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ORIGINAL ARTICLE
Energy-saving benefits from plug-in hybrid electric
vehicles: perspectives based on real-world measurements
Boya Zhou
1,2
&Shaojun Zhang
3
&Ye Wu
1,4
&
Wen wei Ke
1
&Xiaoyi He
1
&Jiming Hao
1,4
Received: 19 February 2017 /Accepted: 25 July 2017
#Springer Science+Business Media B.V. 2017
Abstract Promoting plug-in hybrid vehicles (PHEV) is one important option to mitigate
greenhouse gas emissions and air pollutants for road transportation sector. In 2015, more than
220,000 new PHEVs were registered across the world, indicating a 25-fold growth during
2011–2015. However, more criticizes have been put forward against the current energy
efficiency regulations for vehicles that are mostly depended on laboratory measurements. To
better understand the real-world energy-saving and emission mitigation benefits from PHEVs,
we conducted on-road testing experiments under various operating conditions for two in-use
PHEVs in Beijing, China. Our results indicate that air condition usage, congested traffic
conditions, and higher loading mass could significantly increase energy consumption and
shorten actual all-electric distance for PHEVs. For example, the worst case (14.1 km) would
occur under harshest usage conditions, which is lower by at least 35% than the claimed range
over 20 km. In charge sustaining (CS) mode, real-world fuel consumption also presents a large
range from 3.5 L/100 km to 6.3 L/100 km because of varying usage conditions. Furthermore,
various vehicle users have significantly different travel profiles, which would lead to large
heterogeneity of emission mitigation benefits among individual PHEV adopters. Therefore,
this study suggests that the global policy makers should use real-world energy efficiency of
emerging electrified powertrain techniques as criteria to formulate relevant regulations and
supportive policies.
Mitig Adapt Strateg Glob Change
DOI 10.1007/s11027-017-9757-9
Boya Zhou and Shaojun Zhang contributed equally to this study.
*Ye Wu
ywu@tsinghua.edu.cn
1
School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution
Control, Tsinghua University, Beijing 100084, China
2
China Automotive Technology & Research Center, Tianjin 300300, China
3
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853,
USA
4
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex,
Beijing 100084, China
Keywords Plug-in hybrid electric vehicle .On-road test .Energy consumption .Driving
condition .Utility factor
1 Introduction
The continuing growth in global transportation activity has posed a challenging position to
reduce road transportation greenhouse gas emissions. The Intergovernmental Panel on Climate
Change (IPCC) has clearly highlighted the importance of energy transit for transportation
sector in the Fifth Assessment Report (IPCC 2014). For road passenger transportation, where
light-duty vehicles play a dominant role, developing electric vehicles (EVs) including battery
electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) is an important com-
ponent under the 2 Degree Scenario (IPCC 2014;IEA2016;ICCT2014). Many countries
have adopted various supportive policies to spur the rapid growth of EVs. For example,
according to the International Energy Agency, the global amount of new registered PHEVs in
2015 climbed to over 220,000, indicating a 25-fold increase just during 2011–2015. Another
important observation in the global megatrend of fleet electrification is that China has played
an important role along with major traditional automobile manufacturing countries (IEA
2016). In 2015, China has replaced the United States (USA) and become the largest EV
market across that world, which has substantially motivated by multiple concerns including
present challenges concerning energy security and air quality and ambitions to hold a leading
position in the future era of electrified transportation (Ke et al. 2017;Wuetal.2017;Zhang
et al. 2014a;Gongetal.2013;Tongetal.2016).
Different from BEV that is purely powered by battery electricity, PHEV capable of
grid recharging typically can run on battery electricity for 20 to 50 km and then operate
the internal combustion engine to power the extensive range. Thus, such a powertrain
category has its technical advantage of balancing energy efficiency and travel range
(Zhou et al. 2015;Tongetal.2015;Wangetal.2015a). However, the real-world energy-
saving and emission mitigation benefits from PHEVs could be more complicated com-
pared with the BEV counterparts. It is noted that real-world emissions have become a
global issue since the diesel emission scandal, because vehicle emission regulations
acrosstheworldaremajorlydevelopedbasedonlaboratorytestingprotocolsthatmay
not represent real-world vehicle operating conditions and could be artificially optimized
(Zhang et al. 2014b; ICCT 2014). The laboratory measurements could be even artificially
optimized by vehicle manufacturers. The International Council on Clean Transportation
(ICCT) has noted that this road-to-laboratory discrepancy for internal combustion engine
vehicles (ICEV) became significantly enlarged with increasingly stringent carbon dioxide
(CO
2
) emission limits in Europe, from below 110% in 2000 to approximately 140% in
2014 (ICCT 2014). Previous studies have expressed similar concerns with respect to all
electric ranges (AERs), fuel consumption (FC), and exhaust CO
2
emissions of PHEVs
that could vary significantly (Karabasoglu and Michalek 2013; Millo et al. 2014; Wang
et al. 2015a). Researchers have applied advanced experimental and simulation techniques
to understand the effects of traffic conditions on FC of PHEVs (Fontaras et al. 2008;
Marshall et al. 2013). These existing efforts have resulted in EC values closer to those of
the real world but still may underscore impacts from other variables, such as air
conditioning (AC), passenger load, and charging loss, as well as overall complexity
(Zhang et al. 2014b,c; Paffumi et al. 2015). Therefore, since the on-road testing profiles
Mitig Adapt Strateg Glob Change
of PHEV are not readily available, we are motivated to measure FC of PHEVs and
characterize comprehensive affecting factors from a real-world perspective.
Technically, for PHEVs, real-world evaluations of gasoline consumption (GC) in the
charge-sustaining (CS) mode can be conducted in a high time resolution (e.g., second by
second) by employing a portable emission measurement system (PEMS) or using on-board
sensors (e.g., oxygen level, engine revolution, air intake, and fuel injection) (Zhou et al. 2016;
Hu et al. 2014). In the charge-depleting (CD) mode with zero tailpipe emissions, an appro-
priate on-board diagnostics (OBD) decoder paired with a global positioning system (GPS)
receiver can be used to collect real-time vehicular and traffic information. The complex effects
of real-time travel patterns and vehicle operating conditions can be analyzed for tested PHEVs
in either CD or CS mode, including state of charge (SoC), speed (Wu et al. 2015; Zhang et al.
