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Analysis of the Deviation Factors between the Actual and Test Fuel Economy

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Authors:
Masayoshi Tanishita at Chuo University
Masayoshi Tanishita
Takashi Kobayashi
Takashi Kobayashi
Takashi Kobayashi
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Abstract and Figures

The Worldwide harmonized Light duty Test Procedure saw its light first as the United Nations Economic Commission for Europe Global Technical Regulation in 2017. However, it remains unclear how much the deviation is between the actual and test fuel economy. In this study, we analyzed the deviation between the actual and test (JC08 and WLTC) fuel economy and examined how well regional characteristics such as average travel speed and temperature could explain the deviation using 182–1035 drivers and 19–52 car models data in Japan. As a result, (1) more than a 30% discrepancy was observed between the actual and JC08 mode test fuel economy, and the higher the test fuel economy, the larger the deviation; (2) regarding WLTC mode fuel economy, the deviation is 19% and constant regardless of the test fuel economy; (3) average travel speed and temperature can explain approximately 8% of the discrepancy.
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Article
Analysis of the Deviation Factors between the Actual and Test
Fuel Economy
Masayoshi Tanishita * and Takashi Kobayashi


Citation: Tanishita, M.; Kobayashi, T.
Analysis of the Deviation Factors
between the Actual and Test Fuel
Economy. Vehicles 2021,3, 162–170.
https://doi.org/10.3390/vehicles
3020010
Academic Editor: Ulugbek Azimov
Received: 26 March 2021
Accepted: 21 April 2021
Published: 22 April 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Department of Civil and Environmental Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku,
Tokyo 112-8551, Japan; kobayashi.58b@g.chuo-u.ac.jp
*Correspondence: mtanishita.45e@g.chuo-u.ac.jp; Tel.: +81-3-3817-1810
Abstract:
The Worldwide harmonized Light duty Test Procedure saw its light first as the United
Nations Economic Commission for Europe Global Technical Regulation in 2017. However, it remains
unclear how much the deviation is between the actual and test fuel economy. In this study, we
analyzed the deviation between the actual and test (JC08 and WLTC) fuel economy and examined
how well regional characteristics such as average travel speed and temperature could explain the
deviation using 182–1035 drivers and 19–52 car models data in Japan. As a result, (1) more than a
30% discrepancy was observed between the actual and JC08 mode test fuel economy, and the higher
the test fuel economy, the larger the deviation; (2) regarding WLTC mode fuel economy, the deviation
is 19% and constant regardless of the test fuel economy; (3) average travel speed and temperature
can explain approximately 8% of the discrepancy.
Keywords: fuel economy; JC08 mode; WLTC mode
1. Introduction
The fuel economy of passenger cars is shown at the time of sale based on the fuel
economy test. This is calculated from the relationship between the distance and fuel
consumption based on the “test cycle” that defines the relationship between time and
speed (acceleration) in the laboratory. Fuel economy tests are used for two primary
purposes: (1) to monitor the compliance of automobile manufacturers with fuel economy
and greenhouse gas emissions standards and (2) to inform consumers about the fuel
economy of passenger cars and light trucks [
1
]. It has been noted that there is a considerable
discrepancy between the actual and test fuel economy. This divergence can lead to errors
in CO
2
emissions reduction by policy interventions and can distort appropriate consumers’
purchase and use of cars.
The actual fuel economy varies greatly. Many factors bring about the divergence
between the test fuel economy, such as drivers (hereafter, actual fuel economy), vehicle
attributes (e.g., engine, accessories, hybrid system), driving environment (e.g., temperature,
congestion level, pavements, vertical alignments), and driver’s behavior (e.g., accelera-
tion, trip distance). It is practically impossible to collect actual fuel economy data in the
laboratory.
Lim et al. (2018) [
2
] showed that the fuel economy is affected by the aging of the engine.
