Hybrid Energy Storage Systems Based on RedoxFlow Batteries: Recent Developments, Challenges, and Future Perspectives
Abstract
:1. Introduction
2. Evaluation of Key Performance Indicators
2.1. Classification of Single Storage Components
2.1.1. RedoxFlow Batteries (RFBs)
2.1.2. LithiumIon Batteries (LIBs)
2.1.3. Sodium–Sulfur Batteries (NaSs)
2.1.4. Lead–Acid Batteries (PbAs)
2.1.5. Supercapacitors (SCs)
2.1.6. Superconducting Magnetic Energy Storage (SMES)
2.1.7. Evaluation of Key Performance Indicators
2.2. Classification of HESSs
2.2.1. Definition of a HESS
 1.
 Primarily ESS cluster: has to satisfy the requirements of higher peak power demand and has to handle the fast transient fluctuations, e.g., load or Renewable Energy Sources (RES) production. This cluster is marked by fast response time, high power peaks, high efficiency, and high cycle lifetime.
 2.
 Secondary ESS cluster: has to comply with the requirement of high storage duration. This cluster is characterized by a low selfdischarge rate and high efficiency.
2.2.2. Evaluation of Key Performance Indicators
3. Coupling Architecture Optimization Strategy
3.1. Coupling Architectures of Hybrid Storage Systems
3.2. HESS Optimization Strategy
4. Energy Management System (EMS) for HESS
4.1. Energy Management Structure for HESS
4.1.1. Application Scenarios
 1.
 Transmission grid (T);
 2.
 Distribution grid (D);
 3.
 Behind the meter at enduser locations (EU).
Source  Application  Purpose  Placement  Control  Duration  Control Parameter  Controller Rate 

