The Range Comfort Zone
of Electric Vehicle Users
Concept and Assessment
Thomas Franke, Madlen Günther, Maria Trantow,
Nadine Rauh, Josef F. Krems
Usable range as challenge for BEV users
Prolog | Concept | Assessment | Results | Conclusion
3
 Challenge:
emissions
2.5
2
– improvement of BEV range = key challenge
– Battery size = ecological footprint & cost effectiveness
 Objective:
ICE
BEV
1.5
1
0.5
0
lifetime mileage
– provide users with maximum mobility resources (i.e., usable range)
based on a given battery capacity
– safeguarding an optimal user experience (i.e., avoid range anxiety)
 Possible solutions:
– Driver information and assistance systems, training approaches, …
 Task
for human factors research:
– Evaluation of utility of range optimization strategies
 Research
objective:
– Examine comfortable range as a possible benchmark variable
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
1
The Range Comfort Zone
(1) Concept…
European conference on Human Centred Design for
Intelligent Transport Systems 2014
2
Understanding usable range
Prolog | Concept | Assessment | Results | Conclusion
 Technical range
vs. usable range:
– Technical range (cycle range): objective range – assessment: driving cycle
– Usable range: really comfortably accessible range – assessment: ???
 Adaptive control of
range (ACOR) framework – 3 psychological range levels:
– Competent range – maximum achievable
– Performant range – everyday available
– Comfortable range – really usable (accessible) range
 Comfortable range
= benchmark variable
– Users’ preferred range safety buffer
– Configuration of available resources & resource needs, still “best feeling state”
 Comfort zone
concept – control theoretic models of driver behavior
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
3
The Range Comfort Zone
(2) …and Assessment
European conference on Human Centred Design for
Intelligent Transport Systems 2014
4
Comfortable range scenario task (CRST)
Prolog | Concept | Assessment | Results | Conclusion
 Scenario description:
– … Imagine you are on a trip with your BEV on a familiar road in a rural area ... 20°C …
– … and you still have 60 km to drive before reaching your destination.
– There are no charging possibilities en route. Yet, at the destination, … opportunity to
recharge …
 Response grid:
1. I am
I Iwill
reach
thecar
destination
withtrip.
my EV.
2. I sure
wish
had
another
to reaching
make this
3. I am
concerned
about
the
destination.
4. On this trip, I will not be worried
about range.
 Item scores:
– i1: 77.5 km
– i2: 72.5 km
– i3: 72.5 km
– i4: 82.5 km
 Mean
77.5
72.5
72.5
82.5
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
score:
– M = 76.25 km
5
Further comfortable range indicators
Prolog | Concept | Assessment | Results | Conclusion

Minimum range safety buffer (MinBuff):
– “Which range buffer do you set for yourself, below which you would not be
willing to drive the BEV anymore (except in exceptional circumstances)?”
 Proportional
range safety buffer (PropBuff):
– “In general, I want to have a safety buffer of x% in the battery. That is: What
percentage should the displayed range be above the total trip distance?”
 Comfortable
trip distance (ComfDist):
– scenario description very similar to the CRST
– “If the BEV shows a range of 100 km, I would still feel good about driving a
total distance of up to x km” (ComfDist100).
 second item: “100 km”  “50 km”(ComfDist50).
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
6
The Range Comfort Zone
(3) …and Results
European conference on Human Centred Design for
Intelligent Transport Systems 2014
7
Data basis
Prolog | Concept | Assessment | Results | Conclusion
BMW ActiveE Leipzig – long-distance commuter
field trial (2012-2015)
N
= 75 private users, 3 months BEV use
 Selection
 BEV:
 For
criteria: at least 90 km driving distance per day
BMW ActiveE, around 130-160 km range
present talk: data from usage phases 1-2 (N = 29)
MINI E Berlin 1.0 & 2.0 field trials (2008-2011)
N
= 110 private users, 6 months BEV use
 Urban
 BEV:
mobility
MINI E, around 160 km range
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
8
Results based on CRST
Prolog | Concept | Assessment | Results | Conclusion
study
LDC
(CRST)
ME1
(RG)
ME2
(RG)
time
point
T0
T1
T0
T1
T0
T1
N
M
M%
α
27
27
37
37
17
17
71.6 km
67.2 km
84.6 km
81.2 km
81.8 km
79.1 km
84%
89%
71%
74%
73%
76%
.93
.97
.91
.94
.91
.93
pT0T1
dT0T1
rT0T1
.005
0.58
.70
.019
0.40
.51
.127
0.39
.43
Note. M% is proportional comfortable range utilization, α is Cronbach's Alpha,
p-values are two-tailed, RG is range game (earlier version of CRST).