Sano et al. (2010) [
3
] analyzed the gap between the actual and 10/15 test cycle mode fuel
economy in Japan and clarified the potential of eco-driving of the on-road real fuel economy
improvement. Lee et al. (2014) [
4
] analyzed the gap between the FTP75 driving cycle and
the actual fuel economy. They showed that the differences in fuel economy ranged from
3.9% to 18.5%, and the key factors were engine friction loss, torque converter loss, and
accessory loss. Zacharof and Fontaras (2016) [
5
] analyzed the NEDC-based type approval
procedure that had mainly been used in Europe. The procedure resulted in shortfall
values ranging between 25% and 35%. They also showed that increased electrical power
load (e.g., A/C, steering assist), aerodynamic alterations (roof box, aerofoils), ambient
Vehicles 2021,3, 162–170. https://doi.org/10.3390/vehicles3020010 https://www.mdpi.com/journal/vehicles
Vehicles 2021,3163
conditions (temperature, wind, rain, and altitude), driving behavior (aggressive driving,
driver training), vehicle condition (lubrication, tire condition), increased vehicle mass
(passengers, additional equipment), and road conditions (road surface, traffic conditions)
affected the in-use fuel consumption.
Fontaras et al. (2017) [
6
] reviewed the influence of different factors that affect fuel
consumption. Factors such as driving behavior, vehicle configuration, and traffic conditions
are reconfirmed as highly influential. Greene et al. (2017) [
1
] showed great variability
in individuals’ fuel economy estimates relative to the official government estimates with
a small bias relative to the sample average. For consumers, the primary limitation of
government fuel economy estimates is imprecision for a given individual rather than bias
relative to the average individual. There is evidence that shows that the shortfall between
test cycle fuel economy estimates and in-use fuel economy estimates has been increasing
since 2005. In addition, Jimenez et al. (2019) [
7
] showed that the gap was found both in
hybrid vehicles and the biggest selling vehicles. Furthermore, the average deviation rate
increased before 2015, but decreased following “Dieselgate”.
Recently, Pavlovic (2020) [
8
] showed that among all factors analyzed, the highest
contribution in the fuel consumption gap’s variance comes from the average vehicle speed,
followed by the road grade, and trip distance. In addition, the impact of driver factors is
not negligible.
After significant efforts from many parties, the Worldwide harmonized Light duty Test
Procedure (WLTP) saw its light as the UNECE Global Technical Regulation in 2017. WLTP
aims to harmonize test procedures on an international level and set up an equal playing
field in the global market. Besides EU countries, WLTP is the standard fuel economy
and emission test also for India, South Korea, and Japan. The WLTP is divided into four
sub-parts, each one with a different maximum speed:
Urban (Low, up to 56.5 km/h);
Suburban (Medium, up to 76.6 km/h);
Rural (High, up to 97.4 km/h);
Highway (Extra-high, up to 131.3 km/h).
In Japan, the “Extra-high” sub-part is not introduced, as average and max speeds are
greatly different from EU countries. The average speed is 46.5 km/h in EU countries, but
changes to 36.6 km/h excluding the “Extra-high” sub-part in Japan. Total distance also
changes from 23.27 km to 15.01 km.
The Worldwide harmonized Light vehicles Test Cycles (WLTC) mode fuel economy
is disclosed based on the average driving ratio of these modes. Pavlovic et al. (2018) [
9
]
analyzed the procedural differences between the WLTP and the New European Driving
Cycle (NEDC), which is the test-procedure that had been used in Europe. They found that
the WLTP was likely to increase the type-approval CO
2
emissions by approximately 25%
in Europe.
However, as far as the author knows, there is no report in Asia regarding the re-
lationship between WLTC and the actual fuel economy. In addition, in Japan, the JC08
mode has been used, but the extent to which it has been used has been reduced by WLTC.
Furthermore, although temperature and travel speed affects fuel economy, it is unclear to
what extent these regional factors affect the deviation.
In this paper, we analyze the divergence between the actual and JC08 modes that have
been used in Japan and the WLTC mode fuel economy using the reported data of actual
fuel economy of various car modes, regions, and drivers. In addition, we show how many
regional characteristics (temperature and average travel speed) can explain the difference
in fuel economy.