2014d), AC usage (Faria et al. 2013), and loading mass (Zhou et al. 2016; Raslavičiusa et al.
2013). Nevertheless, trip distance is another important factor affecting real-world distance
fractions of CS and CD modes for PHEVs with various battery capacities (e.g., AERs) (He
et al. 2016). Thus, real-world FC evaluations of PHEVs should include all previous issues,
which is a key interaction between upstream and operation stages.
In this manuscript, we aim to report real-world energy-saving benefits from in-use PHEVs,
which are measured under a wide spectrum of operating conditions and analyzed based on
actual travel profiles of large-sized individual drivers. We select Beijing as a case to implement
our field experimental study, because Beijing is not only the city with largest vehicle
population within China (Beijing Municipal Bureau of Statistics 2015) but also an important
hub for China’s EV deployment in the early adoption stage (Beijing Municipal Science &
Technology Commission 2014). In this study, two plug-in Toyota Prius PHEV models
participated in a 2-month road test campaign in Beijing, where a series of complex operating
conditions were applied. In addition, thanks to detailed usage profiles of approximately five
private passenger vehicles collected in Beijing (He et al. 2016), we could comprehensively
present the heterogeneity of energy-saving and emission mitigation benefits among individual
vehicle users. This study could suggest global regulations, and supportive policies should
emphasize the real-world energy and climate benefits from emerging electrified transportation
technologies.
2 Methodology and data
The on-road test campaign was performed in the urban area of Beijing from April to June,
2014. The key experimental conditions such as the date, ambient temperature, departure and
arrival time, mode switching time (e.g., CD to CS) and trip distance, electricity consumption
(EC) and GC, and designed operating conditions were carefully recorded. The vehicles were
tested for a few trips (four trips per day typically) during daytime to experience as many traffic
conditions as possible. Most of the road segments are flat except for one underground charging
site and one tunnel. To eliminate any possible bias from different behaviors of different
drivers, the total test distance over 2000 km was completed by one same driver. The data
for two PHEVs were averaged to reduce the inter-vehicle bias. To explore the actual
AER under real-world conditions, all the test trips started after the PHEVs were fully
charged and were longer than the claimed AER value (e.g., designed as 20 km). Two
fixed charging sites 14 km apart from each other were used in our field test, both located
in the urban area of Beijing. In addition, we fixed the departure and destination locations
Mitig Adapt Strateg Glob Change
for all trips, although the entire route may be not in completely accord between various
trips. As we noted above, traffic conditions, AC usage, and loading mass are major real-
world affecting factors, which were covered by various experimental conditions designed
in this study.
2.1 Key parameters of the PHEV and charging equipment
The PHEVs of a same model were originally produced in Japan, and their key parameters are
listed in Table 1. The lithium-ion battery can independently power the synchronous motor to drive
over 20 km as designed (i.e., AER of 20 km) after being fully charged. In terms of the electric
vehicle supply equipment (EVSE), indoor household power (220 V/10 A) was used to charge the
PHEVs. We recorded the total power from the local network using a handheld local network
power collector (model HP 9800), which combined the charging and discharging loss. The
maximum charging power was 2.3 kW, which was gradually reduced as the actual SoC increased.
The real battery SoC is designed not to fall to zero to promote driving safety and warrantee
battery lifespan. On the other hand, the dashboard SoC indicator performs as follows: as the
dashboard SoC reaches zero, the PHEV immediately shifts from CD mode to CS mode. Then,
the SoC would remain approximately stable representing a dynamic equilibrium of frequent
shifts between CD and CS modes (Samaras 2013), although all of the tests may not be
absolutely the same. For example, we charged the battery right after a complete test trip. Our
results indicated that the total electricity gained from local power was 3.0 ± 0.3 kWh. The inter-
trip and inter-vehicle discrepancy (e.g., both within ±10%) could be identified in Appendix A.
2.2 Fuel consumption calculation
Technically, PHEVs could operate in the CD, CS, and CD blended modes according to SoC
and traffic conditions. In this study, we primarily focused on pure CD and CS modes by
avoiding the blended mode, as the blended mode would not commonly occur in the urban area
with lower speed limits. In the CD mode, we set two rules in the road test to keep the engine
off and eliminate any blended conditions. First, the driver was required to step on pedal
appropriately to avoid unnecessary boost acceleration, and second, the instantaneous speed
was not higher than 80 km/h. The second rule was also in accordance with the speed limits
(less than 80 km/h) for most of the urban roads in Beijing.
Tabl e 1 Key vehicle parameters
of the PHEVs tested in Beijing Index Data
OEM Toyota
Model type Prius Plug-in Hybrid
ZVW35L-BHXEBW
Gross vehicle weight (kg) 1840
Curb weight (kg) 1420
Battery capacity 21.5 Ah/207 V/4.5 kWh
Engine displacement (ml) 1798
Engine power (kW) 73
Motor power (kW) 60
Charging parameter 10 A/230 V(stable charging)
Usage time <2 years, <10,000 km
Mitig Adapt Strateg Glob Change
We obtained the EC in the CD mode by recording the amount of local network charging
(Eq. 1). EC in the CS mode was calculated by integrating second-by-second GC recorded by
an OBD decoder (Eq. 2). The key parameters, such as the real-time speed of trips, were
collected simultaneously.
ECi−100 ECi.Si;CD ð1Þ
ECi¼100 GCi.Si;CD ¼100 ∫t2
t1qgdt.Si;CS ð2Þ
where s
i,CD
and s
i,CS
are strictly the total distance of the CD and CS modes (km), EC
i
is the total
EC in the CD mode of trip i (kWh), ECiis distance-specific EC in the CD mode of trip i
(kWh/100 km), GC
i
is the CS mode total GC of trip i (L), GCiis the distance-specific CS mode
EC of trip i (L/100 km), t
1
is the shifting time of the CD mode to the CS mode, t
2
is the finishing
time of the CS mode (s), and q
g
is the second-by-second gasoline injection quantity (ml/s).