[38,46,47,48,49,50]  Momentary Reserve  S  T  P  t < msec  f_AC ^{1}  <20 ms 
[1,38,43,46,47,49,50,53,54]  Primary Control  S/G  T  P  t < msec  P_AC f_AC ^{1}  <30 s 
[10,38,42]  Secondary Control  S/G  T  P  s < t > 15 min  P_AC f_AC ^{1}  <5 min 
[10,38,42]  Tertiary Control  S/G  T  P  min < t > 60 min  P_AC f_AC ^{1}  <15 min 
[10,38,42]  Black Start  S    P  s <t > min  $\Delta $P ^{3} f_AC ^{1} U_AC  1–10 s 
[1,10,38,42,44,55]  Island Grid ^{4}  S    E  s < t > days  $\Delta $P ^{3}  1 s–1 min 
[1,38,42]  Transmission Support and Stability  S  T  E  t > h  $\Delta $P ^{3}  1 s–1 min 
[10,38,42,49,56,57]  Voltage Support  G /S  T/D  P  15 min < t >h  $\Delta $U ^{2}  1–15 min 
[1,10,38,42,43,46,49,50,52]  Distribution Power Quality  G /S  D  P  s < t > min  $\Delta $P ^{3}  1 s–1 min 
[10,38,43,44,52]  Peak Shaving (all time scales)  M /G  EU  P  s < t > 15 min  $\Delta $P ^{3}  30 s–1 min 
[38]  Uninterruptible Power Supply  M  EU  P/E  s < t > h  P f_AC U  <20 ms 
[38,46,47,49,50,52,56,57]  Energy Time Shifting  M  EU  E  15 min < t > days  $\Delta $P ^{3} t  1–15 min 
[1,38,43]  Energy Trading, Arbitrage  M    E  15 min < t > h  $\Delta $P ^{3} EUR/kW EUR/kWh  1–15 min 
4.1.2. Control and Optimization Parameters
4.2. Energy Management Optimization for HESS
4.2.1. Prediction
4.2.2. EMS Control Techniques
 1.
 Lowlevel optimization functions control the AC/DC bus voltage and the electric current flow.
 2.
 Highlevel optimization functions control many energy management strategies, among which are power performance, SoC monitoring, ESS charge/discharge cycles, and energy cost reduction.
 1.
 Classical control techniques mainly include filtrationbased control, dead beat control, droop control, and sliding mode control. These techniques are the most used in the literature, as demonstrated in Table 7, and are mainly applied for offline implementation independently of the filtrationbased control technique.
 2.
 Intelligent control techniques are classified into rulebased techniques and optimizationbased techniques. Rulebased techniques are among the most widely adopted in previous work due to their simplicity in implementation (see Table 7). However, these techniques are still far from perfect, as they require deep knowledge of the domain and the definition of rules for a complex system is a challenging task. Recently, there has been considerable interest in realtime optimization techniques, with a rapid rise in the use of Deep Learning (DL) and Machine Learning (ML) algorithms, e.g., Neural Network (NN) and Reinforcement Learning (RL). ML techniques deliver accurate results in real time, but on the other hand, they require a lot of training data and suffer from high computational complexity.
5. Related Work
6. Conclusions
 The advance of realtime optimization of EMS came at a very high computational cost. One solution to address this issue is the use of the Digital Twin (DT) concept. DT uses realworld data to create a simulation that predicts system future performance [100]. DT has been recently adopted in many application fields due to several advantages, in particular energy management and operation optimization improvement.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AC  Alternating Current 
AFE  Active Front End 
ANN  Artificial Neural Network 
AORFB  Aqueous Organic RedoxFlow Battery 
ARIMA  Auto Regressive Integrated Moving Average 
BC  Battery Converter 
BMS  Battery Management System 
CNN  Convolution Neural Networks 
D  Distribution Grid 
DC  Direct Current 
DT  Digital Twin 
E  Energy 
EC  Energy Component 
EES  Electrical Energy Storage 
EMS  Energy Management System 
ESS  Energy Storage System 
EU  Behind the Meter at EndUser Locations 
FC  Fuel Cell 
GA  Gradient Descent 
GAN  Generative Adversarial Network 
HESS  Hybrid Energy Storage System 
ISC  Supercapacitor Current 
Isol  Isolated 
G  Grid 
KPI  Key Performance Indicator 
LIB  LithiumIon Battery 
LSTM  Long Short Term Memory 
M  Manage 
MAE  Mean Absolute Error 
MAPE  Mean Absolute Percentage Error 
MDPI  Multidisciplinary Digital Publishing Institute 
MILP  Mixed Integer Linear Programming 
MINLP  Mixed Integer Nonlinear Programming 
MLP  Mixed Linear Programming 
MPC  Model Predictive Control 
NaS  Sodium–Sulfur Battery 
NCM  Lithium–Nickel–Cobalt–Manganese Oxide 
NN  Neural Network 
Non Isol  Not Isolated 
NRMSE  Normalized RootMeanSquare Error 
N/S  Not Specified 
OMEI  Open Mobility Electric Infrastructure 
P  Power 
PbA  Lead–Acid Battery 
PC  Power Component 
PSO  Particle Swarm Optimization 
RE  Renewable Energy 
RES  Renewable Energy Sources 
RFB  RedoxFlow Battery 
RL  Reinforcement Learning 
RMSE  RootMeanSquare Error 
RNN  Recurrent Neural Networks 
S  System 
SC  Supercapacitor 
SCC  Supercapacitor Converter 
SCM  Supercapacitor Module 
SoC  State of Charge 
SMES  Superconducting Magnetic Energy Storage 
T  Transmission Grid 
UPS  Uninterruptible Power Supply 
VRFB  Vanadium RedoxFlow Battery 
VSC  Voltage at Supercapacitor Module 
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LIB  SC  NaS  PbA  RFB  

Battery  oriented  Energy density in Wh/kg  
Power density in W/kg  
Efficiency in %  
Selfdischarge in %/day  
Reaction Time in s  
Application  oriented  Cost in EUR/kW  
Cost in EUR/kWh  
Lifetime in cycles  
Shelf life in years  
Design Flexibility  
Ecologic impact  
Safety  
Storage duration  mindays  mshour  mindays  mindays  weeks 
SC+LIB  SC+NaS  SC+PbA  SC+RFB  LIB+RFB  