2. Materials and Methods
In Japan, the 10/15 mode fuel economy had been used since 1991 for passenger
cars. However, it was pointed out that the 10/15 mode deviated from the actual driving
condition; therefore, the JC08 mode fuel economy was introduced in April 2011. Since
Vehicles 2021,3164
then, discussions on integration have progressed around the world, and the WLTC mode
fuel economy has been set since 2017. Figure 1shows the test cycle of JC08 and the WLTC.
Compared with JC08, the WLTC has a longer cycle time and a complicated speed change.
Vehicles 2021, 3, FOR PEER REVIEW 3
2. Materials and Methods
In Japan, the 10/15 mode fuel economy had been used since 1991 for passenger cars.
However, it was pointed out that the 10/15 mode deviated from the actual driving condi-
tion; therefore, the JC08 mode fuel economy was introduced in April 2011. Since then,
discussions on integration have progressed around the world, and the WLTC mode fuel
economy has been set since 2017. Figure 1 shows the test cycle of JC08 and the WLTC.
Compared with JC08, the WLTC has a longer cycle time and a complicated speed change.
Figure 1. JC08 test cycle (left) and WLTC (right). X-axis is time (time) and Y-axis is speed (km/h). Source:
https://www.mlit.go.jp/common/001184850.pdf (accessed on 21 April 2021); https://www.naltec.go.jp/publication/regula-
tion/fkoifn0000000ljx-att/fkoifn00000060rh.pdf (accessed on 21 April 2021). (Access: 10APR2021).
Figure 2 shows the relationship between the JC08 mode and the WLTC mode fuel
economy. It can be seen that the WLTC mode fuel economy is lower than the JC08 mode.
Regarding the three fuel economies of WLTC (urban, suburban, and rural), when the
WLTC fuel economy is less than 20 km/L, the fuel economy improves in the order of urban
< suburbs < rural, but when it exceeds 20 km/L, the order is changed: urban < rural < sub-
urbs. Since hybrid vehicles use a lot of batteries, the fuel economy tends to deteriorate
when driving in rural areas.
Figure 2. Comparison of JC08 and WLTC fuel economy. (L: urban, M: suburban, H: Rural, WLTC mode
fuel economy is shown in X mark). Source: https://www.mlit.go.jp/jidosha/jidosha_mn10_000002.html
(accessed on 21 April 2021). (Access: 10APR2021).
In this study, the fuel economy ratio (=actual fuel economy/test fuel economy) is used
as an index of deviation. Using the logarithm of this fuel economy ratio as an explained
variable and using a linear mixed-effects model that can consider both fixed effects and
Figure 1.
JC08 test cycle (
left
) and WLTC (
right
). X-axis is time (time) and Y-axis is speed (km/h). Source: https:
//www.mlit.go.jp/common/001184850.pdf (accessed on 21 April 2021); https://www.naltec.go.jp/publication/regulation/
fkoifn0000000ljx-att/fkoifn00000060rh.pdf (accessed on 21 April 2021). (Access: 10APR2021).
Figure 2shows the relationship between the JC08 mode and the WLTC mode fuel
economy. It can be seen that the WLTC mode fuel economy is lower than the JC08 mode.
Regarding the three fuel economies of WLTC (urban, suburban, and rural), when the WLTC
fuel economy is less than 20 km/L, the fuel economy improves in the order of urban <
suburbs < rural, but when it exceeds 20 km/L, the order is changed: urban < rural <
suburbs. Since hybrid vehicles use a lot of batteries, the fuel economy tends to deteriorate
when driving in rural areas.
Vehicles 2021, 3, FOR PEER REVIEW 3
2. Materials and Methods
In Japan, the 10/15 mode fuel economy had been used since 1991 for passenger cars.
However, it was pointed out that the 10/15 mode deviated from the actual driving condi-
tion; therefore, the JC08 mode fuel economy was introduced in April 2011. Since then,
discussions on integration have progressed around the world, and the WLTC mode fuel
economy has been set since 2017. Figure 1 shows the test cycle of JC08 and the WLTC.
