A Comprehensive Review of Hybrid Energy Storage Systems: Converter Topologies, Control Strategies and Future Prospects

Abstract

The ever increasing trend of renewable energy sources (RES) into the power system has increased the uncertainty in the operation and control of power system. The vulnerability of RES towards the unforeseeable variation of meteorological conditions demands additional resources to support. In such instance, energy storage systems (ESS) are inevitable as they are one among the various resources to support RES penetration. However, ESS has limited ability to fulfil all the requirements of a certain application. So, hybridization of multiple ESS to form a composite ESS is a potential solution. While integrating these different ESS, their power sharing control plays a crucial role to exploit the complementary characteristics of each other. Therefore, this article attempts to bring the numerous control strategies proposed in the literature at one place. Various control techniques implemented for HESS are critically reviewed and the notable observations are tabulated for better insights. Furthermore, the control techniques are classified into broad categories and they are briefly discussed with their limitations. From the carried-out analysis, the challenges faced towards the implementation of HESS for standalone and grid connected microgrid systems are presented. Finally, the future directions are laid out for the researchers to carry out the research and implementation of HESS technologies. Overall, this article would serve as a thorough guide on various control techniques implemented for HESS including their features, limitations and real-time applications.

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Digital Object Identifier
A comprehensive review of hybrid
energy storage systems: converter
topologies, control strategies and future
prospects
THANIKANTI SUDHAKAR BABU1* (Member, IEEE), KRISHNAKUMAR R. VASUDEVAN1
(Graduate Student Member, IEEE), VIGNA K. RAMACHANDARAMURTHY1(Senior
Member, IEEE), SULEIMAN BALA SANI1, SANSUBARI CHEMUD2, and ROSLI MAT LAJIM3
1Institute of Power Engineering, Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga National, Jalan IKRAM-UNITEN,
43000 Kajang, Selangor, Malaysia.
2Strategy & Policy, Asset Management, Tenaga Nasional Berhad, Malaysia.
3Smart Grid Research Group, TNB Research Sdn. Bhd., 43000, Kajang, Malaysia.
Corresponding author: * Thanikanti Sudhakar Babu (e-mail: sudhakarbabu66@gmail.com).
This research was supported by Tenaga Nasional Berhad, Malaysia seed fund; U-TD-RD-19-22, for the project titled "Hybrid Energy
Storage System to Enhance Renewable Energy Integration".
ABSTRACT The ever increasing trend of renewable energy sources (RES) into the power system has
increased the uncertainty in the operation and control of power system. The vulnerability of RES towards
the unforeseeable variation of meteorological conditions demands additional resources to support. In such
instance, energy storage systems (ESS) are inevitable as they are one among the various resources to support
RES penetration. However, ESS has limited ability to fulfil all the requirements of a certain application. So,
hybridization of multiple ESS to form a composite ESS is a potential solution. While integrating these
different ESS, their power sharing control plays a crucial role to exploit the complementary characteristics
of each other. Therefore, this article attempts to bring the numerous control strategies proposed in the
literature at one place. Various control techniques implemented for HESS are critically reviewed and the
notable observations are tabulated for better insights. Furthermore, the control techniques are classified
into broad categories and they are briefly discussed with their limitations. From the carried-out analysis,
the challenges faced towards the implementation of HESS for standalone and grid connected microgrid
systems are presented. Finally, the future directions are laid out for the researchers to carry out the research
and implementation of HESS technologies. Overall, this article would serve as a thorough guide on various
control techniques implemented for HESS including their features, limitations and real-time applications.
INDEX TERMS Hybrid energy storage system; Microgrid; Intelligent control; Renewable energy; Energy
management; Power electronics.
I. INTRODUCTION
FROM the past few years, the growing concerns on
environmental effects due depletion of fossil fuels has
resulted in transition towards RES to satisfy the global energy
demand. Eco-friendliness, scalability and other extensive
features of RES has attracted its deployment in commercial,
industrial, and residential sectors. This is further supported
by the quick progression in power electronics [1], which aids
the complete control of RES limited by stochastic natural
conditions [2]. Moreover, RES have various limitations like
poor load following, intermittent power generation and non-
dispatchable nature. Due to these factors, their coordina-
tion in the grid system is a challenging task for efficient
operation, particularly for high capacity systems [3]. The
notable challenges faced during the integration of RES are
voltage instability, load discrepancy, frequency fluctuation,
poor power quality and load-following [4].
The integration of ESS is a promising solution to overcome
these limitations and to facilitate stable operation of grid.
The integration of ESS with RES into the microgrid can
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Sudhakar et al.: Review of HESS control techniques
Nomenclature
Acronyms
AC alternating current
ANFIS adaptive neuro-fuzzy inference system
ANN artificial neural network
BESS battery energy storage system
BEV battery electric vehicle
CAES compressed air energy storage
CVT continuous variable transmission
DC direct current
DG distributed generation
DP dynamic programming
EDC extended droop control
EDLC electrical double layer capacitor
EGP Enel Green Power
EMS energy management system
ESS energy storage system
FBC filtration based control
FC fuel cell
FFSVM feed-forward-space-vector-modulation
FJC faster joint control
FLC fuzzy logic control
FR frequency control or regulation
FR frequency regulation
HES high energy storage
HESS hybrid energy storage system
HF high frequency
HPF high-pass filter
HPS high power storage
HSS hydrogen storage system
ID integral droop
ISO independent system operator
LF low-frequency
Li-ion lithium-ion
LPF low pass filter
LPSP loss of power supply probability
LR load regulation
MF membership function
MIAD multiplicative-increase- additive-decrease
MLD mixed logic dynamic
MMC modular multilevel converter
MPC model predictive control
NaS sodium-sulfur
NFC neuro-fuzzy controller
PCH port-controlled hamiltonian
PMP pontryagin’s minimum principle
PQ power quality
PSO particle swarm optimization
RBC rule based control
RES renewable energy resource
RL reinforcement learning
SC supercapacitor
SFL shuffled frog leap
SMC sliding mode control
SMES superconducting magnetic energy storage
SOC state of charge
SVM support vector machine
SVR secondary voltage regulation
V-P voltage-power
VCD virtual capacitance droop
VF Vanadium flow
VFB vanadium flow battery
VID virtual impedance droop
VR voltage regulation
VRD virtual resistance droop
WCA water cycle algorithm
WES wind energy system
avoid power fluctuations, improve power quality, frequency
regulation and enable additional ancillary services [5]. There-
fore, various ESS technologies have been evolved in recent
years which can be categorized as electrical, electrochemi-
cal, chemical and mechanical storage systems. The widely
used ESS are SC, SMES, flywheel, pumped hydro storage,
batteries, CAES and hydrogen tanks. Among these technolo-
gies, batteries are treated as one of the most significant and
promising ESS for maintaining the stability of power system
networks [6]. Furthermore, ESS in the off-grid system plays a
vital role in managing the momentary power fluctuations and
quality of power. Based on the analysis of various ESS, their
important characteristics are listed in Table. 1. Meanwhile,
the well-known different types of batteries along with their
suitable applications, limitations, and features are presented
in Table. 2. Furthermore, ESS plays a significant role in the
advancement of electric energy systems and extending RES
to power the remote locations of the globe [7]–[10].
The global energy storage market is increasing substan-
tially, since the last decade. It is predicted that the ES market
may increase more than 26 billion USD in yearly sales by the
year 2022, with a compounded annual growth rate of 46.5%
[11]. Countries like Australia, the USA, India, UK, Chile,
Japan, Italy, South Korea, and Germany are concentrating
on the leading technologies of battery storage systems. Be-
sides that, the national policies and subsidies provided by
different countries further accelerate the growth of ESS. The
combination of RES and ESS can reduce the dependency on
the energy import, improve resiliency and reliability of the
system and also help move towards decarbonization of grid.
Furthermore, the developing nations are moving towards
smart cities to achieve their goals such as environmental
sustainability, adequate power supply, efficient mobility and
the adoption of electric vehicles, where RES and ESS play a
critical role. The capacity of ESS is exponentially increasing,
which doubled during the year of 2017 and 2018 to 8 GWh
[12]. The current global installed capacity of ESS is shown
in Fig. 1. In addition to that, the adoption of ESS for various
applications is presented in Fig. 2.
It is worth noting that, every ESS has its own limitations
which confines its range of application, since an ideal appli-
cation demands both high energy and high power. However,
ESS are limited either by their power or energy capacity.
Thus, it is necessary to build a system with a combination of
two or more ESS to form HESS [16]. For example, batteries
have features of low specific power, high specific energy, less
life-cycle, less capacity of self-discharge and less cost/Wh.
On the other hand, the SC exhibits less specific energy, more
specific power, fast charging, longer lifetime and high self-
discharge [17]. Thus, the combination of battery-SC can
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Sudhakar et al.: Review of HESS control techniques
FIGURE 1: Global scenario of energy storage adoption [13].
FIGURE 2: Applications of energy storage [13].
utilize the complementary properties of each other. This
combination has been popular due to its homologous working
principle, ample availability, and low initial cost.
The benefits of implementing HESS are (see Fig. 3):
•Minimizes the initial cost contrasted with a single en-
ergy storage system ( due to the decoupling of power
and energy, the secondary storage system has to cover
only the average demand for power).
•Enhances overall system efficiency.
•Improves storage capacity and lifetime of plant (min-
imizes the dynamic stress of the secondary storage
system and optimizes the operation).
Due to these credible features of HESS, numerous re-
searchers and industrial experts have focussed on the devel-
opment of HESS technologies for integration of RES into the
grid. Therefore, this paper collates various control techniques
implemented for the development of HESS. Since control
techniques are treated as the heart of the system, this article
presents a detailed analysis of various control techniques,
their benefits and limitations along with the future perspec-
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Sudhakar et al.: Review of HESS control techniques
TABLE 1. Characteristics of different energy storage elements [14], [15]
Type of Storage
device
Energy den-
sity in W h/l
Installation
costs in
C/kW
Installation
cost in
C/kWh
Reaction
time
Self-discharge
rate
Lifetime in
years
System effi-
ciency in %
Technology ma-
turity
Application
SC 2-10 150-200 10000-20000 < 10ms Up to 25 % in 1st
48 h
15 77-83 Demonstration Distributed generation and microgrid
SMES 0, 5-10 High High 1-10 ms 10-15 %/day 20 80-90 Demonstration Power quality, system stability, LF oscillation
Flywheel 80-200 300 1000 >10 ms 5-15 %/h 15 80-95 Commercial FR, auxiliary service, PQ, enterprise UPS
Lead-acid 50-100 150-200 100-250 3-5 ms 0, 1-0, 4 %/day 5-15 70-75 Commercial,
Demonstration
Peak load shifting, transportation, communica-
tion, national defense, reserve power supply,etc.,
Lithium-ion 200-350 150-200 300-800 3-5 ms 5%/month 5-20 80-85 Demonstration,
Commercial
All aspects of generation, transmission, distribu-
tion use.
Nas 150-250 150-200 500-700 3-5ms 10 %/day 15-20 68-75 Commercial LR, peak load shifting, PQ, large-scale grid-
connected RES etc.,
Redox-flow20-70 1000-1500 300-500 > 1 s 0,1-0,4 %/day 10-15 70-80 Demonstration Peak LR, large-scale grid-connected RES, UPS,
emergency power supply,etc.,
Hydrogen 750/250 bar
2400/liquid
1500-2000 0,3-0,6 10 min 0,003-0,003
%/day
20 34-40 Commercial Peak LR
Pumped hydro 0,27-1,5 500-1000 5-20 >3 min 0,005-0,02 %/day 80 75-82 Commercial FR,peak LR, black start, phase shift.
