Empirical Evaluation of V2G Round-trip Efﬁciency
Thijs van Wijk
Wilfried van Sark
Abstract—The business case of vehicle-to-grid (V2G) technol-
ogy and its potential to provide grid services is heavily dependent
on the round-trip efﬁciency of this technology. Surprisingly,
very little empirical research is conducted to determine the
V2G round-trip efﬁciency of electric vehicles currently available
in the market, resulting in a wide range of efﬁciency values
used in V2G modelling studies. This study aims to create more
insight in the current V2G round-trip efﬁciency to stimulate that
more uniform and realistic efﬁciency values are used in other
studies. A ﬁeld experiment is executed to measure the round-trip
energy efﬁciency of V2G for different dates, current rates and
average states of charge. It was found that the average round-
trip efﬁciency (i.e., combined inverter and battery efﬁciency)
when charging between a state of charge 25% and 75% with
3x16 Ampere was 87.0%. However, various external factors could
inﬂuence the measured efﬁciencies, which had a total range from
79.1% to 87.8%. Charging at lower ambient temperatures and
lower current rates had a statistically signiﬁcant adverse effect
on the round-trip efﬁciency. Efﬁciency at high and low state of
charge was found to be marginally lower than around medium
state of charges. Two different electric vehicle + charging station
models were tested, one with on-board AC/DC converter, which
is a novel V2G setup, and one with external AC/DC converter,
rendering no statistically signiﬁcant different efﬁciency values.
Index Terms—V2G, V2G efﬁciency, EV charging efﬁciency,
electric vehicles, ﬁeld experiment
Vehicle-to-grid (V2G) technology is gaining prominence
with increasing adoption of electric vehicles (EVs). The infeed
of electricity from EV battery systems to the grid through V2G
expands the opportunities of grid operators to stabilize the
grid using EVs. It has been demonstrated that power quality
and congestion problems in low-voltage grids can be mitigated
using V2G , while V2G also can provide ﬁnancial beneﬁts
as well as environmental beneﬁts .
In recent years, multiple car models suitable for V2G have
been introduced to the market (e.g., Nissan LEAF, Mitsubishi
Outlander), while charging stations compatible for bidirec-
tional charging are now also introduced to the streets . The
round-trip efﬁciency of V2G charging cycles is crucial for
future adoption of V2G as it directly affects the business case
and environmental impact of batteries in general, and of V2G
speciﬁcally , .
This study was supported by the EU’s ERDF in the project ‘Smart Solar
Charging regio Utrecht’ and by the Dutch RVO in the project ‘Slim laden
met ﬂexibele nettarieven (FLEET)’. The authors want to thank ElaadNL for
allowing their EV testing lab to be used for this study and want to thank
Bram van Eijsden for his contributions throughout the research process.
1Both authors contributed equally to this work.
An overview of the used V2G round-trip efﬁciencies in an
non-exhaustive list of model studies in Table I indicates that
the ambiguity about the efﬁciency is high, as V2G round-trip
efﬁciencies range from 55% to 100%. Surprisingly, the number
of empirical evaluations of the V2G round-trip efﬁciency is
low. Refs. , – arrived at round-trip efﬁciency values
of between 53-70%, which is very low compared to the
efﬁciency of stationary lithium-ion based battery systems.
A single measurement was carried out in , arriving at a
round-trip efﬁciency of 87%. However, this study did not
consider the effect of e.g. State-of-Charge (SoC), current
and temperature. A laboratory experiment in  arrived at
a round-trip efﬁciency of 77%, but did not use an actual
EV in determining this value. Refs.  and  arrived at
efﬁciencies of around 80%, but only considered the efﬁciency
of the charging station. The inconclusiveness on the value of
the V2G round-trip efﬁciency among researchers is highlighted
by the discussions in the following comment papers , .
