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

Abstract

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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Abbreviations

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

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

SCBANNHC:

Speed condition-based artificial neural network

UCp:

Ultra-capacitor

EM:

Electric motor

MESM:

Multiple energy storage model

HEVs:

Hybrid electric vehicles

SCAP:

Supercapacitor

ICE:

Internal compunction engine

EMS:

Energy management strategy

SOC:

State of charge

HPS:

Hybrid power source

UDC:

Unidirectional converter

BDC:

Bidirectional converter

ZVT:

Zero voltage transition

SRM:

Switched reluctance motor

MCC-LSSVR:

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

PMSM:

Permanent-magnet synchronous motor

SSRM:

Segmented-rotor switched reluctance motor

SBBBC:

Switching bi-directional buck-boost converter

V2G:

Vehicles- to-grid

ESS:

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). https://doi.org/10.1007/s00202-020-01052-0

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Keywords

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