Battery  oriented  Energy density in Wh/kg  
Power density in W/kg  
Efficiency in %  
Selfdischarge in %/day  
Reaction time in s  
Application  oriented  Cost in EUR/kW  
Cost in EUR/kWh  
Lifetime in cycles  
Shelf life in years  
Design flexibility  
Ecologic impact  
Safety  
Storage duration  msdays  msdays  msdays  msweeks  minweeks 
Hybrid influence:  positive influence  no/medium influence  negative influence 
Legend:  = negative  = medium  = positive 
+ =  + ; + =  + ; + = 
Architecture Proposal  

Design Parameter  a  b  c  
Power converted by SCC (kW)  25  30  25  
Power converted by BC (kW)  5  5  30  
Overall conversion power installed (kW)  30  c35  55  
Voltage ratio SCC  TBD  H  L  L 
Voltage ratio BC  H  L  H  H 
Maximum power processed with low voltage ratio (kW)  25  5  30  25 
Minimum power processed with high voltage ratio (kW)  5  30  5  30 
Application  Voltage Support  Distribution Power Quality  Peak Shaving  Energy Time Shifting  

Hybrid Component  PC  PC  PC  PC/EC  
Island grids  EC  Improving transient response, increase efficiency/performance and lifetime of the EC, grid (voltage) quality, supply security [63,64,65]  Operational limits operation, self sufficiency, economic efficiency, efficiency, reduce energy costs [66,67]  
Uninterruptible Power Supply  EC  Utilization of UPS EC, economic efficiency, stability of power system [68]  
Peak Shaving  EC  Minimizing the power fluctuation, selfsufficiency, grid quality, optimizing the capacity ratio of EC, PC [69]  Dimensioning, efficiency, economic efficiency, lifetime, smoothing the current of EC [70]  
Energy time shifting  EC  Dimensioning, efficiency, economic efficiency, lifetime, smoothing the fluctuation of RE [71]  Selfsufficiency, reduce of max. power consumption/generation, utilization of RE, efficiency, dimensioning, lifetime [72]  
Energy Trading/Arbitrage  EC/PC  Economic efficiency (operational costs), efficiency, reduce energy costs [73] 
Predicted Data  Prediction Techniques  Evaluation Metrics 

Charging demand [80,81,82]  CNN, LSTM, RNN  MAPE, MAE, NRMSE 
RE production [75,76,77,79]  CNN, MILP, NN, RNN, ARIMA, GAN, MLP, LSTM  MAPE, RMSE 
ESS Capacity [78,79]  MILP, MINLP, NN  RMSE 
Charging scheduling and pricing [83,84]  MILP, RL, ANN  N/S 
Charging station placement [85,86]  GA, RL, Linear Regression, Decision Trees  N/S 
Paper  Energy Storage System  Electric Topologies  Optimization  General Control Techniques  Used Data  

