Skip to main content

Advertisement

SpringerLink
Log in

Thermal state's impact on the efficiency of a PI control for speed’s tracking in EV

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

In this paper, an advanced electrothermal model was developed in order to provide real-time information about the thermal states of the electrical components of the vehicle’s powertrain and determine the evolution of losses and that of powertrain parameters versus temperature during vehicle traffic. Then, electrothermal models were introduced in order to study the impact of the thermal behavior of the motor–inverter combination on the speed control of an electric vehicle. The obtained results show that the efficiency of the adopted PI regulators with predetermined parameters is heavily affected by the variation of powertrain parameters caused by temperature change. To improve the performance of the vehicle speed control and make the PI regulators adaptable to the variation of the different parameters during a drive cycle, the fuzzy logic technique was adopted.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Abbreviations

E on :

Turn-on energy loss

E off :

Turn-off energy loss

Err :

Recovery energy of diode

E sw :

Switching loss energy

C aero :

Resistant torque corresponding to aerodynamic effect

C em :

Electromagnetic torque of the motor

C roult :

Resistant torque corresponding to the friction at bearings

C p :

Resistant torque corresponding to the gravity effect

C w :

Useful torque on wheels

C x :

Drag coefficient

f r :

Coefficient to bearing pneumatic

i d :

Direct component of the current

i q :

In squaring components of the current

L d :

Direct induction

L q :

In squaring inductance

L s :

Statoric inductance

M va :

Density of the magnets

M v :

Vehicle mass

p:

Number of pole pairs

r c :

Dynamic IGBT resistance

r d :

Dynamic diode resistance

R roue :

Wheel radius

R s :

Statoric resistance

S f :

Frontal surface

t :

Time

T :

Temperature

T j :

Junction temperature

V :

Instantaneous EV speed

V ce :

Collector–emitter voltage

V d :

Forward voltage

v d :

Direct component of the voltage

v q :

In squaring components of the voltage

Ω:

Angular motor’s speed

References

  1. Sun X, Cao J, Lei G, Guo Y, Zhu J (2021) A composite sliding mode control for spmsm drives based on a new hybrid reaching law with disturbance compensation. IEEE Trans Transp Electrif. https://doi.org/10.1109/TTE.2021.3052986

    Article  Google Scholar 

  2. Sun X, Wu M, Lei G, Guo Y, Zhu J (2020) An improved model predictive current control for PMSM drives based on current track circle. IEEE Trans Ind Electron. https://doi.org/10.1109/tie.2020.2984433

    Article  Google Scholar 

  3. Chen Q, Kang S, Zeng L, Xiao Q, Zhou C, Wu M (2020) PMSM control for electric vehicle based on fuzzy PI. Int J Electr Hybrid Veh 12(1):75–85. https://doi.org/10.1504/IJEHV.2020.104271

    Article  Google Scholar 

  4. Gasbaoui B, Nasri A, Abdelkhalek O (2016) An efficiency PI speed controller for future electric vehicle in several topology. Procedia Technol 22:501–508. https://doi.org/10.1016/j.protcy.2016.01.109

    Article  Google Scholar 

  5. Luo C, Shen Z, Evangelou S, Xiong G, Wang FY (2019) The combination of two control strategies for series hybrid electric vehicles. IEEE/CAA J Autom Sin 6(2):596–608. https://doi.org/10.1109/JAS.2019.1911420

    Article  Google Scholar 

  6. Parida SM, Choudhury S, Rout PK, Kar SK (2018) A new self-adjusting PI controller for power control in a wind turbine generator. World J Eng 15(3):362–372. https://doi.org/10.1108/WJE-10-2017-0323

    Article  Google Scholar 

  7. Tabatabaei M (2019) Parametric optimization-based design of PI controllers: application to a DC servomechanism. World J Eng 16(3):351–356. https://doi.org/10.1108/WJE-05-2018-0178

    Article  Google Scholar 

  8. El Azzaoui M, Mahmoudi H (2017) Fuzzy-PI control of a doubly fed induction generator-based wind power system. Int J Autom Control 11(1):54–66. https://doi.org/10.1504/IJAAC.2017.10001059

    Article  Google Scholar 

  9. Sun X, Hu C, Lei G, Yang Z, Guo Y, Zhu J (2020) Speed Sensorless Control of SPMSM Drives for EVs with a Binary Search Algorithm-Based Phase-Locked Loop. IEEE Trans Veh Technol 69(5):4968–4978. https://doi.org/10.1109/TVT.2020.2981422