Given the high importance of the V2G round-trip efﬁciency for
future research, the current research presents a re-evaluation
of the V2G round-trip efﬁciency in a ﬁeld experiment setting,
which resembles the natural environment of V2G. The V2G
round-trip efﬁciency is evaluated for different dates, SoCs,
charging currents and EV charging systems. Thereby this study
includes a proof-of-concept of performing V2G using an on-
board AC/DC converter.
The paper is outlined as follows: Section II presents the
setup of the ﬁeld experiment and describes the methods used
to determine the round-trip efﬁciency. The results of the ﬁeld
experiments are presented in Section III. This is placed in a
wider context in the Discussion in Section IV. Concluding
remarks are presented in Section V.
NON -EXH AUS TIV E OVE RVIE W OF US ED V2G RO UN D-
TR IP EFFI CIE NC IES I N LI TER ATUR E.
Efﬁciency EV charging models
73% , 
81% , –
100% , 
978-1-7281-4701-7/20/$31.00 ©2020 IEEE
II. ME TH OD S
This study used two experimental setups considering two
types of charging systems, as depicted in Fig. 1a and Fig.
1b. The ﬁrst experimental setup considered a charging system
with the AC/DC inverter inside the charging station. A Nis-
san LEAF (MY2018) was charged and discharged using the
eNovates DC V2G 10 kW charging station. The second ex-
perimental setup considered a charging system with a AC/DC
inverter on board of the EV, using a V2G prototype of the
Renault ZOE and a WeDriveSolar v1.1. charging station with
a Last Miles Solutions controller. This second setup is a novel
approach, which means that this research also serves as a
proof-of-concept of V2G technology using the AC/DC inverter
on board of the EV.
The EV batteries in both systems reported the SoC of the EV
on a two second basis. Multiple charging/discharging cycles
were performed, which were based on the communicated SoC
of the EV battery. In a charging/discharging cycle, an EV
starts charging from the predetermined starting SoC until the
predetermined ﬁnal SoC is reached, after which it discharges
until the starting SoC is reached again. Discharging is enforced
by sending a computer signal to the Open Charge Point
Interface Protocol (OCPI) protocol of the charging station
to change the direction of the current. Fig. 2 illustrates the
charging power and the SoC during one charging/discharging
cycle. In both charging systems, power ﬂows between the
charging station and the AC grid were measured on a two
second basis, at the measuring point depicted in Fig. 1a and
Fig. 1b. The efﬁciency of one charging/discharging cycle was
determined by taking the ratio between the energy exported
from the charging station Eout and the energy fed into the
charging station Ein in one charging/discharging cycle, as
outlined in eq. (1). Hence, the losses consist of all conversion
losses in the charging station and in the EV battery in a
full charging/discharging cycle. Ein and Eout are determined
considering the charging power over time (Pt), the duration
of one timestep (∆t), the starting moment of charging at the
starting SoC (tSoCmin,start), the moment the ﬁnal SoC is reached
(tSoCmax) and the moment the starting SoC is reached again
Note that the discharging power, in the numerator of (1), is
negative by convention - as also visible in Fig. 2. Therefore,
the minus sign is added to obtain a positive value for the
III. RES ULTS
Table II provides an overview of all performed tests and
the average efﬁciency per experimental set-up. The measured
efﬁciencies ranged from 79.1% to 87.8%. Highest efﬁciencies
were found for the Nissan LEAF when charging and discharg-
ing at maximum current, namely 87.0%. However, various
external factors were found to have an impact on the efﬁciency,
which will be discussed in the next sections.
A. Impact of Date on Efﬁciency
Fig. 3 illustrates a comparison between the test results of
two tests: one performed on 23 April 2019 and one performed
on 28 November 2019. Average efﬁciency of the former was
87.0%, whereas efﬁciency of the latter was 85.6%. Despite
the small sample size (three and four cycles, respectively) this
difference was statistically signiﬁcant (two-sample t-test; p <
Results could be explained by the difference in temperature
between these two days; the average ambient temperature
of the test performed on 23 April was 15.3°C, while the
average of the tests performed on 28 November was 5.5°C.
The decreased performance of EVs on cold days is a well-
known factor in EV user experiences and EV modelling .