CAES 3-6 700-1000 40-80 3-10 min 0,5-1 %/day Ca. 25 60-70 Commercial,
Demonstration
Peak LR, grid connected RES
TABLE 2. Applications, limitations and features of different types of battery technologies.
Type Energy storage Applications Advantages Disadvantages
Power Qual-
ity
Voltage Reg-
ulation
Load Level-
ing
Grid Exten-
sion
Voltage Reg-
ulation
Demand
Management
Sodium-sulfur (NaS) 3 3 3 3 3 3 High efficiency & energy density High production cost, recycling
need for sodium
Lead-acid 3 3 3 3 3 3 Less cost of investment Less energy density
Lithium-ion 3 3 3 3 3 3 High power density, energy & effi-
ciency
Cost of lithium is high and require
recycling
Ultra Battery 3 3 3 7 3 3 Superior performance than lead-
acid & less cost of investment
Less energy density
Metal-air 3 3 3 3 3 3 Less cost, eco friendly & high en-
ergy density
Less recharging capability
Nickel-cadmium 3 3 3 3 3 3 High energy, power density & effi-
ciency
Involves toxic components highly
FIGURE 3: Benefits of HESS.
tives.
The remaining sections of the paper are organized as fol-
lows: The different converter topologies used for the HESS
are discussed in section II. The detailed discussion on con-
ventional control techniques of HESS and its comparative
analysis is listed in section III. The intelligent control meth-
ods for managing the power flow between HESS devices are
given in section IV and section V deals with the applications
of HESS and discussion on case studies of real-time HESS
plants implemented worldwide. Furthermore, the challenges
in installation of HESS and future research directions are
given in section VI and conclusions are made in section VII.
II. INTERCONNECTION TOPOLOGIES
The interconnection topology of HPS, HES dictates the con-
trol flexibility, dynamic performance, efficiency and lifetime
of ESS. The HPS and HES can be connected to the system
directly or through power converters. The direct connection
offers a simple system architecture, low cost and control
complexity. Albeit, the use of power converters ensures de-
coupled control of HPS and HES and offers enhanced power
regulation. The HESS can be either connected to the DC bus
or use a separate DC-AC converter to connect with the AC
bus. The interconnection topologies can be classified into
passive, semi-active and active. The selection of topology
vastly varies based on the system requirement and the func-
tions of energy management system. A critical analysis of
interconnection topologies is presented in Table 3.
A. PASSIVE
Passive architecture is the simple approach to interconnect
HPS and HES to the system (see Fig. 4). The ESS are
directly connected together without employing power con-
verters [18], [19]. The matching of voltage levels of ESS
with the DC bus voltage or the load voltage is a prerequisite
for direct connection [20]. The passive architecture resembles
parallel operating synchronous generators, where the load is
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Sudhakar et al.: Review of HESS control techniques
shared based on the impedance ratio. Similarly, the load is
shared based on the internal resistance of ESS and its output
characteristics. Where the resistance is greatly affected by the
temperature and the instantaneous state of charge.
FIGURE 4: Passive interconnection topology of HESS
For instance, in a battery and SC hybrid, the high power
pulses are absorbed by SC due to its low impedance. Thus,
SC is analogous to a low pass filter. Furthermore, the charg-
ing and discharging characteristics vary considerably which
offers poor regulation of voltage and load. The power balance
of the system is given by Eq. 1. Where Pgen,Phess ,Pload
are power generated, power exchanged by HESS (negative
during power consumption) and power consumed by load
respectively. The power exchanged by HESS is the sum of
power contributed by HPS and HES as given by Eq. 2.
Pgen ±Phess =Pload (1)
Phess =Phps +Phes (2)
Initially, this topology has been used for pulse loads
[21], vehicular applications with battery-SC hybrid and later
adopted to power system applications [22]. Lately, battery-
SC with passive interconnection has been used to mitigate the
intermittency of RES in isolated microgrids [23]. However,
this topology did not gain much attention due to the following
limitations:
•No control flexibility and the power sharing varies based
on the source impedance.
•ESS are vulnerable to cascaded failure during contin-
gencies as they are directly connected to the system.
•The voltage of ESS should be strictly matched with the
DC bus or load voltage.
B. SEMI-ACTIVE
This topology is the extension of passive topology with one
power converter to control HPS. Where a bidirectional DC-
DC converter is used to control the power exchanged by HPS
along with an appropriate control algorithm (see Fig. 5). The
power exchanged by HESS with this topology is given by Eq.
3. Where αdenotes the controllability of HPS and determines
the power share of HPS.
Phess =αPhps +Phes (3)
FIGURE 5: Semi active topology of HESS
The peak power requirements of the system are satisfied
by HPS and the remaining demand is met by HES. In a study,
the semi active topologies with controlled SC and controlled
battery have been analysed. The power converter of SC is
oversized to handle the pulse power output. Whereas the
topology with controlled battery has variations in DC link
voltage [24]. So, relatively high energy storage is necessary
to maintain the DC bus voltage whenever a HES is interfaced
with a converter. Recently, a battery-SMES hybrid has been
used to support the WES in an isolated microgrid. The SMES
has been used to extend the lifetime of battery by absorbing
high frequency power variations. Similarly, a battery-SC
hybrid has been used to suppress the fluctuations of small
WES. Where the SC has been interfaced using bidirectional
DC-DC converter and the battery is directly connected to the
DC bus [25]. Although it offers partial flexibility there are a
few limitations as follows:
•The DC bus voltage varies when a HPS is directly
connected to the system.
•The DC-DC converter should be designed to handle the
large power spikes when interfaced with a HPS.
C. ACTIVE
The active topology employs separate bidirectional DC-DC
converter to control HPS and HES (see Fig. 6). This topology
offers the highest possible controllability with the decoupled
control of both the ESS. It facilitates the energy management
strategy to exploit the complementary characteristics of HPS
and HES. Furthermore, it accommodates the adoption of
wide variety of control strategies. However, all these advan-
tages come at the expense of increased power conversion
losses and high cost of converters. The power exchanged by
HESS with this topology is given by Eq. 4. The variables α
and βrepresent the controllability of HPS and HES respec-
tively. The control strategies determine these variables based
on several factors like SOC, frequency of power variation and
deterioration rate of battery.
Phess =αPhps +βPhes (4)
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Sudhakar et al.: Review of HESS control techniques
FIGURE 6: Active topology of HESS
By far, this topology has been widely utilized in the
application of HESS in power system. This can be subdivided
into parallel active and series active topologies. The parallel
topology employs two separate set of converters for interfac-
ing HPS and HES in parallel [26], [27]. Whereas the series
topology has cascaded HPS and HES with power converter
to decouple it from the DC bus [28]. The series topology is
often neglected as it demands the power converter to be rated
to the total power rating of HESS. So, parallel active topology
has been widely used for power system applications. Various
advantages of this topology are:
•Improved flexibility with decoupled control of HPS and
HES.
•A wide range of control strategies can be employed.
•The voltage levels of ESS are independent of system
voltage.
•It has inherent fault tolerance capacity with converters
decoupling ESS from the system
III. CLASSICAL STRATEGIES
Design and implementation of optimal controller is the most
significant concern in HESS. The selection of suitable control
technique for HESS relies upon an array of parameters.
To name a few, extension of storage lifespan, reduction
of power intermittency, power quality, controller response
time, expense of controller and structure of hybridization.
Implementation of a suitable realistic controller technique is
essential to accomplish consistent, effectual and safe opera-
tion of HESS. The implementation of EMS focus into two
levels of architecture, a lower level control system which
controls the DC bus voltage and maintains the current flow.
Whereas the high level control focus on allocation of power,
SOC monitoring and other objectives of the system. [29],
[30].
The goals of implementing HESS for off-grid system in-
clude optimizing micro grid performance, improving stability
of system and reducing the cost of operation (see Fig. 7). To
achieve these goals, different techniques were proposed and
they are classified in this paper as classical and intelligent
control strategies (see Fig. 8).
FIGURE 7: The goals of implementation of EMS.
FIGURE 8: Detailed classification of HESS control tech-
niques.
A. FILTRATION BASED CONTROL TECHNIQUES
The power transfer between ESS can be classified into HF
and LF components. The HF components are the result of
sudden variation in load or irregularities in power generation
by RES. The LF components are the ones that occur during
the regular behaviour of RES. HF components need ESS with
rapid response time, whereas the LF components require ESS
having high energy density [31]. FBC separates the power
demand into low and high frequency components with help
of filter circuit which leads to flattening of battery current
variations. With this motivation, S.K. Kollimalla et. al., in
[32] developed LPF based FBC EMS to control the charging
levels of battery. The proposed controller has been evaluated
to enhance the life span of battery. This method exhibits
faster dynamic response and has minimised computational
burden. The conventional HESS scheme along with regular
FBC, novel FBC scheme proposed by authors in [32] are
shown in Fig. 9. In addition to that, a FBC based on high
pass filter is proposed by the authors of [33] are exhibited in
Fig. 10.
The method based on rate limit controller for effective
management of HESS is implemented in [27], [32], [34],
[35]. This method is more efficient in controlling the bat-
tery charge and discharge conditions by producing optimal
current reference signal for the HESS. It is less complex and
well suited for isolated DC microgrids. Similar to the rate
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Sudhakar et al.: Review of HESS control techniques
TABLE 3. Comparison of interconnection topologies of ESS to form HESS
Topology Cost Flexibility Range of
control
strategies
adoption
DC bus volt-
age fluctua-
tion
Fault
tolerance
Space
requirement
Control com-
plexity
Recommendations
Passive [18], [19] LowNo LowYes No Less LowIt can be used when the cost is
the deciding factor in small capacity
systems.
Semi-active [24],
[25]
Moderate Partial Moderate Yes when
HPs is
connected
directly
Only HPS Higher than
passive
Moderate It is recommended when the slight
increase in the cost can be compro-
mised to extend the battery life.
Active [26]–[28] High Full High No Yes High High Itis more suitable for large capacity
systems which require a superior
dynamic response.
(a)
(b)
FIGURE 9: Various types of FBC, (a) Conventional FBC
with LPF [32] (b) FBC with feed forward control [32]
FIGURE 10: FBC with high pass filter [33]
limit, multilevel control based EMS for HESS is proposed in
[36], [37]. In this work, the authors considered different types
of batteries to form a DC microgrid and used centralized
controller. This centralized controller helps in enhancing
system stability and consistency of power supply. It also has
the features of SVR and autonomous SOC recovery.
With an aim to reduce the active power variations and
to achieve better voltage regulation, MMC grid-tied HESS
system has been proposed in [38]. In this approach, SC
and batteries have been divided as upper and lower arms
to decouple the power. The SC and batteries can mitigate
the low and high frequency fluctuations, thereby achieving
better reactive power regulation by SC. The proposed power
decoupling strategy is shown in Fig. 11.
