Driving Simulation Conference 2012

Paris, France, September 6-7, 2012

Using a Driving Simulator as an Electric Vehicle
Hardware Development Tool
Dirk Kok 1, Michael Knowles 1, Adrian Morris 1(1) Institute for Automotive and Manufacturing Advanced Practice (AMAP), University of Sunderland, Colima
Avenue, Sunderland, SR5 3XB, UK, E-mail : {Dirk.Kok, Michael.Knowles, Adrian.Morris}@sunderland.ac.uk
Abstract – Driving simulators have been used tosupport the development of new vehicle systems formany years. The rise of electric vehicles (EVs) as ameans of reducing carbon emissions has lead to theemergence of a number of new design challenges
related to the performance of EV components andthe flow of power under a variety of circumstances. In
this paper we describe the integration of an EV drive
train test system with a driving simulator to allow theperformance of EV systems to be investigated while
under the control of real drivers in simulated
scenarios. Such a system offers several potential
benefits. The performance of EV drive trains can beevaluated subjectively by real world users while theelectrical and mechanical properties can be tested
under a variety of conditions which would be difficult
to replicate using standard drive cycles.
Key words: Driving Simulation, Hardware testing,Electric Vehicles, Simulator Control, Vehicle Design

1.

2.

3.

Research and development in these areas makes
extensive use of the hardware-in-the-loop (HIL)
methodology where hardware can be combined withsimulated elements to achieve HIL tests (c.f. [Bou1],[Ros1]). Zha and Zong [Zha1] describe the use of anelectric motor to act as a dynamometer for simulating
the inertia of an electric vehicle. Jun Liu et al [Jun1]
describe the use of an electrical load for simulating
the motor in drive train experiments. Such activities
utilise drive cycles which consist of a pattern of use interms of vehicle speed through time. At present muchof the power system simulation and modelling work isbased on standard drive cycles. This has theadvantage of providing a snapshot of performanceand allowing easy comparison. The usefulness ofstandard drive cycles is, however, restricted by theaccuracy of the assumptions upon which it is based.
In order to achieve greater realism it is necessary tolook for more realistic drive cycles [Saj1][Ado1] and avariety of techniques are beginning to receive
attention to address these [Wal1][Hiw1].

Introduction
As the world faces ever growing pressures to reducecarbon emissions, Electric Vehicles (EVs) are seen
as a potential replacement for conventionally fuelledvehicles. First generation mass produced orconverted electric vehicles are now on the market
and are receiving widespread recognition. Many
drawbacks remain, however, and it is crucial that
improvements are made to make the next generationof vehicles to suit the requirements of users. The
driveability of vehicle as experienced by the user is aparticular area of concern.

In this paper we will describe an architecture forusing a driving simulator as a control mechanism for
HIL tests creating a new development platform
capable of supporting Human-in-the-Loop (HuL)
testing. A bespoke dynamometer developed at the
University of Sunderland [Kno1] will be integrated
with a Forum8 3D driving simulator to allow hardware
to be tested under the direct control of a driver
travelling around a real route.

Alongside the experience gathered from current EV
models and trials, we believe that driving simulators
have a major part to play in ensuring human factors
are given appropriate consideration in the design
process. The key technical requirements for future
EV development include thermal management, range
optimisation, control strategies and transmission
design. Alongside these technical considerations
Crolla et al [Cro1] have identified 3 areas for
additional research to ensure EVs are viable andattractive in real world conditions:

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Driveability – optimisation research should bebased upon realistic driving conditions rather
than standard patterns.
Braking behaviour – regenerative braking
means energy can be recovered but such
systems should not compromise safety.
Practical design – research currently done
leaves many implementation issues; e.g.
some methods for control are just too
computationally intensive.

The remainder of the paper is structured as follows.
The motivation for the test system is summarised in
the following section. This is followed by a description-1-

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Article Titleof the two major system components: thedynamometer and the driving simulator. The
integration strategy for these two components is thendiscussed followed by a section considering theongoing work to realise such a system.

Motivation
An integrated hardware test system controlled by arealistic immersive driving simulator has manypotential benefits. Electric vehicle (EV) design issues
listed above can be evaluated and improvements
developed
using
realistic
driving
patterns.
Furthermore feedback can be obtained on theperformance of traction system configurations bydrivers without the need to create expensive test
vehicles. The effect of control strategies on driver
behaviour can also be investigated in real time using
easily varied control systems. Finally, the driving
simulator software can be used to create different
test routes which can be either designed specificallyto investigate certain aspects of performance or
which recreate real road systems, the latter will also
allow for cross validation with data captured from test
vehicles driven around standard laps. It is ourcontention that the development of such a system
has much to offer EV research and development
programmes.