It is also in line with laboratory research performed on lithium-
ion battery charging and discharging, which found higher heat
generation (which indicates conversion losses) in the battery at
low ambient temperatures than at high ambient temperatures
B. Impact State of Charge on Efﬁciency
Fig. 4 illustrates the efﬁciencies of tests performed around
an SoC of 15%, 50% and 85%. Results indicate that charging
efﬁciency is higher for medium SoCs than for SoCs either on
the low or high extremes (84.6% versus 83.7% and 83.0%,
respectively). For low SoCs, this is in line with previous
research on lithium-ion batteries; it was found that batteries
exhibit higher internal resistance and heat generation at low
SoCs for both charging and discharging .
However, differences are relatively small and statistically
insigniﬁcant. This indicates that V2G can also be performed
for low and high SoCs of the EV without considerably
compromising the efﬁciency.
C. Impact Current on Efﬁciency
Fig. 5 illustrates the various efﬁciencies on full load and
partial load. Partial load (3x8A) signiﬁcantly reduces the
round-trip efﬁciency of V2G (two-sample t-test, p <0.01). De-
creasing the current to 3x4A further decreases the efﬁciency.
These results are not in line with , who found that heat
generation in batteries (which indicate losses) increase with
increasing current. However, the C-rates (i.e. power-to-energy
ratio) in that study were between 1 and 4, whereas C-rates
were below 1 in our study, which makes results incomparable.
Our results are in line with , who explain this ﬁnding by
noting that EV chargers must be designed for a large range
of charging conditions, but cannot be optimized for all these
D. Impact EV and Charging Station Type on Efﬁciency
Fig. 6 compares the efﬁciencies of the two tested charg-
ing systems. Results indicate that similar efﬁciencies can be
obtained with different V2G conﬁgurations. As the AC/DC
Fig. 1. a) Experimental setup of V2G measuring system with AC/DC inverter in the charging station, using a Nissan LEAF. Image of Nissan LEAF is taken
at testing lab of ElaadNL. b) Experimental setup of V2G measuring system with AC/DC inverter onboard of the EV, using a Renault ZOE prototype. Image
of Renault ZOE is a stock photo from Renault.
Fig. 2. One charging / discharging cycle, in this case between a SoC of 45%
and 55%. The start and end times of a half cycle are indicated by tSoCmin,start,
tSoCmax and tSoCmin,start. Note that the EV only communicates integers, which
explains the step-wise increase in SoC.
converter of the Renault ZOE is still a prototype, better efﬁ-
ciencies can potentially be obtained with further development
of the technology.
Fig. 3. Efﬁciencies of tests performed in spring and in late autumn, with SoC
range 25% to 75%. Height of bars indicate the average of the tests; error bars
indicate the minimum and maximum found efﬁciency within the tests.
E. Charging and Discharging Power at Different SoCs
Fig. 7 illustrates the relationship between SoC and power
for charging and discharging (V2G) of the Nissan LEAF. In
general, power rates of charging are somewhat higher. This
is because of the location of the measuring point, which is
OVE RVIE W TES T RE SULT S.
Start Time End Time Current SoC limits EV + EVSE Number
3x16A 25%-75%Nissan LEAF + eNovates 4 87.0%
3x16A 25%-75%Nissan LEAF + eNovates 3 85.6%
1/05/2019, 15:30 2/5/2019, 06:31 3x8A 30%-70%Nissan LEAF + eNovates 3 84.6%
25/4/2019, 05:02 3x4A 25%-75%Nissan LEAF + eNovates 1 79.2%
3x16A 80%-90%Nissan LEAF + eNovates 3 83.0%
3x16A 45%-55%Nissan LEAF + eNovates 3 84.6%
3x16A 11%-19%Nissan LEAF + eNovates 3 83.7%
9/10/2019, 11:31 9/10/2019, 14:49 3x16A 25%-35%Renault ZOE (prototype) 3 85.1%
Fig. 4. Efﬁciency at different average SoCs, with SoC range of 11% to 19%,
45% to 55% and 80% to 90%. Height of bars indicate the average of the
tests; error bars indicate the minimum and maximum found efﬁciency within
between the charging station and the AC grid. Hence, charging
power rates are before conversion losses and discharging
rates after conversion losses. What further stands out, is the
jagged shape in the charging curve - despite the fact that
the current signal that is sent to the charging station is
constant. Apparently, either the charging station or the EV
readjusts the voltage at speciﬁc SoCs. A possible explanation
is that the EV some modules of the battery pack are charged
consecutively instead of in parallel, however, the underlying
reason is difﬁcult to verify. The discharging curve follows
the more well-known power curve of a battery, with higher
voltages at higher SoCs .
