=Paper=
{{Paper
|id=Vol-2399/paper06
|storemode=property
|title=Pre-Estimation of Electric Vehicle Energy Consumption on Unfamiliar Roads and Actual Driving Experiments
|pdfUrl=https://ceur-ws.org/Vol-2399/paper06.pdf
|volume=Vol-2399
|authors=Toshiaki Uemura
|dblpUrl=https://dblp.org/rec/conf/vldb/Uemura19
}}
==Pre-Estimation of Electric Vehicle Energy Consumption on Unfamiliar Roads and Actual Driving Experiments==
https://ceur-ws.org/Vol-2399/paper06.pdf
ceur-ws.org/Vol-2399/paper06.pdf
Pre-Estimation of Electric Vehicle Energy Consumption on
Unfamiliar Roads
and Actual Driving Experiments
Toshiaki Uemura
supervised by Takashi Tomii
Yokohama National University
uemura-toshiaki-xh@ynu.jp
ABSTRACT As described in this report, we propose a system that sup-
For this study, we constructed a system for pre-estimation ports an EV user’s travel planning on unfamiliar roads. We
of electric vehicle (EV) energy consumption on unfamiliar present a solution for pre-estimating the EV energy con-
roads. Drivers of EVs must make plans based on estimated sumption range: minimum energy consumption Emin and
energy consumption because they fear that an EV might run maximum energy consumption Emax . We present Emin and
out of power and stop on the road. Our constructed system Emax to assist planning. In addition, if Emin and Emax are
pre-estimates ranges at which an EV can be expected to correct, actual energy consumption Ereal is in the range of
be forced to stop on a road. The range is calculated using Emin – Emax . Therefore, to evaluate the accuracy of the
EV driving simulation on a route that is input by a driver. proposed system, we conducted EV driving experiments on
The driving simulation requires only map data and the EV roads with two conditions and confirmed that Ereal is in the
car specifications. Moreover, we assessed a system using a range of Emin – Emax .
real EV. Results show that the system produced sufficiently
correct ranges on the roads used for experimentation. Ad- 2. RELATED WORKS
ditionally, we evaluated the accuracy of ranges output by Studies of many types have estimated EV energy con-
our system. For evaluation, we used numerous accumulated sumption and therefore the EV travel range. Using a mo-
daily driving logs for EVs. tion equation model and actual driving logs collected by a
probe car, most of these studies have produced methods to
calculate EV energy consumption or travel range. Grubwin-
1. INTRODUCTION kler et al. estimated EV energy consumption from statistical
In recent years, energy-efficiency and CO2 emission re- analysis of driving data generated from large amounts of col-
duction have become necessary worldwide because of cli- lected driving data[5]. Ito et al. estimated EV travel ranges
matic variation and scarcity of fossil fuels. Given that back- from averaging energy consumption maps from a probe car
ground, electric vehicles (EVs) are attracting global atten- database[6]. Zhang et al. proposed estimation of EV travel
tion. Reportedly, EVs present the benefit that so-called range using driving logs, traffic conditions, and weather[12].
well-to-wheels CO2 emissions are lower than those of in- Styler et al. proposed a means of controlling a Range EX-
ternal combustion vehicles (ICVs). In addition, EVs have tender (REX) EV more efficiently using estimated energy
no emissions when they are running. Many countries have consumption generated from probe car data[9]. Yang et al.
formulated EV deployment goals for the future. Therefore, proposed a means of estimating energy consumption and
EVs are expected to penetrate markets gradually worldwide. CO2 emissions from average speed and stop frequency data
Nevertheless, many difficulties arise when a user operates acquired by passage sensors at an intersection[11].
