Skip to main content

Advertisement

SpringerLink
Log in

Exponential state observer based finite time control of fully active hybrid energy storage system

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

This paper suggests an observer based control approach for fully active hybrid energy storage system (HESS) comprising of two storage elements such as supercapacitor (SC) and battery, two bidirectional DC–DC converters and variable load. In order to formulate a control approach for this hybrid system, the unmeasured dynamics of the system must be available. This engenders the design of a robust exponential state observer in this paper to estimate various unknown system variables such as load, battery and SC currents/voltages. Furthermore, this paper also renders an integral terminal sliding mode control (ITSMC) concatenated with Proportional Integral (PI) based Lyapunov’s function technique to robustly track the reference battery current, SC current and load voltage in finite time in the presence of lumped system uncertainties. The robustness, stability and finite time convergence properties of the closed loop system with the proposed technique are demonstrated through analytical approach. Simulated results are validated with the proposed methodology through various case studies and compared with LMI (linear matrix inequalities) based robust control techniques presented in literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

References

  1. Aneke M and Wang M 2016 Energy storage technologies and real life applications–A state of the art review. Appl. Energy 179: 350–377

    Article  Google Scholar 

  2. Wang Y, Liu C, Pan R and Chen Z 2017 Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator. Energy 121: 739–750

    Article  Google Scholar 

  3. Manandhar U, Tummuru N R, Kollimalla S K, Ukil A, Beng G H and Chaudhari K 2017 Validation of faster joint control strategy for battery-and supercapacitor-based energy storage system. IEEE Trans. Ind. Elect. 65(4): 3286–3295

    Article  Google Scholar 

  4. Zhang L, Hu X, Wang Z, Sun F and Dorrell D G 2018 A review of supercapacitor modeling, estimation, and applications: A control/management perspective. Renew. Sustain. Energy Rev. 81: 1868–1878

    Article  Google Scholar 

  5. Jung H, Wang H and Hu T 2014 Control design for robust tracking and smooth transition in power systems with battery/supercapacitor hybrid energy storage devices. J. Power Sour. 267: 566–575

    Article  Google Scholar 

  6. Li W, Joós G and Bélanger J 2009 Real-time simulation of a wind turbine generator coupled with a battery supercapacitor energy storage system. IEEE Trans. Ind. Elect. 57(4): 1137–1145

    Article  Google Scholar 

  7. Li J, Xiong R, Yang Q, Liang F, Zhang M and Yuan W 2017 Design/test of a hybrid energy storage system for primary frequency control using a dynamic droop method in an isolated microgrid power system. Appl. Energy 201: 257–269

    Article  Google Scholar 

  8. Camara M B, Gualous H, Gustin F and Berthon A 2008 Design and new control of DC/DC converters to share energy between supercapacitors and batteries in hybrid vehicles. IEEE Trans. Veh. Technol. 57(5): 2721–2735

    Article  Google Scholar 

  9. Gao L, Dougal R A and Liu S 2005 Power enhancement of an actively controlled battery/ultracapacitor hybrid. IEEE Trans. Power Elect. 20(1): 236–243

    Article  Google Scholar 

  10. Trovão J P, Silva M A, Antunes C H and Dubois M R 2017 Stability enhancement of the motor drive DC input voltage of an electric vehicle using on-board hybrid energy storage systems. Appl. Energy 205: 244–259

    Article  Google Scholar 

  11. Incremona G P, Cucuzzella M, Magni L and Ferrara A 2017 MPC with sliding mode control for the energy management system of microgrids. IFAC-PapersOnLine 50(1): 7397–7402

    Article  Google Scholar 

  12. Song Z, Hou J, Hofmann H, Li J and Ouyang M 2017 Sliding-mode and Lyapunov function-based control for battery/supercapacitor hybrid energy storage system used in electric vehicles. Energy 122: 601–612

    Article  Google Scholar 

  13. Ashok R S, Shtessel Y B and Ghanes M 2017 Sliding mode control of hydrogen fuel cell and ultracapacitor based electric power system: Electric vehicle application. IFAC-PapersOnLine 50(1): 14794–14799

    Article  Google Scholar 

  14. Wang B, Xu J, Yan Z, Cao B and Yang Q 2017 Duty-ratio based adaptive sliding-mode control method for boost converter in a hybrid energy storage system. Energy Proc. 105: 2360–2365

    Article  Google Scholar 

  15. Abeywardana D B W, Hredzak B and Agelidis V G 2016 A fixed-frequency sliding mode controller for a boost-inverter-based battery-supercapacitor hybrid energy storage system. IEEE Trans. Power Elect. 32(1): 668–680

    Article  Google Scholar 

  16. Vigneysh T and Kumarappan N 2016 Autonomous operation and control of photovoltaic/solid oxide fuel cell/battery energy storage based microgrid using fuzzy logic controller. Int. J. Hydrogen Energy. 41(3): 1877–1891