We chose the Toyota Corolla (2014 model year) as an ICEV counterpart for comparison,
which is equipped with a 1.8-l gasoline engine. Based on 767 individual car users’records
through a smartphone app, the average on-road GC is 8.24 ± 1.22 L/100 km (Xiaoxiongyouhao
2016). Although vehicle performance attributes of Toyota Corolla and Prius are not exactly
consistent, the comparison could also provide useful reference for compact vehicles. In addition,
we applied the speed correction curve for urban driving conditions (e.g., average speed lower than
70 km/h) based on previous real-world PEMS measurements for 41 gasoline ICEVs in China
(Zhang et al. 2014d). The key parameters for the reference ICEV are listed in Appendix B.
2.3 Designed operating scenarios of on-road tests
We developed two-stage test scenarios, a benchmark scenario, and three additional scenarios
of various AC usage and loading mass conditions. In the benchmark scenario, the PHEVs ran
with AC off and without extraweight representing Bpassengers.^Additional scenarios involved
different operating situations with AC on and loading mass increased. For example, AC was
on with the temperature set to 19 °C, and the fan set at a medium speed. The lower and upper
ambient temperatures were 8–19 and 20–26 °C, respectively. A load of 300 kg represented by
water buckets was added, which approximated a total weight of four adults. Four typical speed
ranges indicated by average speed, free flow (40–50 km/h), light traffic (30–40 km/h), normal
traffic (20–30 km/h), and heavy traffic (10–20 km/h), were investigated to evaluate the impact
of traffic conditions on the FC of the PHEV (CD and CS modes separately) and the gasoline
ICEV in each scenario. Equation 3represents the speed-dependent FC models under various
operating scenarios, which are best fitted with power functions. Equation 4is used to
normalize various speed points in each speed range to reduce bias.
FC c;vðÞ¼acv−bcð3Þ
FC0
ic;vðÞ¼FCic;vðÞ
FC c;vc
ðÞ
FC c;vðÞ
ð4Þ
where FC c;vðÞis the speed-dependent FC function under scenario c, kWh/100 km (in CD
mode) or L/100 km (in CS mode); a
c
and b
c
are the fitting parameters of speed-dependent FC
curves under various scenarios; FC
i
(c,v) represents the tested FC of route i; FC′
i
(c,v)isthe
normalized FC under the speed of v;andv
c
is the central speed of one speed range, km/h.
Mitig Adapt Strateg Glob Change
2.4 The utility factor and energy-saving benefits under typical travel patterns
In this study, the utility factor (UF) is defined as the distance fraction of the CD mode
in one entire trip. Equation 5illustrates the speed-dependent function of AERs under
scenario c. The parameters c
c
and d
c
are two fitting coefficients. Given the trip length s
(km) under scenario c, the UF is evaluated according to the actual AER and sas Eq. 6
illustrates.
AER c;vðÞ¼ccv−dcð5Þ
UF c;v;sðÞ¼
AER c;vðÞ
s100%;AER c;vðÞ<s
100%;AER c;vðÞ≥s
(ð6Þ
Given a trip with certain traffic parameters (e.g., average speed and total length), UF
can be estimated for further calculating the integrated FC of a PHEV (i.e., EC and GC
combined). We consolidated the energy type for integrated comparison, and the equiv-
alent GC was applied for convenient comparison between the PHEV and ICEV.
Equation 7shows how to convert the EC in the CD mode to GC equivalent (ECeq)
in terms of heating value. Finally, the FC of the PHEV for a given trip is calculated by
Eq. 8, and the maximum FC from the replacement of the PHEV (Prius) with the ICEV
(Corolla) could be simulated.
ECeq ¼EC HV e
HVg
ð7Þ
FCPHEV ¼UF c;v;sðÞECeq þ1−UF c;v;sðÞðÞGC ð8Þ
where ECeq is the equivalent EC of gasoline, L/100 km; HV
e
is the heating value of electricity,
3.6 MJ/kWh; HV
g
is the low heating value of gasoline, 43.11 MJ/l.
The UF and energy use profiles support the evaluation of on-road EC performance in
terms of variable travel patterns. Estimation of travel characterization is usually based on
the large populations of vehicle use data, for example, the National Household Travel
Survey (NHTS) in the USA (Tamor et al. 2013). We analyzed vehicle usage data
regarding start, stop, and trip trajectory for hundreds of personal passenger vehicles in
Beijing by using portable GPS data loggers. Detailed information and results of the
vehicle usage survey were documented by He et al. (2016). In summary, the vehicle
owners were typically distributed according to their home location, office location,
and occupation. GPS data collection for each vehicle lasted at least 1 month (note:
some vehicles lasted up to 6 months) to improve the real-world representativeness of
trip length, road coverage, and traffic conditions. We selected 7702 trips from
approximately 500 vehicles with travel distances less than 100 km and average speeds
below 60 km/h. Figure 1illustrates a distribution of these trip candidates grouped by
distance and average speed, where we create square-like bins within speed and
distance intervals of 10 km/h and 10 km, respectively. The probability of bin k(P
k
)
Mitig Adapt Strateg Glob Change
means that the fraction of total trips that has the trip parameters of vkand s
k
,asEq.9
shows.
Pk¼
Nkvk;sk
Nð9Þ
The bin with the highest distribution probability of 28.6% represents travel of less than
10 km, with a speed range of 10–20 km/h. Nine bins within the 10–40 km trips with a speed
range of 0–30 km/h make up 81.2% of the selected distribution (in red, see Fig. 1). As the trip
length increases, the users prefer to choose express roads with no signal light for their travel.
Therefore, the average speed tends to increase accordingly. The green bins in Fig. 1account
for a total fraction less than 0.15%, which indicates that it is quite unlikely to travel a long
distance while at a low average speed.
The overall FC combines the equivalent FC in each bin and its probability weight (Eq. 10).
Following a similar method, ICEV EC within the Beijing travel pattern and the benefits of
PHEVs in Beijing can be evaluated (Eq. 11).