Fig.1. Forum8 driving Simulator

This simulator has been selected for the work for thefollowing reasons:••

Driving simulator

Dynamometer

The University of Sunderland driving simulator
laboratory was established in 1999 and has beenused to support research in a variety of areas relating
tovehicledesignandhumanfactors(c.f.[Mid1][Mid2][Mid3]). The laboratory presently
houses two driving simulators. The system which has
been utilised for these experiments is the most recent
system which has been used for a variety of work
including eco-driver training [Sco1] and assessmentof driving style [Kno2].

The Dynamometer which will be used for the test
system was rebuilt in 2011 to provide bespoke test
facilities for EV drive train components. The system
consists of a Froude Hofmann EC38TA (Eddy
Current) dynamometer, shown in figure 2, which iscontrolled from a Texcel V4 ECE/HE controller,
shown in figure 3.

The hardware component of the simulator is a
Forum8 Driving Simulator and is illustrated in Figure
1. The hardware is based around a vehicle cockpit
comprising all the usual controls including steering
wheel, transmission selector, parking brake,
accelerator andbrake. Instruments include
speedometer and engine speed measurement. The
display consists of three 32 inch LCD screens, eachwith a resolution of 1024x768 pixels and a fourth,
smaller 8.4 inch LCD TFT screen with a resolution of
800x600 pixels which can be used for display ofnavigational information or other data to the driver.

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The ease with which simulated versions ofreal routes can be created using GPS or
mapping data using the LandXML data
format for import.
The provision of a plug-in based architecture
allowing bespoke software components to be
developed giving access to the appropriate
internal data structures.

Fig. 2. Dynamometer hardware-2-

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The data capture system currently consists of 4 parts:
1.

2.

Fig.3. Dynamometer control and datalogging equipment3.
The dynamometer is currently fitted with an EV drive
train which consists of:4.
••

A 15kW (30kW peak) induction motor
controlled through a Curtis motor controller.
The Curtis motor controller can be
programmed and controlled via CAN bus.
A Lithium-ion phosphate pack of 29 3.2V
90Ah cells (8352 kWh 100% discharged) withindividual
battery
management
cellcontrollers and overall battery managementmonitoring module with CAN connector.

Both the CAN connections (2) and analogue data
capture (4) are done through the PV750 at a
maximum speed of 50Hz.
The current system has been designed primarily toinvestigate the electrical behaviour of EV drive
systems, however it should be emphasised that the
instrumentation and data capture system can beeasily customised using the reprogrammable natureof the Murphy display unit and the Curtis motor
controller.

These drive train components can be fully or partiallyreplaced with any system which requires testing.
Other components currently available for testing
include alternative lithium-ion power cells and avariety of hydrogen fuel cells.

Integration design

In terms of instrumentation and control, the system
features a Murphy Power View (PV)750 display
which features 3 separate CAN ports, 3 analogue I/O
ports and internal memory for data logging. In its
current configuration two options exist for controllingthe system:
1.

2.

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A 4 band digital oscilloscope which isconnected to a computer for data logging.
This system is used to record electricaltransients in the motor power system withhigh speed recording.
Two CAN connections allow for data logging
from the motor controller (voltages, current,
motor and controller temperature) and BMS(cell number of highest voltage, cell numberof lowest voltage and temperatures ofrespective cells).
A USB connection from the Battery
Management System (BMS) to computer for
capturing voltages, temperatures and state ofcharge (SoC) for cells within the battery pack.
Analoguedata
capturefromthedynamometer controller via the Murphy
PV750 of torque and speed.

Control of the test motor on the dynamometer will beachieved using data captured from the simulator
controls. The mechanical load imposed by the
dynamometer will be based around the mechanicalcharacteristics of the vehicle as well as surface
properties of the road and aerodynamic factors andthe gradient upon which the simulated vehicle istravelling [Lar1]. The speed at which the motor turns
under that load will be measured from the
dynamometer and fed back via an inverse vehicle
model to the simulator to control the speed of thesimulated vehicle. The proposed system structure isillustrated in figure 4 below.

Manually through a linear actuator, much likea throttle pedal in a vehicle. This option is
primarily used for testing purposes.
Programmed operation via the Murphy
display. The PV750 can be loaded with adrive cycle and when started it will control the
motor controller and the dynamometer, while
recording data.

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Control positions andgeographical information(gradient etc)
Forum8
Driving
Simulator

Motor power withfull data logging

Motor control
Motor
Controller

Dynamic
Model

Mechanical loading based on
gradient and vehicle properties

Vehicle
speed

Motor

Dynamometer

Dynamometer
Controller
Motor speed under
specified load
Fig. 4 System structure illustrating data flow

Upon detection of both a brake and a throttle
signal, the brake signal will be passed on to
the controller while the throttle signal will be
set to zero. This practise is used inside theCurtis motor controller as well.

