the vehicle dynamics are reviewed. In the final
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`part of the paper, the use of Janus in simulating the per-
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`formance of both electric and internal combustion engined
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`road vehicles is demonstrated. In the case of the electric
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`vehicle, this is achieved by simulating the General Electric
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`ETV-1 electric car [15]; while when considering the inter-
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`nal combustion engined vehicle its usefulness in examining
`new vehicle concepts is demonstrated by investigating the
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`influence of‘fuel off at idle’,
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`2
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`Vehicle dynamics
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`To provide the necessary propulsion power, any vehicle
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`drive train must be able to provide sufficient tractive effort
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`at the road wheels to overcome aerodynamic drag,rolling
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`resistance and hill gradient effects, while still providing the
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`necessary vehicle acceleration. Consequently, at any parti-
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`cular velocity and acceleration,
`tractive effort
`the net
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`required at the road wheels can be expressed as the alge-
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`braic sum of these components, i.e.
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`E=]%+T+%Z+N
`where the respective componentsoftractive effort are
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`Ty = 1/2pC, AV + VP N
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`T,=C,WgN
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`266
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`(1)
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`(2)
`(3)
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`T, = Wg sin 6 N
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`T.=WaN -
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`and
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`(4)
`(5)
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`C, = drag coefficient
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`A = vehicle frontal area, m?
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`C, = coefficient of rolling resistance
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`W = vehicle mass, kg
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`@ =hill severity and percentage grade = 100 tan }, %
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`V = vehicle velocity, m/s
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`V,, = head wind velocity, m/s
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`a = vehicle linear acceleration, m/s”
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`p = density of air = 1.226 kg/m? (at 15°C
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`and 10° Pa (1 bar) ambient conditions)
`In eqn. 3 the coefficient of rolling resistance is dependent
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`on the type of tyre used, the tyre pressure and the vehicle
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`speed. However,
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`is difficult
`to quantify reliably for
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`vehicle simulation studies, and a number of authors have
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`used a quadratic, velocity-dependent equation for rolling
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`resistance [7, 9, 11, 16], while others assumethe coefficient
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`of rolling resistance to be a constant [17]. In general, the
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`variation in the coefficient of rolling resistance with veloc-
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`ity is small up to about 90 km/h, with the major changes
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`occurring at speeds significantly greater than this. Indeed,
`in tests on the General Electric ETV-1 electric car the
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`energy required to overcome
`rolling resistance was
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`observed to decrease with speed [15], possibly due to
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`increased tyre heating at higher speeds. To allow for simu-
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`lation flexibility the coefficient of rolling resistance used in
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`Janus contains both a constant and:a velocity-dependent
`term, both of which can be defined by the user.
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`The accelerative tractive effort in eqn. 5 relates solely to
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`the linear acceleration of the vehicle and takes no account
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`of the rotational inertia of the road wheels and engine. The
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`inertia of the road wheels can increase the effective vehicle
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`weight by about 2% during acceleration, and is included
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`in eqn. 5 by using an effective accelerative weight W’given
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`by
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`6)
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`I
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`W=W+ Ske
`YD
`where
`I, = inertia of the road wheels, kg m?
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`rp = rolling radius of the wheel, m
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`The engine inertia is not included directly in the tractive
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`effort but as a local energy demand within the engine algo-
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`rithm itself. It is interesting to note that, if referred to the
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`wheels through the overall gear ratio, the engine inertia
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`can increase the effective accelerative weight by about 20%
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`in first gear and 2%in top gear.
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`3
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`Janus—its structure and operation
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`the use of separate
`A fundamental feature of Janus is
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`Fortran subroutines to represent individual vehicle com-
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`ponents. The necessary subroutines can then be easily
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`assembled into a master program to simulate a particular
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`type of vehicle. This approach is adopted as many of the
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`components required by the conventional
`internal com-
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`bustion engined vehicle are encountered in an electric
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`vehicle while components used in both these appear in the
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`hybrid electric vehicle. Each component block is written to
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`ANSI Fortran VII standard. The master program can also
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`include additional Fortran statements if required. Such
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`versatility is of prime importance, as it allows the user to
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`increase the degree of output information to suit his own
`TEE PROCEEDINGS,Vol. 132, Pt. A, No. 5, SEPTEMBER 1985
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`BMW1014
`Page 2 of 15
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`BMW1014
`Page 2 of 15
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`particular requirements, or to restructure the control com-
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`mands when simulating a hybrid vehicle.
