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Empirical Evaluation of V2G Round-trip Efficiency


Abstract and Figures

The business case of vehicle-to-grid (V2G) technology 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.
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Empirical Evaluation of V2G Round-trip Efficiency
Wouter Schram*1
Utrecht University
Nico Brinkel1
Utrecht University
Gilbert Smink
Thijs van Wijk
Wilfried van Sark
Utrecht University
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 [1], while V2G also can provide financial benefits
as well as environmental benefits [2].
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 [3]. 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 [4], [5].
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.
*Corresponding author
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. [4], [6]–[8] 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 [9], 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 [10] arrived at
a round-trip efficiency of 77%, but did not use an actual
EV in determining this value. Refs. [11] and [12] 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 [4], [7].
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.
Efficiency EV charging models
55% [13]
72% [14]
73% [15], [16]
77% [17]
81% [1], [18]–[21]
84% [22]
86% [23]
87% [24]
94% [25]
100% [26], [27]
978-1-7281-4701-7/20/$31.00 ©2020 IEEE
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
tSoCmax Ptt
tSoCmin,start Ptt(1)
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
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 [28].
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 [29].
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 [29], 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 [6], 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
Start Time End Time Current SoC limits EV + EVSE Number
of cycles
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
the tests.
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 [30].
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 [9], but substantially
higher than the highest reported V2G round-trip efficiency
in multiple other studies [4], [6], [10], [11]. 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 [30]. 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 [4], [5]. 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.
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.
[1] 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,
[2] 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.
[3] Utrecht University, “King launches network of charging stations to
charge and discharge electrical cars,” 2019.
[4] 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.
[5] 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.
[6] 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.
[7] 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,
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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,
[14] 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.
[15] 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.
[16] 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.
[17] 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–
4167, 2019.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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–
190, 2017.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
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The expansion of photovoltaic systems as well as heat pumps and charging stations for electric vehicles are creating new challenges for low-voltage grids, as peaks in load and generation can lead to grid congestions. A promising approach to these challenges is the use of flexibility from various flexibility options to prevent these grid congestions. In this paper a system for flexibility-based grid congestion management is presented. Furthermore in this paper, different flexibility options are compared in terms of availability and cost to provide flexibility for grid congestion management.
... The on-board charger developed by Radimov et al. is a bi-directional, three-stage, onboard charger with a peak efficiency of 96.65 % [43]. Schram et al. determined the V2G round-trip efficiency of a Renault Zoe with a bi-directional on-board charger to be 85.1 % and of a Nissan Leaf connected to an external charging station to be 87.0 % [44]. Due to the increasing demand for power electronic devices that are also used in EV powertrain systems researchers work on their improvements. ...
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A comprehensive electric vehicle model is developed to characterize the behavior of the Smart e.d. (2013) while driving, charging and providing vehicle-to-grid services. To facilitate vehicle-to-grid strategy development, the EV model is completed with the measurement of the on-board charger efficiency and the charging control behavior upon external set-point request via IEC 61851-1. The battery model is an electro-thermal model with a dual polarization equivalent circuit electrical model coupled with a lumped thermal model with active liquid cooling. The aging trend of the EV’s 50 Ah large format pouch cell with NMC chemistry is evaluated via accelerated aging tests in the laboratory. Performance of the model is validated using laboratory pack tests, charging and driving field data. The RMSE of the cell voltage was between 18.49 mV and 67.17 mV per cell for the validation profiles. Cells stored at 100% SOC and 40 °C reached end-of-life (80% of initial capacity) after 431–589 days. The end-of-life for a cell cycled with 80% DOD around an SOC of 50% is reached after 3634 equivalent full cycles which equates to a driving distance of over 420,000 km. The full parameter set of the model is provided to serve as a resource for vehicle-to-grid strategy development.
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The application of smart charging to battery electric buses can provide opportunities for bus operators to reduce the operational costs of their bus fleet. This research aims to create insight into the impact of different charging strategies for battery electric bus fleets on charging costs and the grid load. It proposes a novel framework to model the charging process of battery electric buses for different charging strategies: charging-on-arrival, peak-shaving, day-ahead market optimization with and without vehicle-to-grid (V2G) functions, including the provision of Frequency Containment Reserves (FCR) and automatic Frequency Restoration Reserves (aFRR) for system balancing in ancillary services markets. Model simulations are conducted to compare the charging costs and grid impact of different charging strategies, using three depots of bus operator Qbuzz in the Netherlands as a case study. Results indicate that the application of smart charging algorithms can considerably reduce charging costs for bus operators. Application of the peak-shaving strategy was found to reduce charging costs by 23-32% compared to the reference case of charging-on-arrival. Charging costs can be further reduced by 6-11% when considering day-ahead market optimization. Participation in ancillary services markets for system balancing is economically attractive for bus operators, particularly in the aFRR market, characterized by a cost reduction potential of 90->100% compared to the charging-on-arrival strategy. The grid impact analysis indicates that charging-on-arrival can result in high charging demand peaks, which can be drastically reduced by the application of peak-shaving or day-ahead market optimization charging strategies. However, the provision of aFRR and FCR using the battery electric bus charging process can have a severe impact on the local grid in terms of high peak demand.