A number of key requirements for the system havebeen determined. These will be discussed in thefollowing sections.

All values sent to the motor anddynamometer controller will be capped attheir respective minimum or maximum values

Data rate

Upon switching off the simulator or if theconnection with the simulator is lost thecontrol signals to the motor will be set tozero.

One of the key considerations for the system viability
is the speed and latency of the system such that the
dynamometer system reacts to changes in throttle
position and feeds back speed changes fast enoughto ensure that the driver feels the experience isrealistic. The effect of ‘transport delays’ has been
widely investigated over a number of years (c.f.
[Lee1]). The frame update rate offered by thesimulator is 20 Hz (under current development
conditions) so the objective for this integration is forthe total round trip communication time to be within
the time period of the frame update i.e. 50ms.

Simulator control
As discussed previously, a plug-in software
component is under development for the driving
simulator. This will fulfil a dual role:
Data will be extracted from the simulation
regarding the throttle and brake controls, aswell as the gradient on which the vehicle sits.

SafetyData from the dynamometer will be used tocontrol the speed of the simulated vehicle.

The primary safety concern for the dynamometer is astall condition since this will be detrimental to themotor and controller potentially causing extensive,
costly to repair damage. Also, sudden stall could leadto a break in the drive shaft. Such a condition should
therefore be avoided at all times. In addition to
standard safety features already installed on the
dynamometer such as cut out switches andtemperature monitoring, the following additionalsafety protocols have been identified as being
necessary:

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The plug-in is under development in the Delphi
programming language using the Forum8 UC-Win
Road SDK version 5.02.04.

Dynamic model
The dynamic model of vehicle properties is based
upon that proposed by Larminie and Lowry [Lar1].
This model is used in the first instance to determine
the mechanical torque which must be applied to the-4-

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motor to simulate the tractive effort required to propel
the vehicle. This will depend on a number of factors
including the gradient of the road – if the vehicle istravelling uphill more effort is required.

(6)

Where r represents the vehicle’s wheel radius and G
is the overall gear ratio between the motor and thewheels.

The tractive effort Fte is the sum of four distinct
factors:

The throttle/brake demand is communicated directlyfrom the control position in the simulator to the motor
controller. This throttle / brake command will cause
the motor to respond which results in a speed
measured by the dynamometer. The controller
manages the throttle and brake inputs and allows fortweaking of input response. Based on the above
model a new torque value is calculated and sent to
the dynamometer. The start torque is derived directlyfrom the rolling resistance since this is the only force
at no speed.

(1)

The four factors are:
1. Rolling road resistance. This is caused by theeffect of frictional forces acting at the wheel/roadinterface and is dependent on the coefficient of rolling
friction µrr:(2)

The power applied to the motor will cause it to work
against the torque applied by the dynamometer. The
speed of the resultant rotation will be recorded andused to update the dynamic model and will be
converted to vehicle speed which will be
communicated back to the simulator.

Where m is the mass of the vehicle and g is theacceleration due to gravity.
2. Aerodynamic drag. This is the frictional effect ofthe vehicle moving through the air:(3)

System Implementation
Implementation of the system is underway. The
following activities have been completed:

Where ρ is the density of air, A is the vehicle frontal
area and Cd is the drag coefficient, a value based onA.

Communications between the simulator andthe dynamometer control systems isachieved via a TCP connection operatingover a Local Area Network.

3. Hill climbing force. This is the mechanical effort
required to overcome a gradient, expressed as anangle, α. If the vehicle is travelling downhill then thiswill become negative:

Dynamometer control is achieved using
National Instruments’ PCI-MIO-16E-4 realtime data acquisition card. This card
operates its own operating system and
Virtual Instruments (VI’s) can be downloaded
to the card through LabVIEW 7 RT.

(4)Bi-directional communication has beenachieved with 2 computers running LabVIEW
software. The first computer contains the I/O
card and runs the real-time VI. The second
computer runs as a TCP/IP server and sends
required data to the first computer andreceives the data sent from the firstcomputer. This Setup allows simple testingand improves development speed.

4. Lateral Force. This force represents the inertia ofthe vehicle and its rotating components and isestimated on the basis the inertia as a percentage ofthe vehicle mass, I and the vehicles acceleration a.

