Safety and failure analysis of electrical powertrain for fully
electric vehicles and the development of a prognostic health
monitoring system
A R Ruddle*, A Galarza†, B Sedano †, I Unanue‡, I Ibarra* and L Low*
*MIRA Limited, UK (e-mail: alastair.ruddle@mira.co.uk), †CEIT, Spain, ‡Jema, Spain
Keywords: electrical powertrain; fully electric vehicle;
hazard analysis; safety.
Abstract
As it is not practicable to address the entire vehicle in the
HEMIS project, this analysis focuses on those elements of the
system that are important for the PHMS and the electrical
powertrain components that this system aims to monitor.
One of the main aims of the EC project HEMIS is to design a
prognostic health monitoring system for electrical powertrain
components in order to enhance the safety and maintainability
of electric vehicles. This paper outlines some of the
preliminary work carried out in the areas of safety and failure
analysis, based on a generic architecture that aims to represent
common features of a wide range of electric vehicles.
Some assumptions concerning the nature of the vehicle were
required in order to describe the vehicle architecture at a level
that is suitable for analysis. Firstly, it is assumed that the
target application for the HEMIS PHMS is probably a nearfuture, high-end passenger vehicle. Previous EU research
projects (e.g. [1]–[3]) indicate that vehicles of this type can be
expected to be equipped with the following:
1 Introduction
x
Progress towards mass production of hybrid and electric
vehicles presents vehicle manufacturers with new challenges
due to the relative immaturity of the new technologies that are
involved. The most notable of these is the electrical
powertrain, comprising the electric traction machine and its
associated power electronics controller. The defining feature
of fully electric vehicles (FEVs) is that they are wholly reliant
on the electrical powertrain to provide traction. One of the
main aims of the EC project HEMIS is therefore to design a
Prognostic Health Monitoring System (PHMS) for the electric
powertrain in order to enhance the safety and maintainability
of FEVs. In order to achieve this, a generic electric vehicle
architecture has been defined and analysed in order to
investigate relevant safety and reliability issues and hence
derive requirements for the PHMS.
x
x
an in-vehicle data network, partitioned between a
number of “functional domains”;
car-car/infrastructure communications capabilities;
Advanced Driver Assistance Systems (ADAS).
Although the physical and electrical architectures of
alternative powertrain vehicles vary widely, the focus of the
HEMIS project is fully electric vehicles (FEVs) as defined in
the context of the European Green Cars Initiative [4], which
includes:
x
x
x
2 Generic electric vehicle architecture
electrically-propelled
vehicles
that
provide
significant driving range on purely battery-based
power;
including vehicles with range extenders;
including small light-weight passenger and light duty
vehicles.
The FEV concept therefore includes series hybrid
architectures and vehicles equipped with other energy sources
such as fuel cells, as well as the purely battery powered. Thus,
the initial assumptions that have been made concerning the
electrical powertrain for the purposes of HEMIS are that:
An underlying architecture is required as an input to the
RAMS (Reliability, Availability, Maintainability and Safety)
analysis tasks in the HEMIS project. As the target market for
the HEMIS PHMS is a broad class of electric vehicles, rather
than any specific vehicle, the architecture that is defined has
to be generic, representing the common features of electric
vehicles that are relevant to the HEMIS PHMS. The generic
electric vehicle architecture that is proposed must therefore
reflect a balance between the desire to make the analysis
generic (and therefore high level) whilst also considering
sufficient detail to make the RAMS analysis practicable. It is
anticipated that the architecture may need to be further
developed in the course of the RAMS analysis activities.
x
x
x
traction power is provided only via electrical
machines, and not mechanically from any on-board
source such as an internal combustion engine (ICE);
the electrical machine may be operated as a traction
motor, or as a generator under braking conditions;
the vehicle contains at least one such machine, but
possibly more (e.g. one in each wheel, or one for
each axle).
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x
The assumed network architecture, based on [3], is illustrated
in Figure 1, while Figure 2 provides a functional view of the
generic FEV architecture and its external interfaces.
electrical energy storage is provided by a high
voltage traction battery, as this is the most
commonly used solution;
It is assumed that energy may be obtained from:
Vehicle Backbone Network
x
x
x
the electricity grid (by conductive or inductive
charging – note that the latter may be achieved by
wireless power transfer, during which the vehicle
may active but temporarily stationary above a source
coil embedded in the road, or possibly even while in
motion as in [5]);
energy recovery during regenerative braking;
possibly from an on-board energy source (which
could be an ICE or turbine coupled to a generator, or
a fuel cell system generating electricity).