an EV. One is the difficulty of EV travel planning when Moreover, many studies have solved optimization prob-
a user navigates unfamiliar roads. Planning must be done lems of energy consumption and driving using motion equa-
while considering an EV travel range and when and where tions and other data. Karbowski et al. proposed a means
one might stop at a charging station. However, EV travel of controlling plug-in hybrid EVs (PHEVs) using an energy
ranges change drastically because of road gradients and traf- consumption simulation generated from traffic, road maps,
fic conditions. Therefore, average users have difficulty mak- and Markov Chain[7]. Kurtulus and Inalhan proposed a
ing a precise plan for unfamiliar routes. route decision algorithm for REXEV considering energy con-
sumption calculated from traffic, weather, maps, and the
destination[8]. De Souza et al. proposed a traffic assign-
ment algorithm that minimizes EV travel time and energy
consumption[1]. Felipe et al. estimated energy consump-
tion using an artificial neural network into which driving
styles and route features are input[4]. Fei et al. proposed
hybrid models incorporating a motion equation model and
a machine learning model[3]. Unlike these studies, we make
Proceedings of the VLDB 2019 PhD Workshop, August 26th, 2019. Los our contribution by evaluating the practicality of our system
Angeles, California. Copyright (C) 2019 for this paper by its authors. using large amounts of data acquired in di↵erent regions.
Copying permitted for private and academic purposes.
Table 1: Variables of Expression (1) Table 2: Experiment Trips
Variable Unit Meaning How to get Route ID Distance Type Charging Spot
gravitational kitami 1 163 km Long 121 km point
g m/s2 constant
acceleration kitami 2 126 km Long 67 km point
⇢ kg/m3 air density constant 85 km point
rolling kitami 10 163 km Long
121 km point
µ - resistance constant outward 27 km Short do not care
coefficient homeward 24 km Short do not care
air resistance car
Cd -
coefficient specification
face area of car
A m2
the vehicle specification
mass of car
M kg
the vehicle specification
inertia mass of car
Mi kg
the vehicle specification
↵ rad road gradient map data
constant
v m/s velocity
speed is set
conversion car
⌘ -
efficiency specification
3. PRE-ESTIMATION SYSTEM
To pre-estimate EV energy consumption, the EV user in-
puts only an origin and a destination and anticipated stop
locations (sightseeing spots or stores, etc.) during a trip.
Next, the system generates origin-destination (OD) trip sim-
ulation logs running on candidate routes at a constant speed Figure 1: Altitude and Distance of Experiment
vc from these inputs. Trip simulation logs are normalized Trips.
by time. We set speed vc in advance, for example, a speed
limit on a road. accelerates and decelerates when stopping at every stop lo-
Then, trip simulation logs are input to an EV energy con- cation and every signal, assuming bad conversion efficiency
sumption model. We use a model based on a motion equa- during acceleration and deceleration.”
tion[2]. Then Emin and Emax are calculated from outputs Therefore, we define Emax as shown in Expression (3)
of the EV energy consumption model. because we want to express it easily. Eacc is described in
Expression (4). Additionally, N stands for the number of
3.1 Emin Calculation stops when an EV stops at every stop location and every
signal.
This subsection presents a description of minimum energy
Emin calculation. For this report, Emin is defined as “energy
consumption when an EV runs at constant speed vc and does Emax = Emin + Eacc [kW h] (3)
not stop.”
First, an EV energy consumption log is calculated every
second by inputting a trip simulation log into the EV en- 1 1
Eacc = N ⇥ (M + Mi )vc2 ⇥ [kW h] (4)
ergy consumption model. Expression (1) represents the EV 2 ⌘
energy consumption model. Table 1 presents variables of In Eacc , we consider two situations. First, an EV makes no
Expression (1), in which c represents 1/3600/1000 J/kW h, gains from kinetic energy through regenerative braking when
and t denotes a time. slowing the vehicle. Second, we chose ⌘ = 0.7 empirically
1 for estimating the worst conversion efficiency.
et = c(( ⇢Cd Av 2 + µM g cos ↵ + M g sin ↵
2
dv 1 4. EXPERIMENT
+(M + Mi ) ) ⇥ ⇥ v) [kW h] (1)
dt ⌘ This section presents comparison of Emin and Emax with
Ereal . The actual energy consumption was Ereal for our
Finally, Emin is calculated as the summation of et (Ex- experiment. We used trips of two types for experimenta-
pression (2)). Also, n represents the number of simulation tion: long trips and short trips (Table 2). Figure 1 presents
logs of an OD trip. the altitude and distance of experiment trips. We ignored
n
X charging spots on short trips because the trip distance is
Emin = et [kW h] (2) sufficiently short that additional charging is not required.
