A review on charging behavior of electric vehicles: data, model, and control

Abstract

The adoption and usage of electric vehicles (EVs) have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution. As a part of the smart grid, EVs can provide valuable ancillary services beyond consumers of electricity. However, EVs are gradually considered as nonnegligible loads due to their increasing penetration, which may result in negative effects such as voltage deviations, lines saturation, and power losses. Relationship and interaction among EVs, charging stations, and micro grid have to be considered in the next generation of smart grid. Therefore, the topic of smart charging has been the focus of many works where a wide range of control methods have been developed. As one of the bases of simulation, the EV charging behavior and characteristics have also become the focus of many studies. In this work, we review the charging behavior of EVs from the aspects of data, model, and control. We provide the links for most of the data sets reviewed in this work, based on which interested researchers can easily access these data for further investigation.

Full text

Control Theory Tech, Vol. 18, No. 3, pp. 217–230, August 2020
Control Theory and Technology
http://link.springer.com/journal/11768
A review on charging behavior of electric vehicles:
data, model, and control
Qing-Shan JIA†, Teng LONG
Center for Intelligent and Networked Systems (CFINS), Department of Automation,
Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
Received 11 March 2020; revised 1 May 2020; accepted 7 May 2020
Abstract
The adoption and usage of electric vehicles (EVs) have emerged recently due to the increasing concerns on the greenhouse
gas issues and energy revolution. As a part of the smart grid, EVs can provide valuable ancillary services beyond consumers of
electricity. However, EVs are gradually considered as nonnegligible loads due to their increasing penetration, which may result in
negative effects such as voltage deviations, lines saturation, and power losses. Relationship and interaction among EVs, charging
stations, and micro grid have to be considered in the next generation of smart grid. Therefore, the topic of smart charging has
been the focus of many works where a wide range of control methods have been developed. As one of the bases of simulation,
the EV charging behavior and characteristics have also become the focus of many studies. In this work, we review the charging
behavior of EVs from the aspects of data, model, and control. We provide the links for most of the data sets reviewed in this
work, based on which interested researchers can easily access these data for further investigation.
Keywords: Electric vehicle, charging behavior, data and model
DOI https://doi.org/10.1007/s11768-020-0048-8
1 Introduction
Energy saving and CO2emission reduction have be-
come international issues. Carbon emission reduction
target has been made in China, U.S.A., and many coun-
tries in Europe. The transportation sector was reported
to be responsible for 62.3% oil consumption in the
world, which is considered as one of the main causes
of the increase of CO2emissions [1]. Electric vehicles
(EVs) provide an alternative for the fuel-based automo-
biles to shift the energy demand from fossil fuels to elec-
tricity. Since electricity can be obtained from a variety
of renewable sources, such as wind, solar, and heat, it
can ease people’s worries about the increasing depletion
of oil resources. Meanwhile, EVs can provide valuable
ancillary services other than pure electricity consumers
†Corresponding author.
E-mail: jiaqs@tsinghua.edu.cn.
This work was supported in part by the National Key Research and Development Program of China (No. 2016YFB0901900), the National
Natural Science Foundation of China under grants (No. 61673229) and the 111 International Collaboration Project of China (No. BP2018006).
© 2020 South China University of Technology, Academy of Mathematics and Systems Science, CAS and Springer-Verlag GmbH Germany,
part of Springer Nature

218 Q. S. Jia, T. Long / Control Theory Tech, Vol. 18, No. 3, pp. 217–230, August 2020
in a smart grid. Compared with the traditional mode of
transportation, EVs has various advantages which are
listed as follows:
•Environment-friendly vehicle. EVs generally have
high energy efficiency and low gas emission which is
a benefit for sustainable development [2].
•Diverse energy sources. Electricity can be generated
by a variety of renewable energy such as wind, solar, and
biomass which can help the transportation sector move
away from dependence on fossil fuel.
•Better fuel economy. The cost per kilometer of EVs
is lower than vehicles driven only by the internal com-
bustion engine (ICE).