Optimization Function  Real Time  
[9]  (H${}_{2}$/Br${}_{2}$) RFB, SC  DC coupled  Power  Yes  Mathematical model  Microgrids 
[87]  Battery, SC  DC coupled  Power allocation of different ESS  Yes  Classical Realtime optimization  Microgrids/Simulated 
[4]  Battery, SC  DC coupled  Reduces measurement inaccuracies  N/S  Classical  N/S 
[8]  VRFB, SC  Active topology  Current, SoC  No  Classical Fuzzy logic  EV charging park/Real 
[5]  LiIon battery, SC  DC coupled  N/S  N/S  Fuzzy logic  Ships 
[88]  VRFB, SC  Active topology  Power thresholds  No  Rulebased  Industrial grid—Real/Synthetic EV charging park 
[63]  Batteries, SC  DC coupled  Constant voltage to the DC bus  No  Classical  PV, AC and DC Loads/Simulated 
[89]  Battery, SC  DC coupled  N/S  N/S  Global optimization Realtime optimization  Electric vehicle 
[90]  LiIon battery, SC  DC coupled  Meet power demand Reduce the cost of energy storage device  Yes  Classical Realtime optimization  Ship load 
[91]  Battery, SC  DC coupled  Power allocation  Yes  Classical Rulebased  EV application 
[64]  Fuel cell, Battery, SC  DC coupled  Provide power for load in time Good tracking performance of HESS current Obtain a stable voltage of the dc bus  Yes  Projection operator adaptive law  N/S 
[69]  Battery, SC  DC coupled  Minimizing the power fluctuation Optimizing the capacity ratio of each ESS  Yes  Realtime optimization  N/S 
[92]  Battery, SC  N/S  N/S  Yes  Rulebased Global optimization Realtime optimization  Electric vehicle 
[93]  battery, SC  DC coupled  Power Charge/Discharge cycle  Yes  Realtime optimization  PV power generation 
[67]  LiIon battery, SC  AC coupled  Optimize the cycle life of the HESS  Yes  Mathematic model  Microgrids 
[72]  Battery, SMES  DC coupled  Control charge/discharge prioritization  No  Classical  Offgrid load profile/Simulated Sea wave energy conversion/Simulated 
[68]  Battery, fuel cell,  AC coupled, On grid  Power  N/S  N/S  Grid data/Real 
[94]  Battery, SC  Threelevel NPC Converter  N/S  N/S  Classical  Electric vehicle 
[71]  Battery Superconducting magnetic ESS  One DC/AC converter Two DC/ DC converters  Smoothing the fluctuations of the wind power output  N/S  Device/systemlevel control strategies  Wind power generation 
[95]  Battery, SC  DC coupled  N/S  N/S  Rulebased  Electric vehicle 
[73]  Battery, fuel cell, electrolyzer  DC coupled, On grid AC  Energy costs, power  N/S  Rulebased  Predicted daily data 
[66]  Fuel cell, battery, SC  DC coupled, Off grid  Power  Yes  Realtime optimization  Grid data/Real 
[65]  Battery, SC  DC coupled  N/S  N/S  Microgrid  
[96]  PbA and LiIon battery, SC  Three different architectures  Maintain the grid power and voltage  No  Classical  Residential load/Literature data 
[97]  battery, SC  DC coupled  Current, voltage  Yes  Realtime optimization  N/S 
[98]  Fuel cell, SC  DC converters  Voltage  No  Classical  Electric vehicle/Simulated 
[70]  Battery, SMES  DC coupled, On grid  Current  N/S  N/S  Grid data/Real 
[99]  Fuel cell, Battery, Electrolyzer  AC bus and DC bus considered  N/S  Yes  Realtime optimization  Residential load 
RealTime EMS Control Techniques  Advantages  Limitations  

Classical  Filtration [91] 

 
Intelligent  Rulebased  Fuzzy logic [88,91,92,95] 


Realtime optimization  MPC [7,92,97] 

 
NN [7,77] 

 
RL [85] 

 
PSO [69,89] 


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Share and Cite
Schubert, C.; Hassen, W.F.; Poisl, B.; Seitz, S.; Schubert, J.; Usabiaga, E.O.; Gaudo, P.M.; Pettinger, K.H. Hybrid Energy Storage Systems Based on RedoxFlow Batteries: Recent Developments, Challenges, and Future Perspectives. Batteries 2023, 9, 211. https://doi.org/10.3390/batteries9040211
Schubert C, Hassen WF, Poisl B, Seitz S, Schubert J, Usabiaga EO, Gaudo PM, Pettinger KH. Hybrid Energy Storage Systems Based on RedoxFlow Batteries: Recent Developments, Challenges, and Future Perspectives. Batteries. 2023; 9(4):211. https://doi.org/10.3390/batteries9040211
Chicago/Turabian StyleSchubert, Christina, Wiem Fekih Hassen, Barbara Poisl, Stephanie Seitz, Jonathan Schubert, Estanis Oyarbide Usabiaga, Pilar Molina Gaudo, and KarlHeinz Pettinger. 2023. "Hybrid Energy Storage Systems Based on RedoxFlow Batteries: Recent Developments, Challenges, and Future Perspectives" Batteries 9, no. 4: 211. https://doi.org/10.3390/batteries9040211