Powertrain
Controller
Charging
Power
Interface
Charging
Data
Interface
Chassis & Safety
Controller
Battery Management
System
Driver
Interfaces
ACC System
Powertrain Thermal
Management
Powertrain Sensors
(e.g. PNRD, driver
pedals, speed etc.)
On-board Energy
Source
Powertrain Domain
Network
Lighting
Control
Passive Safety
Airbag
LV
Source
Bus
Mobile Device
Telephone
Infotainment
Domain
Network
Seat
Control
Body Electronics
Domain Network
Diagnostic
Systems
Infotainment
Interfaces
Powertrain
Demand
Driving
Demand
Diagnostic
Interface
Nearby
Vehicles
ACC Radar
Antenna
LV DC Power Bus
ACC
System
Steering
Chassis
Sensors
CHASSIS AND
SAFETY
INFOTAINMENT
Pedal and
Direction
Sensors
BODY ELECTRONICS
Battery Management
System
Navigation
Figure 1: HEMIS generic FEV architecture: network view.
COMMUNICATIONS
Vehicle Backbone Network
Auxiliary
LV Battery
HV DC
Source
Bus
Display/
Video
Chassis & Safety
Domain Network
Functional Domains
Charging
System
USB
Audio
Climate
Control
Steering Sensors
(steer angle)
Chassis Sensors
(e.g. yaw rate,
lateral/longitudinal
acceleration etc.)
PHMS Analysis and
Warning
Door
Modules
Instrument Panel
Energy
System
Infotainment
Controller
Instrument
Panel
Environmental
Sensors
Passengers
Communications
Antennas
Body Electronics
Controller
Braking System
Driver
External
Networks
DSRC
Bluetooth
Inverter Controller
In addition, it is assumed that the vehicle is equipped with a
HEMIS PHMS that is focused on the electrical transmission
components (i.e. the electrical machine(s) and their associated
power electronics and control systems).
Vehicle Charging
Station
Communications GPS/Galileo
UMTS
Unit
Diagnostic
Interface
HEMIS PHMS
Steering
Sensors
EM Field
Monitor
Braking
System
Analysis &
Warning
POWERTRAIN
Powertrain
Controller
HV Traction
Battery
DC/DC
Converter
Thermal
Bus
Transmission
Sensors
Powertrain Domain Network
Powertrain Thermal
Management
Thermal
Bus
Wheels
Control
Mechanical
Thermal
High voltage (HV)
Low voltage (LV)
Signals/Data
Gas
On-board Energy
Source
HV AC
Power
Bus
Electrical
Machine
Electrical Transmission
Vehicle
HV DC Power Bus
Air Intake
Exhaust
Transmission
Mechanical
Transmission
Radiator
Environment
Atmosphere
Figure 2: HEMIS generic FEV architecture: functional view.
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R
o
a
d
required as an input to the hill-hold function that is
implemented by the Powertrain Controller and Electrical
Transmission.
3 Main vehicle functions
In Figures 1–2 it is assumed that the vehicle systems are
distributed amongst five functional domains, described as
Powertrain, Chassis and Safety, Body Electronics,
Infotainment and Communications. Each domain has its own
network providing intra-domain communications, and all
domains are connected to a Vehicle Backbone Network that
provides inter-domain communications. Each domain has a
domain controller that acts as a gateway to the Vehicle
Backbone Network and may also take control of domain
system functions as a back-up if the associated systems fail.
4 Vehicle hazard analysis
A preliminary hazard analysis (PHA) is carried out by
reviewing the high level functions of a system together with
its operating environment. In this way it is possible to identify
the hazards that may result when the system mission is not
fulfilled. As the PHA is intended to be systematic and
repeatable the use of guidewords is encouraged. The PHA
distinguishes between system hazards and failures, and the
system under analysis is to be considered without any
safeguards or mitigations. Furthermore, implementation
details are not relevant for this type of study.
Although the domain of primary interest in HEMIS is the
Powertrain Domain, some systems of the Chassis and Safety
Domain may also have significant interactions with systems
in the Powertrain Domain. The Body Electronics Domain is
also of interest, primarily in terms of its role in providing
information and warnings to the driver. The Infotainment and
Communication domains, however, are less significant for the
operation of the PHMS.