FCvehicle ¼∫FCvehicle kðÞPkdk ð10Þ
Δ¼FCICEV −FCPHEV ð11Þ
Fig. 1 The trip distribution of 7702 trips from 200 vehicles in Beijing and the probability distribution of trip bins
Mitig Adapt Strateg Glob Change
3 Results and discussion
3.1 PHEV energy consumption in the CD and CS modes
When PHEVs operate on urban roads (i.e., average speed below 60 km/h), their EC under the
CD mode is generally reduced as the average speed gets increased. However, this trend
becomes less significant when the average speed is over 30 km/h. Power functions could have
strong correlations between EC in the CD mode and average speed for all scenarios (R
2
>0.6)
except for the scenario of EL and AC on (R
2
= 0.41, indicating medium correlation) (see
Table 2). For example, in the benchmark scenario, the average EC under the normal traffic
condition (average speed of 25 km/h) is 14.9 ± 0.7 kWh/100 km (Table 2). The average EC
rises to 17.0 ± 2.0 kWh/100 km under the heavy traffic condition, representing an increase of
14% than the normal traffic condition. The average EC of PHEVs achieves 13.9 ± 1.2 kWh/
100 km under the light traffic condition and 13.8 ± 0.9 kWh/100 km under the free traffic
condition, respectively. Furthermore, variations in EC in the CD mode due to various traffic
conditions would lead to changes in real-world AERs. For example, the average AER under
the benchmark scenario (i.e., normal traffic, EL, and AC off) could reach 20.4 km, which
complies with the level as vehicle original equipment manufacturer (OEM) designed. When
the traffic speed becomes higher (e.g., light traffic and free flow conditions in Table 2), the
average AER value remains stable. However, when traffic congestion becomes increasingly
serious (heavy traffic, EL, and AC off), for example, during rush hours, the average AER has
difficulty reaching the designed value and deteriorates by 17%, only 16.9 km.
Although we have discerned increased EC due to congested traffic conditions, real-world
EC under the CD mode for tested PHEVs is significantly less sensitive to changes in speed
than that of the ICEV counterparts. For example, Zhang constructed speed-dependent func-
tions for the EC of gasoline light-duty passenger vehicles (LDPVs) and found that the real-
world EC under the heavy-traffic condition would be elevated by 43% for gasoline LDPVs
(Appendix C) and by 33% for diesel LDPVs (Zhang et al. 2014d). In contrast, the EC of
PHEVs in CD mode would be increased by 14% for PHEVs (Table 2). This is because the
Tab l e 2 Averaged fuel consumption and AER under various operating conditions
Traffic condition Load A/C EC (kWh/100 km) AER (km) GC (L/100 km)
Heavy (ave. speed 10–20 km/h) Empty off 17.3 ± 1.7 16.9 ± 1.5 4.5 ± 0.6
Full off 18.5 ± 0.4 16.4 ± 0.3 5.3 ± 0.3
Empty on 19.1 ± 1.3 15.4 ± 0.9 5.6 ± 0.3
Full on 21.9 ± 0.2 14.1 ± 0.1 6.3 ± 0.3
Normal (ave. speed 20–30 km/h) Empty off 14.7 ± 0.5 20.4 ± 1.8 4.2 ± 0.4
Full off 16.7 ± 1.6 18.6 ± 0.6 4.7 ± 0.5
Empty on 18.2 ± 2.4 16.6 ± 1.5 4.8 ± 0.4
Full on 20.3 ± 1.3 15.3 ± 0.5 5.0 ± 0.8
Light (ave. speed 30–40 km/h) Empty off 14.4 ± 1.1 20.5 ± 2.1 3.7 ± 0.3
Full off 15.7 ± 1.5 19.2 ± 1.5 4.0 ± 0.4
Empty on 16.6 ± 0.4 17.8 ± 0.9 4.0 ± 0.3
Full on 17.7 ± 1.7 16.5 ± 0.3 4.3 ± 0.2
Free flow (ave. speed 40–50 km/h) Empty off 13.8 ± 1.0 20.5 + 2.2 –
Full off ––3.6 ± 0.2
Empty on 15.7 ± 0.1 17.9 ± 0.6 3.5 ± 0.1
Full on 15.2 ± 0.1 17.2 ± 0.2 –
The AER has been normalized by Eq. 4
Mitig Adapt Strateg Glob Change
internal combustion engine has a prime working area characterized by high combustion
efficiency. The EC of ICEV has a significantly increasing gradient when the engine-
operating conditions are apart from this area (Gao et al. 2014). For PHEV, the characteristic
curve of electric motor and the brake energy regeneration allow a high and stable powertrain
efficiency among traffic conditions in the CD mode.
The results of multiple scenarios indicated that the impact of AC usage on EC in the CD
mode is greater than the impact of increased load mass. Compared with the benchmark
scenario, AC on and FL could shorten the average AERs to 16.6 and 18.6 km, respectively,
under the normal traffic condition, indicating reductions of 19 and 9% (Table 2). Evidences
from other international studies could also support the significant impact of AC usage on EC
and AER of electric powertrain systems. For example, Fontaras et al. (2008) and Faria et al.
(2013) indicated that AC usage could reduce the AER of BEV by 12 to 17% in summer when
the daytime ambient temperature ranged 25–30 °C. Farrington and Rugh (2000) used simu-
lation methods and suggested that the AER of BEV could be reduced by 38% if increasing the
AC power to 3 kW. Rugh (2010) further reported that the PHEVAER values were reduced to
different extents under different driving cycles, e.g., 18% under a high-speed cycle and 30%
under an urban cycle. Our experimental results suggest that, when the PHEVs were operated
with AC on and a full load, their average AER would drop to 15.3 km under the normal traffic
condition. This drop represents a reduction of 25% compared with the benchmark scenario. To
make matters worse, if the traffic conditions are more congested (heavy traffic, FL and AC on;
namely the harshest scenario in our study), the average AER for tested PHEVs could last only
14.1 km, which is only 69% of the benchmark result.
Therefore, our results indicate that attainment of the designed AER of 20 km is conditional
and may not be guaranteed under some unfavorable conditions that may often occur in reality
(Table 2). Logistically, the OEMs first set the AER target and later determine battery capacity
accordingly. However, the opportunity to achieve the designed AER is often diminished
because the driver has to maintain smooth driving, select the proper travel time to avoid
congestion, and switch off on-board AC system. According to the Beijing travel pattern
survey, the length of 64% of the single trips is shorter than 14 km. The users therefore have
a high probability of finishing 14 km in CD mode over a wide range of combined vehicular
and road conditions. The PHEV could extend the range by starting the gasoline engine.