    Article  Google Scholar 

  17. Dey S, Mohon S, Pisu P, Ayalew B and Onori S 2015 Online state and parameter estimation of battery-double layer capacitor hybrid energy storage system. In: Proceedings of the 2015 54th IEEE Conference on Decision and Control (CDC), (pp. 676-681). IEEE

  18. Wang B, Xu J, Xu D and Yan Z 2017 Implementation of an estimator-based adaptive sliding mode control strategy for a boost converter based battery/supercapacitor hybrid energy storage system in electric vehicles. Energy Conv. Manag. 151: 562–572

    Article  Google Scholar 

  19. Pavković D, Lobrović M, Hrgetić M and Komljenović A 2016 A design of cascade control system and adaptive load compensator for battery/ultracapacitor hybrid energy storage-based direct current microgrid. Energy Conver. Manag. 114: 154–167

    Article  Google Scholar 

  20. Ashok R and Shtessel Y 2015 Control of fuel cell-based electric power system using adaptive sliding mode control and observation techniques. J. Franklin Inst. 352(11): 4911–4934

    Article  MathSciNet  Google Scholar 

  21. Liu J, Laghrouche S and Wack M 2014 Observer-based higher order sliding mode control of power factor in three-phase AC/DC converter for hybrid electric vehicle applications. Int. J. Control. 87(6): 1117–1130

    Article  MathSciNet  Google Scholar 

  22. Deepika D, Narayan S and Kaur S 2020 Globally robust adaptive critic based neuro-integral terminal sliding mode technique with UDE for nonlinear systems. Int. J. Prec. Eng. Manuf. 21(3): 403–414

    Article  Google Scholar 

  23. Deepika D, Kaur S and Narayan S 2020 Integral terminal sliding mode control unified with UDE for output constrained tracking of mismatched uncertain non-linear systems. ISA Trans. 101: 1–9

    Article  Google Scholar 

  24. Chiu C S 2012 Derivative and integral terminal sliding mode control for a class of MIMO nonlinear systems. Automatica. 48(2): 316–326

    Article  MathSciNet  Google Scholar 

  25. Deepika D, Narayan S and Kaur S 2019 Synthesis of a new robust exponential sliding mode differentiator with its observer applications. Asian J. Control. 21(1): 387–396

    Article  MathSciNet  Google Scholar 

  26. Song Z, Hofmann H, Li J, Hou J, Han X and Ouyang M 2014 Energy management strategies comparison for electric vehicles with hybrid energy storage system. Appl. Energy 134: 321–331

    Article  Google Scholar 

  27. Xiong R, Cao J and Yu Q 2018 Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Appl. Energy 211: 538–548

    Article  Google Scholar 

  28. Trovão J P, Pereirinha P G, Jorge H M and Antunes C H 2013 A multi-level energy management system for multi-source electric vehicles–an integrated rule-based meta-heuristic approach. Appl. Energy 105: 304–318

    Article  Google Scholar 

  29. Song Z, Hofmann H, Li J, Han X and Ouyang M 2015 Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach. Appl. Energy 139: 151–162

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Punjab Engineering College (Deemed To Be University), Chandigarh, India

    Deepika Deepika

  2. Production and Industrial Engineering Department, Punjab Engineering College (Deemed To Be University), Chandigarh, India

    Nikhilesh Singh

Authors
  1. Deepika Deepika
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikhilesh Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepika Deepika.

List of Symbols

List of Symbols

{SC,bat}:

Subscripts showing variables with supercapacitor and battery

\({V_{bat}}\) :

Battery voltage [V]

\({V_{SC}}\) :

Super-capacitor voltage [V]

\({L_1},{L_2}\) :

Inductances of converters [H]

\({C_1},{C_2}\) :

Capacitances of converters [F]

\({C_L}\) :

Capacitance at load end [F]

\({R_L}\) :

Resistive load [Ω]

\({i_1},{i_2}\) :

Current through L1and L2, respectively [A]

\( {i_L}\) :

Load current [A]

\({R_{L1}},{R_{L2}}\) :

Resistances of converters [Ω]

\({R_1}\) :

Internal resistance of battery [Ω]

\({R_2}\) :

Internal resistance of super-capacitor [Ω]

\({u_1},{u_2}\) :

Control inputs

\({\hat V_{bat}},{\hat V_{SC}},{\hat i_1},{\hat i_2},{\hat V_L}\) :

Estimated parameters through proposed observer

\({\sigma_1},{\sigma_2}\) :

Sliding surfaces

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deepika, D., Singh, N. Exponential state observer based finite time control of fully active hybrid energy storage system. Sādhanā 47, 21 (2022). https://doi.org/10.1007/s12046-021-01797-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-021-01797-9

Keywords

Search

Navigation