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Author: search.filters.author.Kanharin, PhatcharaphongHas files: No

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    Small Scale Electric Vehicle Simulator Using PI and Adaptive Neuro Fuzzy Inference System Controllers
    (2025-01-01)
    Cheapanich, Siriruch
    ;
    Kinnares, Vijit
    ;
    Kanharin, Phatcharaphong
    This paper proposes the experimental implementation of a small-scale Electric Vehicle (EV) simulator system using PI and Adaptive Neuro Fuzzy Inference System (ANFIS) controllers. The proposed power train system includes lithium-ion battery, a bidirectional dc-dc converter, a Permanent Magnet DC (PMDC) motor drive acting as a load emulator and a Brushless DC (BLDC) motor drive acting as EV traction. The designed simulator is a reduced scale referring to the realistic EV. Charging and discharging operation of the lithium-ion battery as a power source is controlled by two PI controllers namely one for constant battery charging current and the another for constant dc link voltage using the bidirectional DC-DC converter in buck and boost modes of operation, respectively. A discontinuous PWM scheme for each switch in controlling battery current and dc bus voltage is employed for such bidirectional dc-dc converter to reduce switching loss. The PI based torque control of the PMDC motor is designed and implemented to meet requirement of road profiles. The ANFIS based speed control is used to obtain accurate speed as required values of the BLDC motor driving EV wheels. The actual speed is compared to the reference speed used as an input of the ANFIS technique in order to obtain the suitable motor voltage and frequency. The overall system performances such as regenerative braking and motoring modes of operation in different speeds according to road profile scenarios, dynamic response, and so on have been investigated. It is found that the testing results are satisfactory. The proposed EV simulator is useful for educational purposes.
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    A Comparison of the Response and Voltage Regulation Performance of the Single-Channel DC/DC Boost Converter Circuit with Artificial Neural Networks, Fuzzy Logic and PID Controllers
    (2023-02-01)
    Chaithanakulwat, Arckarakit
    ;
    Thungsuk, Nuttee
    ;
    Savangboon, Teerawut
    ;
    Ngao-Ngam, Somchai
    ;
    Kanharin, Phatcharaphong
    Fixed-speed wind turbines for generating electricity are also important because they are clean energy and do not pollute the environment. Developing maximum wind energy tracking and increasing DC voltage to optimal values is also an important factor designers must consider so that the power from generators connected to wind turbines can function efficiently. Therefore, in this researcher paper proposed a single-channel dc/dc boost converter control, three forms of algorithms consisting of neural networks, fuzzy algorithms and PID algorithms. The purpose of bringing these algorithms controlled because they wanted to compare the response and voltage control performance of the single-channel DC/DC boost converter to be associated with a three-phase inverter that controls PWM signal modulation with space vector technique. However, the principles and methodologies in this article are presented to simulate the algorithmic response using the MATLAB/Simulink program and compare it with the prototype mechanism. A comparison of the response performance and voltage regulation of the single-channel DC/DC boost converter showed that the three algorithms have different advantages and disadvantages but can be used together to achieve high efficiency.