The advantages mentioned above encouraged coun-
tries like Canada, China, and the U.S.A. to expand the
EV industrial scale in terms of policies and economy,
such as tax exemption, purchase subsidy, and pollutant
emissions limitation [3]. In some cities of China, the
access to the ICE vehicle license plate is far more diffi-
cult for EVs [4]. Since the number of EVs is increasing
in these years, the charging load of EVs is becoming
a significant load to the electric power grid. Even at a
low penetration, the uncontrolled charging of EV can
become a major challenge to the power quality of the
distribution infrastructure and may lead to high load
peaks, voltage deviations, and power losses [5, 6]. The
results in [7] show that the power demand and line cur-
rent may exceed the transformer ratings considering the
uncontrolled EV charging. Rezaei et al. [8] investigated
the impact of EV charging loads enabling vehicle-to-grid
(V2G) and grid-to-vehicle (G2V) systems on the stability
and reliability of the power grid. Therefore it is of prac-
tical interest to study how to charge the EVs both for a
single EV and for a fleet. EVs are reported to be idle dur-
ing 95% of the time on average and have the elasticity
of charging, which implies huge optimization space and
potential economic benefits. At the same time, the wave
of EV industry has also spread to the charging infrastruc-
ture and related fields, bringing a series of technological
changes such as EV aggregators [9], V2G, renewable
energy sources (RES).
An EV charging system consists of EVs, charging sta-
tions, the electric power grid, and the communication
network. The charging behavior, which involves the fre-
quency, demand, and standard of charging, and the
power level, is the basis for EV scheduling. Therefore
it is important to study the model and simulation of
the charging behavior. The aggregated charging behav-
ior affects the upper-level agencies such as the charging
stations and the local distribution network.
There exist many studies on EV charging, such as the
propulsion systems [10], modeling [11], charging infras-
tructure [12], optimization [13, 14], supply-demand co-
ordination [15], power electronic converter topologies
with RES [16], and fast charging [17]. Some key issues
and challenges are reviewed in [18] and [19]. Despite
many works on the model and control of EV charging
behavior, there is a lack of review on the real data of EV
charging behavior. We consider this important problem
in this work and provide a comprehensive review of the
existing data sets and models for EV charging behavior
and the related control methods.
We make the following major contributions in this
work. First, we provide a comprehensive collection on
data sets of EV charging behavior. This includes data
such as sales volume, driving, charging, and battery per-
formance. Most of these data sets are accompanied with
download links. Second, we discuss some models of EVs
and charging points (CPs) such as the charging standard,
mode, connector, and system. Third, we identify the im-
pact of EV charging behavior on EVs, charging stations,
and the local grid. Some related issues are also dis-
tinguished including an optimization framework of EV
smart charging.
The remainder of this paper is organized as follows.
We review the data sets and models of EV charging be-
havior in Section 2. We introduce some main standards
of CPs in Section 3 while discussing the impact and re-
lated research of the EV charging behavior in Section 4.
The review ends with the concluding remarks in Sec-
tion 5.
2 Data sets and models of EV charging be-
havior
The first generation of EVs came out in 1997 [20].
After more than 20 years of rapid development, a chain
of industries of EV has been established and is grow-
ing. Various makes and modes of EVs are now available
in the market. There are many ways to classify EVs,
such as the degree of hybridization and the structure
of the propulsion system [12]. EVs might be divided
into three groups, including the plug-in hybrid electric
vehicle (PHEV), battery electric vehicle (BEV), and fuel
cell electric vehicle (FCEV) [21]. A PHEV is an upgraded
hybrid electric vehicle (HEV) with an onboard battery
charger (OBC) and storage system driven by both elec-
tricity and liquid fuel, which allows charging from the
power grid and renewable energy sources while parked.
The electricity stored onboard may only drive the PHEV
for a limited range. A BEV is mainly driven by the electric
motor (EM) rather than the ICE. In terms of power trans-

Q. S. Jia, T. Long / Control Theory Tech, Vol. 18, No. 3, pp. 217–230, August 2020 219
mission, the electric energy for driving the EM mainly
comes from the rechargeable battery or other storage
devices onboard. BEVs can be connected to CPs and
recharged from the state grid. An FCEV is driven by hy-
drogen. However, due to the high cost, production, and
storage problems of hydrogen, it’s not widely accepted
so far. The typical one is the Mirai of Toyota. We summa-
rize the common categories and representative vehicles
of EVs in Fig. 1. Further details on the classification of
EVs are available in [10] and [22].
