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Abstract—Range anxiety is a major barrier for the mass
adoption of electric vehicles (EVs), a contributing factor to this
is the variability of the predicted range remaining presented to
the driver in the vehicle. This study aims to better understand
the causes of potential inaccuracies and how ITS can help
resolve these issues. Eleven participants completed 141 logged
journeys, with results showing that range (as predicted by the
EV and presented to the driver) was overestimated by
approximately 50% in comparison to journey distance. Driving
style had the most significant impact on range prediction
accuracy, where a more aggressive driving style led to greater
inaccuracies. However, journey distance and type of road
driven, which can be calculated from Satnav systems, were
factors which were correlated with having a significant effect
on range accuracy. Therefore incorporating these into future
range prediction algorithms has the potential to increase the
accuracy of information and subsequently increase driver trust.
Index Terms—Electric vehicle; Driver behaviour; Driver
information systems; Intelligent transportation systems; Range
anxiety; Range accuracy; Real-World driving
I. INTRODUCTION
Electric Vehicles (EVs) have long been proposed as a
viable method to accelerate the reduction in carbon
emissions of road transport. Carbon emissions in the EU (i.e.
EU-28) have been reducing overall steadily since 1990 and
by 1.3% between 2011 and 2012, with a strong decline in
road transport emissions of 5% in 2012 alone [1]. However,
this reduction may be slightly exaggerated by the global
economic downturn which resulted in a reduction in both
passenger and freight transportation by up to 20% in some
EU countries [1]. Despite this, road transportation in general
has a key role in delivering CO2 reductions in line with the
International Energy Agency’s (IEA) “2DS” (2°C Scenario),
where the sector’s potential share of overall CO2 reductions
world-wide is predicted to be 21% by 2050. In order to meet
this share, three-quarters of all vehicle sales by 2050 would
need to be plug-in electric of some type [2].
Research supported by the Technology Strategy Board through the
WMG centre High Value Manufacturing Catapult
S. A. Birrell is with WMG, University of Warwick, Coventry, CV4 7AL,
UK (+44 24 7657 3752; S.Birrell@warwick.ac.uk).
A. McGordon is with WMG, University of Warwick, Coventry, CV4
7AL, UK (A.McGordon@warwick.ac.uk).
P. A. Jennings is with WMG, University of Warwick, Coventry, CV4
7AL, UK (Paul.Jennings@warwick.ac.uk).
Research and innovation into electrically and alternatively
powered vehicles is continuing at pace, specifically
surrounding battery technology and light-weighting. Despite
these efforts there still remain many barriers to adoption for
electric vehicles, which have thus far limited EV sales
primarily to early adopters of innovative technologies and
company fleets looking to reduce carbon emissions. A report
for the European Council suggested that the main barriers
for adoption were: high purchase price; range anxiety;
uncertainties associated with battery life; and other factors
relating to new and unfamiliar technology [3]. Range anxiety
is a common term given when drivers experience anxiety
about their car’s ability to cover the distance required before
needing to be recharged. It occurs almost exclusively in EV
drivers because of limited charging infrastructure [4]. More
complex than the phenomenon of range anxiety is how to
combat it in users in order to remove one of the barriers to
mass uptake of EVs. Long term goals would be to increase
electric range to over 300 miles (similar to that of a
traditional internal combustion engine (ICE) vehicle), and /
or to make plug-in charging facilities as commonplace as
fuel stations are today. Both of these are vastly expensive
options in terms of research and infrastructure costs, and are
unlikely to be achieved in the next 10 years. Intelligent
Transportation Systems (ITS) can offer solutions to these
problems in the shorter term by developing integrated
charging management and services (such as pre-booking and
payment of charging points), improved traffic management
and delivering eco-driving advice [5]. In addition by
improving route guidance to minimise energy consumption
of EVs, by taking into account congestion levels and
topography [5, 6]. However accurate range prediction, which
is subsequently presented to the diver in the vehicle,
underpins these solutions.
Range prediction in EVs is not a simple procedure with
algorithms needed which take into account aspects such as
Peukert’s coefficient, temperature and depth of discharge
[7]. The driver also has significant influence on range
predictions with the use of heating, ventilation and air
conditioning (HVAC) and auxiliary use, or driving style.