Ski resorts are becoming perfect demonstrations for the integration of renewable sources. Moreover, the ever-growing global fleet of electric cars (EV) and the increase of battery capacities allow the vehicles to be used for purposes other than driving. This paper aims to study the implementation of a vehicle-to-grid (V2G) application and the development of an MPC-based energy management system (EMS) within a ski resort in the Trentino-Alto Adige Italian region. Using real data for load and production power estimation, the study analyses the economic and environmental impact in three different scenarios by considering the resort’s future trends. The results show that photovoltaic power generation has a significant impact on both cost and environmental aspects, leading to a reduction in total expenditure and CO2 of about 7%. However, assuming an increasing number of charging stations, the exploitation of V2G technology for energy arbitrage does not lead to significant total cost and CO2 reductions (≈2%) for the future scenarios. Conversely, self-consumption improves (≈100%) using different charging strategies. Despite this, a sensitivity analysis on PV production and a joint increase in EVs and energy price gap leads to a significant reduction in total costs of up to 7.7% and 15.2%, respectively.
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In an electrical microgrid, distributed renewable generation is one of the main tools used to achieve energy sustainability, cost efficiency and autonomy from the grid. However, reliance on intermittent power sources will lead to a mismatch between generation and demand, causing problems for microgrid management. Flexibility is key to reducing the mismatch and providing a stable operation. In such a context, demand response and energy storage systems are the main factors that contribute to flexibility in a microgrid. This paper provides an assessment of the technical and economic impacts of a microgrid at the building level, considering photovoltaic generation, battery energy storage and the use of electric vehicles in a vehicle-to-building system. The main novel contributions of this work are the quantification of system efficiencies and the provision of insights into the design and implementation of microgrids using real on-site data. Several tests were conducted using real on-site data to calculate the overall efficiencies of the different assets during their operation. An economic assessment was carried out to evaluate the potential benefits of coordinating battery storage with a vehicle-to-building system regarding the flexibility and cost�efficient operation of the microgrid. The results show that these two systems effectively increase the levels of self-consumption and available flexibility, but the usefulness of private electric vehicles in public buildings is constrained by the schedules and parking times of the users. Furthermore, economic benefits are highly dependent on the variability of tariffs and the costs of energy storage systems and their degradation, as well as the efficiency of the equipment used in the conversion chain.
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With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer.
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The need to limit climate change has led to policies that aim for the reduction of greenhouse gas emissions. Often, a trade-off exists between reducing emissions and associated costs. In this paper, a multi-objective optimization framework is proposed to determine this trade-off when operating a Community Energy Storage (CES) system in a neighbourhood with high shares of photovoltaic (PV) electricity generation capacity. The Pareto frontier of costs and emissions objectives is established when the CES system would operate on the day-ahead spot market. The emission profile is constructed based on the marginal emissions. Results show that costs and emissions can simultaneously be decreased for a range of solutions compared to reference scenarios with no battery or a battery only focused on increasing self-consumption, for very attractive CO2 abatement costs and without hampering self-consumption of PV-generated electricity. Results are robust for battery degradation, whereas battery efficiency is found to be an important determining factor for simultaneously decreasing costs and emissions. The operational schedules are tested against violating transformer, line and voltage limits through a load flow analysis. The proposed framework can be extended to employ a wide range of objectives and / or location-specific circumstances.
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Cloud transients cause rapid fluctuations in the output of photovoltaic (PV) systems, which can significantly affect the voltage levels in a low-voltage (LV) grid with high penetration of PV systems. These voltage fluctuations may lead to violation of the existing power quality standards. This study estimates the impact of rapid PV output fluctuations on the power quality in an existing LV grid by performing load flow analyses for scenarios in the years 2017, 2030 and 2050 using PV data with 20-second resolution. In this study, we propose a system for the mitigation of PV output fluctuations by altering the charging processes of electric vehicles (EVs) and we assess the effectiveness of the proposed system. Results indicate that PV output fluctuations have minor impact on the voltage levels in the year 2030, but PV output fluctuations induce considerable voltage fluctuations in the year 2050. The magnitude of the voltage fluctuations is dependent on the location in the grid, the installed PV capacity and the grid configuration. These voltage fluctuations can induce visible and annoying light flicker for a significant part of the day in the year 2050. Implementing the proposed system shows that EV technology can contribute in reducing the amount of visible and annoying light flicker considerably, however at the expense of increased charging costs for EV owners.