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`The advantages of this block structure approach can be
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`demonstrated by considering the layout of the internal
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`combustion engined vehicle shown schematically in Fig. 1.
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`WHEELS
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`COUPL
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`TRANS
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` ICENG
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`1c engine [OI
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`Fig.1
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`Conventionalinternal combustion engine vehicle drive train
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`The software blocks representing this vehicle are shown in
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`the flow diagram of Fig. 2, and it is only the components
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`within the dotted frame that are assembled by the user. To
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`simplify program writing, the names of the software blocks
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`have been selected to be similar to those of the com-
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`ponents they represent. For operational
`flexibility each
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`subroutine is divided into three sections termed, respec-
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`the initial section, dynamic section and output
`tively,
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`section. The initial and dynamic sections are further
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`divided into two subsections. During the initialisation
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`phase, each of the component subroutines is entered in
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`
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`turn, and the vehicle power train and driving cycle par-
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`
`
`
`
`
`ameters
`specified. Unfortunately,
`in some subroutines
`
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`
`
`information may be required from components upstream
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`
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`
`
`of the one currently being accessed, and as such will have
`
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`
`
`yet to be defined. To overcome this problem a two pass
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`
`
`system is used. On thefirst pass full details of the individ-
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`
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`
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`
`
`ual componentsare specified, while on the second pass any
`
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`calculations requiring information from an upstream com-
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`
`ponent are completed. Such a system is necessary,
`for
`
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`
`
`
`example, when automatic weight generation is used. In
`
`
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`
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`
`
`such circumstances,
`the weight of the gearbox and final
`
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`
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`
`
`drive depends on the specified engine torque and power,
`
`
`
`
`
`
`
`one of the last componentsto be defined.
`Having established the initial operating conditions the
`
`
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`
`
`
`simulation enters the dynamic section, the main computa-
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`
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`
`
`
`tional part of the program.In this part of the program, the
`
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`
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`
`
`fuel efficiency of the vehicle and all the component effi-
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`
`
`
`
`
`
`ciencies are calculated. For the majority of driving cycles,
`
`
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`
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`
`
`
`such as the ECE-15 urban cycle shown in Fig. 3,
`the
`
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`
`
`vehicle velocity is explicitly defined as a function of time,
`
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`
`
`
`
`set t=0
`
`
`set flag for
`
`
`
`initial pass
`
`
`
`
`VEHICLE
`
`
`specifies all
`vehicle
`
`
`
`parameters.
`
`DCYCLE
`
`
`acceleration and
`
`velocity at time
`
`
`
`t for specific
`
`
`
`driving cycle
`
`
`
`WHEELS
`
`
`torque and speed
`reset
`initial
`at wheels
`
`
`
`
`conditions
`
`
`
`AXLE
`
`
`torque and speed
`at drive shaft
`
`
`
`
`account of axle
`
`
`
`
`
`efficiency
`
`
`TRANS
`
`
`transmission type,
`
`
`
`gear,torque and
`
`speed at engine
`
`
`
`
`COUPL
`
`
`friction clutch
`
`or torque con.
`
`
`
`
`ICENG
`
`
`fuel consumption
`over interval
`
`
`
`
`at
`
`yes
`
`
`
`set flog for
`set flag for
`
`
`dynamic initial
`
`
`dynamic
`
`
` Fig. 2
`
`IEE PROCEEDINGS,Vol. 132, Pt. A, No. 5, SEPTEMBER 1985
`
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`
`BMW1014
`Page 3 of 15
`
`Flowchart for a conventional internal
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`combustion engine
`
`
`267
`
`BMW1014
`Page 3 of 15
`
`

`

`enabling the program to step through the driving cycle at
`
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`
`one second intervals (default value) calculating vehicle
`
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`
`
`
`
`40
`
`60
`
`50
`
`30
`
`40r .
`g 20
`:
`= 30 E
`
`S
`G
`
`8778 4
`¥
`$
`10
`
`0
`
`o
`
`Fig.3
`
`
`
`
`
`
` ECE-15 urban driving cycle
`
`
`
`
`20
`
`40
`
`60
`
`8&0
`
`100
`
`time,s
`
`
`
`120
`
`
`
`140
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`
`
`160
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`
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`180 200
`
`
`fuel used during the last time step and the energy loss in
`
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`each of the drive-train components is recorded. The timeis
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`
`then updated and the process repeated until the driving
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`
`
`
`cycle is completed.