Road parameters also have an important impact on energy
usage for a journey (and hence range prediction) these
include topography, number of stop/starts and sharp turns,
traffic congestion and speed limit. Many of the parameters
mentioned above are not unique to EV range prediction but
common to ICE distance to empty calculations too. However
Defining the Accuracy of Real-World Range
Estimations of an Electric Vehicle
Stewart A. Birrell, Andrew McGordon, and Paul A. Jennings
this coupled with the reduced absolute range (300-500 miles
for an ICE compared to approximately 100 with an EV) and
lack of convenient refuelling leads to range anxiety on
behalf of EV drivers. For these reasons research has shown
that EV users deploy ‘safety buffers’ for ensuring that they
do not run out of range, these buffers have been shown to be
as much as 25% of vehicle range capacity [8, 9].
One of the key factors for increasing anxiety is a lack of
trust in the range prediction presented via the in-vehicle
display and the subsequent use of high safety buffers. The
accuracy of range prediction has also been identified as one
of the top five priorities for research and development into
ITS to support electromobility [5]. In order to facilitate
improvements in range predictions first we need to
understand where the inaccuracies are present and what
impact the have on EV range. This paper evaluates range
predictions over 140 journeys taken by 11 drivers on
familiar commuting routes during real-world driving.
II. METHODOLOGY
A. Data Collection
A 2011, right-hand drive, Nissan Leaf full battery EV was
used for the data collection. According to the owner’s
manual [10] this specific EV has a fully charged range of
‘approximately 100 miles’, adding ‘the majority of drivers
will experience vehicle ranges between 62 - 138 miles based
on the many factors that affect vehicle range’. No
modifications were made to the vehicle; however a GPS data
logger was installed for all trials.
Eleven participants took part in the trials; with journeys
taken predominately over familiar routes (i.e. commute to
and from work). Each driver typically completed six
journeys in a one week period, with a minimum of four
journeys being considered the threshold for inclusion in the
analysis. The mean age of the participants was 38 years
(Standard Deviation = 9.3 years), all were employees of
WMG at the University of Warwick, with positions from
technicians, engineers and researchers. Given the nature of
the trials those who volunteered were intrinsically interested
and curious regarding EVs and new technology; however
were novice EV drivers.
Two phases of data collection were adopted with four of
the 11 participants completing trials in both phases 1 and 2.
The two phases were adopted for two reasons, firstly to
evaluate any possible effects of range prediction over a
greater array of environmental temperature (phase 1 was UK
summer, phase 2 autumn), and secondly to allow more data
to be collected (table 1).
TABLE I
PERTINENT INFORMATION RELATING TO THE PHASES OF DATA COLLECTION
Phase 1
Phase 2
Dates
23/05/13 –
25/07/13
16/09/13 –
27/10/13
Mean Temperature
16.2 deg C
13.1 deg C
No of Participants
9
6
No of Trials
85
56
B. Procedure
All participants were emailed an information sheet
containing details of the study, they were also given some
basic information about the EV (i.e. charging, conditions of
use etc.). Also to avoid participants being placed in difficult
situations regarding the distance of journeys they wish to
take, they were informed of the range prediction accuracy
with wording similar to that contained in the owner’s
manual. Inclusion criteria for the study were simply that they
were registered to drive a University owned vehicle on the
University’s insurance policy.
On the participants first day of the trials they were given
an introduction to the driving controls and in-vehicle
systems of the EV, they were also shown how to plug in and
charge the vehicle. The principal form of data collection for
the study (see below) was data captured from the in-vehicle
systems relating to range and energy use. This data were
collected via a log sheet, which the participants were also
instructed how to complete. After a brief practice drive
participants were left to their own devices with the EV to
experience use in real-world driving scenarios.
C. Variables Collected
As indicated above data were collected via a datasheet
based on what they could read on the in-vehicle information
system (IVIS; figure 1), or what they could select as driving
related options. Route characteristics were also recorded.