However, for a BEV equipped with similar battery and electric motor systems, the safety
estimate discount of real-world AER may be 30% or higher, as noted earlier.
The GC in the CS mode falls as speed increases in the downtown area, but the speed
dependence tends to be stable compared to ICEVs. Table 2shows the GC performance in all
speed ranges with different AC and loading conditions. For example, in the benchmark
scenario, the average GC of the CS mode is 4.2 L/100 km (normal traffic), and the PHEV
achieves 4.5 L/100 km under the heavy traffic condition. When the city traffic becomes less
congested, the GC drops 10% to 3.7 L/100 km. Although less energy effective under the heavy
traffic condition, the PHEV could save larger amounts of gasoline in its CS mode than the
gasoline counterpart (Appendix C). In the benchmark scenario, the GC saving rates of the
PHEV range from 66% (heavy traffic) to 47% (light traffic). In the CS mode, the hybrid system
uses the battery as a reservoir for harsher operating conditions. When working outside the best
fuel economy conditions, the hybrid system keeps the engine operating in the high-efficiency
area as long as possible by using the battery to balance the residual power demand. Mean-
while, the engine can charge the battery when the engine power is beyond the demand, e.g.,
during braking or idling conditions.
Mitig Adapt Strateg Glob Change
Similar to the CD mode, added loading mass and AC usage would increase the GC in
the CS mode under all scenarios. For example, under the normal traffic condition (20–
30 km/h), the average GC is increased by 19% when the AC is on plus full load relative
to the benchmark (i.e., 5.0 L/100 km vs. 4.2 L/100 km). Under the heavy and light traffic
conditions, the harshest conditions could increase the average GC by 40 and 16%,
respectively. Similar to the CD mode, AC usage is a significant contributor to extra fuel
demand in the CS mode. As seen in Table 2, AC usage could increase the GC singly by
24, 14, and 8% on average under the heavy, normal, and light traffic conditions,
respectively, whereas the full load could increase GC by 18, 12, and 8%. The largest
difference between AC and load impact occurs in the low speed range, whereas the AC
impact weakens as speed increases. However, unlike the CS mode, the AC and load
impact gaps exist in most speed ranges and remain relatively stable in the CD mode,
usually around 5%.
In addition, the PHEVon-road gasoline benefit decreased when the speed range exceeded
the city travel pattern. We also tested the highway GC results in only the CS mode (EL, AC
off) and found that the EC increases when the average speed surpasses the inflection around
approximately 60 km/h. Because our previous PEMS tests provided limited data under high-
speed conditions, we refer to the COPERT4 model for the speed corrections over 70 km/h
(Ntziachristos and Samaras 2014). As Appendix Cindicates, PHEVs could save approximate-
ly 40% of GC if the average speed exceeds 70 km/h, lower than that of the CS mode in city
travel (i.e., 66% under the heavy condition, EL, AC off). Thus, considering that trip lengths of
highway travels (e.g., inter-city) are very likely to exceed the AER, PHEVs tend to achieve
less energy reduction relative to ICEVs when they are operated on highways rather than for
urban travels.
3.2 The utility factor and equivalent FC of the PHEV under different scenarios
Based on the real-world travel pattern data, we evaluated the UF for urban trips for every
10 km/h from 5 to 55 km/h and every 10 through 100 km. In particular, the UF change from 10
to 25 km was calculated in a finer distance resolution of 1 km, because it represents a distance
range in which the shift between CD and CS modes mostly likely occurs. As the surface shows
in Fig. 2, each point in the surface represents the maximum UF of one trip (fully charged), with
a certain trip length and speed under prescribed AC and loading mass conditions. Furthermore,
with the UF surfaces and FC functions of the CD and CS modes, the total FC could be
simulated by combining two separate parts.
Trip length and speed are proven to be two major factors in real-world maximum UF. First,
under the benchmark scenario (i.e., AC off and EL), a fully charged PHEV can achieve a UF of
100% until the trip lengthens to 19 km at an average speed of 25 km/h. However, when average
speed varies from 5 to 55 km/h, the longest distance to achieve a UF of 100% varies from 16 to
22 km. As the length continues to increase, the UF becomes lower as the speed decreases,
indicating that the distance fraction of the trip powered by the engine tends to be greater.
If AC, passenger load, and battery SoC are all fixed, trip length and speed would
synergistically affect the on-road FC of the PHEV. The low FC domain (i.e., indicated in
orange, lower than 2 L (eq.)/100 km) in Fig. 3represents the CD mode of the PHEV, and the
other part of the FC surface is mixed with the CD and CS modes. Shorter trip length is a
positive factor, resulting in lower FC for PHEVs. For example, the FC of a fully charged
PHEV (i.e., 25 km/h, EL, and AC off) stays at 1.7 L (eq.)/100 km before the trip distance is
Mitig Adapt Strateg Glob Change
over 19 km (Fig. 3a). As the trip distance becomes longer, the FC would approach the result of
the CS mode, and the benefit of high energy efficiency in the CD mode decreases. The overall
FC (i.e., 25 km/h) increases gradually to 3.6 L (eq.)/100 km for a trip of 100 km, which is only
reduced by 12% compared with the average FC in the CS mode (i.e., 4.1 L/100 km). Average
speed also affects the equivalent FC significantly. For example, as the average speed changes
from 10 to 55 km/h, the benchmark scenario FC to travel 19 km varies by 1.5–2.5 L (eq.)/
100 km (Fig. 3a), whereby the PHEV under the heavy traffic condition (i.e., 15 km/h) has
already switched to the CS mode. Therefore, short trip length and higher speed result in highly
efficient PHEVs, whereas short trip length and low speed conditions bring about larger energy-
saving benefits than the gasoline counterparts.