The charging control of EVs is not a simple point-
to-point process, but instead involves the coordination
of multiple systems including the battery management
system (BMS), the central controller, the inverter (con-
nector), the CP and the fleet operator (FO). Inside EVs,
the BMS is responsible for monitoring and reporting the
battery health status and state of charge (SOC). As the
information interface with the outside world, the central
controller becomes the actual operator of the EV charg-
ing process. The inverter and connector act as transfer
stations of energy flow and ensure the stability of charg-
ing behaviors. In particular, the power converter for the
AC source is generally onboard. However, due to the
onboard weight limitation, charging power, and space
constraints, fast-charging operations usually performed
through an external off-board DC power converter and
source [23]. Externally, the FO and CPs are also involved
as terminals for information flow and energy flow, re-
spectively. The detailed interactions between these sys-
tems are shown in Fig. 2.
It is of practical interest to simulate EV charging behav-
iors. Though these behaviors may be described either by
models or by real data, a combination of model-based
and data-driven simulations has been shown to be use-
ful in practice. In the rest of this section, we review the
related data sets and models in more detail.
Fig. 1 A classification of vehicles.
Fig. 2 EV charging framework.

220 Q. S. Jia, T. Long / Control Theory Tech, Vol. 18, No. 3, pp. 217–230, August 2020
2.1 Datasets on EV charging behavior
In order to understand the patterns in charging and
driving of EVs, several projects around the world have
been launched, which led to several data sets. These
data sets focus mainly on four aspects, namely industry
trends, driving patterns, charging habits, and batteries.
We list some of these data sets in Table 1. The data
sets reviewed here are still limited, and there may be
important data sets not included in this paper.
Table 1 Some data sets on EV charging behavior.
Category Database/Project Country Website Citation
Sales Statista U.S.A. https://www.statista.com/markets/419/topic/487/vehicles-road-traffic/
Sales U.S. PEV Sales U.S.A. http://www.ev-volumes.com/datacenter/
Sales EV Volumes U.S.A. https://afdc.energy.gov/data/
Sales Evpartner China http://www.evpartner.com/daas/
Sales NEVI China https://nevi.bbdservice.com/global/analysis
Drive KiD 2010 Germany http://www.kid2010.de
Drive MOP Germany http://mobilitaetspanel.ifv.kit.edu/
Drive MiD Germany http://www.mobilitaet-in-deutschland.de/
Drive SrV 2008 Germany https://idp2.tu-dresden.de/
Drive NHTS U.S.A. https://nhts.ornl.gov/ [24]
Drive UP 2013 U.S.A. https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city
Drive Puget Sound U.S.A. https://www.psrc.org/puget-sound-trends [25, 26]
Trends
Drive Volt Stats U.S.A.& https://www.voltstats.net/ [27, 28]
Canada
Drive NYC’s Taxi U.S.A. https://chriswhong.com/open-data/foil_nyc_taxi/
Trip 2013
Drive Chicago’s Taxi U.S.A. https://data.cityofchicago.org/Transportation/Taxi-Trips/
Trip 2013
Drive Roma’s Taxi Italy http://crawdad.org/roma/taxi/20140717/ [29]
Trip 2014
Drive Shanghai’s Taxi China https://www.cse.ust.hk/scrg/ [30]
Trip 2007
Battery Battery U.S.A. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
Charging #battery
Battery Randomized U.S.A. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
Battery Usage #batteryrnddischarge
Battery VCS Test U.S.A. https://avt.inl.gov/project-type/data
Charge The EV Project U.S.A. https://www.energy.gov/eere/vehicles/avta-ev-project [31, 32]
[33, 34]
Charge ACN-Data U.S.A. https://ev.caltech.edu/dataset
Charge CCVRP U.S.A. http://www.arb.ca.gov/msprog/aqip/cvrp.htm [35]
Charge StarCharge China http://www.starcharge.com/
2.1.1 EV sales data sets
The sales data of EVs can reflect the development
of the EV industry, technical maturity, and penetration
of EVs. It also shows why it is necessary to schedule
EV charging on a large scale, the potential economic
benefits, and the impact on the grid. Statista [36] and EV
volumes.com [37] provide some paid statistics and facts
of the EV industry in various countries. Alternative Fuels
Data Center (AFDC) of the U.S. Department of Energy’s
Office has collected the data and analyzed the trend of
sales by the PEV model from 2011 to 2019 [38]. Data
about EV sales in China can be obtained online from

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NEVI [39] and Evpartner [40].