Below is listed the data that were collected, and options
given to the participants to select from:
Figure 1. Nissan Leaf in-vehicle driver interface (taken from Nissan Leaf
2011 Owner’s Manual [10]).
Time and Date of journey taken
Temperature of the external environment according to
IVIS (figure 1, number 5)
Driving Mode: The EV has two selectable driving modes,
either ‘D’ or ‘Eco’. D, or Drive mode, is intended for all
normal driving and delivers all of the available power when
accelerating, with regenerative braking delivering similar
deceleration when removing the foot from the throttle as a
typical ICE vehicle. Eco mode is intended to help extend the
driving range by limiting power when accelerating, more
aggressive regenerative braking and reduced HVAC
capability. Depending on external conditions and previous
driving behaviour, when fully charged the Eco mode
predicts that between 10 and 20 more miles could be
completed on a single charge
Start and End of journey predicted range remaining
according the IVIS (figure 1, number 12)
HVAC: Heating and air conditioning use draws a
significant amount of power from the battery and hence
reduces range, therefore recording accurately its use is
essential. Options given for the driver to record were: AC;
Heater; Fan; Occasional (indicating cyclic or intermittent use
HVAC); Demist; or None. More than one option could be
recorded
AUX: Again use of the auxiliary power systems will draw
power from the battery, options available were: Radio; CD;
MP3; Satnav; 12v Charger; Lights; Wipers; or None (Status
screen). For analysis this was simplified to: Infotainment;
Navigation; Safety; or None. More than one option could be
recorded
Driving Style: In an attempt to establish driver behaviour
a self-rating of driving style was also recorded, options
included: Eco; Conservative; Normal; Progressive;
Aggressive; or Mixed. For the analysis this was simplified
to: Eco; Normal; or Progressive
Traffic flow was also estimated by the driver, options
available were: High; Med; Low; Mixed Density; or Free
flow (i.e. busy but moving freely)
Route: The percentage of driving time (not distance) on
each type of road category was estimated by participants. A
percentage of driving time was recorded for: Urban (town or
city); Rural (or countryside); Inter-Urban (major A-roads
linking conurbations); Motorway (aka freeway, highway
etc.)
Distance of the journey was also recorded.
Comments: In the final sections participants were
encouraged to make free-form comments on: Range
Estimations; IVIS; Driveability; and Charging
Statistical data analysis was conducted in SPSS 21 and
significance was accepted at p<0.05. The nominal data
(Driving Mode, HVAC, Aux, Driving Style and Traffic
Density) were analysed using a One-Way ANOVA, and the
numerical data (Temperature, Start and End range, Route
and Distance) analysed using regression analysis.
D. Normalisation
A normalisation process was needed in order to evaluate
the accuracy of the EVs ability to predict range used for a
specific journey. Simply using distance minus range used
would not accurately reflect the prediction accuracy for long
or short journeys; therefore the following equation was used:
Normalised Range = (Journey Distance – Range Used)
Journey Distance
A normalised range value of zero would indicate that the
range prediction was completely accurate (i.e. range used
equals journey distance). If the equation returned a positive
value then the EV was underestimating the range needed to
complete the journey, a negative value was an
overestimation (i.e. the range used was greater than the
journey distance).
III. RESULTS AND DISCUSSION
A. General Findings
Results from the study (table 2) show that the average
journey distance completed by the participants in this study
was 16.8 miles (SD = 13.1). The maximum journey distance
was 65.4 miles, with 56.7% of journeys being longer than 10
miles. The average range used was 25.3 miles, and
normalised range accuracy was -0.48. This suggests that the
range predicted for an average journey was almost 50%
greater than the actual journey distance. This results in a
real-world theoretical maximum range for this particular EV
(when fully charged) of approximately 70 miles. This is
similar to results found in the literature which showed
average energy consumption of 1.5% State of Charge
(SoC)/mile which extrapolated to a theoretical range of 66.7
miles (for a vehicle with a 100 mile certified range) [7], or
1.9% SoC/mile with a maximum range of 53 miles (70 mile
certified range) [11].