FIGURE 11: Power decoupling control between batteries and
supercapacitors [38]
B. RULE BASED CONTROL TECHNIQUES
In general, RBC is based on the sequential decision making
process pertaining to the control objective. The rules are
defined either with system expertise or using mathematical
models. Among various rule based approaches, thermostat
is one of the simple and easier one. In this approach the
high energy storage is constrained by the SOC limits. An
enhanced method based on state machine control is intro-
duced in [39], which can include numerous rules (to be
characterized based on heuristic or experience of experts). In
this work, the authors focused to implement a combination
of fuel cell and battery with ratings (fuel cell power – 1.2 kW
and battery voltage 220 V DC, capacity 10 Ah). To optimize
the power flow, system operation has been divided into 9
regions and accordingly rules have been framed to achieve
effective control. Rule based techniques have been widely
used due to its less computational burden, easy to implement
and simple attribute [40]. RBC for off grid microgrid system
with HESS is implemented in [41], [42]. In this work, the au-
thors suppressed the fluctuations by filtering the wind speed
and solar irradiation levels which is not practical in the real
scenario. Thereby this method has limited implementation.
RBC based on state machine control is shown in Fig. 12
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FIGURE 12: State machine control [30]
C. DEAD BEAT CONTROL
Deadbeat control works based on the model of the system. It
generates the ratio of duty cycle to minimize error regulation
in one control cycle. Thereby, it overcomes the state variable
errors as well as effectively maintains the power sharing be-
tween ESS. Fast dynamic response and high control accuracy
are the additional features of deadbeat controller. Further-
more, it also acquired the features of conventional controls
like simple implementation and easier process involvement.
In [43], the authors regulated the SC to respond for tran-
sient power demand and minimized the stress on battery to
enhance its lifespan. The developed deadbeat controller is
shown in Fig. 13.
FIGURE 13: Control scheme of deadbeat control for HESS
[43]
D. DROOP CONTROL
A HPF based droop controller and VCD is incorporated
together to regulate battery and SC in [44]. This hybrid
controller is proposed to provide solutions for effective power
sharing, avoid voltage fluctuations, and limit the SOC of bat-
tery and SC. Furthermore, in [44] they have implemented de-
centralized controller via VCD and VRD for SC and battery
to limit low and high frequency components. Incorporation
of SOC recovery loop is feature of this work, which smooths
the dynamics of transient power sharing.
The other widely used type of droop controller for power
management of HESS is the VID. It is developed with
the combination of VCD and VRD controller. Due to this
combination, the controller is able to manage high and low
frequency power sharing among SC and battery. However,
this method, fails to control voltage deviation. To nullify
this effect, authors in [45] introduced SVR controller for
batteries to manage the bus voltage. In addition to that, SVR
is developed to act under fast recovery of SOC irrespective of
the leakage current [45] and the control scheme is shown in
Fig. 14. Furthermore, an EDC presented in [46]. This method
FIGURE 14: Secondary voltage controller [45]
is also developed with the combination of VRD and VCD
to control the power flow of HESS in DC microgrid. The
authors focused to maintain constant power of battery by
avoiding fluctuations of high frequency. The Fig. 15 shows
the proposed control structure with the combination of VRD
and VCD.
(a)
(b)
FIGURE 15: (a) Equivalent circuit (b) and control architec-
ture of extended droop control [46]
A dynamic droop control technique, with the considera-
tion of frequency as a control function is proposed in [47].
The proposed system has been successfully implemented
in the Uligam Island in Maldives. This proposed method,
effectively manages the synergistic operation of two different
kinds of ESS even though both (battery, SMES) has dissimi-
lar integral behavior. The droop control is extensively used as
it can control the different control units and generate precise
droop values. The droop range selection for the battery and
SMES is shown in Fig. 16.
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(a)
(b)
FIGURE 16: The selection of droop ranges; (a) battery, (b)
SMES [47]
The architecture of frequency control function used to pro-
pose dynamic droop controller is given in Fig. 17. Through
FIGURE 17: primary frequency control concept of the MG
system with HESS [47].
rigorous research on droop control techniques, ref [48] pro-
posed a novel ID control to improve the performance of
VID. The proposed co-ordinated control between V-P and
ID, fundamentally develops the HPF/LPF to acquire optimal
dynamic power allocation between considered ESS. The
dynamic power sharing can be accomplished at decentralized
level which is the additional feature of ID. The control
schemes proposed for ID and V-P are shown in Fig. 18 and
Fig. 19 for a better insight.
FIGURE 18: Integral droop and double PI controller [48].
FIGURE 19: Traditional V-P droop and double PI controllers
[48].
The combination of cache control and adaptive droop is
implemented in [49] to integrate PV and wind system with
HESS. The cache control is preferred to combine dissimilar
features of storage devices and an adaptive droop is used
to achieve coordinated control of battery for long term op-
eration. In addition to that, it also controls the online SOC,
and schedules the SOC of BESS. The cache control scheme
proposed is shown in Fig. 20.
FIGURE 20: Cache control scheme applied for BESS and
SCES [49]
E. SLIDING MODE CONTROL
Sliding mode control is a type of non-linear control which
toggles between the control laws based on the state vector.
Abeywardana et al., in [50] implemented improved SMC
technique by resolving the problem of variable switching fre-
quency in conventional SMC. With this, authors minimized
the power dissipation and design complexity of system. By
combining the hysteresis control to the SMC an adaptive
SMC is proposed in [51] with an aim of managing multimode
HESS to overcome fluctuations in current. From the carried-
out literature, the comparative analysis between conventional
control strategies for HESS are tabulated in Table. 4.
IV. INTELLIGENT CONTROL TECHNIQUES
To overcome the limitations of classical control based EMS
techniques, a real-time, intelligent based techniques such as
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Sudhakar et al.: Review of HESS control techniques
TABLE 4. Comparison of conventional control strategies of HESS
Controller technique Features Limitations
Filtration based control [27],
[32]–[36], [38] •These strategies are straightforward, financially
savvy and widely preferable for real-time im-
plementations.
•Designing of filter components is complex and
selection of cut-off frequency decides the sys-
tem performance.
•Requires accurate mathematical model of con-
sidered system.
•The effectiveness of reducing the peak power
demand of battery using FBC is less, it can
process only the frequency of power demand.
Rule based control [39]–
[42] •Working with this type of controller is easy,
involves less computation and simple to imple-
ment.
•The controller performance is not accurate, if
there is an increase or decrease in the number
of ESS.
•This method is more sensitive to change in
parameters.
•Rigid in nature, not suitable for implementing
in real-time system conditions as it involves
predetermined thresholds, rules and operations.
Droop based controller
[44]–[47], [49] •These techniques are highly reliable, decentral-
ized and can be implemented with ease.
•Power sharing capability among the energy
storage devices exhibits less accuracy.
•It is limited by slower response, and it is prefer-
able where the response time can be relaxed.
Integral droop control [48]
•It overcomes the limitations of LPF/HPF.
•Fully autonomous and decentralised control
system.
•Slower response when compared to traditional
droop control.
Dead beat control [43]
•This type of controller require minimal amount
of sensors than regular PI controller based tech-
niques and also exhibits faster response during
sudden change in load conditions.
•Fast dynamic response and high control accu-
racy.
•Simple process in implementation.
•Highly sensitive to change in parameters of
controller and requires exact model of the sys-
tem.
PI [52]
•Easy and simple to design. •The response of the system will be poor, if
the operating point of the system is not within
limited range.
•Uncertainty with respect to parameter varia-
tion.
LPF/HPF [32], [53]
•Easy to implement and simple in structure. •It experiences repetitive LPF/HPF enforced in
power converters, which results in system in-
stability.
multi-mode fuzzy logic [54]
•Faster response •Real-time implementation of this technique is a
challenging task.
Hierarchical control [37],
[55], [56] •Highly effective in controlling the vulnerability
of connected sources and loads.
•Accurate load balance and power sharing capa-
bilities among the energy storage devices.
•The chances of communication failure in the
system are high.
Sliding mode control [50],
[51] •High robust controller.
•Less sensitive to change of parameters.
•Involves complex design procedure.
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Sudhakar et al.: Review of HESS control techniques
adaptive PMP, meta-heuristics and MPC, are developed for
the application of energy management in HESS. The systems
implemented with these techniques were proven to have
enhanced control performance, however, the time taken for
the calculations at each cycle is high and the system cost is
also high [57]. The detailed discussion on intelligent control
techniques developed for HESS are discussed in following
subsections.
A. MODEL PREDICTIVE CONTROLLER (MPC)
MPC is a plant process optimization technique that forecasts
the effect of future control decisions on the changing state
of the plant. It is an efficient and systematic technique used
to deal with restricted multivariate control problems typically
in process control of industries [58]. The greatest benefit of
MPC is that it enables optimization of the current time frame
while preserving the future time frames. MPC is likely to
predict future incidents and can thus take corrective steps
[59]. In [60] the authors implemented an off-grid based
wind/PV HESS via MPC to observe the SOC, load varia-
tions and levels of hydrogen tanks. The simulation findings
reveal that the projected battery capacity and total device
performance in comparison to the state controller system
have improved by 12.23% and 14.65% [60]. Furthermore, to
control the discrete/continuous dynamics in HESS system,
MPC with MLD system is proposed in [61]. By using MPC,
future control inputs and future system responses can also
be predicted and it exhibits optimal control scheme. It has
the capability of controlling numerous control variables in a
large scale system. The control scheme of MPC proposed by
authors in [62] is shown in Fig. 21.
FIGURE 21: MPC based control scheme [62]
A hybrid system combination of fuel cells and SC are
controlled with a MPC in [63]. In this work, the SC are
interlinked directly in parallel with load and the fuel cell
is connected to the load via DC-DC converter. The main
limitations of the work carried-out by the authors are listed as
follows: authors did not permit the gradual current changes
to the considered power sources, the performance of the
system may be affected due to rapid current changes as only
the first order model of SC is considered. While designing
the controller no actual DC-DC converter is considered.
Moreover, the system performance has been evaluated only
through simulation studies. Its major drawback in the reliance
of MPC on mathematical model of the system.
MPC strategy is proposed in [64] with a combination of
SC and batteries as energy storage devices. The main features
of the work implemented are: the proposed MPC technique
ensures the SOC of battery and SC are maintained within the
limits, it gives unique steps to deal with the controller. On
the other hand, the limitations exist in this work are, since
by using of two state space models for two converters, the
system is highly computationally insensitive as opposed to
classical MPC. In addition to that, this method is not capable
of regulating DC bus voltage. By extending the same concept
given in [64], authors in [65], proposed less complex con-
troller for effective management of HESS. Even though this
proposed method, behaves less complex, the consideration
DC bus voltage as constant will not be applicable in real-time
DC microgrid system. Therefore, the limitations has to be
addressed for its implementation in microrgrids. The direct
control of converters using MPC based EMS for HESS are
shown in Fig. 22.
FIGURE 22: MPC controller based direct control of convert-
ers for HESS [66]
B. NEURAL NETWORK AND FUZZY LOGIC
In search of the trade-off between complexity and efficiency,
other suitable methods for real-time EMS are proposed based
on fuzzy logic, neural networks and RL [67]–[69].
Smart control strategies like FLC and ANN are way more
effective than the conventional control strategies [70], [71].
Smart control strategies can enhance the system dynamic
behavior without requiring the exact system model, yet they
don’t guarantee optimum performance [72].
ANN is a mathematical model which was developed with
the desire to recognize and process parallel data. The ANN
consists of several machine neuron layers. These layers are
divided into three types; namely input, hidden, and output
layers. Thanks to its non-linear and adaptive mechanisms,
generalization abilities and design independence with regard
to parameter systems, hence ANN is ideal for use in control
systems [73], [74]. However, due to its ’black box’ nature
and the instructional issue in the network [73], [74], ANN
lacks rules to define the structure (cells and layers). In a
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study, an ANN control system is designed to learn how to
efficiently exploit system resources over time by adjusting
the energy storage system’s control strategy to change power
production. However, the main disadvantage of a control
strategy based on ANN is that the learning and tuning process
needs historical data [64]. In [69] an ANN-based control
strategy for an off-grid wind / PV power network with a HSS
/ battery HESS is proposed. The ANN-based management
strategy uses a feedback loop to learn from new experiences
and data in a new way. Compared with conventional methods,
the proposed system has quick response [69]. However, the
computational complexity depends on the number of data
sets used to train the ANN [64].