2.1.2 Driving data sets
Driving data, such as the origin and destination, the
start and end time, and the purpose of each trip, may
reflect the statistics of the driving pattern. This is the
most common type of data set used to simulate the
track and charging demand of EVs since it is relatively
easy to access. In Germany, several studies and sur-
veys have been launched to understand the big picture
of the motorized road transport such as Motor Vehi-
cle Traffic in Germany 2010 (KiD 2010) [41], Mobil-
ity Panel Germany (MOP) [42], Mobility in Germany
(MiD) [43] and Mobility in Cities 2008 (SrV 2008) [44].
These data sets can help researchers and practitioners
to understand the driving habits of car owners in Ger-
many. In U.S.A., similar surveys have been conducted
by some government departments such as the Federal
Highway Administration (FHWA) and the city govern-
ments in New York and Chicago [45–47]. Taxi trip and
roundabout mobility data are also collected by countries
like Italy and China [48,49]. Private companies have also
contributed to driving data by taking advantage of busi-
ness areas [50–52].
2.1.3 Battery data sets
Despite the difference between the driving behavior
of ICE vehicles and PHEVs, sometimes the travel data of
ICE vehicles is combined with the battery consumption
data to simulate PHEVs. The frequency, location, and
load of charging are important data for such a simula-
tion. The capacity, life cycle, operating temperature, and
self-discharge rate are important factors of an EV battery.
Lithium batteries are preferred in EVs recently due to the
light weight, small size, and high power density [53]. The
Prognostics CoE at NASA Ames and Advanced Vehicle
provide the data of charging and discharging lithium bat-
teries at different temperatures [54, 55]. The data set of
randomized battery usage is provided by NASA Ames
which can provide reference benchmarks for battery
state of health [56].
2.1.4 Charging data sets
Charging data including the charging power, the fre-
quency, and the charging time is directly related to the
charging behavior of EVs. Office of Energy Efficiency
and Renewable Energy provides a data set including
12,500 (public and residential) charging stations and
8,650 PHEVs in 18 cities [57]. However, the data of
charging events frequency, time length, power, and lo-
cation are generally difficult to collect and obtain due to
privacy. In fact, most of these charging data are held by
some large private EV charging operators and EV man-
ufacturers, and may only be accessed through payment
or project-based collaborations [58–60].
2.2 Models of the EV charging behavior
2.2.1 Battery for EVs
The on-board battery has been the bottleneck for the
larger adoption of EVs. There are several types of bat-
teries that are based on Pb-acid, NiCd, NiMH, NaNiCl,
Li-ion, and Lithium-ion Polymer, respectively. These bat-
teries are different in energy density, cycle stability, and
lifetime. Li-ion and Lithium-ion Polymer batteries usually
provide larger power storage capacity and good energy
density but are more expensive and instable when over-
loaded. A good review/survey of EV batteries is available
in [61] and [62].
A battery in EV is usually composed of multiple packs,
each of which is in turn composed of many cells con-
nected in serial and parallel. Though each cell only out-
put 3–4 V and 15–40 Ah DC power, by arranging hun-
dreds of these cells in series and parallel together, an EV
battery usually output 300–380 V and 120–240 Ah DC
power and with a capacity of 40–80 kWh. The charging
process of a battery may be described by a dynamic
curve of the state-of-charge (SOC) of the battery. In or-
der to assess the performance of BMS, voltage based
models are widely used to monitor voltage losses in the
battery [63].
2.2.2 Utility factor
A PHEV may be regarded as a combination of an ICE
vehicle and a BEV and maybe driven either in pure con-
ventional fuel mode or in pure electric mode. According
to the SAE J2841 standard [64,65], the utility factor (UF)
is defined as the ratio of electric model driving distance
among the total driving distance. The UF may be used
to access the environmental performance of a PHEV.
In the SAE J2841 standard, it is assumed that an EV is
charged once per day and always fully charged before
starting the trip in a day. In order to relax these as-
sumptions, some alternative definitions of the UF have
been proposed [66,67]. Such an improved UF may bet-
ter assess the fuel consumption and CO2emissions of
PHEVs [68,69].