Results from this current study and surrounding literature
suggest that inaccurate real-world range prediction is a
common EV problem and not specific to any particular
manufacturer. Anecdotal evidence from consumer blogs
suggests that newer EV have improved range accuracy
predictions and increased real-world ranges over first
generation vehicles. This could be as result of more efficient
management of the HVAC system
1
, by presenting actual
state of charge on the in-vehicle display
2
, or over-the-air
updates to range algorithms
3
.
1
http://insideevs.com/nissan-leaf-side-by-side-range-comparison-2012-
vs-2013/
2
http://sfbayleafs.org/commentary/2013/09/2013-vs-2011-nissan-leaf-
whats-new-whats-gone-whats-changed/
3
http://www.plugincars.com/nissan-software-update-201112-leafs-
yields-promising-results-127532.html
TABLE II
MEAN RESULTS FOR EACH PARTICIPANT, INDIVIDUAL PHASES AND THE
GROUP AS A WHOLE RELATING TO RANGE ACCURACY AND
ESTIMATIONS. RANGES AND DISTANCES PRESENTED ARE IN MILES.
Start
Range
End
Range
Journey
Distance
Range
Used
Normalised
Accuracy
1
72.76
56.35
12.07
16.41
-0.22
2
96.35
61.71
23.24
34.65
-0.58
3
88.27
46.40
25.25
41.87
-0.73
4
84.00
72.17
7.07
11.83
-0.31
5
67.00
48.00
12.17
19.00
-0.53
6
73.33
50.00
15.17
23.33
-0.54
7
83.89
63.78
12.00
20.11
-0.82
8
77.00
47.15
18.44
29.85
-0.65
9
66.33
11.00
44.03
55.33
-0.19
10
76.38
28.50
31.88
47.88
-0.42
11
75.44
64.44
6.28
11.00
-0.78
Phase 1
75.19
51.31
15.96
23.88
-0.48
Phase 2
84.11
56.59
17.97
27.52
-0.47
Mean
78.73
53.40
16.76
25.33
-0.48
SD
25.23
26.20
13.09
20.60
0.75
Twenty-five (or 17.3%) of the 141 trips taken resulted a
positive (or zero in two cases) normalised accuracy (i.e.
journey distance being greater than or equal to range used;
figure 2), and of these 25 just over three-quarters were less
than 10 miles in length. Of the remaining 82.7% of trips
which had a negative normalised accuracy approximately
two-thirds of these were greater than 10 miles in length. This
highlights that the longer the trip, the more problems the EV
had with accurately predicting range. Given that many such
journey prediction systems use history based algorithms, it
could be expected that a longer journey would give the
algorithms more time to adapt to driver style or traffic
conditions, and hence become more accurate. This was not
observed in this current study with increased distance
leading to reduced accuracy.
Figure 2. Normalised range accuracy for each individual trial. Dotted line
indicates zero, or range used equates to journey distance.
Figure 3 shows the majority of trips were conducted in
Drive mode, with no AC, heating or auxiliary load drawing
power from the vehicle. Drivers were also predominantly
driving in a self-rated eco style in low traffic densities, over
a mixed driving route. These findings suggest that drivers
were actively trying to increase the range of the EV by
adopting behaviours and driving in real-world situations
which could be considered advantageous for maximising
energy usage from an EV. This further highlights the
importance of accurate range prediction for users, as shown
in this study range prediction was about 50% out even with
users being conscious of the influence of power demand on
EV range. What was not amended was driving route for
individual journeys, i.e. avoidances of high speed roads or
taking the shortest (as opposed to quickest) routes.
Figure 3. Results relating to a) Drive Mode; b) HVAC; c) Auxiliary usage
(Info means Infotainment, i.e. radio, CD or MP3); d) Driving Style; e)
Traffic Density; and f) Route.
As mentioned previously two phases of data collection
were completed – separated by 3-4 months. Statistical
analysis revealed that only ‘Temperature’ and ‘Start Range’
differed significantly (p<0.05) between the phases. Mean
temperature being 16.2 compared to 13.1° C, and start range
being 75.2 and 84.1 miles for phases 1 and 2 respectively.
