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Optimal Performance of Lithium-Ion Battery and Ultra-Capacitor with A Novel Control Technique Used In E-Vehicles

Bibliographic Data

ID19518074
AuthorsRaghavaiah Katuri (0000-0002-3315-1900, Vignan's Foundation for Science, Technology & Research, corresponding author), Srinivasarao Gorantla (Vignan's Foundation for Science, Technology & Research)
LanguageEN

Smooth switching between energy source, leads to obtain the optimal performance of the battery and UC, which has been achieved by a novel control technique. The main objective of this work is to design a control strategy of a multiple energy storage structure (MESS) to switch the battery and ultracapacitor (UC) as per the electric vehicle (EV) requirement. To achieve the objective, a condition-based controller (CBC) has been designed by considering four math functions further those can individually be programmed according to the speed of the electric motor (EM). The speed of an EM plays a vital during switching of energy sources present in multiple energy storage structure (MESS), and the main function of designed CBC to regulate the pulse signals which is given to the switches present in the unidirectional converter (UDC) and bidirectional converter (BDC). In this work, four different controllers' proportional-integral controller (PI), proportional integral derivative (PID) controller, the fuzzy logic controller (FLC), and artificial neural network (ANN) are used individually for generating required pulse signals to the switches present in DC-DC converters. Finally, the proposed objective can be achieved by combining CBC individually with PI, PID, FLC, ANN made a separate four hybrid controllers. Comparative analysis has been made among four hybrid controllers based on different time domain specifications. The entire circuit can be implemented with four hybrid controllers separately in MATLAB/Simulink with four cases based on the speed of the EM

Capacitor · Electrical engineering · Ion · Lithium-ion battery · Physics · Psychiatry · Thermodynamics · Voltage · Advanced battery technologies research · Advanced DC-DC Converters · Computer Science · Electric and Hybrid Vehicle Technologies · Engineering · Materials Science · Psychology · Automotive Engineering

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