In total, the round-trip efﬁciency of 23 full V2G charging
+ discharging cycles was determined. An efﬁciency of 87%
was found for charging the Nissan Leaf at 3x16A. This value
Fig. 5. Efﬁciencies of tests for different values of current, with SoC range
of 25% to 75% for 3x16A and 3x4A and 30% to 70% for 3x8A.. Height of
bars indicate the average of the tests; error bars indicate the minimum and
maximum found efﬁciency within the tests. The test of 3x4A was performed
only once, hence the non-existent error bar.
is in line with the values reported in , but substantially
higher than the highest reported V2G round-trip efﬁciency
in multiple other studies , , , . The higher
efﬁciency values measured in this study indicate that other
studies assuming a considerably lower efﬁciency values might
have underestimated the business case and potential to provide
grid services of V2G.
Lower temperatures and partial load seem to have a negative
impact on V2G round-trip efﬁciency. The considerably lower
efﬁciency values with partial load has considerable impli-
cations for EV charging models. Where most EV charging
models consider that the charging and discharging efﬁciency is
independent of the charging load, this study indicated that this
assumption is invalid. It is recommended that the dependency
between charging current and charging efﬁciency is considered
in future charging models.
Fig. 6. Efﬁciencies of the Nissan LEAF (MY2018) combined with the
eNovates DC V2G 10kW charger, with SoC ranges between 25% and 75%
and the Renault ZOE with on-board AC/DC converter (prototype), with SoC
range of 25% to 35%. Height of bars indicate the average of the tests; error
bars indicate the minimum and maximum found efﬁciency within the tests.
Fig. 7. State of Charge versus a) charging power and b) discharging (V2G)
power of EV of three charging cycles at 3x16 Ampere of the Nissan LEAF
between SoCs of 25% and 75%.
It should be noted that the experimental setup was a ﬁeld
experiment, which is both a strength and a weakness of
the present study. The disadvantage of this setting is that it
is impossible to make conclusive statements on the relation
between independent and dependent variables. The advantage
is that this setting resembles the natural environment of V2G,
increasing the external validity of the tests.
Inaccuracies in measurements could occur from the com-
municated SoC by the EV battery. An important factor for
the EV to determine its SoC is the voltage level in the battery
system. However, the voltage in the battery can also be affected
by different parameters, including the battery temperature and
previous operation history . Therefore, the same reported
SoC does not necessarily represent the same energy level in the
battery. By performing multiple charge and discharge cycles
of a large SoC-range, the effect of this potential inaccuracy is
Our results have important practical implications. Since
the efﬁciencies reported in this study are determined in a
ﬁeld experiment, results give a realistic estimation of V2G
efﬁciencies in real-world applications. As mentioned before,
efﬁciency is of large importance for the ﬁnancial and envi-
ronmental impact of battery operation in general, and V2G
speciﬁcally , . Aggregators could use the efﬁciency
values obtained in this research to explore V2G business cases
for EV users. Furthermore, the environmental impact of V2G
can be determined more accurately.
V. CONCLUDING REMARKS
This study is the ﬁrst to perform a broad ﬁeld experiment
on the round-trip efﬁciency of V2G. Furthermore, it provides
a proof-of-concept of V2G using the AC/DC inverter on board
of the EV. The results give guidance to EV modellers on
appropriate efﬁciencies to assume in EV charging models and
provide insight to e.g. charging ﬂeet operators on the factors
impacting the EV charging efﬁciency.