On contrary to ANN, FLC is understandable and insen-
sitive to parameter variations. In addition to that, the FLC
does not need an exact program and training method model.
The FLC algorithm is based on the rules and MF. In [75],
the authors proposed FLC for an HSS / battery HESS wind
/ PV power off-grid network. The proposed control strat-
egy aims to minimize the cost of HSS and regulate level
of hydrogen and SOC of battery. The results show that
the proposed program optimizes the consumption costs by
achieving a cumulative savings of 13 per cent over the regular
program with RBC [75]. Indeed, FLC’s MF are typically
calculated by a trial and error process. This approach takes
time and does not ensure optimal results [70], [71], [73],
[74]. As the number of variables increases, the process of MF
optimization becomes more complex. A multi-mode fuzzy
based controller is introduced by authors to overcome the
limitations of haar wavelet method which fails to consider
the impacts of SOC [54]. The proposed multi-mode fuzzy is
verified under long and short term scenarios. As a short term
ac side performance is observed and in long term scenario,
the problems of battery aging and efficiency are improved.
The aforementioned goals are achieved by allocating FLC to
three power sharing modes.
The NFC takes advantage of the inference ability of FLC,
learning and process data in parallel features of ANN. The
NFC applies neural learning rules for defining and optimizing
MF of FLC. This methodology increases the accuracy of the
fuzzy model in a limited period of time. In [76] the REPS
control strategy was launched with the ANFIS [76]. MF of
FLC are tuned and configured using the back-propagation
adaptive error process. In the large scale operation (one-year
simulation), the proposed system is compared with the RBC.
The results of the simulation show that the two systems can
provide the necessary energy while holding the SOC bat-
tery within operating limits. The program proposed strategy
increases the performance by offering higher efficiency for
battery and hybrid system [76].
C. OPTIMIZATION BASED METHODS
In addition to the above discussed FLC and ANN based
controllers few other modern optimization based control
techniques were also implemented for effective management
of HESS. As the bio-inspired algorithms have capability of
handling multi objective functions effectively and provide
optimal solution which is proved in various fields of appli-
cations [77], [78]. Therefore researchers introduced the com-
bination of FLC with optimization algorithms for effective
framing of rules, which are discussed as follows.
PSO is a computational approach that optimizes the prob-
lem by iteratively trying to develop a solution for a specific
objective. In [79], the authors proved that the PSO optimized
FLC can be used for an HSS / Battery HESS off-grid PV
/ Wind power network. The MF of FLC are calculated by
PSO algorithm and the network proposed is combined with
an optimized FLC system. Results from the simulation show
that the O & M and LPSP costs of the proposed system
are 57% and 33% lower than optimized systems [79]. With
the motivation of PSO+FLC technique, WCA is proposed
which is enlivened by the normal water cycle. In [80] off-
grid HESS using WCA with FLC is proposed. MF of FLC
are optionally tailored using WCAs to reduce LPSP and O &
M costs. The combination of FLC with LPF control strategy
is implemented in [53], where the LPF is used to attenuate
high dynamic components of battery and FLC was focused to
manage peak current of battery. To get better performance of
the FLC, the membership functions are optimized via PSO.
The control scheme proposed in [53] are presented in Fig. 23.
The proposed system has been successfully implemented and
tested in real-time rural house hold applications.
FIGURE 23: FLC controller with PSO [53]
Chia Y Y et.al., in [81] proposed a predictive energy
management system based on SVM for battery / SC HESS.
The predictive SVM-based strategy correctly predicts the
demand with a precision of 100% and a classification time
of 0.004866. With a load prediction, the proposed system
reduces the battery stress and prolongs the battery life by
operating the SC at 200 ms before demand for maximum
power takes place [81]. However, the amount of SC power is
uncontrollable. The other innovative predictive management
technique for a battery HESS is proposed for off-grid wind
power system [68]. Based on the predicted wind and load
profile, the strategy adapts the operation of the batteries and
FC so that the network does not face blackout when the
wind is weak or the energy reserves are inadequate [68].
However, the proposed predictive analysis approach has a
pre-determined threshold which limits its application.
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A hybrid on-grid HESS using predictive control is devel-
oped in [82]. The proposed system forecasts daily and weekly
photovoltaic power output, wind speed, electric demand,
electricity hourly price data and ambient temperature. The
daily / weekly FLC is optimized by using SFL and PSO
according to the predicted daily and weekly parameters. The
simulation findings reveal that 9.27 per cent of SOC for
weekly SFL-optimized FLCs, regular SFL-optimized FLCs,
weekly PSO-optimized FLCs and regular PSO-optimized
FLC are up by 17.8% and 16.89% respectively and weekly
operating costs are raised by weekly SFL-optimized FLC,
daily SFL-optimized FLC, weekly PSO-optimized FLC, and
daily PSO-optimized FLC. The other optimization based
EMS for HESS using MIAD is introduced in [83]. In this
work, the authors proved that the proposed algorithm has
less iterations to accomplish the objectives. The considered
objectives are reducing fluctuations in battery current flow,
diminishing the energy losses produced by SC.
D. UNIFIED CONTROLLER
A unified EMS for the DC microgrid with HESS is devel-
oped by Tummuru et al., in [84]. The proposed technique
proved the extension of life span of SC and battery under
various operating conditions. The performance achieved by
the proposed system gives superior than the EMS imple-
mented in [85] with respect to voltage regulation, charge and
discharge rates of battery and processing time. The similar
type of unified controller given in [86] has features of faster
dynamic voltage regulation, effective power sharing under
any sort of disturbances, reducing fluctuations in rate of
charge/discharge of battery, and power quality enhancement.
The developed unified controller is presented as a flowchart
in Fig. 24
Supervisory power management for HESS with hybrid
microgrid is implemented by authors in [87], [88]. In these
works, the authors have focused on cooperation between grid
and microgrid. In addition, the adequacy of HESS to deal
with various conditions from the PV and grid were also
analyzed. Another control technique named FJC is proposed
in [26] for control of HESS in DC microgrids. In this system,
the efficiency of the system is improved by the use of SC to
cater for the unmet load by batteries.
FFSVM to manage the power flow between batteries and
EDLC is proposed in [89]. An asymmetrical cascaded multi-
level converter is used to manage the HESS. In each mode of
operation, either battery or EDLC is chosen to give or receive
the power and other storage devices are used to maintain the
output voltage. As per the DC and AC voltage reference the
FFSVM produces switching times and sequences of the state
vector.
A hierarchical control strategy to control power flow be-
tween SMES and batteries is proposed in [55]. Empirical
mode decomposition tool is used for signal analysis and to
achieve effective power allocation to eradicate the time delay
unlike traditional methods. The structure of hierarchical con-
trol is shown in Fig. 25. The usage of SMES is increasing in
FIGURE 24: Flow chart of the unified controller for EMS of
HESS [86]
HESS than SC due to its features such as efficiency high,
low self-discharge, long life span and high power rating
levels. Based on the complete analysis carried-out form the
literature, the different intelligent control techniques used for
power management of HESS for the application of micro-
grids are tabulated in Table. 5.
FIGURE 25: Hierarchical controller for HESS [56]
V. CASE STUDIES AND APPLICATIONS
A. REAL-TIME CASE STUDIES
A variety of HESS combinations are common in simula-
tion software’s such as HOMER and MATLAB, real life
applications are not as common though. A lot of pilot and
demonstration projects are currently taking place in different
parts of the world. Limited information is known about
these projects as the companies and governments involved
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TABLE 5. Critical analysis of intelligent control strategies of HESS
Controller technique Features Limitations
ANN [67]–[75]
•FLC is less sensitive to change of parameters
and easy to implement the controller.
•The exact system model and training process is
not required.
•This method consumes high computational
time as membership functions are tuned by trial
and error basis.
Evolutionary methods [53],
[79]–[81] •These methods can handle multiple objective
functions to optimize at a time and gives better
response than conventional techniques.
•The selection of parameters can be done on
random values which may take time to give
optimized solutions and sometimes results may
not be accurate as expected.
Multi objective [68], [82]
•Several objective functions and constraints can
be controlled at a time.
•This type controller behaves like evolutionary
methods.
MPC [58]–[65]
•With help of this controller can predict the
future behavior of system and its performance.
These can also be optimized at regular inter-
vals.
•Provides uniform approach to design control
system.
•Easy incorporation of constraints.
•Can also control high scale system with numer-
ous control variables.
•Without accurate model of the system, the con-
troller will not function.
•High computational burden.
•Computationally intensive.
Robust control [90]
•This controller gives superior performance than
conventional controllers and is less sensitive to
change in parameters.
•The implementation of this type of controller
is complex due to involvement of immense
calculations in every switching period.
don’t provide detailed information about these projects until
noticeable progress is achieved. Most of the real life HESS
applications are for renewable energy sources integration,
frequency control and back-up power. They are considered
to enhance the operation of the grid and to save money
for the clients by lowering energy wastage and improving
efficiency. A few examples of real-time scenarios of HESS
implementation are:
•A 1.2 MW SC/Battery HESS in North Carolina, United
States was installed by Duke Energy in 2016 to han-
dle peak demand response, load shifting and support.
The hybrid SC-battery HESS provides multiple service
applications, extended operational life, rapid response,
real-time solar smoothing and load shifting. Benefit of
this combination is that it provides long term grid sup-
port and easily integrates renewable installations [91].
•A 11.5 MW Li-ion, NaS battery HESS commenced op-
eration in 2018 in Varel Niedersachsen, Germany. The
energy service provider EWE and its Japanese partners,
Japanese industrial development agency NEDO, Hitachi
Chemical, NGK Insulators and Hitachi Power Solutions
worked on the unique project. The purpose of the HESS
combination was to balance out frequency fluctuations
from renewable energy sources in the regional electric-
ity network. Li-ion battery will act as a high power
provider with its ability of quick discharge while the
NaS battery will act as a high energy provider which
is suitable for long term storage [92].
•A 1 MWh VF/Li-ion battery HESS was developed for
Monash University, Australia by redT energy storage
solution in 2018. The largest behind the meter Commer-
cial and Industrial ESS to be installed worldwide. The
main purpose of the HESS is for storing and dispatching
energy from multiple sources including a 1MW solar
panels [93].
•A 20 MW brand new concept of power to heat/battery
storage was developed in Bremen Germany by AEG
power solutions. Energy is stored in a battery and an
electrical heating system which are then connected to
a converter. Its main function is frequency control for
power operations and renewable energy sources integra-
tion [94].
An overview of world wide real-time implementation of
HESS are tabulated in Table. 6.
B. APPLICATIONS
HESS is predominantly deployed in the following sectors
Power sector, Transport sector and Renewable energy sector.
The applications of HESS in electric power system include
grid stabilisation, frequency regulation, backup power and
renewable energy sources integration.
