Design and analysis of a Fuzzy Logic Controller for the optimal switching of drives for Hybrid Electric Vehicles

Sreenath, M; Indulal, S.

doi:10.1109/iccc.2015.7432874

Cited by 1 publication

(1 citation statement)

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“…If the battery SOC is above a particular threshold then, it proceeds further to check if the engine speed is above a set threshold, and if it is above the set threshold then the HEV continue to run in electric mode and battery is also charged at the same time. But if the engine speed is below a certain threshold value in this case, then the vehicle continues to run in the electric mode without charging thee battery [11]. This is the simplest case that we have taken to explain the algorithm where a threshold value is fixed for both the battery SOC and the engine speed.…”

Section: Battery Charge Controller For Hybrid Electric Vehiclementioning

confidence: 99%

Development of Progressive Fuzzy Logic and ANFIS Control for Energy Management of Plug-In Hybrid Electric Vehicle

et al. 2021

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Hybrid electric vehicles are an effective alternative to the conventional fuel engine vehicles and thus efficient and intelligent energy management is the key for establishing a significant market for the hybrid electric vehicles globally. Recent developments in the field of intelligent techniques and demand to make the energy systems intelligent have become a means to develop energy efficient hybrid electric vehicles. The energy management issue becomes vital in order to enhance the autonomy of hybrid electric vehicles and to reduce the costs. Therefore, a novel approach with intelligent techniques, to control the plug-in hybrid electric vehicles, in front of different customer profiles has been presented. This paper presents the battery performance improvement of a plug-in hybrid electric vehicle using fuzzy logic controller and neural fuzzy logic controller with battery state of charge as a deciding parameter and consequently comparing the performance of both cases. The battery state of charge and engine speed as input has been selected and based on their values the advanced controller decides the accurate torque required to be converted to energy which could be used to charge the battery and this can be achieved by controlling the forward gain value. For this, an advanced fuzzy controller and advanced adaptive nuero fuzzy inference system controller are used to decide the value of forward gain. Simulink environment is used to simulate the performance of the proposed system. This could be helpful in deciding which type of intelligent system is to be used for the power efficient operation of the hybrid electric vehicle. The results of both the control techniques are compared and the better controller is recommended for energy management of a plug-in electric vehicles. The results indicate that advanced control techniques provide the good performance and improving the fuel budget of hybrid electric vehicles.

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Section: Battery Charge Controller For Hybrid Electric Vehiclementioning

confidence: 99%