`
`
`the simulation
`Once the driving cycle is completed,
`
`
`
`
`
`
`
`enters the output section, where full details of the vehicle,
`
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`
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`
`
`driving cycle and the individual drive-train componentsis
`
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`given with graphical presentation of time-varying quan-
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`tities, engine fuel maps etc. Besides displaying component
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`efficiencies, losses and the overall vehicle fuel economy, the
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`
`percentage of the total cycle time spent in each area of the
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`engine fuel mapis also given. Such fuel map informationis
`
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`
`invaluable, particularly when detailed studies on the effect
`
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`of the vehicle component sizing and control] on fuel effi-
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`ciencies are being undertaken. Alternatively, if a detailed
`
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`
`component breakdownis not required, a reduced output
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`facility may be selected which simply details the vehicle
`
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`range and/or fuel consumption when operated contin-
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`
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`uously over a particular driving cycle.
`Once a simulation run has been completed, the control-
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`ling software allows further runs to be conducted over a
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`
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`
`
`different driving cycle. Alternatively, modifications may be
`
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`
`
`madeto the individual power-train components and/or the
`
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`
`
`
`vehicle parameters.
`
`The different component subroutines available within
`
`
`
`
`
`Janus are listed in Table 1 and, in many cases, contain
`
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`
`
`
`Table 1: Component subroutine names
`
`
`
`
`Component
`Simulation name
`
`_ Vehicle definition
`VEHICLE
`
`Oriving cycle
`DCYCLE
`
`Wheels
`WHEELS
`Final drive
`AXLE
`
`Transmission
`TRANS
`Clutch or torque convertor
`COUPLE
`
`
`
`internal combustion engine
`ICENG
`
`
`Series DC motor
`DCSER
`
`
`Separately excited OC motor
`DCSHUNT
`
`
`
`DC switched reluctance motor
`OCREL
`
`
`
`AC inductance motor
`ACINDUC
`
`
`DC generator
`OCGEN
`
`Field chopper
`FCHOPR
`
`Armature chopper
`ACHOPR
`
`Traction battery
`BATTERY
`
`‘Gearing’ far connecting two
`DRIVE
`
`
`
`prime movers
`
`Battery switching
`BATSWCH
`
`Summing block
`SUM
`
`Torque splitting module (hybrids)
`TOROQSPLT
`
`
`
`Powersplitting module (hybrids)
`POWSPLT
`
`
`
`
`Vehicle controllers VEHCONT
`
`
`performance details and simulation algorithms on a
`
`
`
`
`
`
`numberof related items, For example, the tree structure of
`
`
`
`
`
`
`
`
`
`Fig. 4 describes the specific components available within
`
`
`
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`
`
`
`the transmission module, any of which can be selected
`
`
`
`
`
`
`
`
`from within the program.
`
`
`
`
`velocity and acceleration directly from the driving-cycle
`
`
`
`
`
`
`data. The tractive effort at the road wheels is calculated at
`
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`
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`
`
`
`
`
`each time instant using eqn. 1 and converted into a torque
`
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`
`
`
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`
`
`and rotational speed demand in the subroutine WHEELS.
`
`
`
`
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`
`
`This torque requirement
`is then reflected back through
`
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`
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`
`
`
`each of the drive train components to the engine output
`
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`
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`
`
`
`shaft. At each stage, account is taken of the gear ratio and
`instantaneous loss within each of the transmission com-
`
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`
`
`
`ponents. Fuel usage is now obtained from the engine fuel
`
`
`
`
`
`
`
`
`
`map by assuming the engine load to be constant over the
`
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`
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`
`
`
`
`
`step interval. By sequentially repeating this process,
`the
`
`
`
`
`
`
`
`total fuel used over the driving cycle can be found.