 Comfortable
range vs. real range utilization behavior:
– LDC: r = -.43, p = .027
 CRST at T1
 minimum displayed SOC of a user over entire trial
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
9
Results based on further indicators
Prolog | Concept | Assessment | Results | Conclusion
item
M
M%
M
PropBuff
M%
M
ComfDist100
M%
M
ComfDist50
M%
MinBuff
T0
T0+1
T1
T2
pT0T1
13.8 km 14.3 km 7.4 km 6.9 km
<.001
12.4%
15.0%
11.1%
9.9%
.227
88%
85%
89%
90%
85.0 km 80.9 km 92.1 km 93.9 km
.002
85%
81%
92%
94%
39.1 km 37.2 km 43.2 km 44.7 km
.089
78%
74%
86%
89%
dT0T1
0.74
0.23
0.63
0.33
Note. M is in original item units, M% is proportional comfortable range utilization,
p-values are two-tailed.
 Comfortable
range vs. real range utilization behavior:
– MinBuff r = .44, p = .017
– PropBuff r = .37, p = .046
– ComfDist100 r = -.54, p = .003
– ComfDist50 r = -.62, p < .001
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
10
Conclusion
Prolog | Concept | Assessment | Results | Conclusion
 Methodology
may provide a valuable tool for
evaluating range-optimization strategies
 However:
–Also some thing to keeps in mind
when using this method
–…and possible further improvements
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
11
Thanks a lot for your attention!
Contact: ActiveE@tu-chemnitz.de
For further reading on comfortable range see for example:
Franke, T., & Krems, J.F. (2013). Interacting with limited mobility resources: Psychological range levels in
electric vehicle use. Transportation Research Part A: Policy and Practice, 48, 109-122.
Franke, T., Neumann, I., Bühler, F., Cocron, P., & Krems, J.F. (2012). Experiencing range in an electric vehicle understanding psychological barriers. Applied Psychology: An International Review, 61(3), 368-391.
Franke, T., Cocron, P., Bühler, F., Neumann, I., & Krems, J.F. (2012). Adapting to the range of an electric vehicle
– the relation of experience to subjectively available mobility resources. In Valero Mora, P., Pace, J.F.,
Mendoza, L. (Eds.). Proceedings of the European Conference on Human Centred Design for Intelligent
Transport Systems, Valencia, Spain, June 14-15 2012 (p. 95-103). Lyon: Humanist Publications.
This study was funded by the German Federal Ministry for the
Environment, Nature Conservation, Building and Nuclear Safety.
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
12
Conclusion
Prolog | Concept | Assessment | Results | Conclusion
 Methodology may provide valuable
tool for evaluating range-optimization
strategies
 Things to keep
in mind:
– High degree of variability among individual scores (individual differences)
 Interpretation in absolute sense: consider other statistical parameters (e.g.,
80th percentile of range safety buffers)
 Design for all approach!
– Comfortable range only one of three psychological range levels in ACOR model
 CRST focuses on gap between comfortable & performant range
 Yet: range elasticity also important design goal for range optimization
– Gap between performant & competent range
– Partly addressed in CRST, yet more direct assessment of perceived range
elasticity advisable
European Conference on Human Centred Design for
Intelligent Transport Systems 2014
13
Download

The Range Comfort Zone of Electric Vehicle Users Concept and