Future research could perform more sophisticated analyses
on the V2G round-trip efﬁciency by performing more charge
and discharge cycles and by considering different types of
charging stations and EVs. In addition, future research should
implement power ﬂow meters behind the inverter to be able to
separate V2G efﬁciency losses into battery losses and inverter
losses. Also the range of experiments can be extended by
testing other potential impacting factors, including different
weather conditions and battery age.
 B. S. K. Patnam and N. M. Pindoriya, “DLMP Calculation and Con-
gestion Minimization With EV Aggregator Loading in a Distribution
Network Using Bilevel Program,” IEEE Systems Journal, pp. 1–12,
 N. Brinkel, W. Schram, T. AlSkaif, I. Lampropoulos, and W. van Sark,
“Should we reinforce the grid ? Cost and emission optimization of
electric vehicle charging under different transformer limits,” Applied
Energy, vol. 276, no. October, 2020.
 Utrecht University, “King launches network of charging stations to
charge and discharge electrical cars,” 2019.
 Y. A. Shirazi and D. L. Sachs, “Comments on “Measurement of power
loss during electric vehicle charging and discharging” – Notable ﬁndings
for V2G economics,” Energy, vol. 142, pp. 1139–1141, 2018.
 W. L. Schram, T. Alskaif, I. Lampropoulos, S. Henein, and W. G. J.
H. M. V. Sark, “On the trade-off between Environmental and Economic
Objectives in Community Energy Storage Operational Optimization,”
IEEE Transactions on Sustainable Energy, 2020.
 E. Apostolaki-Iosiﬁdou, P. Codani, and W. Kempton, “Measurement of
power loss during electric vehicle charging and discharging,” Energy,
vol. 127, pp. 730–742, 2017.
 E. Apostolaki-Iosiﬁdou, W. Kempton, and P. Codani, “Reply to Shirazi
and Sachs comments on “Measurement of Power Loss During Electric
Vehicle Charging and Discharging”,” Energy, vol. 142, pp. 1142–1143,
 C. Heymans, S. B. Walker, S. B. Young, and M. Fowler, “Economic
analysis of second use electric vehicle batteries for residential energy
storage and load-levelling,” Energy Policy, vol. 71, pp. 22–30, 2014.
 A. Whitehead, C. L. Smith, and J. M. Grace, “Vehicle-to-Grid Fleet
Demonstration Prototype Assessment,” Tech. Rep. June, Lincoln Labo-
ratory, Massachusetts Institute of Technology, 2018.
 C. Capasso and O. Veneri, “Experimental study of a DC charging station
for full electric and plug in hybrid vehicles,” Applied Energy, vol. 152,
pp. 131–142, 2015.
 A. Zecchino, A. Thingvad, P. B. Andersen, and M. Marinelli, “Suitability
of Commercial V2G CHAdeMO Chargers for Grid Services Suitability
of Commercial V2G CHAdeMO Chargers for Grid Services,” in EVS
31 & EVTeC 2018, 2018.
 A. Kieldsen, A. Thingvad, S. Martinenas, and T. M. Srensen, “Efﬁciency
test method for electric vehicle chargers,” EVS 2016 - 29th International
Electric Vehicle Symposium, 2016.
 P. Papadopoulos, S. Skarvelis-Kazakos, I. Grau, L. M. Cipcigan, and
N. Jenkins, “Electric vehicles’ impact on British distribution networks,”
IET Electrical Systems in Transportation, vol. 2, no. 3, pp. 91–102,
 F. Safdarian, L. Lamonte, A. Kargarian, and M. Farasat, “Distributed
optimization-based hourly coordination for V2G and G2V,” 2019 IEEE
Texas Power and Energy Conference, TPEC 2019, pp. 1–6, 2019.