(5)

Communications between Simulator andDynamics computer have been tested andvalidated

Since the dynamometer is not connected to the motor
by a final drive gearbox it is necessary to account forthis. Therefore the torque to be applied at the
dynamometer is give by τ:Paper Number

The Dynamic Model has been implemented in
LabVIEW as a VI running on the I/O card. A
screenshot of the interface where the model
properties are configured is shown in figure 5.

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Article Title[Jun1] Jun Liu, Li Fang Wang “Research of a Novel
Bi-Directional Flexible Load for Electric Vehicle Test
Bench”. Applied Mechanics and Materials, 2011,
pp55-57, 512[Kno1] Knowles M.J. Kok D. Baglee D. And Morris
A.C. “Design and Development of an Electric Vehicle
Drive Train Test Bed”. To appear in Proceedings of
The Ninth International Conference on Condition
Monitoring and Machinery Failure Prevention
Technologies (BINDT CM 2012) June 2012
[Kno2] Knowles M.J Scott H. Baglee D. “The effectof driving style on electric vehicle performance,
economy and perception”. Submitted to International
journal of electric and hybrid vehicles, April 2012

Fig.5. Dynamic model screenshot[Lar1] Larminie J. and Lowry J. “Electric Vehicle
Technology Explained”. John Wiley and Sons. 2003
0-470- 85163-5.

Conclusions and future work
The benefits and difficulties involved in connecting adriving simulator to an EV hardware test system toallow human control of hardware tests have been
described. Work is ongoing to achieve successful
integration and proof of the concept of human-in-theloop hardware testing. Once the concept is proved itwill be necessary to develop suitable test strategies
to ensure the maximum benefit is achieved from thesystem.

[Lee1] Lee, Woon-Sung. "Compensation ofTransport Delay in Driving Simulator," American
Control Conference, 1991, pp.1050-1055, 26-28 June
1991

References[Mid2] Middleton, H., Litchfield, D. & Westwood, D.,“Gaze transitions of young novice, young
experienced and older experienced drivers at asimulated junction,” Vision-in-Vehicles 11, Dublin,
Ireland, 2006.

[Mid1] Middleton, H., Litchfield, D., and Westwood,D., "Driving performance and gaze behaviour ofyounger and older drivers at a simulated junction"
26th International Congress of Applied Psychology,
Athens,Greece, 2006.

[Ado1] Adornato B. Patil R. Filipi Z. Baraket Z.
Gordon T.
“Characterizing naturalistic driving
patterns for Plug-in Hybrid Electric Vehicle analysis”.
IEEE Vehicle Power and Propulsion Conference,
2009. VPPC '09. Pp 655 – 660.

[Mid3] Middleton, H., Westwood, D., Robson, J. &Kok, D. “Assessment and decision criteria for driving
competence in the elderly” In G. Underwood (Ed.).
Traffic and Transport Psychology (pp. 101-113)
Elsevier Ltd. UK.

[Bou1] Bouscayrol A. “Different types of hardware-inthe-loop simulation for electric drives”. IEEE
International Symposium on Industrial Electronics,
2008, pp 2146 – 2151.

[Ros1] Rosario L.C. “Power and Energy
Management of Multiple Energy Storage Systems inElectric Vehicles”. PhD Thesis, Cranfield University.
2007.

[Cro1] Crolla, D. A., Ren, Q., Eldemerdash, S. & Yu,
F. “Controller design for hybrid vehicles - state of theart review”. Vehicle Power and Propulsion
Conference, 2008. VPPC '08. IEEE. Harbin, China.

[Saj1] Sajith A.H. Nagendra Babu S. Vinay Kumar
and Prakash R. “Optimisation of an Electric Drive
Train for “On Road” Electric Vehicle”. SAE TechnicalPaper. 2009. http://papers.sae.org/2009-26-0041[Hiw1] Hiwatari R. Ikeya T. Okano K. Yamamoto H.
Ito T. Takagi M. Iwafune Y. Yamaji K. “A New
Approach to Analyze Effectiveness of Charging
Infrastructure for Electric Vehicle by Road Traffic
Simulator”.
SAE
Technical
Paper,
http://papers.sae.org/2011-39-7216

Paper Number-6-

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Article Title[Sco1] Scott H. Knowles M. Morris A. Kok K. “Therole of a driving simulator in driver training to increase
fuel economy”. Submitted to Driving simulator
conference 2012, Paris, September 2012.
[Wal1] Waldowski P. Schuppel F. Schindler V.
“Battery Optimisation in Electric Vehicles and Range
Extended Electric Vehicles based on Measured
Individual Operational Profiles”. VDE Kongress 2010
– E-Mobililty. Leipzig.
[Zha1] Zha H. and Zong Z. “Emulating Electric
Vehicle’s Mechanical Inertia Using an Electric
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Paper Number-7-

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