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TABLE 6. HESS real-time case studies
S.No Combination Location Capacity
(MW &
MWh)
Company Application Year
[91] SC/Battery United States 1.2 MW Duke Energy Peak demand response, load shifting
and support
2016
[92] Li-ion/NaS
battery
Varel
Niedersachsen,
Germany
11.5
MW
EWE, NEDO, Hitachi
Chemical Co, Hitachi
Power solutions Co ltd
and NGK insulators
To balance out frequency fluctuations in
the regional electricity network
2018
[93] VFB/Li-ion bat-
tery
Monash Univer-
sity, Melbourne,
Australia
1MWh RedT The hybrid system will act as a flexi-
ble platform, integrating with building
management systems and EV charg-
ing stations whilst enabling cutting-edge
“peer-to-pool” energy trading.
2018
[94] Power to
heat/Battery
Bremen,
Germany
20 MW AEG Power Solutions Frequency control power 2018
[95] lead-acid batter-
ies and lithium-
ion capacitors
Japan 1.5 MW Hitachi Ltd and Shin-Kobe
Electric Machinery Co Ltd
Renewable energy integration 2014
[96] Li-ion batter-
ies/hydrogen
storage
Cerro Pabellón,
Chile
125
kWp PV
plant
EGP Grid stability 2017
1) Power Sector
HESS in the power sector is used for ancillary services like
FR or VR and backup power for the grid. Traditionally single
ESS were used for such services but due to their limited
capability in terms of energy, power density and dynamic re-
sponse, HESS became the better alternative [66]. Battery/SC
and battery/flywheel are the most common combinations of
power sector HESS. They are used in microgrids where
lifecycle of batteries is increased due to less stress from
charging and discharging. Overall HESS application in the
power sector is for grid stability, FR, back up and power
quality services.
2) Transport Sector
Peak power demand from sudden accelerations in EV draws a
lot of energy from batteries, which means that the duration of
power supply will be reduced and lifecycle of the battery will
also be lowered. Increasing the battery capacity to deliver
more power can solve the problem but this will be expensive
and will add more weight and volume. Alternatively the
HESS combination of a battery/super-capacitor or battery
flywheel will effectively manage large fluctuations from sud-
den accelerations and regenerative breaking [97], [98].The
battery performance and lifetime will also be increased due to
the HESS combination by reducing the charging/discharging
rate and battery stress [99].
HESS is also used in electric trains to flatten fluctuations
from various acceleration and deceleration cycles. They can
be installed on the train or railway substation. They are
utilized to reuse regenerative energy from vehicle breaking so
that large power is feedback to the source during deceleration
and power will be drawn during train acceleration [100].
3) Renewable Energy Sector
HESS in renewable energy sector is mainly used to improve
the power quality and system efficiency of renewable energy
sources. HESS aids integration of intermittent renewable
sources into the grid by providing long term energy bal-
ancing, short term power quality and frequency regulation
services. It is used instead of a single ESS to meet peak power
demand in some systems [100]. A HESS is a better solution
in terms of cost effectiveness, practicality and durability for
complete system implementation [5]. It is used to increase
the storage and system lifetime by optimising lifespan and
reducing dynamic stress on the high energy storage unit
[101]. The noticeable observations and applications of HESS
were tabulated in Table 7.
VI. CHALLENGES AND FUTURE DIRECTION
The investments on ESS is showing an increasing trend in
the recent years. It is primarily dictated by the RES and
microgrid infrastructure development in the present system.
The batteries and SC are widely used for grid connected
systems and EV applications due to its easy accessibility.
In addition to that, the conventional ESS like pumped hydro
storage and CAES were also used in wide range of appli-
cations. However, to improve the successful development
of HESS technologies, the following challenges should be
encountered:
•Creating awareness among public for enhancement
of HESS technologies towards achieving the reliable
power supply to the consumer.
•Providing support on infrastructure development, instal-
lation and regular maintenance should be guaranteed to
increase the installation capacity of HESS.
•Providing incentives for investment in HESS technolo-
gies is necessary which will bring attention towards the
growth of HESS in the market.
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Sudhakar et al.: Review of HESS control techniques
TABLE 7. HESS applications in various fields
Ref. Section Category Key points Summary(merits/demerits)
[55] Transport SMES/BES A novel method of making high power fast charging
load controllable is designed to generate real time power
demand for HESS.
High energy density of Battery energy storage (BES) and
rapid response of SMES can limit the power change rate
and power magnitude of fast charging stations. SMES
with its low self-discharge rate, long cycle life and high
efficiency brings down the operation cost compared to
other high power devices.
[56] Renewable Energy(PV sys-
tems)
Power heat/battery, Power
heat/battery/hydrogen,
SC/battery, Battery/battery
Amodular experimental test bed for HESS systems has
been described in its components, structure and function-
ality.
Double low pass filtering and peak shaving based power
flow decomposition principal approach was presented.
Configurations of four HESS for decentralized PV sys-
tems was presented. ESS coupling architecture and en-
ergy management concepts like hierarchical control and
optimization based energy management were discussed.
[66] Microgrid, Renewable En-
ergy
SC/battery, FC/battery,
SMES/battery, FC/SC,
battery/flywheel,
FC/flywheel and
battery/CAES
MG and RES challenges can be solved by HESS. The
services HESS can provide are: 1)Renewable system
intermittency improvement. 2)Storage lifespan improve-
ment 3)Power quality improvement HESS. 4)Stability
Acomprehensive review of Microgrid (MG) application
of HESS from different point of view was presented.
HESS sizing, applications in control and electrical sys-
tems for the adaptation of MG and current. Connections
and configurations of HESS and optimization control of
the process were discussed.
[102] Transportation (BEV) SC/Battery HESS performance in terms of lifetime extension can be
improved by continuously variable transmission (CVT).
BEV dynamic and economic performance is increased by
CVT.
Electrified CVT performance for a BEV equipped with
HESS was studied in the paper.it showed that regardless
of powertrain configurations by the addition of SC to
ESS, the charging current in the battery was controlled.
[103] Microgrid Battery/Flywheel Enhanced performance of the MG is noticed due to the
HESS. Lifecycle of the battery has increased and im-
provement of the quality of power during grid integration
is noticed.
Adynamic analysis of a HESS consisting of battery
and flywheel coupled to a PV generation plant and a
residential load up to 20 kW is provided.
[104] Microgrid Battery/SC MG with the aid of HESS can be installed in isolated
areas saving money without the need for costly inefficient
transmission and distribution infrastructure.
Development and technological advancements of
SC/battery HESS in standalone MG system was
discussed and reviewed in this paper. The effectiveness
of EMS in HESS for mitigating battery stress was
presented in a case study.
[100] Electrified transport,
Renewable energy
Battery/SC, Battery/SMES,
Battery/flywheel,
CAES/ SC, CAES-
SMES, CAES/flywheel,
CAES/battery, Fuel cell/SC,
Fuel cell/ SMES, Fuel
cell/battery, Pumped hydro/
power supplier ESS
Suitable for transport and utility services. Transport in-
cludes private EV and electrical powered train. The elec-
trical powered train is variable with so many accelera-
tions and deceleration’s. HESS can be used to reuse re-
generative energy from braking. Reliability, stability and
power quality issues from renewable energy integration
can be mitigated.
Outstanding features of HESS materialise in specific
applications that single ESS units cannot perform. HESS
enhances the performance in several applications that use
single ESS. HESS is used as an energy source in several
applications ranging from grid support to transport sector.
[105] Power systems Battery-SC Frequency regulation in electricity market Asubstantial amount of money can be saved by regula-
tion service provider from using HESS instead BES alone
for frequency regulation in power system operation.
[106] Power systems, Renewable
energy integration (PV)
Battery- SC The combination reduces stress on the battery, hence
increasing its lifespan.
A new control scheme and a selected combined topology
to control battery and SC power sharing was proposed.
Based on the PV power curves, a method for ESS sizing
and hybrid distribution was introduced.
[107] Transport (EV) Battery-SC/Fywheel Primary ESS is Li-ion battery while the secondary ESS
is either a flywheel or SC. During high power demand
the secondary source delivers/recovers energy. It also
increases the lifetime of the battery.
The reduction of battery intervention during start up and
regenerative breaking of EV was the main objective of
the paper. This is to increase the lifecycle of the battery,
respond to dynamic requirements of the vehicle during
extreme braking and traction operations and to enhance
capacity performance. In terms of energy density, vol-
ume, power density and cost, flywheel is better suited
than SC for EV application. SC is more convenient in
terms of weight and specific power. Breaking energy
recovery while driving is better achieved by SC.
•The upgradation of poor energy markets and weak grids
is required which will attract the power distribution
company towards deployment of HESS.
•The universal guidelines for HESS selection and its
operating procedures should be improved according to
the present technological development.
•The research on HESS technology is stagnant on labo-
ratory scale with only theoretical perspective, so, devel-
opment of projects to promote commercialization and
industrialization of HESS technology is necessary.
•It is advisable to come up with HESS solutions with the
support from researchers, economical advisors, electric-
ity companies, consumers and social organizations.
The advancement of HESS needs modernization and break-
through in long lifespan, minimal cost and high security. In
addition to that, the following recommendations are worth
addressing in near future:
•An intelligent EMS controller with superior perfor-
mance of HESS will facilitate the adoption of smart
grids or microgrids in near future. So, HESS can enable
the development of microgrids ranging from medium
scale to large scale towards the transition of smart-grid.
•The innovative approach of “energy internet” for future
energy supply and distribution system will highly de-
pend on the performance, flexibility and reliability of
HESS [108], [109]
•Development of new battery technologies namely alu-
minium, lithium-air, sodium-ion and graphene are need
to replace with current batteries with substantial devel-
opment in lifespan and performance [110], [111].
•Advancements in battery technologies will further in-
crease the adoption rate of HESS for grid connected
applications [112].
•Novel combination of ESS across different medium
(mechanical, thermal) will widen the options of HESS
for various applications. For instance, hybridization of
16 VOLUME 4, 2016

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10.1109/ACCESS.2020.3015919, IEEE Access
Sudhakar et al.: Review of HESS control techniques
fast responding high power ESS with high energy ESS
like CAES, thermal energy storage and pumped hydro
storage can be developed.
•The development of new control strategy for HESS via
multi objective optimization by taking into considera-
tion of technical and economical limitations is yet to be
targeted.
•There exist inadequate research works have been
carried-out using predictive controllers for implementa-
tion of HESS, with the improvement of these techniques
for HESS, penetration of renewable energy can be fur-
ther increased.
•As authors proposed in [113], a reconfigurable ES bank
to change the configuration of battery bank dynamically
to minimize the load on each battery and enhances the
performance of HESS and give long life span. Perform-
ing research on these type of system bringing these into
regular usual market is challenge in the present world.
VII. CONCLUSION
In this paper, an extensive review of various control strategies
implemented for HESS is presented. The different control
strategies used for autonomous and grid connected micro-
grids are elucidated. The recently published articles on HESS
are critically reviewed and noticeable characteristics of each
control technique are summarized in tables. In addition to
that, a detailed discussion of the pros and cons of intelligent
and conventional control techniques are also presented. As
an added value, HESS adopted in real-time applications
throughout the globe is analyzed and discussed. Finally, a
pathway for the future researchers to carry out their research
in this area is laid out, and the main challenges faced to-
wards the implementation of HESS is presented. Therefore,
this article helps the researchers and practitioners to have a
complete idea on control technologies implemented for better
coordination of ESS forming the HESS, thereby driving the
penetration of RES into the grid.
REFERENCES
[1] O. Ellabban, H. Abu-Rub, and F. Blaabjerg, “Renewable energy re-
sources: Current status, future prospects and their enabling technology,”
Renewable and Sustainable Energy Reviews, vol. 39, pp. 748–764, 2014.