`
`
`
`
`
`
`
`
`
`Such a direct calculation method is adequate for the
`
`
`
`
`
`
`
`
`majority of situations, as the performance of each drive
`
`
`
`
`
`
`
`
`train component can be considered to be independent of
`
`
`
`
`
`
`
`
`the other components. Normally the componentefficiency
`
`
`
`
`
`
`will depend only on the torque and speed it is required to
`
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`
`
`
`
`
`
`
`
`transmit at that time instant. However, in some cases, the
`
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`
`
`
`
`
`
`
`performance and behaviour of two or more of the drive
`
`
`
`
`
`
`
`
`
`train components may be
`strongly dependent. For
`
`
`
`
`
`
`example, in an electric vehicle, the behaviour of both the
`
`
`
`
`
`
`
`
`
`electronic controller and the traction motor depends on
`
`
`
`
`
`
`
`the battery terminal voltage. In turn, the battery terminal
`
`
`
`
`
`
`
`
`voltage depends on load current. In such instances, it may
`
`
`
`
`
`
`
`
`
`be necessary to iterate around the vehicle drive train until
`
`
`
`
`
`
`
`
`
`a stable operating condition is achieved, when component
`
`
`
`
`
`
`
`efficiencies can then be calculated. To accommodatethis, a
`
`
`
`
`
`
`
`
`two-pass system is used at each time step. On the first
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`pass, all the vehicle variables such as torques, speeds etc.
`
`
`
`
`
`
`
`
`
`are calculated from the road wheels to the energy source,
`
`
`
`
`
`
`
`
`using an iterative process to establish the stable operating
`
`
`
`
`
`
`
`condition. Once the stable operating condition has been
`
`
`
`
`
`
`
`
`
`reached the second dynamic pass is started, in which the
`
`TRANS
`
`gearbox
`
`CVT
`
`automatic
`(as manual)
`
`
`268
`
`manual
`(numberof gears: 2-6,
`
`
`
`gearratios, upchange
`
`
`schedule, downchange
`
`schedule)
`
`hydromechanical
`
`
`
`Perbury
`
`
`
`'V’ belt
`
`
`
`cone
`
`rofler
`
`
`constant
`efficiency
`
`Fig.4
`
`
`
`
`Subdivision oftransmission module
`
`
`
`IEE PROCEEDINGS,Val. 132, Pt. A, No.5, SEPTEMBER 1985
`
`
`
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`
`
`BMW1014
`Page 4 of 15
`
`BMW1014
`Page 4 of 15
`
`

`

`and speed increments and stored as a 20 x 20 two-
`
`
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`
`
`
`
`
`dimensional array. At each time interval through the cycle,
`
`
`
`
`
`
`
`
`
`140,
`
`1000
`
`%000
`
`5000
`
`4
`Componentsimulation
`
`
`417° Transmission
`
`the
`simulates
`The transmission subroutine (TRANS)
`
`
`
`
`
`behaviour of the vehicle transmission system and includes
`
`
`
`
`
`
`
`models of manual and automatic, fixed ratio, gearboxes
`
`
`
`
`
`
`
`and a variety of continuously variable transmissions
`
`
`
`
`
`
`(CVTs). The fixed ratio gearbox model is based on aneffi-
`
`
`
`
`
`
`
`
`
`
`ciency algorithm that predicts
`the gearbox efficiency,
`
`
`
`
`
`
`depending on the transmitted torque and speed. The algo-
`
`
`
`
`
`
`
`
`rithm also allows the number of gears, gear ratios and the
`
`
`
`
`
`
`
`
`
`
`gear change schedule to be specified. Both the up-change
`
`
`
`
`
`
`
`
`and the down-change schedule can be specified as a func-
`
`
`
`
`
`
`
`
`tion of vehicle speed or, alternatively,
`the ECE-15 gear
`
`
`
`
`
`
`
`
`change schedule maybe used. As the time taken during the
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`gear change is small compared with the total cycle time,
`
`
`
`
`
`the simulation assumes instantaneous gear changes.
`Theefficiency of a gearbox depends on the powertrans-
`
`
`
`
`
`
`
`
`mitted, the operational speed, the gear ratio and the gear
`
`
`
`
`
`
`
`
`
`3000
`2000
`
`speed rev/min
`profile and will vary with different gearbox designs.