Compared with JC08, the WLTC has a longer cycle time and a complicated speed change.
Figure 1. JC08 test cycle (left) and WLTC (right). X-axis is time (time) and Y-axis is speed (km/h). Source:
https://www.mlit.go.jp/common/001184850.pdf (accessed on 21 April 2021); https://www.naltec.go.jp/publication/regula-
tion/fkoifn0000000ljx-att/fkoifn00000060rh.pdf (accessed on 21 April 2021). (Access: 10APR2021).
Figure 2 shows the relationship between the JC08 mode and the WLTC mode fuel
economy. It can be seen that the WLTC mode fuel economy is lower than the JC08 mode.
Regarding the three fuel economies of WLTC (urban, suburban, and rural), when the
WLTC fuel economy is less than 20 km/L, the fuel economy improves in the order of urban
< suburbs < rural, but when it exceeds 20 km/L, the order is changed: urban < rural < sub-
urbs. Since hybrid vehicles use a lot of batteries, the fuel economy tends to deteriorate
when driving in rural areas.
Figure 2. Comparison of JC08 and WLTC fuel economy. (L: urban, M: suburban, H: Rural, WLTC mode
fuel economy is shown in X mark). Source: https://www.mlit.go.jp/jidosha/jidosha_mn10_000002.html
(accessed on 21 April 2021). (Access: 10APR2021).
In this study, the fuel economy ratio (=actual fuel economy/test fuel economy) is used
as an index of deviation. Using the logarithm of this fuel economy ratio as an explained
variable and using a linear mixed-effects model that can consider both fixed effects and
Figure 2.
Comparison of JC08 and WLTC fuel economy. (L: urban, M: suburban, H: Rural, WLTC
mode fuel economy is shown in X mark). Source: https://www.mlit.go.jp/jidosha/jidosha_mn10_
000002.html (accessed on 21 April 2021). (Access: 10APR2021).
In this study, the fuel economy ratio (=actual fuel economy/test fuel economy) is used
as an index of deviation. Using the logarithm of this fuel economy ratio as an explained
variable and using a linear mixed-effects model that can consider both fixed effects and
random effects, we analyze how regional characteristics such as temperature and travel
speed can explain this divergence.
logyijkt/y0
j=α+xj+xkt +ui+εijkt (1)
Vehicles 2021,3165
Here,
yijkt
: actual fuel economy (km/L) of driver i, car model j, prefecture k, and month
t;
y0
j
: test fuel economy (km/L) of car model j;
α
: intercept;
xj
: car model (fixed effects);
xkt
:
average travel speed and temperature in prefecture k and month t;
ui
: random effects of
driver i;εijkt: error term. Random effect and error terms follow a normal distribution.
We estimate the following three regression models for JC08 and WLTC mode:
a. Car model (fixed effect);
b.
Car model + regional characteristics (average temperature and average travel speed);
c. Car model + regional characteristics + drivers (random effect).
The three data sources presented below were used for the analysis.
(1)
E-NENPI
E-NENPI is a system in which car users record the amount of refueling and mileage
and manage the fuel consumption of their cars. Refueling data of tens of thousands of
times a month can be obtained from more than 650,000 users nationwide. In this study,
we used data from odd-numbered months in 2016. We obtained the car model, the month
of refueling, the distance to refueling, the amount of refueling, and the user’s zip code of
registered drivers in E-NENPI.
As these are self-reported data, we removed the data based on the following four
criteria:
a. No prefecture/zip code listed;
b. No description of mileage or 0;
c. Refueling amount exceeds 97-L;
d. The mileage is 60 km or less.
The number of samples is shown in Table 1. As car models with WLTC fuel economy
are limited in 2016, the number of samples of WLTC mode is smaller than that of JC08. The
number of drivers of JC08 mode and WLTC mode is 1035 and 182, respectively.
Table 1. Number of samples, car models, postal codes, and drivers.