[2] F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus, “Overview
of control and grid synchronization for distributed power generation
systems,” IEEE Transactions on industrial electronics, vol. 53, no. 5, pp.
1398–1409, 2006.
[3] J. Zhu, W. Gu, G. Lou, L. Wang, B. Xu, M. Wu, and W. Sheng, “Learning
automata-based methodology for optimal allocation of renewable dis-
tributed generation considering network reconfiguration,” IEEE access,
vol. 5, pp. 14 275–14 288, 2017.
[4] A. Kyritsis, D. Voglitsis, N. Papanikolaou, S. Tselepis, C. Christodoulou,
I. Gonos, and S. A. Kalogirou, “Evolution of pv systems in greece and
review of applicable solutions for higher penetration levels,” Renewable
Energy, vol. 109, pp. 487–499, 2017.
[5] L. W. Chong, Y. W. Wong, R. K. Rajkumar, R. K. Rajkumar, and D. Isa,
“Hybrid energy storage systems and control strategies for stand-alone
renewable energy power systems,” Renewable and sustainable energy
reviews, vol. 66, pp. 174–189, 2016.
[6] G. J. May, A. Davidson, and B. Monahov, “Lead batteries for utility
energy storage: A review,” Journal of Energy Storage, vol. 15, pp. 145–
157, 2018.
[7] K. R. Vigna, V. Gomathi, J. Ekanayake, S. Tiong et al., “Modelling
and simulation of variable speed pico hydel energy storage system for
microgrid applications,” Journal of Energy Storage, vol. 24, p. 100808,
2019.
[8] M. I. Hlal, V. K. Ramachandaramurthy, A. Sarhan, A. Pouryekta, and
U. Subramaniam, “Optimum battery depth of discharge for off-grid solar
pv/battery system,” Journal of Energy Storage, vol. 26, p. 100999, 2019.
[9] M. Azharuddin Shamshuddin, T. S. Babu, T. Dragicevic, M. Miyatake,
and N. Rajasekar, “Priority-based energy management technique for
integration of solar pv, battery, and fuel cell systems in an autonomous
dc microgrid,” Electric Power Components and Systems, vol. 45, no. 17,
pp. 1881–1891, 2017.
[10] M. A. Shamshuddin, S. B. Thanikanti, V. K. Ramachandaramurthy, and
R. Kennel, “Implementation and design of an autonomous residential dc
nanogrid,” in PCIM Europe 2019; International Exhibition and Confer-
ence for Power Electronics, Intelligent Motion, Renewable Energy and
Energy Management. VDE, 2019, pp. 1–8.
[11] “Supercharged: Challenges and opportuni-
ties in global battery storage markets,”
https://www2.deloitte.com/content/dam/Deloitte/bg/Documents/energy-
resources/gx-er-challenges-opportunities-global-battery-storage-
markets.pdf.
[12] “World energy outlook 2018,” International Energy Agency, Paris,
France;, 2019.
[13] G. E. S. Database, “Global energy storage database, u.s. department of
energy,” https://energystorageexchange.org/projects, accessed December
25, 2019.
[14] Y. Kim and N. Chang, Design and management of energy-efficient hybrid
electrical energy storage systems. Springer, 2014.
[15] T. Bocklisch, “Hybrid energy storage systems for renewable energy
applications,” Energy Procedia, vol. 73, no. 2015, pp. 103–111, 2015.
[16] H. Chen, T. N. Cong, W. Yang, C. Tan, Y. Li, and Y. Ding, “Progress in
electrical energy storage system: A critical review,” Progress in natural
science, vol. 19, no. 3, pp. 291–312, 2009.
[17] C. Spataru, M. Barrett, and Y. Kok, “Physical energy storage employed
worldwide,” Energy Procedia, vol. 62, pp. 452–461, 2014.
[18] J. P. Zheng, T. R. Jow, and M. Ding, “Hybrid power sources for pulsed
current applications,” IEEE transactions on aerospace and electronic
systems, vol. 37, no. 1, pp. 288–292, 2001.
[19] A. Lahyani, P. Venet, A. Guermazi, and A. Troudi, “Bat-
tery/supercapacitors combination in uninterruptible power supply
(ups),” IEEE transactions on power electronics, vol. 28, no. 4, pp.
1509–1522, 2012.
[20] S. Vazquez, S. M. Lukic, E. Galvan, L. G. Franquelo, and J. M. Carrasco,
“Energy storage systems for transport and grid applications,” IEEE
Transactions on industrial electronics, vol. 57, no. 12, pp. 3881–3895,
2010.
[21] R. A. Dougal, S. Liu, and R. E. White, “Power and life extension of
battery-ultracapacitor hybrids,” IEEE Transactions on components and
packaging technologies, vol. 25, no. 1, pp. 120–131, 2002.
[22] J. Cao and A. Emadi, “A new battery/ultracapacitor hybrid energy storage
system for electric, hybrid, and plug-in hybrid electric vehicles,” IEEE
Transactions on power electronics, vol. 27, no. 1, pp. 122–132, 2011.
[23] T. Ma, H. Yang, and L. Lu, “Development of hybrid battery–
supercapacitor energy storage for remote area renewable energy sys-
tems,” Applied Energy, vol. 153, pp. 56–62, 2015.
[24] Z. Song, H. Hofmann, J. Li, X. Han, X. Zhang, and M. Ouyang, “A
comparison study of different semi-active hybrid energy storage system
topologies for electric vehicles,” Journal of Power Sources, vol. 274, pp.
400–411, 2015.
[25] A. M. Gee, F. V. Robinson, and R. W. Dunn, “Analysis of battery lifetime
extension in a small-scale wind-energy system using supercapacitors,”
IEEE transactions on energy conversion, vol. 28, no. 1, pp. 24–33, 2013.
[26] U. Manandhar, N. R. Tummuru, S. K. Kollimalla, A. Ukil, G. H. Beng,
and K. Chaudhari, “Validation of faster joint control strategy for battery-
and supercapacitor-based energy storage system,” IEEE Transactions on
Industrial Electronics, vol. 65, no. 4, pp. 3286–3295, 2017.
[27] S. K. Kollimalla, M. K. Mishra, A. Ukil, and H. Gooi, “Dc grid voltage
regulation using new hess control strategy,” IEEE Transactions on Sus-
tainable Energy, vol. 8, no. 2, pp. 772–781, 2016.
[28] I. J. Cohen, D. A. Wetz, J. M. Heinzel, and Q. Dong, “Design and
characterization of an actively controlled hybrid energy storage module
for high-rate directed energy applications,” IEEE Transactions on Plasma
Science, vol. 43, no. 5, pp. 1427–1433, 2014.
VOLUME 4, 2016 17

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3015919, IEEE Access
Sudhakar et al.: Review of HESS control techniques
[29] P. Bajpai and V. Dash, “Hybrid renewable energy systems for power
generation in stand-alone applications: A review,” Renewable and Sus-
tainable Energy Reviews, vol. 16, no. 5, pp. 2926–2939, 2012.
[30] Y. Zhang, Z. Jiang, and X. Yu, “Control strategies for bat-
tery/supercapacitor hybrid energy storage systems,” in 2008 IEEE Energy
2030 Conference. IEEE, 2008, pp. 1–6.
[31] S.-J. Ahn, J.-W. Park, I.-Y. Chung, S.-I. Moon, S.-H. Kang, and S.-R.
Nam, “Power-sharing method of multiple distributed generators consider-
ing control modes and configurations of a microgrid,” IEEE Transactions
on Power Delivery, vol. 25, no. 3, pp. 2007–2016, 2010.
[32] S. K. Kollimalla, M. K. Mishra, and N. L. Narasamma, “Design and
analysis of novel control strategy for battery and supercapacitor storage
system,” IEEE Transactions on Sustainable Energy, vol. 5, no. 4, pp.
1137–1144, 2014.
[33] N. Mendis, K. M. Muttaqi, and S. Perera, “Active power management of
a super capacitor-battery hybrid energy storage system for standalone op-
eration of dfig based wind turbines,” in 2012 IEEE Industry Applications
Society Annual Meeting. IEEE, 2012, pp. 1–8.
[34] D. Wu, R. Todd, and A. J. Forsyth, “Adaptive rate-limit control for energy
storage systems,” IEEE Transactions on Industrial Electronics, vol. 62,
no. 7, pp. 4231–4240, 2014.
[35] S. K. Kollimalla, A. Ukil, H. Gooi, U. Manandhar, and N. R. Tummuru,
“Optimization of charge/discharge rates of a battery using a two-stage
rate-limit control,” IEEE Transactions on Sustainable Energy, vol. 8,
no. 2, pp. 516–529, 2016.
[36] J. Xiao, P. Wang, and L. Setyawan, “Multilevel energy management
system for hybridization of energy storages in dc microgrids,” IEEE
Transactions on Smart Grid, vol. 7, no. 2, pp. 847–856, 2015.
[37] ——, “Hierarchical control of hybrid energy storage system in dc micro-
grids,” IEEE Transactions on Industrial Electronics, vol. 62, no. 8, pp.
4915–4924, 2015.
[38] L. Zhang, Y. Tang, S. Yang, and F. Gao, “A modular multilevel converter-
based grid-tied battery-supercapacitor hybrid energy storage system with
decoupled power control,” in 2016 IEEE 8th International Power Elec-
tronics and Motion Control Conference (IPEMC-ECCE Asia). IEEE,
2016, pp. 2964–2971.
[39] K. Jin, X. Ruan, M. Yang, and M. Xu, “A hybrid fuel cell power system,”
IEEE Transactions on Industrial Electronics, vol. 56, no. 4, pp. 1212–
1222, 2008.
[40] S. Teleke, M. E. Baran, S. Bhattacharya, and A. Q. Huang, “Rule-based
control of battery energy storage for dispatching intermittent renewable
sources,” IEEE Transactions on Sustainable Energy, vol. 1, no. 3, pp.
117–124, 2010.
[41] T.-L. Pan, H.-S. Wan, and Z.-C. Ji, “Stand-alone wind power system with
battery/supercapacitor hybrid energy storage,” International Journal of
Sustainable Engineering, vol. 7, no. 2, pp. 103–110, 2014.
[42] R. Cozzolino, L. Tribioli, and G. Bella, “Power management of a hybrid
renewable system for artificial islands: A case study,” Energy, vol. 106,
pp. 774–789, 2016.
[43] B. Wang, U. Manandhar, X. Zhang, H. B. Gooi, and A. Ukil, “Deadbeat
control for hybrid energy storage systems in dc microgrids,” IEEE
Transactions on Sustainable Energy, vol. 10, no. 4, pp. 1867–1877, 2018.
[44] Q. Xu, J. Xiao, X. Hu, P. Wang, and M. Y. Lee, “A decentralized power
management strategy for hybrid energy storage system with autonomous
bus voltage restoration and state-of-charge recovery,” IEEE Transactions
on Industrial Electronics, vol. 64, no. 9, pp. 7098–7108, 2017.
[45] M. Shi, X. Chen, J. Zhou, Y. Chen, J. Wen, and H. He, “Advanced sec-
ondary voltage recovery control for multiple hesss in a droop-controlled
dc microgrid,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3828–
3839, 2018.
[46] Q. Xu, X. Hu, P. Wang, J. Xiao, P. Tu, C. Wen, and M. Y. Lee, “A
decentralized dynamic power sharing strategy for hybrid energy storage
system in autonomous dc microgrid,” IEEE transactions on industrial
electronics, vol. 64, no. 7, pp. 5930–5941, 2016.