`
`
`
`
`
`
`
`
`Although efficiency is high at normal full-load operating
`Fig.5=Enginefuel mapfor a typical 1850 cc engine
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`See Reference 18
`conditions, typically in excess of 96%, at part load, parti-
`
`
`
`
`
`
`
`
`
`
`
`@ Maximum torquecurve
`cularly high-speed part-load operation, efficiency drops
`
`
`
`
`
`b Constant SFC 400g/kWh
`
`
`
`and cansignificantly effect the vehicle fuel economy.
`
`
`
`
`
`
`
`
`
`
`c Power for constant 115 km/h
`
`
`
`
`
`ad Power far constant 80 km/h
`Gearbox lasses can be broadly divided into two com-
`
`
`
`
`
`
`
`
`
`
`
`
`
`e Power for constant $0 km/h
`ponents, a no-load loss due to gears churning in the
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`gearbox oil and a load friction loss due to the transfer of
`
`
`
`
`
`
`
`
`
`
`load between the gears. This last componentof lass is also
`
`
`
`
`
`
`
`
`
`
`dependent on the gear profile. Such losses are approx-
`
`
`
`
`
`
`
`
`imated in Janus by the power loss expression
`
`
`
`
`
`
`
`
`Poor = (1 — nealP; + 1.14 x 10782” W
`
`
`
`
`
`
`
`
`
`
`(7)
`
`where P, is the gearbox input power and n¢, is the partial
`
`
`
`
`
`
`
`
`
`
`
`efficiency relating to thefriction loss. The second velocity-
`
`
`
`
`
`
`
`
`dependent term is associated particularly with the churn-
`
`
`
`
`
`
`
`ing loss. The friction loss component is dependent on gear
`
`
`
`
`
`
`
`
`
`ratio and in the simulation is changed such that non-
`
`
`
`
`
`
`
`
`
`meshing gears,
`i.e. gear ratio of 1:1, have a higher effi-
`
`
`
`
`
`
`
`
`
`
`ciency than the meshing gears. Typically, 7g, is taken as
`
`
`
`
`
`
`
`
`
`0.99 for nonmeshing gears and 0.98 for meshing gears. For
`
`
`
`
`
`
`
`
`
`an automatic gearbox,a single value of 0.97 is assumed.
`
`
`
`
`
`
`
`
`At high rotational speeds, particularly at low load, the
`
`
`
`
`
`
`
`
`value of the churning loss speed index v becomescritical as
`
`
`
`
`
`
`
`
`
`
`gearbox efficiency is dominated by this loss term. To
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`obtain gearboxefficiency values typical of those quoted in
`the literature [17, 22], a speed index of 2.1 is assumed.
`
`
`
`
`
`
`
`
`
`The transmission is coupled to the engine by the
`
`
`
`
`
`
`
`
`COUPLEroutine, which contains models for both a
`
`
`
`
`
`
`
`torque convertor anda friction clutch.
`
`
`
`
`Efficiency curves similar to those described by eqn. 7 are
`
`
`
`
`
`
`
`
`
`also used in the final drive model. However, as the final
`
`
`
`
`
`
`
`
`
`
`drive may be constructed from either bevel or spur gears, a
`
`
`
`
`
`
`
`
`
`
`different friction loss componentis required for each gear
`
`
`
`
`
`
`
`
`type, bevel gears having a higherfrictional loss. The final
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`drive simulation block also contains efficiency values rep-
`resentative of both chain and belt drives, as these have
`
`
`
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`
`
`
`
`
`
`
`
`
`
`
`
`been used previously in electric vehicles (15, 20].
`
`4.2 Internal combustion engine
`
`
`
`Accurate engine maps giving the specific fuel consumption
`
`
`
`
`
`
`
`at different loads and speeds are essential if fuel consump-
`
`
`
`
`
`
`
`
`
`tion over urban driving cycles is to be accurately com-
`
`
`
`
`
`
`
`
`
`puted. Such fuel maps are commonly presented,either in a
`
`
`
`
`
`
`
`
`
`power/speed,
`brake-mean-effective-pressure
`(BMEP)/
`
`
`speed, or torque/speed form. An example ofthe latter is
`
`
`
`
`
`
`
`
`
`shownin Fig. 5 for a typical 1850 cc engine. For computa-
`
`
`
`
`
`
`
`
`
`
`tional purposes the fuel map is divided into twenty BMEP
`
`
`
`
`
`
`
`
`
`TEE PROCEEDINGS,Vol. 132, Pt. A, No. 5, SEPTEMBER 1985
`
`
`
`
`
`
`
`
`
`
`the fuel consumption is calculated for the particular load
`
`
`
`
`
`
`
`
`and engine speed by linearly interpolating between the
`
`
`
`
`
`
`
`four specific fuel values adjacent to the operating point.