The generic architecture outlined in section 2 formed the
basis of the HEMIS PHA. The focus of this analysis was to
identify hazards associated with acceleration and deceleration
(i.e. unavailable, un-demanded, excessive, insufficient and
reversed), as well as those affecting vehicle handling and
stopping distance. The high level functions outlined in section
3 were assessed in order to identify functional failures that
could result in potential hazards. The functional domains that
were analysed included the Powertrain Domain and the
Chassis and Safety Domain, with the Electrical Transmission
and Energy Systems being the primary interests.
The high level functions of the systems were specified, based
on the architectural description. In the Powertrain Domain,
the Energy System provides facilities for obtaining,
generating and storing energy, as well as supplying energy to
the Electrical Transmission. The Powertrain Thermal
Management system ensures that the temperatures of key
powertrain sub-systems including the HV Traction Battery,
DC/DC Converter, On-Board Energy Source, Control, and
Electrical Machine are controlled for optimum performance.
Driver demand for the powertrain is delivered through the
Pedal and Direction Sensors, which monitor the accelerator
pedal and PNRD (Park, Neutral, Reverse and Drive) selection
lever.
The hazard identification was carried out in two parts: the
first part identified hazards related to functional failures of the
system, while the second part identified non-functional
hazards that are inherent in the novel technologies assumed in
the vehicle. The latter included high voltage traction batteries,
high voltage electrical power networks and on-board energy
generation such as by fuel cells. The potential hazards
associated with these include fire, explosion, and exposure to
hazardous substances, and exposure to high voltages.
The basic function of the electrical powertrain (the Electrical
Transmission of Figure 2) is to supply torque to the Wheels
via the Mechanical Transmission. More sophisticated
functions of the electrical powertrain, such as idle creep, hillhold and torque vectoring, are assumed to be implemented by
the Electrical Transmission under the control of the
Powertrain Controller. A regenerative braking function is also
considered, but is assumed to be Category A as defined in [6],
essentially providing energy harvesting when the accelerator
pedal is released if the rotor of the machine is still rotating.
The objective of the PHA is to translate system hazards into
design constraints, or functional safety requirements. Once
the hazards were identified, each was assessed in terms of
their potential consequences (“severity”), probability of
exposure to the hazard (“exposure”) and opportunities for the
driver to influence the outcome (“controllability”), using
qualitative classifications described in ISO 26262 [10]. A risk
graph was then used in order to establish and classify the
associated risks in terms of the Automotive Integrity Levels
(ASILs) of ISO 26262 [10].
The primary source of braking is the Braking System of the
Chassis and Safety Domain. This system is also assumed to
provide a number of enhanced braking functions, including
anti-lock braking (mandatory since 2007), brake assist
(mandatory since 2009 [7]) of Category A as described in [8],
electronic stability control (mandatory since 2011 [9]) and an
electric parking brake. Other systems of the Chassis and
Safety domain that interact with the electrical powertrain
include an Adaptive Cruise Control (ACC) system and the
Steering Sensors and Chassis Sensors. The ACC system has a
speed control function and the Chassis Sensors include
parameters such as longitudinal acceleration, which is
In addition, fault tree analysis (FTA) and failure mode and
effects analysis (FMEA) were also used to identify the
specific systems and functions that may lead to the potential
hazards. The FMEA and FTA methods are complementary
because the focused, deductive nature of FTA may identify
failures that might be missed by the broader, inductive FMEA
approach. Conversely, the broad coverage provided by FMEA
may identify relevant failures that are outside the scope of the
more narrowly focused FTA evaluations.
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relationships between all of the potential causes, from which
one is more readily able to identify the root causes of the
problem. The FMEA approach provided a mechanism for
identifying and prioritizing those failure modes that would
require corrective action in order to ensure that functional
safety targets are satisfied.
5 Electrical Transmission architecture
The architecture of the Electrical Transmission was further
refined, as outlined in Figure 3, in order to facilitate more
detailed analysis of the components that are intended to be
monitored by the PHMS.
6 Electrical Transmission faults
Powertrain Domain
Network
LV DC
Power Bus
Inverter Controller
Voltage
Regulator
Thermal
Bus
ENERGY
SYSTEM
Gate
Drivers
HV DC
Power
Bus
Microcontroller
Inverter
Instrumentation
DC Bus
Link
Capacitor
Inverter Gates
Inverter
Control
Specific information regarding faults in automotive traction
machines and associated power electronics converters is not
readily available as these technologies are still in relative
infancy. However, some information is available concerning
similar types of equipment used in other applications.