AC usage and full load reduce the highly efficient CD mode area (mostly below 2.8 L (eq.)/
100 km, Fig. 4), and AC contributes more significantly to extra FC than the full load, as
mentioned earlier in this paper. Compared with the benchmark scenario, the equivalent FC of a
19-km-long trip increases to 2.8 L (eq.)/100 km under the harshest conditions (i.e., 25 km/h,
AC on, FL), an increase of 65%. The CD mode ends as the trip reaches 16 km, and the
remainder has to be powered by the engine. As the trip extends to 100 km, the equivalent FC
Fig. 2 The utility factor profile of certain trips under a wide range of traffic (speed) and vehicle (AC and
passenger load) conditions. We controlled the initial SoC and blend CD mode situations (i.e., initial SoC at 100%
and no CD blend) to simplify the cases
Mitig Adapt Strateg Glob Change
rises to 4.5 L (eq.)/100 km (i.e., 25 km/h, AC on, FL), which is increased by 10% over the
benchmark scenario result.
In a word, the real-world FC correction of a PHEV is not a fixed value but should be
prudently derived from the test profiles and to follow a series of settings for vehicular and
travel conditions. According to the Beijing travel pattern survey, drivers seldom drive long
distances at a low average speed in cities (the green bins of Fig. 1). The actual UF or FC
profiles tend to be smaller than the profiles shown in Figs. 2and 3. Thus, the actual FC
profile is only a portion of that illustrated (the top EC is likely to stay below 4.0 L (eq.)/
100 km), and travel pattern impacts should be included to accomplish the determination
of on-road FC.
3.3 On-road PHEV energy-saving potential: a case study of Beijing
In this section, we discuss the impact of travel patterns on real-world FC for
PHEVs. To simplify the analysis, the benchmark scenario is selected with AC off
Fig. 3 The equivalent FC of PHEV for different trip distances and speeds, AC, and passenger load conditions; a
combination FC of CD mode and CS mode
Mitig Adapt Strateg Glob Change
and no additional passengers other than one driver. The number in each bin of Fig. 4shows the
amount of equivalent gasoline saved if a PHEV replaces a conventional gasoline ICEV.
Although the PHEV have lower FC for high-speed and short-distance trips, but the energy-
saving benefit relative to the ICEV is most significant for congested and short-distance trips.
First, the FC gap between PHEV and ICEV narrows as the average speed increases. The FC
difference drops from 11.3 L (eq.)/100 km in the domain of 0–10 km and 10–20 km/h to 3.9 L
(eq.)/100 km in the domain of 0–10 km and 50–60 km/h. Then, the trip length also changes the
energy-saving potential. According to Fig. 4, when the trip is lengthened to 100 km while
keeping the speed stable (i.e., 50–60 km/h), the FC gap drops further to 2.2 L (eq.)/100 km.
Although the longer trip increases the FC of PHEVs, these trips seldom occur in the entire
Beijing travel pattern.
Combining the FC results of bins (Fig. 4), the maximum (fully charged) probability-
weighted FC of PHEVs under the Beijing travel pattern is 1.9 L (eq.)/100 km. Similarly, the
ICEV achieves 11.5 L/100 km, and the PHEV reduces the FC by 83%. Meanwhile, if all trips
are undertaken after the dashboard SoC fell to zero, the PHEV would behave like an HEVand
could save 64% of total energy use. When focusing on the nine red bins described in Fig. 1,the
probability-weighted PHEV FC is 1.8 L (eq.)/100 km on average, which represents 81% of the
total trip distribution and dominates the travel pattern-based EC. The total 7702 trips indicate
the representative travel pattern of 24.0 km/h of average speed and 13.6 km of trip length. The
20 km of AER is not abundant for long-distance travel, but based on the travel pattern survey,
Fig. 4 The maximum PHEV gasoline saving benefit distribution under different trip speed and length bins in
Beijing (EL, AC off). The number of each bin shows the energy savings (L (eq.)/100 km) of PHEV compared to
gasoline ICEVs under the same conditions; the color means different distribution probability
Mitig Adapt Strateg Glob Change
82% of the trips are shorter than 20 km. Although AC usage and higher load mass may shorten
the actual AER, a fully charged PHEV may still be able to finish a major part of the first of the
daily trips using electricity. Further, 77% of the total trips occurred with an average speed
lower than 30 km/h and 44% under 20 km/h. Therefore, the ICEV user has few chances to
drive a long distance in the most efficient fuel economy zone of their daily travel. Based on the
single trip distribution, the PHEV (AER of 20 km) is a good choice to replace the ICEV in
typical Chinese cities, either in the CD, CS, or combined modes.
Daily VKT consists of several single trips and has different trip distribution characteristics.
According to the GPS data investigated, the probability-weighted single trip distance and daily
VKT of 500 Beijing users are 13.6 and 44.0 km. However, the speed distribution for the daily
VKT is more complex than the speed distribution of the single trips. Large samples and long-
term observation are needed to improve the representative of travel data and the estimation
accuracy of energy consumption. If the average daily travel pattern is simply defined as
44.0 km with a speed of 24.0 km/h, the white-frame bins in Fig. 4best represent the daily
travel-based energy-saving benefits on the presumption of fully charged only during nighttime.
Under this circumstance, the energy-saving potential of PHEV could be up to 71% (EL, AC
off) at most in the daily travel pattern. For the short AER PHEV, the CD mode could cover
daily VKT partially (UF ~48%). For a PHEV equipped with a larger battery, its energy-saving
benefits may be improved but accompanied by higher vehicle cost to customers, which needs
further analysis to optimize.
4 Policy implications
Although PHEVs could achieve high energy-saving rates compared with the ICEV
counterparts, their real-world energy use could be much higher than that under ideal
scenarios. For example, we gathered real-world GC data for eight PHEV models avail-
able in China reported by 236 car drivers (Xiaoxiongyouhao 2016). The type-approval
FC (electricity consumption excluded) values for those PHEV models range from 1.6 to
2.4 L/100 km. By contrast, their real-world GC results (note: EC data unknown) are
significantly higher by 109 to 391% than type-approval values, ranging from 5.0 to
9.8 L/100 km (see Table 3). In addition, the correlation variances are distributed
approximately from 30 to 40%, which are higher than their ICEV counterparts (MIIT
2016) and indicate great bias in vehicle usage conditions.
Significantly higher real-world FC of PHEVs can be primarily attributed to several
major issues. The current type-approval regulation and test procedure for PHEVs in
China is quite different with their real-world travel patterns. First, the EC is separately
informed and not taken account into final type-approval fuel consumption results (see
Appendix D) (GAQSIQ 2005). The EC is separately informed and not taken account, and
the final type-approval FC level exclusively consider the GC result (GAQSIQ 2005).