 N. B. Brinkel, M. K. Gerritsma, T. A. AlSkaif, I. I. Lampropoulos,
A. M. van Voorden, H. A. Fidder, and W. G. van Sark, “Impact of rapid
PV ﬂuctuations on power quality in the low-voltage grid and mitigation
strategies using electric vehicles,” International Journal of Electrical
Power and Energy Systems, vol. 118, jun 2020.
 Y. Shirazi, E. Carr, and L. Knapp, “A cost-beneﬁt analysis of alter-
natively fueled buses with special considerations for V2G technology,”
Energy Policy, vol. 87, pp. 591–603, 2015.
 Y. Huang, “Day-Ahead Optimal Control of PEV Battery Storage Devices
Taking into Account the Voltage Regulation of the Residential Power
Grid,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 4154–
 E. B. Iversen, J. M. Morales, and H. Madsen, “Optimal charging of
an electric vehicle using a Markov decision process,” Applied Energy,
vol. 123, pp. 1–12, 2014.
 M. van der Kam and W. van Sark, “Smart charging of electric vehicles
with photovoltaic power and vehicle-to-grid technology in a microgrid;
a case study,” Applied Energy, vol. 152, pp. 20–30, 2015.
 T. W. Hoogvliet, G. B. M. A. Litjens, and W. G. J. H. M. V. Sark,
“Provision of regulating- and reserve power by electric vehicle owners
in the Dutch market,” Applied Energy, vol. 190, pp. 1008–1019, 2017.
 S. Faddel, A. Aldeek, A. T. Al-Awami, E. Sortomme, and Z. Al-Hamouz,
“Ancillary Services Bidding for Uncertain Bidirectional V2G Using
Fuzzy Linear Programming,” Energy, vol. 160, pp. 986–995, 2018.
 G. M. Freeman, T. E. Drennen, and A. D. White, “Can parked cars and
carbon taxes create a proﬁt? The economics of vehicle-to-grid energy
storage for peak reduction,” Energy Policy, vol. 106, no. March, pp. 183–
 A. Zakariazadeh, S. Jadid, and P. Siano, “Multi-objective scheduling of
electric vehicles in smart distribution system,” Energy Conversion and
Management, vol. 79, pp. 43–53, 2014.
 G. Chandra Mouli, P. Bauer, and M. Zeman, “System design for a
solar powered electric vehicle charging station for workplaces,” Applied
Energy, vol. 168, pp. 434–443, 2016.
 A. Trivi˜
no-Cabrera, J. A. Aguado, and S. de la Torre, “Joint routing
and scheduling for electric vehicles in smart grids with V2G,” Energy,
vol. 175, pp. 113–122, 2019.
 L. Agarwal, W. Peng, and L. Goel, “Using EV battery packs for vehicle-
to-grid applications: An economic analysis,” 2014 IEEE Innovative
Smart Grid Technologies - Asia, ISGT ASIA 2014, pp. 663–668, 2014.
 X. Han, H. Zhang, X. Yu, and L. Wang, “Economic evaluation of grid-
connected micro-grid system with photovoltaic and energy storage under
different investment and ﬁnancing models,” Applied Energy, vol. 184,
pp. 103–118, 2016.
 J. Lindgren and P. D. Lund, “Effect of extreme temperatures on
battery charging and performance of electric vehicles,” Journal of Power
Sources, vol. 328, pp. 37–45, 2016.
 G. Liu, M. Ouyang, L. Lu, J. Li, and X. Han, “Analysis of the
heat generation of lithium-ion battery during charging and discharging
considering different inﬂuencing factors,” Journal of Thermal Analysis
and Calorimetry, vol. 116, no. 2, pp. 1001–1010, 2014.
 J. B. Gerschler and D. U. Sauer, “Investigation of open-circuit-voltage
behaviour of lithium-ion batteries with various cathode materials under
special consideration of voltage equalisation phenomena,” 24th Interna-
tional Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and
Exhibition 2009, EVS 24, vol. 3, no. January, pp. 1550–1563, 2009.