Realization of prototype hardware model with a novel control technique used in electric vehicle application


Battery and ultra-capacitor (UCp) combination forms the multiple energy storage model (MESM), which provides the optimum benefit to hybrid electric vehicles/electric vehicles (EVs) for its successful operation. The inherent high power density characteristic of UCp is used during starting and momentary periods of EV. On the other hand, the battery provides the average power to the EV, during the steady-state periods. The development of the supervisory energy management strategy, corresponding to the EV dynamics is one of the key issues. In this paper, a new control technique is proposed to attain a smooth and automatic transition between energy sources in MESM according to the EV requirement. A speed condition-based (SCB) controller is designed with four individual math functions, corresponding to the speed of the electric motor (EM). A combination of the SCB controller and the artificial neural network (ANN) formed a SCBANN hybrid controller (SCBANNHC). To identify the proper power split between energy sources, the proposed SCBANNHC is applied to the main circuit in four different case studies corresponding to the load on the EM. Four different case study circuit models are realized in the MATLAB/Simulink environment along with a prototype hardware model for validation of the proposed control technique.

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\(S_{1} ,S_{2} ,S_{3}\) :

Switches of DC–DC converters

\(U_{1} ,U_{2} ,U_{3} ,U_{4}\) :

SCB controller outputs

\(i^{ * }\) :

Low-frequency current dynamics

\(E_{0}\) :

Constant voltage

\(i_{t}\) :

Extracted capacity

\(i_{f}\) :

Leakage current

\(i_{0}\) :

Exchange current density

\(X_{2}\) :

Helmholtz layer length

\(\alpha\) :

Charge transfer coefficient

\(T_{{{\text{ref}}}}\) :

Nominal Ambient temperature

\(T\) :

Internal temperature

\(T_{{\text{a}}}\) :

Ambient temperature

\(\beta\) :

Arrhenius rate constant

\(R_{{{\text{th}}}}\) :

Thermal resistance

\(t_{{\text{c}}}\) :

Thermal time constant

\(i\) :

Current density

\(Q\) :

Electric charge

\(N_{{\text{p}}}\) :

Number of parallel UCp’s

\(N_{{\text{s}}}\) :

Number of series UCp’s


Speed condition-based artificial neural network




Electric motor


Multiple energy storage model


Hybrid electric vehicles




Internal compunction engine


Energy management strategy


State of charge


Hybrid power source


Unidirectional converter


Bidirectional converter


Zero voltage transition


Switched reluctance motor


Maximum-correntropy-criterion-based least squares support vector regression


Permanent-magnet synchronous motor


Segmented-rotor switched reluctance motor


Switching bi-directional buck-boost converter


Vehicles- to-grid


Energy storage system


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Correspondence to Raghavaiah Katuri.

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Katuri, R., Gorantla, S. Realization of prototype hardware model with a novel control technique used in electric vehicle application. Electr Eng (2020).

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  • Artificial neural network controller (ANN)
  • Speed condition-based (SCB) controller
  • Electric vehicles (EVs)
  • Ultracapacitor (UCp)
  • Battery