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2.2.3 Driving and charging pattern
To simulate the driving and charging pattern of an EV,
one usually need to specify the start and end of charging,
and the load of the charging, which may be described by
statistical models or real-data. Li et al. [70] used random
simulation and statistical analysis to model the charg-
ing demand of a single PHEV and used queueing theory
to describe the charging behavior of multiple PHEVs.
The chi-square distribution is commonly used to fit the
distribution of driving distances [71]. Normally, short-
term trips on weekdays are more predictable since the
working schedules and routes are fixed for drivers. A
typical daily duty cycle is proposed in [72] to fit the
energy requirements of users. Yang et al. [73] used dif-
ferent transition probabilities to describe the trips of EVs
among residential and commercial buildings.
For simulating large EV fleet driving profiles, Soares
et al. [74] developed a simulation tool for smart grid op-
erators. If more sophisticated simulations are needed,
some commercial software like Anylogic [75] can pro-
vide such services considering the characteristics such
as vehicle choices, batteries efficiency, and congestion
level.
2.2.4 Vehicle to grid
An EV receives electric power from the grid during
charging. In addition, the V2G technology allows EVs
to discharge the on-board battery and supply electric
power back to the grid. In this case, an EV may be re-
garded as energy storage or a distributed generator [76]
and may provide ancillary services to the power grid
such as peak load shaving, spinning reserve, and fre-
quency control [77,78]. Despite the potential benefit of
the V2G technology, it is still at the preliminary stage in
practice. The frequent charge and discharge usually lead
to more battery degradation [79]. Currently, the pene-
tration of the V2G system is still low. More investment
for upgrading and maintenance of the charging system is
needed before V2G can better provide ancillary service
to the grid [80].
2.2.5 Battery Swap
The long charging time of the EV batteries is a bottle-
neck for more adoption of EVs. Besides the recent study
and pilot project [81] on ultra-fast battery charging, an-
other alternative is to swap batteries with low-SOC with
fully charged batteries. Such a swap of a battery may take
only 10 minutes [82] and is comparable with the time to
fill in the gas for an ICE vehicle. Battery swap has begun
to pilot in several countries. As of November 2019, there
are 290 battery swap stations (BSSs) in China [83]. The
swap process may be fully automated. An automated
guided vehicle (AGV) with a mechanical arm can com-
plete the swap [84]. When the EVs are operated for
public transportation, or by a taxi or a shared-car com-
pany, the SOC and state-of-health (SOH) of the batteries
may be monitored and reported back to a battery man-
agement center. In a case study on the Xuejiadao BSS
that serves 6 electric bus (EB) routes in Qingdao, China,
Li et al. [85] developed a new routing and scheduling
strategy for EBs and found an optimal scale for the BSS.
A periodic fluid model was proposed [86] to find the
optimal battery purchasing and charging strategy for the
BSS. Beyond the battery swap at the BSS, battery deliv-
ery and on-site swap may be ordered as a service. More
details on battery swap may be found in [82] and [87].
3 Main standards of charging points
The increasing penetration of a massive number of
EVs catalyzes the development of regional charging in-
frastructure [88]. In U.S.A., more than 60,000 CPs are
already in service. While in China, this number ex-
cesses 90,000 in 2019 [36, 39]. In order to improve the
charging stability and efficiency, a series of standards
and charging levels have been continuously developed.
These standards establish a unified charging framework
and communication channel, at least in some areas and
countries. In this section, We briefly review the related
standards of CPs.
3.1 Charging standard
Due to the large differences in the global low-voltage
distribution grid (such as voltage and frequency), the
charging standards of EVs can only be unified at the re-
gional or national level, while still showing differences
globally.
IEC, SAE, and GB/T are the most widely used stan-
dards at present, which have established a complete set
of charging operation specifications, including charg-
ing connectors, communication, and facility construc-
tion [12, 23]. For example, the GB/T 20234.2 and GB/T
20234.3 provide detailed standards about the AC and
DC conductive charging connector. And the IEC 61980-
2 is a part of EV wireless power transfer (WPT) systems
communication protocol which gives specific require-
ments for communication between EVs and the charg-
ing infrastructure. The SAE J2953 standard is published
by the Society of Automotive Engineers (SAE) focusing

Q. S. Jia, T. Long / Control Theory Tech, Vol. 18, No. 3, pp. 217–230, August 2020 223
on test procedures for EV interoperability with EV sup-
ply equipment. In addition, other standards such as ISO
and NBT regulate the charge safety field.