B. Accuracy Correlations with Dependent Variables
When evaluating the data as a complete set of individual
trials, identifying trends within the data compared to
normalized range accuracy was problematic. The broad
range of data from the individual trials seemed to obscure
potential differences, or simply highlight that making
assumptions on range prediction was very difficult. Of the
variables assessed (as outlined in the methodology) only
‘Driving Style’ showed a statistically significant (F(1,140) =
3.01, p<0.05) effect on normalized range accuracy. Post-hoc
analysis (Bonferroni corrected) revealed that this difference
was significant between Eco and Progressive driving styles
(figure 4); showing that when adopting a self-rated Eco
driving style normalized range accuracy improved to -0.34
from -0.75 for a progressive driving style.
Figure 4. Driving style verses normalised range accuracy. Columns
represent mean data and error bars standard error. Asterisk (*) represents
significant (p<0.05) difference from the Progressive condition.
Auxiliary use showed a trend (p=0.080) for an effect on
range accuracy, with post-hoc analysis suggesting the use of
safety devices (e.g. lights and windscreen wipers) over no
auxiliary use being responsible the decreased prediction
accuracy (p<0.05). Whilst the lights of a vehicle may draw
up to 200 W of power from the batteries, this is not a huge
draw compared to the likes of the HVAC system – which
can be up to 3 kW. It is the assumption of the authors that
other driver behavior factors may be accountable for this
difference, such as increased driving speed and reduced
congestion, or reduced visibility leading to increased need
for sharper braking and hence reduced opportunities for
regenerative braking.
External temperature has been shown in previous research
to be strongly linked to battery capacity [12, 13] and hence
range of an EV. However in this current real-world study
figure 5 shows that ambient external temperature – recorded
at the start of the journey – had no significant effect (p>0.05)
on range accuracy. Reasons for this could be that start range
was already reduced due to the temperature, hence accuracy
was not actually affected. Secondly that the mean
temperature changes from 13.1 – 16.2° C were not sufficient
to significantly affect battery capacity, with lab tests
typically utilizing a far greater range in exposed
temperatures (e.g. -25 to +45° C) [12].
Figure 5. External temperature verses normalised range accuracy.
C. Analysis Between Similar Journeys
As highlighted thus far in the results, when considering
individual trips as unique data points, identifying notable
trends is very difficult (with the exception of driving style).
To aid the analysis a subset of the data were considered
separately for analysis. This subset included only journeys
taken by an individual participant which were similar in
nature, i.e. the same distance over the same route, typically a
commute to and from work. As previous results showed no
difference between the two phases of data collection, all data
were combined. A minimum of four journeys were needed
in order to be considered in the analysis. This resulted in
data from seven participants, totaling 66 individual journeys
being identified.
Figure 6. Normalised range accuracy against journey distance for all
journeys logged in the study (a), subset of journeys identified as being
similar in nature (b), and average of participants fixed journeys (c).
From this data more representative mean values for the
journeys could be calculated, as all journeys taken by
individual participants were consistent. An example of this
is illustrated in figure 6, where graph (a) shows very little
correlation when all of the data points for normalized range
accuracy are plotted against journey distance (R2 = 0.003).
When we consider each data point for similar journeys only
(graph b), a comparable pattern is observed with a slightly
stronger trend for a decrease in range accuracy as journey
distance increases (R2 = 0.136). However when we use
average range accuracy for each individual participant over
their specific journey (graph c) we see a significant
correlation between distance and normalized accuracy (R2 =
0.651).
Aspects of the route driven also showed interesting
correlations when considering this subset of data. Figure 7
shows that both the percentage of the journey completed on
either motorway or in the urban environment can have an
impact on range prediction accuracy. An increase in the
percentage of the journey completed on higher speed
motorways had a negative effect on accuracy, whereas
increased urban driving improved average range accuracy.
Despite this, data shows that for only one participant where
urban driving equated to 60% and motorway was 0% of the
total driving time, a positive normalized range accuracy
value was returned (i.e. range used was less then journey
distance). Reasons for these observations may be that an
*
increase in motorway driving may be indicative of an
increase in journey distance (and vice-versa for urban
driving) which as shown above resulted in a decrease in
range accuracy, or that motorway driving involves constant
high draw from the battery with little opportunity for
regenerative braking, which again is the opposite to urban
driving.