Test Cycle Mode Samples Car Model Postal Code Drivers
JC08 12,169 52 579 1035
WLTC 1840 19 152 182
Unfortunately, E-NENPI data contain neither temperature nor vehicle speed data of
each trip. In order to grasp the seasonal variation and regional differences, we used the
following average monthly temperature and average speed for the analysis:
(2)
Japan Meteorological Agency
Monthly average temperature data of the prefectural capital city of 47 prefectures in
Japan.
(3)
NAVITIME (https://www.navitime.co.jp/ (accessed on 21 April 2021))
Average travel speed from the center of the postal code to the city hall where all data
were obtained using the NAVITME website.
Descriptive statistics are shown in Table 2.
Figure 3shows the relationship between the average temperature and the fuel econ-
omy ratio. The difference in fuel economy is smallest when the temperature is 16.5 degrees
Celsius regardless of whether the JC08 or the WLTC mode is used as test fuel economy.
This is because the higher the temperature, the larger the difference in fuel economy due to
the use of air conditioning, and the lower the temperature, the larger the difference in fuel
economy due to mechanical loss.
Vehicles 2021,3166
Table 2. Descriptive Statistics.
Min. Mean Max. s.d.
JC08 (km/L) 8.6 21.27 35.4 7.5
WLTC (km/L) 9.8 16.84 29.7 5.6
Urban (km/L) 7.1 14.31 27.7 6.5
Suburban (km/L) 9.9 17.76 32.1 6.0
Rural (km/L) 11.5 17.81 29.1 4.8
Actual (km/L) 3.7 15.1 44.0 5.1
Distance to refueling (km) 77 467 1403 196
Amount of refueling (liter) 4.6 35.2 74.2 11.6
Average temperature (◦C) −6.6 15.6 29.8 8.2
Average travel speed (km/h) 12 28.4 53.8 8.5
Vehicles 2021, 3, FOR PEER REVIEW 5
Table 2. Descriptive Statistics.
Min.
Mean
Max.
JC08 (km/L)
8.6
21.27
35.4
WLTC (km/L)
9.8
16.84
29.7
Urban (km/L)
7.1
14.31
27.7
Suburban (km/L)
9.9
17.76
32.1
Rural (km/L)
11.5
17.81
29.1
Actual (km/L)
3.7
15.1
44.0
Distance to refueling (km)
77
467
1403
Amount of refueling (liter)
4.6
35.2
74.2
Average temperature (°C)
−6.6
15.6
29.8
Average travel speed (km/h)
12
28.4
53.8
Figure 3 shows the relationship between the average temperature and the fuel econ-
omy ratio. The difference in fuel economy is smallest when the temperature is 16.5 degrees
Celsius regardless of whether the JC08 or the WLTC mode is used as test fuel economy.
This is because the higher the temperature, the larger the difference in fuel economy due
to the use of air conditioning, and the lower the temperature, the larger the difference in
fuel economy due to mechanical loss.
Figure 3. The relationship between the average temperature and the fuel economy ratio when using the JC08 mode (left)
and the WLTC mode (right) as a test fuel economy.
Figure 4 shows a scatter plot of the regional average travel speed and fuel economy
ratio. The faster the average speed, the closer the fuel economy ratio is to 1 regardless of
whether the JC08 or the WLTC mode is used as test fuel economy, but the variation is
extremely large.
Figure 3.
The relationship between the average temperature and the fuel economy ratio when using the JC08 mode (
left
)
and the WLTC mode (right) as a test fuel economy.
Figure 4shows a scatter plot of the regional average travel speed and fuel economy
ratio. The faster the average speed, the closer the fuel economy ratio is to 1 regardless
of whether the JC08 or the WLTC mode is used as test fuel economy, but the variation is
extremely large.
Vehicles 2021, 3, FOR PEER REVIEW 6
Figure 4. A scatter plot of the regional average travel speed and fuel economy ratio using the JC08 mode (left) and the WLTC
mode (right) as a test fuel economy.