The IEC standards are mainly used in countries of Eu-
rope. The SAE standards are mainly adopted in U.S.A.
The GB/T standards are mainly used in China. A com-
prehensive overview of these standards can be found in
Table 2.
Table 2 Overview of EV charging standards [89–92].
Standard IEC SAE GB/T Others
Connector 62196-1 J1772 20234-1
62196-2 20234-2
62196-3 20234-3
Commu- 61850 J2293-2 27930 ISO 15118
nication 61980-2 J2836
61980-3 J2847
Topology 61439-5 J2953 18487-1
61851-1 29781
61851-21 33594
61851-22
Safety 60364-7 J1766 18384-1 ISO 6469-3
60529 J2894-2 18384-3 ISO 17409
61140 37295 NBT 33008
62040
3.2 Charging mode
In order to meet the charging needs of EVs in different
situations, a variety of charging modes have been devel-
oped recently. The Charging modes developed under
SAE J1772, IEC 61851-1, and GB/T 18487-1 standards
are widely used in the world. Mode 1 (also referred to
as AC Level 1) is the most common AC slow charg-
ing mode which can be carried out conveniently from a
household outlet and only the onboard power converter
is needed. In this case, the charging time of Model 1 is
usually several hours long [61].
A possible solution for this issue is to use an in-cable
protection device between the EV and the plug while
charging from a household/industrial socket-outlet. This
charging mode which is known as Mode 2 (AC Level 2)
can meet the power requirement of up to 19.2kW under
the premise of a three-phase voltage AC source [23].
Mode 3 is identified as the AC fast charging. When
EVs are connected to the power grid, the special power
supply equipment with a control pilot function is re-
quired.
To overcome the differences in AC voltage and fre-
quency values in different regions and meet the demand
of higher charging power, Mode 4 with DC offboard
charging device is a promising candidate which can pro-
vide a power rating of 400 kW [93]. In addition, Tesla
offers its charging mode specifications. Table 3 lists the
charging modes of conductive charging systems under
the most used standards.
Table 3 Standard EV charging levels [94–96].
Mode AC/DC Max voltage Max ampere
SAE standards
AC Level 1 AC 120 V 16 A
AC Level 2 AC 240 V 80 A
DC Level 1 DC 600 V 80 A
DC Level 2 DC 1000 V 400 A
ICE standards
Mode 1 AC 250 V (1-phase) 16A
480 V (3-phase) 16 A
Mode 2 AC 250 V (1-phase) 32A
480 V (3-phase) 32 A
Mode 3 AC 480 V 250 A
Mode 4 DC 500 V 400 A
GB/T standards
Mode 1 AC 250 V 8 A
Mode 2 AC 250 V 16 A
Mode 3 AC 250 V 32 A (1-phase)
440 V 63 A (3-phase)
Mode 4 DC 1000 V 250 A
3.3 Charging connector
According to the charging standards and modes of
different countries, diversified charger plug types spec-
ifications are applied. In U.S.A. and Europe, Type 1 and
Type 2 Mennekes connectors are widely applied for
AC charging operations based on SAE J1772 and IEC
62196-2. Those plugs are mainly used for Level 1 and
Level 2 charging. The Combined Charging System (CCS)
connectors for DC charging combines the Type 1 and
Type 2 connectors with two high speed charging pins.
China has the world’s largest EV market and has devel-
oped its charging connectors based on GB/T 20234. The
CHAdeMO connector developed by the Japanese utility
Tepco has become the official DC charger standard in
Japan, which has two central pins for power exchange

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and the other for communication. The communication
protocol of GB/T and CHAdeMO is CAN standard, the
others are based on the PLC standard.
In particular, Tesla provides a proprietary connector
that can accept both AC and DC charging and be only in
Tesla charging stations. Besides, a Tesla charger adapter
cable will give to the owners which allow their vehicles
to use charging stations that have Type 1 connectors.
We summary the main EV charging connectors in Fig. 3.
Fig. 3 Main EV charging connectors [97, 98].
3.4 Charging system
The charging system of EVs usually consists of sev-
eral power sources, inverters/converters, and the battery
onboard [99]. A typical grid-connected charging system
with renewable energy supply is shown in Fig. 4. Renew-
able energy in this system can participate in the charg-
ing process of EVs and transfers the excess power to
the grid. Inverters/converters integrate multiple energy
sources to deliver electricity to EVs in a unified form.