Figure 7. Normalized range accuracy verses % of journey completed on
either (a) motorway or (b) urban roads.
D. Subjective Comments on Range Accuracy
Participant’s subjective views were also collected at the
end of their trials, these comments are summarized below:
Participant 1: For a few of my journeys I got to my
destination with more range than I started with, other times
for he some journey it would drop by 20 miles, I have no
idea why as my driving was the same (I think)
Participant 2: Needs better damping; Range increase in
urban and plummets on motorway; 10 miles in town -
mileage estimation went from 93 to 96, battery blocks went
from full to full-2, charge time went to 2 hours, what’s the
difference? Range estimation in D feels more accurate than
E; Approx. 25 miles reduction in first 3 miles, then 10 in
next 24 miles
Participant 3: Inconsistent and confusing; Would prefer a
band rather than a single number; Estimated range seems to
bear little relationship to driving style - only regen activity;
Battery fuel gauge was far more reliable estimate
Participant 4: Fine on short distances, way out on mid-
range distances that have a short section on fast roads; Being
stuck in traffic seems to lower the range estimations on
otherwise similar drives
Participant 6: I thought the range accuracy was quite
unpredictable, steady driving would reduce range estimate
quite dramatically and this can be worrying for 25+ miles
journeys
Participant 7: It seems to go down rather quickly which
means you feel conscious of it the whole time
Participant 9: 90→12 on vehicle was actually on 56
miles!
A common theme from the subjective comments was the
unpredictable nature of the range estimations, with little
understanding as to what is the actual root cause of the
inaccuracies. One thing identified by the drivers, and
supported by the data analysis, was that range accuracy
decreased with journey distance. What is also very apparent
from the comments is the type of language used, with words
like ‘feel conscious’, ‘unpredictable’, ‘inconsistent and
confusing’, ‘no idea’, ‘dramatically’ and ‘plummets’. These
are very emotive words and are typical of range anxiety
experienced by novice users of EVs. However research has
shown that whilst range anxiety (e.g. being aware of range
while driving) does not reduce with EV experience, the
explicit worry of range anxiety (e.g. being concerned of
running out of energy while driving) does, as users increase
their ability to understand changes in the EV’s
instrumentations [14].
E. Limitations and Future Research
Whilst a large pool of individual journeys were collated
for this study enabling some interesting conclusions to be
made, the findings only describe behaviour for one particular
range estimation algorithm implemented in this specific
vehicle. In addition the authors had no control over the range
algorithm. Future studies will make greater use of the GPS
logged data to better understand objective (rather than self-
reported) driving style, but also to evaluate enhanced GPS
features such as elevation and predicted verses actual road
speeds. Utilising different EVs and an increased number of
drivers will also increase transferability of the results.
IV. CONCLUSIONS
Results from this study have shown that during real-world
EV driving trials the accuracy of the range predictions,
which are presented to the driver via the IVIS, are
overestimated by approximately 50% compared to actual
journey distance. This resulted in a real-world theoretical
maximum range for this particular EV of 70 miles. Self-
reported driving style had a statistically significant impact
on range accuracy, with the more aggressively driven
journeys experiencing less accurate range estimations.
However, external temperature was seen to have no effect on
accuracy. When considering a subset of the data, which
consisted of only similar journeys, the journey distance and
percentage completed on motorways led to increased range
prediction inaccuracies, whereas urban driving decreased
these inaccuracies.
With respect to future ITS, in the near-term results from
this study suggest that journey distance and road category
could be incorporated into future enhanced route guidance
systems (in addition to road topography and traffic
congestion) to improve the accuracy of range prediction. In
addition to this EV manufacturers should consider the
grouping of repeatable and predictable journeys for data
analysis, rather than analysis all individual trips, to refine
range algorithms as more accurate assumptions can be made.
In the longer term, further advancements could be delivered
by the self-learning car, as driver behaviour measured over
time can be incorporated to further increase range accuracy
predictions or deliver specifically tailored eco-driving
advice. However what intelligent systems will not be able to
control will be the driver behind the wheel, as the human is
still the biggest factor for increasing the available range of
an EV.
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