They set the valid tested AER (GAQSIQ 2005) and an assumed 25 km as the weight of
CD and CS modes separately, and the gasoline use of CD is zero in the type-approval FC
calculation. Thus, the longer the AER is, the lower type-approval FC could be achieved.
For example, we tested one Prius PHEV over three consecutive NEDCs, and the first and
the third ones could represent the type-approval gasoline and electricity consumption test
situations for CD (or CD blended) and CS modes, respectively (see Appendix E). The
GC values over the first and third NEDCs were 0.9 L/100 km and 4.6 L/100 km,
Mitig Adapt Strateg Glob Change
Tab l e 3 Real-world fuel consumption voluntarily reported by car drivers for eight PHEV models available in China
a
Manufacturer Model Model
year
Type-approval
fuel consumption
(L/100 km)
Typ e-
approval
AER (km)
Distance
share
of CD mode
b
Real-world fuel consumption Road-to-
lab ratio
Number of
car users
Mean ± standard
deviation
(L/100 km)
Correlation
variance
BYD Qin 1.5 T 2015 1.6 70 0.74 7.30 ± 2.16 30% 456% 6
BYD Qin 1.5 T plus 2015 1.6 70 0.74 6.54 ± 2.44 37% 409% 39
BYD Qin 1.5 T 2014 1.6 70 0.74 6.58 ± 1.88 29% 411% 72
BYD Tang 2.0 T AWD 2015 2 80 0.76 9.81 ± 2.77 28% 491% 69
SAIC Roewe e550 2016 1.6 58 0.70 6.29 ± 2.35 37% 393% 5
SAIC Roewe 550 Plug-in 2014 2.3 58 0.70 5.78 ± 2.29 40% 251% 21
GAIC GA5 Elite 2015 2.4 50 0.67 6.24 ± 2.50 40% 260% 10
GAIC GA5 Exclusive 2015 2.4 50 0.67 5.01 ± 1.61 32% 209% 14
a
The real-world fuel consumption data voluntarily reported by car users are gathered through the smartphone APP Xiaoxiongyouhao [39]. Because only domestic brands of PHEVs can
be subsidized, therefore data for imported PHEVs and international brands of models are very limited
b
The distance share of CD mode for calculating type-approval fuel consumption is estimated as the ratio of the type-approval AER and the total distance that is the sum of AER and
25 km [44]
Mitig Adapt Strateg Glob Change
indicating an overall weighted GC of 3.7 L/100 km as its type-approval FC according to
China’s current regulations. As a result, motivated by purchase subsidy policy and fuel
economy standards in China (Wu et al. 2017), domestic PHEV models tend to have larger
battery capacity (i.e., higher AER) and higher power rating of electric motor (or called
extended range electric vehicles). Thus, the domestic PHEVs can gain low overall type-
approval GC by increasing the distance allocation of CD mode (e.g., 0.67 to 0.76, see
Tab le 3) and avoiding CD blended conditions (note: all the PHEVs have no GC in the
CD mode, namely no CD blended conditions) during the type-approval AER test
procedure. In addition, as we presented above, complex operating conditions such as
traffic congestion, AC use, and high load mass would shorten the AER and increase GC
in CS mode. More importantly, the charging infrastructures are to be developed yet
(CATARC 2016), which limit the charging opportunities of PHEVs. By comparing
results in Tables 2and 3, we could conclude that a major part of existing PHEV users
have not considerably utilize the battery electricity (i.e., CD mode) to power the vehicles,
because their real-world GC levels are close to those fully in CS mode. PHEVs could
have the opportunity to realize very low FC by using battery electricity, however, which
has rarely happened according to the actual GC profiles of PHEVs. Therefore, the
distance allocation of CD mode is significantly overestimated, and the weighting method
may not represent the real-world travel patterns.
Since 2012, the phase III limits of fuel economy standard for LDPVs in China have
introduced an overall fuel consumption bar for each vehicle manufacturer, which is the
Cooperate Average Fuel Consumption (CAFC) (GAQSIQ 2011). The CAFC of each
vehicle manufacturer is weighted by annual sales of each vehicle model. In 2015, the
MIIT released the phase IV limits of China’s fuel economy standard for LDPVs, setting
a target of 5.0 L/100 km as the national average fuel consumption (NAFC) by 2020, a
reduction of 28% compared with the NAFC in 2015 (i.e., 6.9 L/100 km) (GAQSIQ
2014). To promote the production of BEVs and PHEVs with AERs higher than 50 km,
their annual sales will be multiplied an adjustment factor greater than 1 (e.g., adjust-
ment factors of 5 in 2016 to 2017, 3 in 2018 to 2019, and 2 in 2020) (GAQSIQ 2011).
Therefore, deployment of BEVs and PHEVs would be a very important approach by
manufacturers to comply with the increasingly stringent limits of CAFC and spear more
production capacity for high-profit vehicles (e.g., sport utility vehicles (SUVs)) that
usually have high type-approval FC. For example, in 2015, China’s annual sales of
SUVs jumped to 6.22 million, an impressive increase of 52% (China Association of
Automotive Manufacturers 2016). However, the real-world FC data gathered suggest
that PHEVs consume much more gasoline than expected, and the adjustment factor of
weighted sales would further enlarge the gap between CAFC and the actual level.
Furthermore, China’s case study reflects the global challenges of designing regulations and
incentives to promote EV development. A global review of supportive policies for EVs also
indicates the absence of real-world usage factors when formulating relevant regulations and
incentives (ICCT 2014). Either purchase subsidies or tax reductions would be determined
according to vehicle specifications (e.g., battery capacity, AER) or type-approval tests (e.g.,
CO
2
emissions, fuel consumption), which have not taken into real-world impacts or individual
heterogeneity into account. Although PHEVs could bring in fuel cost saving during the in-use
stage, such benefits only play a minor role in total economic incentives to promote PHEVs
compared with other one-time incentives. As we present in this paper, one-time purchase
subsidies or tax exemptions provided by governments could not guarantee the energy-saving
Mitig Adapt Strateg Glob Change
benefits as global policy-makers expect. Thereby, real-world energy efficiency should be
applied as criteria to develop future energy efficiency regulations and supportive policies for
electric vehicle technologies. Policy-makers should encourage the usage of more advanced
real-world energy efficiency monitoring techniques for emerging electrified transportation
modes and switch to a policy framework (e.g., regulations, incentives) emphasizing real-
world climate benefits.