BMS plays the role of battery energy management and
information exchange. More detail about the charging
systems can be found in [16].
Fig. 4 EV charging system.
4 Impact and related research
In the smart grid, EVs, charging stations, and local
power grid exchange information and energy to achieve
stability and economic goals. As the direct embodiment
of loads, the charging behavior of EVs has an important
influence on the other participants. In this section, we
will discuss the impact and related research topics of
the EV charging behavior.
4.1 Impact on EVs–smart charging
Due to the elasticity of EV charging, the charging be-
havior may be schedulable to realize various objectives.
The control of EV charging behavior is also called smart
charging which includes the charging process manage-
ment of a singe EV and the scheduling of EV fleets. An
individual EV owner may take advantage of the time-
of-usage (TOU) price of the electricity [100]. Optimal
charging for a fleet of EVs may save even more cost by
using solar power [101]. And EV owners may purchase
service from a fleet operator (FO). The FO aggregates
the charging behavior of a fleet of EVs into a virtual
power plant. Together these EVs may be charged at a
lower cost [9]. The control of EV charging behavior may
be determined either at a central controller or in a de-
centralized manner. We briefly review these works in
the following, respectively.
4.1.1 Centralized scheduling
In centralized scheduling, the FO gathers information
such as the identification, SOC, charging demand, pref-
erence, and TOU price of electricity from the EVs and
makes decisions in a centralized way [102].
Various methods have been developed for the opti-
mization problem of the FO, such as linear program-
ming [103], nonlinear programming [104], branch-and-
bound/branch-and-cut algorithms [105], stochastic pro-
gramming [106], Benders decomposition [107], and ro-
bust optimization [108], just to name a few. When there
is a renewable energy system, Kou et al. [109] and Guo
et al. [110] applied model predictive control. In large
scale problems, bi-level optimization [111] and event-
based optimization [112] have been developed. And
multi-objective optimization algorithms [113,114] have
been applied to address multiple objective functions at
the same time such as charging cost minimization, peak
procurement minimization, and renewable energy coor-
dination.
4.1.2 Decentralized scheduling
Despite the pervasive application of centralized
scheduling, it suffers from privacy concerns from the
EV owners and the curse of dimensionality in large
scale problems [15]. Decentralized scheduling becomes
an attractive alternative in this case. In decentralized
scheduling, EV owners receive price signals from FOs

Q. S. Jia, T. Long / Control Theory Tech, Vol. 18, No. 3, pp. 217–230, August 2020 225
or other entities and decides their own charging sched-
ules [115]. The private data such as travel plans and trip
histories are kept local and will not be shared with oth-
ers. Various algorithms have been developed to solve
these decentralized scheduling optimization problems
such as the game-theoretical analysis for the multi-agent
confrontation problem [116], semi-cooperative schedul-
ing [73,117], bi-level distributed optimization [118], and
neural network [119].
4.2 Impact on charging stations - position and size
As the intermediate coordinator between the local
power grid and EVs, charging stations not only provide
the commercial services of charging, but also partici-
pate in the smart charging as FOs. Due to the diversity
of EV charging behavior in different regions, the location
and size of charging stations become the key elements
of business decision-making and city planning. Many
factors are usually considered in the deployment and
planning of a charging station, such as EV energy loss,
grid stability, development cost, and road constraints.
Various methods have been developed to solve this
problem, such as the mixed-integer non-linear program-
ming [120], and genetic algorithms [121, 122]. Several
objective functions have been considered such as the
charging reliability and quality maximization [123], cov-
erage maximization [124], and a weighted summation of
reliability, power loss, and voltage drop [125]. The posi-
tion and sizing of charging stations may also be affected
by the renewable energy systems [126].
4.3 Impact on the power grid - ancillary services
Uncontrolled charging load from a large number of
EVs can bring great challenges to the distribution net-
work such as congestion, power losses, and voltage
deviations [6, 70, 127]. Extra peak load generation and
maintenance investment are required [128].
However, if scheduled properly, the EVs may be re-
garded as a storage or even a generator and may pro-
vide ancillary services back to the power grid. Fig. 5
shows the power grid load curves under different charg-
ing strategies. Under an appropriate charging strategy,
EV loads can help the power grid to reduce the peak
load and to fill in the valley. With the support of RES and
V2G technologies, the peak load of the power grid can
be further reduced [90]. Ma et al. evaluate the potential
for peak load reduction through EV charging schedul-
ing [129].
