Realization of prototype hardware model with a novel control technique used in electric vehicle application
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ORIGINAL PAPER
Realization of prototype hardware model with a novel control technique used in electric vehicle application Raghavaiah Katuri1 · Srinivasarao Gorantla1 Received: 22 October 2019 / Accepted: 17 June 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Artificial neural network controller (ANN) · Speed condition-based (SCB) controller · Electric vehicles (EVs) · Ultracapacitor (UCp) · Battery List of symbols S1 , S2 , S3 Switches of DC–DC converters U1 , U2 , U3 , U4 SCB controller outputs i∗ Low-frequency current dynamics E0 Constant voltage it Extracted capacity if Leakage current i0 Exchange current density X2 Helmholtz layer length 𝛼 Charge transfer coefficient Tref Nominal Ambient temperature T Internal temperature Ta Ambient temperature 𝛽 Arrhenius rate constant Rth Thermal resistance tc Thermal time constant i Current density Q Electric charge * Raghavaiah Katuri [email protected] 1
Department of Electrical Engineering, VFSTR Deemed To Be University, Guntur 522213, India
Np Number of parallel UCp’s Ns Number of series UCp’s Abbreviations 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
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