5 Conclusions
Travel patterns affect PHEV energy consumption significantly. Travel patterns could be
employed in the FC profiles to properly simulate the results in typical cities or
individual trip chains. From the microperspective, we constructed the on-road EC
profiles of the CD and CS modes of one type of PHEV. From the macroperspective,
we collected data of the travel patterns of cities and individual trip chains using a large
sample survey.
Many real-world factors affect the energy-saving benefits of PHEVs. In this Beijing test, we
focus mainly on travel patterns (average speed and trip length) and vehicular conditions (AC
and passenger load and the SoC condition). Correspondingly, the PHEV could achieve 20 km
AER under the conditions of AC off, no passenger, and normal or better traffic conditions.
However, the designed AER is shown to be the most optimistic level. Under the worst-case
conditions, the users might only have a much shorter AER as low as 14 km. The safe
estimation of the real-world AER discount should be over 30% of the nominal AER. Real-
world energy use under both the CD and CS modes for tested PHEVs is less sensitive to
changes in speed than that of the ICEV counterparts, resulted in a larger FC reduction benefit
when traveling in the congested urban areas. Considering the vehicular condition factors
affecting FC, AC is a more important contributor than passenger load in both CD mode and
CS mode.
Combining the FC results of trip bins together, the maximum probability-weighted
PHEV FC of Beijing is 1.9 L (eq.)/100 km (empty load and AC off), reducing up to 83%
of the total energy consumed by the ICEV. If the SoC falls to 0%, the PHEV, considered
as a grid-independent BHEV,^could also save approximately 60% of the energy. The
PHEV could perform better under smooth traffic conditions but saves a larger amount of
energy on a congested road because the ICEV is more speed-sensitive than the PHEV.
However, in reality, users cannot be assured of the fully charged condition before every
trip, so the energy-saving benefit varies between fully charged and conventional hybrid
results. For example, real-world FC results for eight PHEV models in China reported by
vehicle users range from 5.0 to 9.8 L/100 km, significantly higher than their type-
approval results. Therefore, our real-world measurement study suggests that future
energy regulations and incentives concerning global EV development use real-world
energy efficiency as one important judgment aspect.
Acknowledgements This work was supported by the Ministry of Science and Technology of China’s Interna-
tional Science and Technology Cooperation Program (2016YFE0106300), the National Natural Science Foun-
dation of China (91544222 and 51378285), and the National Key Research and Development Program of China
(2017YFC0212100). The authors thank Mr. Charles N. Freed, formerly of the US EPA, for his help in improving
this paper, and Mr. Xiong Zhang and Mr. Hongbo Sun of Xiaoxiongyouhao for providing real-world fuel
Mitig Adapt Strateg Glob Change
consumption data. Dr. Shaojun Zhang is supported by Cornell University’s David R. Atkinson Center for a
Sustainable Future. The contents of this paper are solely the responsibility of the authors and do not necessarily
represent the official views of the sponsors.
Appendix A CD mode energy consumption distribution of tested trips
Appendix B Key parameters for the reference ICEV model
Fig. 5 CD mode energy consumption distribution of tested trips; the EC represents the total charging amount
from local power, including the EVSE and charging and discharging loss)
Tab l e 4 Key parameters for the reference ICEV model, under the urban driving conditions
Model type Baseline GC
(l/100 km)
Relative GC of gasoline
ICEV
b
GC in speed ranges (l/100 km)
c
Heavy
traffic
Normal
traffic
Light
traffic
Free
flow
Toyota Corolla
(1.8 L CVT)
8.24
a
RECICEV ¼11:378 v−0:698
(R
2
=0.93)
14.2 9.9 7.8 6.6
a
The baseline GC of reference Corolla was measured by averaging 767 real GC reports from Xiaoxiongyouhao
(Xiaoxiongyouhao 2016)
b
The speed-dependent function for estimating fuel consumption is rewritten according to Zhang et al. (2014d),
which is applicable to average speed lower than 70 km/h (2014)
c
The average speed of heavy, normal, light, and free traffic range is 15, 25, 35, and 45 km/h
Mitig Adapt Strateg Glob Change
Appendix C On-road speed corrections of PHEV and the ICEV
Appendix D Calculation method of type-approval electricity and fuel
consumption for PHEVs in China
Gasoline consumption GCðÞC¼DeC1þDav C2
DeþDav
ð1Þ
Electricity consumption ECðÞE¼DeE1Dav E4
DeþDav
ð2Þ
Fig. 6 On-road speed corrections of PHEV and the ICEV counterpart for their urban travels, average speed
lower than 45 km/h. The on-road fuel consumption for ICEV is estimated according to self-reported data by
Toyota Corolla drivers (Xiaoxiongyouhao 2016) and the speed correction curve based on our previous PEMS
measurement (Zhang et al. 2014d) (see Appendix B)
Fig. 7 On-road speed corrections of PHEVand the ICEV counterpart for their high-speed travels, average speed
higher than 70 km/h. The on-road fuel consumption for ICEV is estimated by using the COPERT4 model
(Ntziachristos and Samaras 2014) for the category of 1.6–2.0 L Euro 4 gasoline cars
Mitig Adapt Strateg Glob Change
where Cis the type-approval GC value, L/100 km; C
1
and C
2
are tested GC values under CD
(or CD blended) and CS modes over the NEDC, L/100 km; Eis the type-approval electricity
consumption value, kWh/100 km; E
1
and E
4
are tested electricity consumption values under
CD (or CD blended) and CD modes, kWh/100 km; D
e
is the type-approval AER tested
according to the regulation, km; and D
av
is the assumed distance of CS mode and is fixed at
25 km (GAQSIQ 2005).
It should be noted that currently China’s type-approval fuel economy for PHEVs only takes
the GC (C) of PHEVs into account for evaluations of CAFC and NAFC.
Appendix E Second-by-second SoC conditions as well as the gasoline
consumption of a PHEV in three consecutive NEDC certification driving
cycles
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