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Aggregation and control of electric vehicles (EVs) via vehicle-to-grid (V2G) technologies is seen as a valid option for providing ancillary power system services. This work presents results from V2G-ready equipment tests and modelling. The technical capabilities of an EV connected to a commercial V2G charger are investigated when controlled either locally or remotely. The charger is characterized in terms of efficiency characteristics, activation time, response granularity, ramping-up/down time, accuracy and precision. Test results show the performance for different operating conditions, highlighting the importance of a good calibration and knowledge of the employed hardware when providing standard-compliant grid regulation services via V2G technology. Ultimately, a set of simulations demonstrates that the designed EV charger model replicates accurately the operating conditions of the real hardware.
This article develops a distributional locational marginal pricing (DLMP) based electric vehicle (EV) aggregator scheduling framework minimizing the congestion in the network. The article has two parts: 1) calculation of DLMPs by solving actual DistFlow equations; and 2) EV aggregator scheduling in a distribution network formulated as a bilevel problem considering EV aggregator as a leader and distribution system operator (DSO) as a follower. The upper level problem is the cost minimization of EV aggregator, whereas the lower level problem is the social welfare maximization of DSO while satisfying the network constraints. The nonlinear bilevel problem is reformulated into the single-level problem using Karush–Kuhn–Tucker conditions and duality theorem. The framework considers the uncertainty of the EV aggregator by using robust programming and tested with the 15-bus radial distribution network for congested and uncongested cases.
This paper first presents an optimization model to flexibly control available plug-in electric vehicle (PEV) battery charging/discharging power based on three-phase power flow and sensitivity approaches. This model can achieve one of two goals: (1) minimizing both battery charging/discharging cost and extra battery degradation cost due to vehicle-to-grid (V2G) activities (cost-reduction strategy) or (2) maximizing local peak load shifting and minimizing extra battery degradation cost due to V2G activities (peak-shifting strategy). The first strategy can determine the appropriate charging/discharging rates of an available PEV battery in order to benefit both the PEV owners and the distribution utilities for the day ahead. The second strategy can reduce the peak loads of the system. Both strategies can improve the power quality at the same time. With the help of a sensitivity method, most of the nonlinear constraints are transformed into linear constraints, and the number of constraints is reduced in the model. An interior point optimization approach is utilized to solve the optimization model. The optimization model is modified further to address unexpected PEV connections and travel scenarios during operation. The effectiveness and accuracy of the proposed method are demonstrated and verified on a 75-node test feeder.
Modern distribution systems with high penetration of Electric Vehicles (EVs) are the focus of increasing attention. EVs charging strategies impact on power networks operation and they can even affect driving patterns when considering Vehicle-to-Grid (V2G) market-driven scenarios. This paper proposes a joint EV routing and charging/discharging scheduling strategy to operate an EV fleet. Particularly, we propose a mathematical framework based on a mixed-integer linear programming problem with the goal of maximizing the revenue of EV users. This approach is illustrated and tested using the IEEE 37-node test feeder. The results show that slight changes in driving patterns can provide benefits to EV users and improve the network operation. The correct design of the price dynamics is concluded to be key for promoting V2G participation.
The operation of bidirectional V2G participating in ancillary services markets is particularly challenging due to various uncertainties. This work proposes an algorithm to optimize the uncertain operation of bidirectional V2G for an electric vehicle (EV) aggregator. The proposed algorithm maximizes the profits of the aggregator while providing additional system flexibility and low charging costs to the EV owners. The proposed algorithm considers electricity market and EV mobility uncertainties using fuzzy linear programming. These uncertainties include those of the regulation and responsive reserve prices, deployment signals, and the energy used for EV trips. Unlike other works in the literature, the proposed algorithm is capable of considering a large number of uncertain parameters without greatly impacting the problem's complexity or simulation time. Also, the algorithm does not result in conservative solutions. The simulation results show the superiority of the proposed fuzzy algorithm over its deterministic counterpart in terms of higher realized profits. Also, the proposed approach results in lower real-time penalties, which ensures its superiority to provide the EVs with the required trip energy at the required time. In addition, it results in lower battery degradation costs, which helps increase the life time of the EVs.
Shirazi and Sachs comment on our publication in Energy "Measurement of Power Loss During Electric Vehicle Charging and Discharging" [1]. Their comment discusses aspects of our methodology and findings on round-trip efficiency during charging and discharging of electric vehicles (EVs) This reply discuss the transformer used in our experimental process, resulting in a round-trip efficiency of 70% and equally inflated commercial assumptions. Also, we suggest that the transformer losses should not be included in the V2G economics.