t=0
4.1 Long trips
3.2 Emax Calculation
This subsection presents our description of how to calcu- 4.1.1 Experiment conditions
late maximum energy Emax . We define Emax as “energy We conducted EV driving experiments for long trips in
consumption when an EV runs at constant speed vc , and Hokkaido in 2017 and 2018. Hokkaido has an area that
Figure 2: Experiment Routes in Hokkaido in 2017.
Figure 4: Ereal in Outward Trips.
Figure 3: Experiment Routes in Hokkaido in 2018.
is the longest interval in Japan between charging stations.
In this area, a fundamental problem arises of whether an
EV can arrive at the next charging station after it leaves
a charging station. Therefore, we conducted experiments
on three routes to ascertain which route is best for an EV
driver.
We simulated kitami 1 and kitami 2 using the system in Figure 5: Ereal in Homeward Trips.
2017. Figure 2 shows kitami 1 and kitami 2 routes. We
designated charging points as CPs. The kitami 1 travel 4.2.1 Experiment conditions
distance is greater, but the elevation di↵erence is smaller. After we accumulated EV energy consumption logs[10] in
Furthermore, the kitami 2 travel distance is shorter, but a database, we evaluated the accuracy of Emin and Emax
the elevation di↵erence is greater. We simulated kitami 10 using EV energy consumption logs accumulated from daily
in 2018 (Fig. 3) because a new charging station is located commuting. We therefore accumulated a large amount of
there. one commuter’s data. The total number of trips was 786:
outbound trips were 434, homeward trips were 352. There-
4.1.2 Experiment results fore, we use these logs as Ereal . We then compare Emin and
Table 3 shows the pre-estimated results. We conducted Emax with Ereal to evaluate their accuracy.
EV driving experiments using a Nissan Leaf (Nissan Motor
Co. Ltd.) as the experiment EV in 2017 and 2018. We used 4.2.2 Experiment results
estimated energy consumption logs[10] calculated from GPS Table 5 presents pre-estimated results. Figures 4 and 5
data as Ereal . In 2017, the experiment EV’s remaining bat- portray histograms of Ereal values. These graphs show that
tery charge was the equivalent of 13.4 kWh when its battery any Ereal values are always between Emin and Emax .
was 80% charged. That charge was achieved through charg-
ing time of 30 min. We selected and ran kitami 2 because 4.3 Overall experiment results
kitami 1 Emin was 15.27 kWh (greater than the 13.4 kWh
As shown in Figure 6, we verified that any Ereal values are
@80%), as Table 3 shows. These calculations indicate that
always between Emin and Emax , even though EVs run on
the EV can run the whole kitami 10 route if the remaining
long trips or short trips. Ereal of outward and homeward
battery charge is greater than 15.01 kWh. The experiment
are mean values of numerous accumulated trips.
EV was 16.7 kWh @100% (more than 15.01 kWh). There-
fore, we chose the kitami 10 route for the 2018 experiment.
Table 4 shows Ereal for the actual driving experiments. We 5. CONCLUSION
can infer that the system outputs “Emin and Emax ” are As described in this report, we proposed a system for pre-
correct because Ereal is between Emin and Emax . estimation of maximum and minimum electric vehicle (EV)
energy consumption for use with unfamiliar roads. We de-
4.2 Short trips fined the minimum energy consumption is achieved when an
Table 3: Emax and Emin in Long Trips
Origin Origin CP CP CP CP
Route ID to CP to CP to next CP to next CP to Destination to Destination
Emax Emin Emax Emin Emax Emin
kitami 1 19.17 kWh 15.27 kWh - - 6.62 kWh 5.88 kWh
kitami 2 12.20 kWh 9.18 kWh - - 5.95 kWh 5.75 kWh
kitami 10 15.01 kWh 11.98 kWh 4.16 kWh 3.29 kWh 6.62 kWh 5.88 kWh
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