`
`
`
`
`
`
`
`
`The position within the fuel map array is then recorded. At
`
`
`
`
`
`
`
`
`
`
`the end ofthe cycle simulation, the total fuel used is calcu-
`
`
`
`
`
`
`
`
`
`
`
`lated and the fuel usage information presented graphically
`
`
`
`
`
`
`
`in the form of a fuel usage map. This map displays that
`
`
`
`
`
`
`
`
`
`
`percentage of the cycle time the engine spent in different
`
`
`
`
`
`
`
`
`
`parts of the operating region. As engine efficiency maps are
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`obtained from steady-state load tests to obtain net engine
`
`
`
`
`
`
`
`torque during an acceleration interval, the engine inertia
`
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`torque must be added algebraically to the output torque.
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`As the engine inertia torque is proportional to the engine
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`angular acceleration, this is readily achieved, except when
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`a gear change-takes place. As a gear change is assumed to
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`occur instantaneously, a step change occurs in the engine
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`speed which can lead to very high, false, inertia torques. In
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`general, engine inertia has only a small
`influence on the
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`vehicle fuel economy;
`thereby, allowing engine inertia
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`effects to be neglected during the computational timestep,
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`the gear change takes place.
`the
`use of BMEP/
`that
`Experience has
`shown
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`normalised speed fuel maps are to be preferred to power
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`maps, as, with the latter, significant errors can be intro-
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`duced at low-torque low-speed conditions; conditions that
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`are commonin urban driving cycles. This arises because
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`under such conditions the power demand is low, possibly
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`less than 10% of maximum power, so that if equal power
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`increments are used in the storage process, accurate spe-
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`cific fuel values corresponding particularly to the lower
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`left-hand corner of Fig. 5 can be lost. By normalising the
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`engine speed, the fuel maps can be ‘stretched’ to either a
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`different engine cubic capacity, and hence power output, or
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`to a different maximum engine speed other than their
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`design value, Although stretching of the engine maps over
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`too large an engine range, and particularly over a different
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`compression ratio range,
`is not
`recommended,
`it does
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`provide substantial additional
`flexibility with negligible
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`additional programming complexity.
`At some stage during the driving cycle, it is possible for
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`the demand torque to be greater than the fuil
`throttle
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`269
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`BMW1014
`Page 5 of 15
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`BMW1014
`Page 5 of 15
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`

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`Fourtypes ofelectric traction motor are currently avail-
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`able within the simulation;
`the DC separately excited
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`motor, the DC series motor, the DC switched reluctance
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`motor and the AC squirrel-cage induction motor. The DC
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`series motor hastraditionally been used for electric vehicle
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`traction as its
`torque characteristic, varying
`inherent
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`inversely as a function of speed, best suits the requirements
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`of the road vehicle. However, the series motor is not so
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`well
`suited to regenerative braking, as
`the separately
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`excited motor and also tends to have a lowerefficiency at
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`low speed. Consequently, with the advent of power elec-
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`tronics there is an increasing use of the separately excited
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`DC motor, particularly with transistor chopper control of
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`field current and SCR armature chopper controlas, for
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`example, used in the Lucas hybrid electric car [24], The
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`advances in powerelectronics have also paved the way for
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`more advanced traction motor types such as electronically
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`commutated DC motors,
`the DC switched reluctance
`motor and the AC induction motor. In the Janus simula-
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`tion the DC switched reluctance and the AC induction
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`motors are both represented as being favourable advanced
`traction motor drives.
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`The torque/speed characteristic and efficiency map for
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`the separately excited motor used in the General Electric
`ETV-1 electric car is shown in Fig. 6 [25, 26]. Such a char-
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`acteristic can be split into four operating regions; regions |
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`and 2 associated with motoring action and regions 3 and 4
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`with regenerative braking. When operating in regions |
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`and 4 the field current, and hence flux, is maintained con-
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`stant and speed control of the motor is achieved by chop-
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`ping the armature supply voltage. In regions 2 and 3 the
`battery is connected directly across the motor armature
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`terminals and speed control is ach