[47] J. Li, R. Xiong, Q. Yang, F. Liang, M. Zhang, and W. Yuan, “Design/test
of a hybrid energy storage system for primary frequency control using a
dynamic droop method in an isolated microgrid power system,” Applied
Energy, vol. 201, pp. 257–269, 2017.
[48] P. Lin, P. Wang, Q. Xu, J. Xiao, I. U. Nutkani, and C. F. Hoong, “An
integral-droop based dynamic power sharing control for hybrid energy
storage system in dc microgrid,” in 2017 IEEE 3rd International Future
Energy Electronics Conference and ECCE Asia (IFEEC 2017-ECCE
Asia). IEEE, 2017, pp. 338–343.
[49] D. N. Huu, “An adaptive control of hybrid battery-supercapacitor storage
for integration of wind and solar,” in 2016 IEEE International Conference
on Sustainable Energy Technologies (ICSET). IEEE, 2016, pp. 157–
162.
[50] D. B. W. Abeywardana, B. Hredzak, and V. G. Agelidis, “A fixed-
frequency sliding mode controller for a boost-inverter-based battery-
supercapacitor hybrid energy storage system,” IEEE Transactions on
Power Electronics, vol. 32, no. 1, pp. 668–680, 2016.
[51] B. Wang, J. Xu, R.-J. Wai, and B. Cao, “Adaptive sliding-mode with
hysteresis control strategy for simple multimode hybrid energy storage
system in electric vehicles,” IEEE Transactions on Industrial Electronics,
vol. 64, no. 2, pp. 1404–1414, 2016.
[52] L. Gao, R. A. Dougal, and S. Liu, “Power enhancement of an actively
controlled battery/ultracapacitor hybrid,” IEEE transactions on power
electronics, vol. 20, no. 1, pp. 236–243, 2005.
[53] L. W. Chong, Y. W. Wong, R. K. Rajkumar, and D. Isa, “An optimal
control strategy for standalone pv system with battery-supercapacitor
hybrid energy storage system,” Journal of Power Sources, vol. 331, pp.
553–565, 2016.
[54] X. Feng, H. Gooi, and S. Chen, “Hybrid energy storage with multimode
fuzzy power allocator for pv systems,” IEEE Transactions on Sustainable
Energy, vol. 5, no. 2, pp. 389–397, 2014.
[55] J. Deng, J. Shi, Y. Liu, and Y. Tang, “Application of a hybrid energy
storage system in the fast charging station of electric vehicles,” IET
Generation, Transmission & Distribution, vol. 10, no. 4, pp. 1092–1097,
2016.
[56] T. Bocklisch, “Hybrid energy storage approach for renewable energy
applications,” Journal of Energy Storage, vol. 8, pp. 311–319, 2016.
[57] B.-H. Nguyen, R. German, J. P. F. Trovão, and A. Bouscayrol, “Real-time
energy management of battery/supercapacitor electric vehicles based on
an adaptation of pontryagin’s minimum principle,” IEEE Transactions on
Vehicular Technology, vol. 68, no. 1, pp. 203–212, 2018.
[58] C. V. Rao and J. B. Rawlings, “Linear programming and model predictive
control,” Journal of Process Control, vol. 10, no. 2-3, pp. 283–289, 2000.
[59] S. Vichik and F. Borrelli, “Solving linear and quadratic programs with an
analog circuit,” Computers & Chemical Engineering, vol. 70, pp. 160–
171, 2014.
[60] J. P. Torreglosa, P. García, L. M. Fernández, and F. Jurado, “Energy
dispatching based on predictive controller of an off-grid wind tur-
bine/photovoltaic/hydrogen/battery hybrid system,” Renewable Energy,
vol. 74, pp. 326–336, 2015.
[61] F. Garcia-Torres, L. Valverde, and C. Bordons, “Optimal load sharing of
hydrogen-based microgrids with hybrid storage using model-predictive
control,” IEEE Transactions on Industrial Electronics, vol. 63, no. 8, pp.
4919–4928, 2016.
[62] R. T. Bambang, A. S. Rohman, C. J. Dronkers, R. Ortega, A. Sasongko
et al., “Energy management of fuel cell/battery/supercapacitor hybrid
power sources using model predictive control,” IEEE Transactions on
Industrial Informatics, vol. 10, no. 4, pp. 1992–2002, 2014.
[63] A. Vahidi, A. Stefanopoulou, and H. Peng, “Current management in a
hybrid fuel cell power system: A model-predictive control approach,”
IEEE Transactions on Control Systems Technology, vol. 14, no. 6, pp.
1047–1057, 2006.
[64] B. Hredzak, V. G. Agelidis, and M. Jang, “A model predictive control sys-
tem for a hybrid battery-ultracapacitor power source,” IEEE Transactions
on Power Electronics, vol. 29, no. 3, pp. 1469–1479, 2013.
[65] B. Hredzak, V. G. Agelidis, and G. D. Demetriades, “A low complexity
control system for a hybrid dc power source based on ultracapacitor–
lead–acid battery configuration,” IEEE transactions on Power Electron-
ics, vol. 29, no. 6, pp. 2882–2891, 2013.
[66] S. Hajiaghasi, A. Salemnia, and M. Hamzeh, “Hybrid energy storage
system for microgrids applications: A review,” Journal of Energy Storage,
vol. 21, pp. 543–570, 2019.
[67] J. N. Forestieri and M. Farasat, “Integrative sizing/real-time energy
management of a hybrid supercapacitor/undersea energy storage system
for grid integration of wave energy conversion systems,” IEEE Journal of
Emerging and Selected Topics in Power Electronics, 2019.
[68] A. O. Haruni, M. Negnevitsky, M. E. Haque, and A. Gargoom, “A novel
operation and control strategy for a standalone hybrid renewable power
system,” IEEE transactions on sustainable energy, vol. 4, no. 2, pp. 402–
413, 2012.
[69] R. Yumurtaci, “Role of energy management in hybrid renewable energy
systems: case study-based analysis considering varying seasonal condi-
18 VOLUME 4, 2016

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3015919, IEEE Access
Sudhakar et al.: Review of HESS control techniques
tions,” Turkish Journal of Electrical Engineering & Computer Sciences,
vol. 21, no. 4, pp. 1077–1091, 2013.
[70] C. Sun, X. Hu, S. J. Moura, and F. Sun, “Velocity predictors for predictive
energy management in hybrid electric vehicles,” IEEE Transactions on
Control Systems Technology, vol. 23, no. 3, pp. 1197–1204, 2014.
[71] I. Altas and A. Sharaf, “A novel maximum power fuzzy logic controller
for photovoltaic solar energy systems,” Renewable Energy, vol. 33, no. 3,
pp. 388–399, 2008.
[72] A. Brka, G. Kothapalli, and Y. M. Al-Abdeli, “Predictive power manage-
ment strategies for stand-alone hydrogen systems: Lab-scale validation,”
International Journal of Hydrogen Energy, vol. 40, no. 32, pp. 9907–
9916, 2015.
[73] N. Altin and ˙
I. Sefa, “dspace based adaptive neuro-fuzzy controller of
grid interactive inverter,” Energy Conversion and Management, vol. 56,
pp. 130–139, 2012.
[74] A. T. Azar, Fuzzy systems. BoD–Books on Demand, 2010.
[75] P. García, J. P. Torreglosa, L. M. Fernandez, and F. Jurado,
“Optimal energy management system for stand-alone wind tur-
bine/photovoltaic/hydrogen/battery hybrid system with supervisory con-
trol based on fuzzy logic,” International Journal of Hydrogen Energy,
vol. 38, no. 33, pp. 14 146–14 158, 2013.
[76] P. García, C. A. García, L. M. Fernández, F. Llorens, and F. Jurado,
“Anfis-based control of a grid-connected hybrid system integrating re-
newable energies, hydrogen and batteries,” IEEE Transactions on indus-
trial informatics, vol. 10, no. 2, pp. 1107–1117, 2013.
[77] A. M. Ajmal, T. S. Babu, V. K. Ramachandaramurthy, D. Yousri, and
J. B. Ekanayake, “Static and dynamic reconfiguration approaches for mit-
igation of partial shading influence in photovoltaic arrays,” Sustainable
Energy Technologies and Assessments, vol. 40, p. 100738, 2020.
[78] D. Yousri, T. S. Babu, D. Allam, V. K. Ramachandaramurthy, and
M. B. Etiba, “A novel chaotic flower pollination algorithm for global
maximum power point tracking for photovoltaic system under partial
shading conditions,” IEEE Access, vol. 7, pp. 121432–121 445, 2019.
[79] S. Safari, M. Ardehali, and M. Sirizi, “Particle swarm optimization based
fuzzy logic controller for autonomous green power energy system with
hydrogen storage,” Energy conversion and management, vol. 65, pp. 41–
49, 2013.
[80] M. Sarvi and I. N. Avanaki, “An optimized fuzzy logic controller by
water cycle algorithm for power management of stand-alone hybrid green
power generation,” Energy conversion and management, vol. 106, pp.
118–126, 2015.
[81] Y. Y. Chia, L.H. Lee, N. Shafiabady, and D. Isa, “A load predictive energy
management system for supercapacitor-battery hybrid energy storage
system in solar application using the support vector machine,” Applied
Energy, vol. 137, pp. 588–602, 2015.
[82] M. Athari and M. Ardehali, “Operational performance of energy storage
as function of electricity prices for on-grid hybrid renewable energy
system by optimized fuzzy logic controller,” Renewable Energy, vol. 85,
pp. 890–902, 2016.
[83] M.-E. Choi, S.-W. Kim, and S.-W. Seo, “Energy management optimiza-
tion in a battery/supercapacitor hybrid energy storage system,” IEEE
Transactions on Smart Grid, vol. 3, no. 1, pp. 463–472, 2011.
[84] N. R. Tummuru, M. K. Mishra, and S. Srinivas, “Dynamic energy
management of renewable grid integrated hybrid energy storage system,”
IEEE Transactions on Industrial Electronics, vol. 62, no. 12, pp. 7728–
7737, 2015.
[85] R. Sathishkumar, S. K. Kollimalla, and M. K. Mishra, “Dynamic energy
management of micro grids using battery super capacitor combined
storage,” in 2012 Annual IEEE India Conference (INDICON). IEEE,
2012, pp. 1078–1083.
[86] U. Manandhar, A. Ukil, H. B. Gooi, N. R. Tummuru, S. K. Kollimalla,
B. Wang, and K. Chaudhari, “Energy management and control for
grid connected hybrid energy storage system under different operating
modes,” IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1626–1636,
2017.
[87] N. Korada and M. K. Mishra, “Grid adaptive power management strategy
for an integrated microgrid with hybrid energy storage,” IEEE Transac-
tions on Industrial Electronics, vol. 64, no. 4, pp. 2884–2892, 2016.
[88] S. Kotra and M. K. Mishra, “A supervisory power management system
for a hybrid microgrid with hess,” IEEE Transactions on Industrial
Electronics, vol. 64, no. 5, pp. 3640–3649, 2017.
[89] W. Jiang, S. Xue, L. Zhang, W. Xu, K. Yu, W. Chen, and L. Zhang, “Flex-
ible power distribution control in an asymmetrical-cascaded-multilevel-
converter-based hybrid energy storage system,” IEEE Transactions on
Industrial Electronics, vol. 65, no. 8, pp. 6150–6159, 2017.