3. Results
3.1. Comparison of Actual and Test Fuel Economy
Figure 5 shows a scatter plot of the actual and test fuel economy of the JC08 and the
WLTC mode, respectively. The estimation result of the logarithmic linear model is added
by the green line. In both modes, most actual fuel economies show lower values than test
fuel economies. Interestingly, as shown in the figure, the better the JC08 mode fuel econ-
omy, the larger the difference in fuel economy between the green line and the red line.
However, the deviation is almost constant regardless of test fuel economy in the case of
the WLTC mode. It is considered that the WLTC mode fuel economy is closer to the actual
driving condition than JC08 mode fuel economy.
Figure 5. Scatterplot of actual and the JC08 mode (left) and the WLTC mode (right) fuel economy.
Figure 4.
A scatter plot of the regional average travel speed and fuel economy ratio using the JC08 mode (
left
) and the
WLTC mode (right) as a test fuel economy.
Vehicles 2021,3167
3. Results
3.1. Comparison of Actual and Test Fuel Economy
Figure 5shows a scatter plot of the actual and test fuel economy of the JC08 and the
WLTC mode, respectively. The estimation result of the logarithmic linear model is added
by the green line. In both modes, most actual fuel economies show lower values than
test fuel economies. Interestingly, as shown in the figure, the better the JC08 mode fuel
economy, the larger the difference in fuel economy between the green line and the red line.
However, the deviation is almost constant regardless of test fuel economy in the case of
the WLTC mode. It is considered that the WLTC mode fuel economy is closer to the actual
driving condition than JC08 mode fuel economy.
Vehicles 2021, 3, FOR PEER REVIEW 6
Figure 4. A scatter plot of the regional average travel speed and fuel economy ratio using the JC08 mode (left) and the WLTC
mode (right) as a test fuel economy.
3. Results
3.1. Comparison of Actual and Test Fuel Economy
Figure 5 shows a scatter plot of the actual and test fuel economy of the JC08 and the
WLTC mode, respectively. The estimation result of the logarithmic linear model is added
by the green line. In both modes, most actual fuel economies show lower values than test
fuel economies. Interestingly, as shown in the figure, the better the JC08 mode fuel econ-
omy, the larger the difference in fuel economy between the green line and the red line.
However, the deviation is almost constant regardless of test fuel economy in the case of
the WLTC mode. It is considered that the WLTC mode fuel economy is closer to the actual
driving condition than JC08 mode fuel economy.
Figure 5. Scatterplot of actual and the JC08 mode (left) and the WLTC mode (right) fuel economy.
Figure 5. Scatterplot of actual and the JC08 mode (left) and the WLTC mode (right) fuel economy.
3.2. Impact of Average Temperature and Travel Speed on the Deviation
Table 3shows the estimation results of the three regression models in JC08 mode and
WLTC mode. Average temperature and average travel speed show statistical significance.
Adjusted R-squared has improved when we added the regional factors and random effect
of drivers. Figure 6summarizes the results of the models. Other refers to the standard
error of the residuals of the regression that could not be explained by the model using
explanatory variables (car model, region, and drivers). The WLTC mode can explain the
difference in fuel economy higher than JC08 mode models. In addition, the improvement
of explanatory power by regional characteristics of average temperature and travel speed
is about 8%.
Vehicles 2021, 3, FOR PEER REVIEW 7
3.2. Impact of Average Temperature and Travel Speed on the Deviation
Table 3 shows the estimation results of the three regression models in JC08 mode and
WLTC mode. Average temperature and average travel speed show statistical significance.
Adjusted R-squared has improved when we added the regional factors and random effect
of drivers. Figure 6 summarizes the results of the models. Other refers to the standard
error of the residuals of the regression that could not be explained by the model using
explanatory variables (car model, region, and drivers). The WLTC mode can explain the
difference in fuel economy higher than JC08 mode models. In addition, the improvement
of explanatory power by regional characteristics of average temperature and travel speed
is about 8%.
Table 3. Estimation results of deviation between actual and the JC08 (top) and the WLTC (bottom)
mode fuel economy.