    Article  Google Scholar 

  10. Aloui H, Ben Jridi A, Chaker N, Neji R (2013) Fuzzy control for speed tracking of an electrical vehicle enclosing an axial-flux synchronous motor. In: 14th International conference on sciences and techniques of automatic control and computer engineering STA 2013, vol 3, pp 36–42. https://doi.org/10.1109/STA.2013.6783102

  11. Ruderman M (2014) Tracking control of motor drives using feedforward friction observer. IEEE Trans Ind Electron 61(7):3727–3735. https://doi.org/10.1109/TIE.2013.2264786

    Article  Google Scholar 

  12. Jurca F, Fodorean D (2012) Axial flux interior permanent magnet synchronous motor for small electric traction vehicle. In: SPEEDAM 2012—21st international symposium on power electronics, electrical drives, automation and motion, pp 365–368. https://doi.org/10.1109/SPEEDAM.2012.6264396

  13. Doumit N, Danoumbé B, Capraro S, Chatelon JP, Blanc-Mignon MF, Rousseau JJ (2017) Temperature impact on inductance and resistance values of a coreless inductor (Cu/Al2O3). Microelectron Reliab 72:30–33. https://doi.org/10.1016/j.microrel.2017.03.037

    Article  Google Scholar 

  14. Sun X, Jin Z, Cai Y, Yang Z, Chen L (2020) Grey wolf optimization algorithm based state feedback control for a bearingless permanent magnet synchronous machine. IEEE Trans Power Electron 35(12):13631–13640. https://doi.org/10.1109/TPEL.2020.2994254

    Article  Google Scholar 

  15. Jebahi R, Chaker N, Aloui H (2019) Electrothermal Modeling of an Embedded Inverter Intended for EV application. In: 4th International conference on recent advances in electrical systems (ICRAES'19), Hammamet, Tunisie, December 23–25, 2019

  16. Jebahi R, Aloui H, Ayadi M (2017) One-dimensional lumped-circuit for transient thermal study of an induction electric motor. Int J Electr Comput Eng 7(4):1714–1724. https://doi.org/10.11591/ijece.v7i4.pp1714-1724

    Article  Google Scholar 

  17. Jebahi R, Chaker N, Aloui H, Ayadi M (2016) Based FE design and performance enhancement of a PMSM intended for a leisure electric vehicle. In: 1st International conference on recent advances in electrical systems (ICRAES'16), Hammamet, Tunisie, December 20–22, 2016

  18. Ye J, Yang K, Ye H, Emadi A (2017) A fast electro-thermal model of traction inverters for electrified vehicles. IEEE Trans Power Electron 32(5):3920–3934. https://doi.org/10.1109/TPEL.2016.2585526

    Article  Google Scholar 

  19. Hanini W, Ayadi M (2018) Electro thermal modeling of the power diode using Pspice. Microelectron Reliab 86(May):82–91. https://doi.org/10.1016/j.microrel.2018.05.008

    Article  Google Scholar 

  20. Lee ST, Kim HJ, Cho JH, Joo D, Kim DK (2012) Thermal analysis of interior permanent-magnet synchronous motor by electromagnetic field-thermal linked analysis. J Electr Eng Technol 7(6):905–910. https://doi.org/10.5370/JEET.2012.7.6.905

    Article  Google Scholar 

  21. SEMIKRON (2015) Application manual power semiconductors

  22. https://www.semikron.com

  23. Rai JN (2012) Speed control of DC motor using fuzzy logic technique. IOSR J Electr Electron Eng 3(6):41–48. https://doi.org/10.9790/1676-0364148

    Article  Google Scholar 

  24. Jridi AB (2016) Speed Commande par les techniques intelligentes d’un Moteur Synchrone de type TORUS, Application: Traction Electrique. Dissertation, University of Sfax

Download references

Acknowledgment

This work is mainly funded and supported by the Tunisian Ministry of high education and research, Tunisia.

Author information

Authors and Affiliations

  1. Laboratory of Advanced Electronic Systems and Sustainable Energy, ENET’Com, University of Sfax, Sfax, Tunisia

    Radhia Jebahi, Alaeddine Ben Jridi, Nadia Chaker & Helmi Aloui

Authors
  1. Radhia Jebahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alaeddine Ben Jridi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nadia Chaker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Helmi Aloui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Radhia Jebahi.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jebahi, R., Jridi, A.B., Chaker, N. et al. Thermal state's impact on the efficiency of a PI control for speed’s tracking in EV. Electr Eng 103, 2637–2646 (2021). https://doi.org/10.1007/s00202-021-01256-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-021-01256-y

Keywords

Search

Navigation