ELECTRICAL
TRANSMISSION
Network Interface
HEMIS
PHMS
HV AC
Power
Bus
Machine
Instrumentation
Traction
Machine
Faults in the connections of electrical machines are reported
to be very unusual at voltages below 1 kV RMS [13], but
more common at higher voltages due to increased dielectric
stresses and forces on conductors. Traction battery voltages
reported for hybrid and electric vehicles range from 120 V
[14] up to 650 V [15], suggesting that connection faults are
perhaps unlikely in traction machines currently used in
automotive applications.
MECHANICAL
TRANSMISSION
Electrical
Machine
Figure 3: Details of Electrical Transmission architecture.
Based on surveys of current vehicles on the market (e.g.
[11]), as well as discussions with vehicle and machine
manufacturers, three types of traction machine were
considered. These included the squirrel cage induction
machine, permanent magnet synchronous machine, and
switched reluctance machine.
However, bearing related faults are widely reported as the
most common cause of failure in electrical machines, with
stator related faults and other rotor related faults providing the
next most significant fault categories. A breakdown of these
categories into more specific component faults is given in
Table 10, which is derived from the surveys reported in [16]
and [17]. The results from both of these surveys are very
similar, with 41% of faults bearing related, 35–36% stator
related, and 9–10% rotor related.
The major components of the three types of machine are
essentially the same (rotor, stator, bearings, windings etc.).
The main difference between them is in the implementation
of the rotor magnetic field source. In the squirrel cage
induction machine the magnetic field of the rotor is provided
by a steel cage, which comprises rotor bars and rotor end
rings. For the permanent magnetic machine the rotor
magnetic field sources are permanent magnets, typically rareearth materials. In the switched reluctance machine the rotor
magnetic field is provided by salient rotor poles formed from
soft-magnetic material projecting from the rotor core. Thus,
there are some minor differences in the failure modes, but
overall the failure behaviours are very similar.
The largest single contributor to machine failures [16]–[17]
relates to stator ground insulation faults, at 22–23%.
However, these figures are dominated by larger, higher
voltage machines with higher vibration and dielectric stress
levels, which may not reflect the characteristics of automotive
traction motors. It is also noted in [13] that bearings in large
machines are generally more reliable than those of smaller
machines. A survey of small (<75 kW), low-voltage machines
(generally squirrel cage IM) indicates that bearing faults
accounted for 95% of the machine failures, with stator and
rotor faults at only 2% and 1%, respectively [18].
Similarly, the nature of the power electronics and controller
used to drive these machines is fundamentally the same.
Thus, the failure behaviours are again very similar for the
different circuit topologies that are required to drive the
different types of machine.
In [19], however, it is suggested that electrical problems may
be much more common in induction motors used for
automotive traction applications than in similar machines
used for industrial applications due to more frequent rapid
temperature rises. These temperature changes may accelerate
insulation degradation and give rise to mechanical stresses
that could cause cracks to form at the junctions between rotor
bars and end rings in squirrel cage induction machines. Thus,
in automotive traction applications, bearing faults may not be
the overwhelming source of machine failures that the results
of [18] would appear to suggest, and insulation and rotor
related faults may also be significant contributors to electrical
machine failures.
Potential functional failure mechanisms of the generic
electrical powertrain were also analysed using FMEA and
FTA, using functions defined based on the architecture
illustrated in Fig. 3. Furthermore, Ishikawa diagrams [12]
were used to develop an overview of how electrical
powertrain faults contribute to the vehicle-level functional
safety hazards, providing a structured representation of all
causes that could contribute to produce the undesirable effect.
This approach provides a graphical representation of the
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Stator current signature monitoring may also provide
information on machine vibration characteristics without
requiring additional sensors (which may be more expensive
and less robust) or the need for access to the machine.
Moreover, the stator current is often already monitored for
other applications, such as protecting the machine against
destructive fault currents, as well as monitoring the
performance of inverters.
From a survey based on 200 products from 80 companies
[20], failures in the converter were reported to be due to faults
in capacitors (30%), PCBs (26%), semiconductors (21%) and
solder (13%). The results of another industry-based survey
[21] reports faults in semiconductors (40%), capacitors (26%)
and gate drivers (24%). These observations suggest that
power semiconductors and DC link capacitors are likely to
account for a significant proportion (perhaps 50–60%) of
possible inverter failures.
Faults associated with vibration that could potentially be
detected through their impact on stator current signature
include damaged bearings, broken rotor bars and air-gap
eccentricity [22]–[23]. For automotive applications, however,
more sophisticated signal processing techniques may be
needed to overcome the wide variations in operating
conditions that result during driving. Techniques based on
short-time Fourier Transform and Wavelet Transform
methods have been shown to be suitable for varying load
conditions [23].