JC08
1: Car Model
2: +Regional Factors
3: +Random Effect of
Drivers
Fixed effect
Coef.
t-value
Coef.
t-value
Coef.
t-value
Intercept
−2.48 × 10−1
−30.94
−5.46 × 10−1
−39.95
−5.42 × 10−1
−18.84
Average temperature
1.67 × 10−2
17.34
1.66 × 10−2
24.31
(Average temperature)2
−5.14 × 10−4
−16.55
−4.89 × 10−4
−22.50
Average travel speed
3.73 × 10−3
14.48
3.20 × 10−3
4.73
Car model dummy
YES
Random effect
Std. dev.
Driver
0.14
Number of samples
12,169
Adj. R-squared
0.27
0.33
0.74
BIC
−4716
−5461
−11,814
WLTC
1: car model
2: + regional factors
3: + random effect of
drivers
Fixed effect
Coef.
t-value
Coef.
t-value
Coef.
t-value
Intercept
−3.30 × 10−1
−14.86
−6.25 × 10−0
−16.68
−5.50 × 10−1
−8.35
Average temperature
1.95 × 10−2
6.94
2.05 × 10−2
9.95
(Average temperature)2
−5.66 × 10−4
−6.49
−6.09 × 10−4
−9.58
Average travel speed
4.54 × 10−3
6.47
4.16 × 10−3
2.35
Car model dummy
YES
Random effect
Std. dev.
Driver
0.14
Number of samples
1840
Adj. R-squared
0.09
0.14
0.63
BIC
−581
−658
−1458
Figure 6. Deviation factors decomposition of standard error of the regression. Note: Y-axis shows
the standard error of the residuals.
0
0.05
0.1
0.15
0.2
0.25
JC08 WLTC
Region Car model Driver Other
Figure 6.
Deviation factors decomposition of standard error of the regression. Note: Y-axis shows
the standard error of the residuals.
Vehicles 2021,3168
Table 3. Estimation results of deviation between actual and the JC08 (top) and the WLTC (bottom) mode fuel economy.
JC08 1: Car Model 2: +Regional Factors 3: +Random Effect of Drivers
Fixed effect Coef. t-value Coef. t-value Coef. t-value
Intercept −2.48 ×10−1−30.94 −5.46 ×10−1−39.95 −5.42 ×10−1−18.84
Average temperature 1.67 ×10−217.34 1.66 ×10−224.31
(Average temperature)2−5.14 ×10−4−16.55 −4.89 ×10−4−22.50
Average travel speed 3.73 ×10−314.48 3.20 ×10−34.73
Car model dummy YES
Random effect Std. dev.
Driver 0.14
Number of samples 12,169
Adj. R-squared 0.27 0.33 0.74
BIC −4716 −5461 −11,814
WLTC 1: car model 2: + regional factors 3: + random effect of drivers
Fixed effect Coef. t-value Coef. t-value Coef. t-value
Intercept −3.30 ×10−1−14.86 −6.25 ×10−0−16.68 −5.50 ×10−1−8.35
Average temperature 1.95 ×10−26.94 2.05 ×10−29.95
(Average temperature)2−5.66 ×10−4−6.49 −6.09 ×10−4−9.58
Average travel speed 4.54 ×10−36.47 4.16 ×10−32.35
Car model dummy YES
Random effect Std. dev.
Driver 0.14
Number of samples 1840
Adj. R-squared 0.09 0.14 0.63
BIC −581 −658 −1458
4. Discussion
As shown in Figure 5, the WTLC mode fuel economy and actual fuel economy are in
a proportional relationship. By simply multiplying the WLTC mode fuel economy by 0.81,
the test fuel economy is close to the actual fuel economy (Figure 7). However, the deviation
is still large. As shown in Figure 6, even though we consider the variety of car models
and regional characteristics (average temperature and travel speed) as fixed effects and
drivers as a random effect, the deviation can explain approximately 40%. More detailed
information is needed to explain the remaining 60%.