EVs are parked for 22 hours a day on the aver-
age [130]. There is a big potential for EVs to contribute
to frequency regulation such as regulation down, reg-
ulation up, and spinning reserve [131]. It is shown
that EVs may significantly reduce the frequency devi-
ations [132]. A load frequency controller is developed
to achieve asymptotic stability of the power grid in [133].
An optimal dispatching strategy is developed in [134] to
maximize the economic benefits of the V2G aggregator
in frequency response.
Fig. 5 Power grid load curve under different charging strategies [13]. (a) Uncontrolled charging. (b) Smart charging. (c) Smart
charging with V2G.
5 Conclusions
EV has become an important component in the in-
frastructure in modern societies. Despite the promising
future in practice, many researchers find it difficult to
build reliable simulation models to study EV charging
and the impact on the power grid as well as other com-
ponents in the society. In this work, we have reviewed
the charging behavior of EVs from three aspects, namely
the data sets, the models, and the control methods. Links
to most of the reviewed data sets are provided for direct
access.
The field of EV charging behavior research is relatively
fresh and there are still some scientific challenges that
are twofold. First, charging data collection is slow and
difficult. Due to the high marketization and privatiza-
tion of EVs, EV owners are hardly motivated to provide

226 Q. S. Jia, T. Long / Control Theory Tech, Vol. 18, No. 3, pp. 217–230, August 2020
their daily charging data. Most of the existing charging
data come from the commercial companies operating
the charging piles or large-scale projects with strong fi-
nancial support. Thus, this situation also slows down the
development of related research. Second, privacy viola-
tions in the control process. The direct control for the
charging behavior of private EVs is rude and unrealistic
since some sensitive data needs to be uploaded, and the
control process is difficult to ensure sufficient security.
Despite the above challenges, the probable future out-
looks are worth to be emphasized. We strongly believe
that at least the following three aspects of work can be
carried out in the future. First, charging behavior control
of commercial EV fleet for passenger transport which is
operated by governments and companies like Uber and
DiDi. It is a foreseeable initial market for EV fleet oper-
ation and probably shows great potential to obtain the
commercial benefit. Second, Group-intelligence based
smart charging. It is a feasible solution for private EV
scheduling. Without centralized information upload and
direct control, it shows greater practicability in user se-
curity. However, the instability of performance caused
by antagonism, game, and randomness between EVs is a
problem that researchers need to tackle next. Third, the
involvement of renewable energy represented by hydro-
gen energy, wind power, and Photovoltaic energy. The
Coordinated optimization of EVs and renewable energy
sources can further improve energy efficiency and clean-
liness, and make another step towards the realization of
the zero-carbon energy system.
Note that there are growing interests in the study of
EVs. Though we try to be comprehensive in this review,
the works that are reviewed here are still limited. We
take full responsibility if important research works are
missing in this review. We hope the researchers in the
field would find this work useful.
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Qing-Shan JIA received the B.Sc. degree in
Automation and the Ph.D. degree in Control
Science and Engineering from Tsinghua Uni-
versity, Beijing, China, in 2002 and 2006,
respectively. He was a Visiting Scholar at
Harvard University, in 2006, at the Hong
Kong University of Science and Technology,
in 2010, and at the Massachusetts Institute
of Technology, in 2013. He is currently an
Associate Professor at the Center for Intelligent and Networked Sys-
tems, Department of Automation, Beijing National Research Center for
Information Science and Technology, Tsinghua University. His current
research interests include theories and applications of cyber physical
systems. E-mail: jiaqs@tsinghua.edu.cn.
Teng LONG received the B.Sc. degree in
Department of Automation from Tsinghua
University, Beijing, China, in 2017. He is
currently pursuing a Ph.D. degree in Con-
trol Science and Engineering supervised by
Qing-Shan Jia with the Center for Intelli-
gent and Networked Systems (Cfins), De-
partment of Automation, Tsinghua Univer-
sity, Beijing, China. His current research
interests include energy management of the smart grid, event-
based optimization, and large-scale optimization problem. E-mail:
lt17@mails.tsinghua.edu.cn.