7 Possible PHMS inputs
The HEMIS PHMS is intended to monitor sensors associated
with the Inverter and Electrical Machine, as well as other
vehicle parameters that may be of relevance to their
performance, in order to assess the condition of these key
components of the electrical powertrain. This information
would be used to identify faults and degradation, as well as to
predict the remaining useful life. The driver could then be
alerted to potential problems and maintenance needs, thus
enhancing reliability, availability, maintainability and safety.
Methods for detecting IGBT failures in a PWM voltage
source inverter drive for an induction machine are described
in [24], based on monitoring the stator current vector. These
approaches are reported to allow the defective semiconductor
to be identified, while data clustering techniques permit a
robust evaluation that is independent of rotor speed. The latter
is of particular interest for FEV applications because of the
variable speed and load conditions.
A review of physical characteristics used to monitor the
symptoms of common induction machine faults, based on the
reviews reported in [22] and [23], is summarized in Table 1
below.
Winding short circuit
Insulation
X
X
X
X
Vibration
X
X
X
X
Temperature
X
X
X
Partial discharge
X
X
Gaseous emission
X
X
Air-gap torque
X
Power
X
Magnetic flux
X
Acoustic emission
X
X
Stator Core
Rotor Bars
X
Rotor Core
Air-gap eccentricity
Current
Fault
Indicators
Rotor Shaft
Bearing and seals
Fault Types
X
X
X
X
X
Failures because of ageing in drive capacitors are mostly
monitored in terms of the capacitor ESR (equivalent series
resistance) from Fourier Transform analyses of current or
voltage signals [25]. In [26] it is concluded that the capacitor
ripple voltage and ripple current are good indicators of ageing
when loads are relatively constant: if not, the ratio of ripple
voltage to ripple current is used. If ripple monitoring is not
available, an alternative technique based on system modelling
is proposed to estimate the ripple voltage from the converter
input current. A real-time condition monitoring alternative is
also proposed in [26], in which the ESR and capacitance
values are estimated using a low-cost analogue circuit.
7 Conclusions
A generic electric vehicle architecture has been proposed and
used as a basis for safety analysis (using the methods of ISO
26262 as well as FMEA and FTA) in order to investigate
requirements for a prognostic health monitoring system
(PHMS) to monitor the electrical powertrain components. The
failure mechanisms associated with the electrical machine and
its associated power electronics have also been analysed in
more detail, also using FMEA and FTA methods as well as
Ishikawa diagrams.
X
Table 1: Possible indicators for faults in induction motors.
Faults associated with bearings, insulation and rotor
components are considered to be the most likely causes of
automotive traction machine failures (see section 6).
Consequently, the results shown in Table 1 suggest that
current, vibration and temperature may be useful indicators
for the HEMIS PHMS.
Opportunities for condition monitoring of electrical
powertrain components have also been briefly reviewed. For
automotive applications, however, more sophisticated signal
processing techniques may be needed to overcome the wide
variations in operating conditions that result during driving.
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Thus, combining results from a number of different sensors,
analysed using a number of different processing techniques, is
expected to improve the scope and reliability of condition
monitoring for electrical powertrain components, as well as
enhancing the associated prognosis capabilities.
[13] P. Tavner, L. Ran, J. Penman and H. Sedding,
“Condition Monitoring of Rotating Electrical
Machines”, Institution of Engineering and Technology,
London, 2008, ISBN 978-0-86341-739-9.
[14] L. Chen, J. Wang, P. Lombard, P. Lazari and V.
Leconte, “High efficiency motor design for electric
vehicles”, Proc. 2012 Flux Conf., Rome, Italy, October
2012.
[15] G. Schmid, R. Überbacher and P. Göth, “ELF and LF
magnetic field exposure in hybrid- and electric cars”,
Proc. Bio-electromagnetics Conf. 2009, Davos,
Switzerland, June 2009, Paper 9–3.
[16] P. O’Donnell, “Report of Large Motor Reliability
Survey of Industry and Commerical Installations, Part
I”, IEEE Trans. Ind. Apps., IA-21(4), July/August 1983,
pp. 853–864.
[17] O.V. Thorsen and M. Dalva, “A survey of faults on
induction motors in offshore oil industry, petrochemical
industry, gas terminals and oil refineries”, IEEE Trans.
Ind. Apps., 31(5), September/October 1995, pp. 1186–
1196.