Vehicles 2021, 3, FOR PEER REVIEW 8
4. Discussion
As shown in Figure 5, the WTLC mode fuel economy and actual fuel economy are in
a proportional relationship. By simply multiplying the WLTC mode fuel economy by 0.81,
the test fuel economy is close to the actual fuel economy (Figure 7). However, the devia-
tion is still large. As shown in Figure 6, even though we consider the variety of car models
and regional characteristics (average temperature and travel speed) as fixed effects and
drivers as a random effect, the deviation can explain approximately 40%. More detailed
information is needed to explain the remaining 60%.
Figure 7. Scatter plot when the WLTC mode fuel economy is multiplied by 0.81. (The size of the
circle indicates the distance to refueling).
For example, the longer the distance to refueling, the higher the actual fuel economy
tends to be. In case of a longer trip length to refueling, it is highly assumed that the driver
will mostly drive on highways. This will contribute to a higher fuel economy. However,
it is not easy to consider trip distance to refueling into test fuel economy, as this distance
is not determined in advance.
In addition, the date and time of driving, the number of passengers, and the weight
of luggage, etc. may also have an effect. However, we could not obtain this information
in this analysis. This is one of the future tasks.
5. Conclusions
In this paper, we analyzed the deviation between the actual and test (JC08 and WLTC
mode) fuel economy as well as the impact of regional factors (average temperature and
average travel speed) on the deviation using various car model owners’ reported fuel
economy data at refueling. The findings of this paper are as follows:
- JC08 mode fuel economy: The better the test fuel economy, the larger the deviation
between the actual and test fuel economy.
- WLTC mode fuel economy: The deviation is almost constant regardless of the test
fuel economy, but there is still a deviation of about 19%.
- The effect of deviation due to average travel speed and temperature is only 8%.
How to present test fuel economy by region or driver characteristics and the impact
of other air pollutant emissions, including freight vehicles [10], should be explored in fu-
ture studies.
Figure 7.
Scatter plot when the WLTC mode fuel economy is multiplied by 0.81. (The size of the
circle indicates the distance to refueling).
Vehicles 2021,3169
For example, the longer the distance to refueling, the higher the actual fuel economy
tends to be. In case of a longer trip length to refueling, it is highly assumed that the driver
will mostly drive on highways. This will contribute to a higher fuel economy. However, it
is not easy to consider trip distance to refueling into test fuel economy, as this distance is
not determined in advance.
In addition, the date and time of driving, the number of passengers, and the weight of
luggage, etc. may also have an effect. However, we could not obtain this information in
this analysis. This is one of the future tasks.
5. Conclusions
In this paper, we analyzed the deviation between the actual and test (JC08 and WLTC
mode) fuel economy as well as the impact of regional factors (average temperature and
average travel speed) on the deviation using various car model owners’ reported fuel
economy data at refueling. The findings of this paper are as follows:
-
JC08 mode fuel economy: The better the test fuel economy, the larger the deviation
between the actual and test fuel economy.
-
WLTC mode fuel economy: The deviation is almost constant regardless of the test fuel
economy, but there is still a deviation of about 19%.
- The effect of deviation due to average travel speed and temperature is only 8%.
How to present test fuel economy by region or driver characteristics and the impact of
other air pollutant emissions, including freight vehicles [
10
], should be explored in future
studies.
Author Contributions:
Conceptualization, M.T. and T.K.; Methodology, M.T.; Software, M.T.; Valida-
tion, M.T. and T.K.; Formal Analysis, M.T.; Investigation, M.T.; Resources, M.T.; Data Curation, M.T.;
Writing—Original Draft Preparation, M.T.; Writing—Review and Editing, M.T. and T.K.; Visualiza-
tion, M.T.; Supervision, M.T.; Project Administration, M.T. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Restrictions apply to the availability of these data. Data were obtained
from EverySence Japan, Inc., Tokyo, Japan, and are available with the permission of EverySense
Japan Inc.
Acknowledgments:
The authors wish to thank the anonymous reviewers for their constructive
comments and suggestions on an earlier version of this manuscript. The authors also thank Yo
Miyagi at Kawasaki City Government for helping with data collection and analysis and Daisuke
Sunaga at Chuo University for the useful comments.
Conflicts of Interest: The authors declare no conflict of interest.
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