4.6 Article

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

期刊

IEEE ACCESS
卷 9, 期 -, 页码 62219-62231

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3073862

关键词

Batteries; State of charge; Hybrid electric vehicles; Torque; Energy management; Synchronous motors; Fuzzy logic; Artificial intelligence; artificial neural network; energy management strategy; fuzzy logic; hybrid electric vehicles

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Hybrid electric vehicles require efficient and intelligent energy management to enhance autonomy and reduce costs. This paper presents a method to improve battery performance of plug-in hybrid electric vehicles using controllers and compares their performance.
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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