[18] P.J. Tavner and J.P Hansson, “Predicting the design life
of high integrity rotating electical machines”, Proc. 9th
IEE Int. Conf. Electrical Machines and Drives,
Canterbury, 1999.
[19] C. Kral, H. Kappeller and F. Pirker, “A stator and rotor
fault detection technique for induction machines in
traction applications for electric or hybrid electric
vehicles”, World Electric Vehicle Association Journal,
1, 2007, pp. 184–189
[20] E. Wolfgang, “Examples for failures in power
electronics systems”, ECPE Tutorial on Reliability of
Power Electronic Systems, April 2007.
[21] S. Yang, A. Bryant, P. Mawby, D. Xiang, L. Ran and P.
Tavner, “An industry-based survey of reliability in
power electronic converters,” Proc. IEEE Energy
Conversion Cong. and Expo. (ECCE), September 2009,
pp. 3151–3157.
[22] D. Basak, A. Tiwari, and S. Das, “Fault diagnosis and
condition monitoring of electrical machines – a review”,
Proc. IEEE Int. Conf. Industrial Technology ICIT 2006,
Mumbai, India, December 2006, pp. 3061–3066.
[23] N. Mehala, “Condition monitoring and fault diagnosis of
induction motor using motor current signature analysis”,
PhD Thesis, National Institute of Technology,
Kurukshetra, India, 2010.
[24] S. Chafei, F. Zidani, R. Nsit-Said and M.S. Boucherit,
“Fault detection and diagnosis on a PWM inverter by
different techniques”, J. Elect. Sys., 4(2), 2008, pp. 235–
247.
[25] H. Ma and L. Wang, “Fault diagnosis and failure
prediction of aluminum electrolytic capacitors in power
electronic converters”, Proc. 31st IEEE Ann. Conf.
Industrial Electronics Society (IECON 2005), Raleigh,
USA, November 2005.
[26] A.M. Imam, “Condition monitoring of electrolytic
capacitors for power electronics applications”, PhD
Thesis, Georgia Institute of Technology, May 2007.
Future work will therefore include selection of the most
appropriate parameters for monitoring by the HEMIS PHMS
and the development of suitable analysis algorithms for
eventual implementation and demonstration in a prototype.
Acknowledgements
The research leading to these results has received funding
from the European Community’s Framework Programme
(FP7/2007-2013) under grant agreement nº 314609. The
authors are grateful for the support and contributions from
other members of the HEMIS project consortium, from CEIT
(Spain), IDIADA (Spain), Jema (Spain), MIRA (UK),
Politecnico di Milano (Italy), VTT (Finland) and York EMC
Services (UK). Further information can be found on the
project website (www.hemis-eu.org).
References
[1] VEESA project, “Vehicle e-Safety Architecture”, EU
project IST-2001-37598, Deliverable D0: Final Report,
29th February 2004.
[2] M. Hiller et al., “General architecture framework”,
EASIS EU Project Deliverable D0.2.4, 13th August
2004.
[3] E. Kelling et al., “Specification and evaluation of esecurity relevant use cases”, EVITA EU Project
Deliverable D2.1, Version 1.2, 30th September 2009.
[4] M. Boukerche, “ICT for Fully Electric Vehicles 4 th call
for proposals Objective GC-ICT-2013.6.7 Electromobility”, PPP European Green Cars Iinitiative Infoday,
Brussels, 9th July 2012.
[5] S. Ahn and J. Kim, “Magnetic field design for high
efficient and low EMF wireless power transfer in on-line
electric vehicle”, Proc. 5th European Conf. Antennas
and Propagation, Rome, April 2011, pp. 3979–3982.
[6] UNECE Regulation No. 13-H, “Uniform provisions
concerning the approval of passenger cars with regard to
braking”, Revision 1 – Amendment 2, 11/11/2009.
[7] Commission Regulation (EC) No 78/2009, O. J. EU,
4/2/2009, L35, pp. 1–31.
[8] Commission Regulation (EC) No. 631/2009, O. J. EU,
25/7/2009, L 195, pp. 1–60.
[9] Commission Regulation (EC) No. 661/2009, O. J. EU,
31/7/2009, L 200, pp. 1–24.
[10] ISO 26262, “Road vehicles – Functional safety”, (9
parts), November 2011.
[11] J. de Santiago et al., “Electrical motor drivelines in
commercial all electric vehicles: a review”, IEEE Trans.
Veh. Tech., 61(2), pp. 475–484.
[12] K. Ishikawa, “Introduction to Quality Control”, 3A
Corporation, Tokyo, 1990.
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