[90] R. Dhaouadi, Y. Hori, and H. Xiaoliang, “Robust control of an
ultracapacitor-based hybrid energy storage system for electric vehicles,”
in 2014 IEEE 13th International Workshop on Advanced Motion Control
(AMC). IEEE, 2014, pp. 161–166.
[91] D. Energy, “Duke energy to put new battery and ultracapacitor system
to the test in n.c,” https://news.duke-energy.com/releases/duke-energy-to-
put-new-battery-and-ultracapacitor-system-to-the-test-in-n-c, 2016.
[92] C. Bartsch, “Intelligent large-scale battery officially commences
operation in varel,” https://www.ewe.com/en/media/press-
releases/2018/11/intelligent-large-scale-battery-officially-commences-
operation-in-varel-ewe-ag, 2018.
[93] S. McGregor, “Monash welcomes world’s largest ci hybrid
storage system,” https://www.smart-energy.com/industry-
sectors/storage/monash-welcomes-worlds-largest-ci-hybrid-storage-
system/, 2018.
[94] C. Pairault, “combined battery energy storage and power
to heat to provide primary-frequency control power,”
https://www.aegps.com/en/company/news/detail/aeg-power-solutions-
announces-that-swb-chose-its-innovative-concept-of-combined-battery-
energy-stora-1/, 2018.
[95] S.-K. E. Machinery, “Hitachi, shin-kobe to plug in 1.5-mw hybrid power
storage system,” https://renewablesnow.com/news/hitachi-shin-kobe-to-
plug-in-15-mw-hybrid-power-storage-system-455781/, 2014.
[96] A. Richter, “Innovation at play at enel’s cerro pabellon geothermal plant
in chile,” https://www.thinkgeoenergy.com/innovation-at-play-at-enels-
cerro-pabellon-geothermal-plant-in-chile/, 2017.
[97] A. Ostadi, M. Kazerani, and S.-K. Chen, “Hybrid energy storage system
(hess) in vehicular applications: A review on interfacing battery and ultra-
capacitor units,” in 2013 IEEE Transportation Electrification Conference
and Expo (ITEC). IEEE, 2013, pp. 1–7.
[98] V. Technologies, “Overview, vehicle technologies
office overview automotive battery rd overview,”
energy.gov/sites/prod/files/2017/06/f34/es000howell2017o.pdf, 2013.
[99] M. Porru, A. Serpi, I. Marongiu, and A. Damiano, “A novel hybrid
energy storage system for electric vehicles,” in IECON 2015-41st Annual
Conference of the IEEE Industrial Electronics Society. IEEE, 2015, pp.
003 732–003 737.
[100] R. Hemmati and H. Saboori, “Emergence of hybrid energy storage
systems in renewable energy and transport applications–a review,” Re-
newable and Sustainable Energy Reviews, vol. 65, pp. 11–23, 2016.
[101] R. Difelice, “Hybrid energy storage systems get the best of both worlds,
(2016).” https://www.greentechmedia.com/articles/read/Hybrid-Energy-
Storage-Systems-Get-Best-of-Both-Worlds, accessed February 1, 2020.
[102] J. Ruan, Q. Song, and W. Yang, “The application of hybrid energy storage
system with electrified continuously variable transmission in battery
electric vehicle,” Energy, vol. 183, pp. 315–330, 2019.
[103] L. Barelli, G. Bidini, F. Bonucci, L. Castellini, S. Castellini, A. Ottaviano,
D. Pelosi, and A. Zuccari, “Dynamic analysis of a hybrid energy storage
system (h-ess) coupled to a photovoltaic (pv) plant,” Energies, vol. 11,
no. 2, p. 396, 2018.
[104] W. Jing, C. H. Lai, S. H. W. Wong, and M. L. D. Wong, “Battery-
supercapacitor hybrid energy storage system in standalone dc microgrids:
areview,” IET Renewable Power Generation, vol. 11, no. 4, pp. 461–469,
2016.
[105] U. Akram and M. Khalid, “A coordinated frequency regulation frame-
work based on hybrid battery-ultracapacitor energy storage technolo-
gies,” IEEE Access, vol. 6, pp. 7310–7320, 2017.
[106] V. M. Miñambres-Marcos, M. Á. Guerrero-Martínez, F. Barrero-
González, and M. I. Milanés-Montero, “A grid connected photovoltaic
inverter with battery-supercapacitor hybrid energy storage,” Sensors,
vol. 17, no. 8, p. 1856, 2017.
[107] K. Itani, A. De Bernardinis, Z. Khatir, and A. Jammal, “Comparative
analysis of two hybrid energy storage systems used in a two front wheel
driven electric vehicle during extreme start-up and regenerative braking
operations,” Energy Conversion and Management, vol. 144, pp. 69–87,
2017.
[108] K. Zhou, S. Yang, and Z. Shao, “Energy internet: the business perspec-
tive,” Applied Energy, vol. 178, pp. 212–222, 2016.
[109] F. F. Wu, P. P. Varaiya, and R. S. Hui, “Smart grids with intelligent
periphery: An architecture for the energy internet,” Engineering, vol. 1,
no. 4, pp. 436–446, 2015.
VOLUME 4, 2016 19

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3015919, IEEE Access
Sudhakar et al.: Review of HESS control techniques
[110] J. Cho, S. Jeong, and Y. Kim, “Commercial and research battery tech-
nologies for electrical energy storage applications,” Progress in Energy
and Combustion Science, vol. 48, pp. 84–101, 2015.
[111] D. Sarkar, A. Shukla, and D. Sarma, “Substrate integrated nickel–iron
ultrabattery with extraordinarily enhanced performances,” ACS Energy
Letters, vol. 1, no. 1, pp. 82–88, 2016.
[112] A. Gallo, J. Simões-Moreira, H. Costa, M. Santos, and E. M. dos Santos,
“Energy storage in the energy transition context: A technology review,”
Renewable and sustainable energy reviews, vol. 65, pp. 800–822, 2016.
[113] Y. Kim, S. Park, Y. Wang, Q. Xie, N. Chang, M. Poncino, and M. Pe-
dram, “Balanced reconfiguration of storage banks in a hybrid electrical
energy storage system,” in 2011 IEEE/ACM International Conference on
Computer-Aided Design (ICCAD). IEEE, 2011, pp. 624–631.
THANIKANTI SUDHAKAR BABU (M’17) re-
ceived the B.Tech. degree from Jawaharlal Nehru
Technological University, Ananthapur, India, in
2009, the M.Tech. degree in power electronics and
industrial drives from Anna University, Chennai,
India, in 2011, and the Ph.D. degree from VIT
University, Vellore, India, in 2017.
He is currently working as a Postdoctoral Re-
searcher in the Department of Electrical Power
Engineering, Institute of Power Engineering, Uni-
versiti Tenaga Nasional (UNITEN), Malaysia. He has published more than
50 research articles in various renowned international journals. Acting as
editorial board members and reviewers for various reputed journals, such as
IEEE and IEEE ACCESS, IET, Elsevier and Taylor and Francis. His areas of
interests include design and implementation of solar PV systems, renewable
energy resources, power management for hybrid energy systems, storage
systems, fuel cell technologies, electric vehicle, and smart grid.
KRISHNAKUMAR R. VASUDEVAN (S’19) re-
ceived the bachelor’s and master’s degree in elec-
trical and electronics engineering from Anna Uni-
versity, Chennai, India, in 2016 and 2018. Cur-
rently, he is pursuing the Ph.D. degree in electrical
power engineering at the Department of Electrical
Power Engineering, Universiti Tenaga Nasional
(UNITEN), Malaysia. He is also working as a
Research Engineer at Institute of Power Engineer-
ing, Universiti Tenaga Nasional, Malaysia since
2019. His areas of interests include energy storage system, pumped hydro
storage, microgrid control, renewable energy and rural electrification. He
has received the best outgoing student award in the bachelor’s degree and
the gold medal in the master’s degree.
VIGNA K. RAMACHANDARAMURTHY (SM’12)
received the bachelor’s degree in electrical and
electronics engineering from the University of
Manchester Institute of Science and Technology
(UMIST), U.K., in 1998, under the Malaysian
Government Scholarship, and the Ph.D. degree
in electrical engineering from UMIST, in 2001.
He then joined the Malaysian electrical utility,
Tenaga Nasional Berhad, in 2002, as an Electrical
Engineer. In 2005, he moved to Universiti Tenaga
Nasional (UNITEN), where he is a Professor with the Institute of Power
Engineering. He is currently a Chartered Engineer registered with the
Engineering Council of U.K., and a Professional Engineer registered with
the Board of Engineers, Malaysia. He is also the Principal Consultant
for Malaysia’s biggest electrical utility, Tenaga Nasional Berhad. He has
completed over 250 projects in renewable energy. He has also developed
several technical guidelines for interconnection of distributed generation and
solar PV in Malaysia and is in the Editorial Board/Associate Editor of IET
Smart Grid, IET RPG, IEEE Smart Grid, and IEEE Access. His areas of
interests include power systems related studies, renewable energy, energy
storage; power quality, electric vehicle, and rural electrification.
SULEIMAN BALA SANI received the bachelor’s
degree in electrical and electronics engineering
from University of Bradford, UK in 2010 and the
MEng in Power Engineering from Bayero Univer-
sity Kano (BUK), Nigeria in 2019, Currently, he is
pursuing the Ph.D. degree in electrical power engi-
neering at the Department of Electrical Power En-
gineering, Universiti Tenaga Nasional (UNITEN),
Malaysia. From 2012 to2015 he worked in Rural
Electrification Board, Kano, Nigeria as a distri-
bution engineer and from 2015 to 2019 he worked in Kaduna Electric, a
distribution company in Kaduna, Nigeria as a distribution engineer. His areas
of interest include rural electrification, renewable energy, hybrid energy
storage systems, energy storage systems and energy storage policy.
SANSUBARI CHEMUD received the bachelor’s
degree from the University of Warwick, England
in Electrical Power, master’s degree in power sys-
tem from the University of Malaya, Malaysia and
master’s degree in business administration from
Universiti Tenaga Nasional, Malaysia. Currently,
he is the Chief Engineer (Asset Management
Policy Strategy) Distribution Network Division,
Tenaga Nasional Berhad (TNB), Malaysia. Before
that, he was senior General Manager for Sus-
tainable Energy Development for TNB. He was the member of Malaysia
Special Committee on RE (SCORE) of Ministry of Energy of Malaysia
and introduced the first commercial grid connected renewable energy power
plant project in Malaysia in 2002. He was also the member of the drafting
committee for developing Malaysia Renewable Energy Master Plan, and
National Renewable Energy Policy which led to the introduction of Feed-
in-Tariff for Malaysian Renewable Energy industry, under the umbrella of
Ministry of Energy, Green Technology and Water in 2010/11. His profes-
sional interest focuses on sustainable energy development in Malaysia.
20 VOLUME 4, 2016

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3015919, IEEE Access
Sudhakar et al.: Review of HESS control techniques
ROSLI MAT LAJIM received the bachelor’s de-
gree in electrical engineering from Indiana Uni-
versity Purdue University Indianapolis (IUPUI),
Indiana, USA. Currently, he is the Principal Re-
searcher at TNB Research Sdn Bhd, Malaysia,
where he is responsible for projects related to de-
mand response, smart city, volt/var optimization.
He is also the Project Manager of the ongoing
energy storage research project. Previously, he
has managed and completed projects related to
SCADA/distribution automation, substation automation system/IEC 61850
and automatic meter reading (AMR).
VOLUME 4, 2016 21