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Author: search.filters.author.Peanjad, Pannawat

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    State of Health Battery Estimation by Using the OCPP of Charging Station Combined with Loss of EV Charging System
    (2023-01-01)
    Peanjad, Pannawat
    ;
    Khomfoi, Surin
    ;
    Phophongviwat, Teeraphon
    ;
    Manee-Inn, Chaitouch
    ;
    Thounthong, Phatiphat
    The research paper focuses on estimating the health of electric vehicle batteries using electrical variables measured by the charger during the charging process. These variables are sent to a central processing system via OCPP (Open Charge Point Protocol), as the charger itself has limitations in directly measuring battery health. To overcome this limitation, this research utilizes electrical variables measured by the DC charger during the charging process to estimate battery health. The Coulomb counting method is employed in combination with an investigation into losses within the vehicle's charging system to enhance the accuracy of State of Health (SoH) estimation. The obtained battery health values will assist electric vehicle users in better trip planning and maintenance scheduling.
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    Electric Vehicle Charging Station incorporating with an Energy Management and Demand Response Technique
    (2022-01-01)
    Peanjad, Pannawat
    ;
    Khomfoi, Surin
    A study of Demand Response (DR) function and Energy management for an electrical vehicle (EV) charger is presented in this paper. The proposed technique is to prevent the electrical system within the rated power by controlling electric vehicle chargers loads to the electrical system. A power management method for limiting the charging current and charging time by using state of charge (SoC) as a priority point index is developed. The power management method is also developed as a calculation algorithm for determining the charging current limit rating for electric vehicle chargers. The simulation model is also developed for validating a DR management function of the electric vehicle charger according to Provincial Electricity Authority of Thailand (PEA) load characteristic data. The results show that the proposed DR function can manage the charging current of each electric vehicle charger appropriately. Also. the proposed technique can prevent the rated power demand of the transformer distribution. The study illustrates that this method is an effective protection for the distribution transformer and can be able to apply as a DR function to manage electrical energy more efficiently.
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    Comparison of LFP battery performance between Screw welding and Laser beam welding
    (2022-01-01)
    Peanjad, Pannawat
    ;
    Khomfoi, Surin
    ;
    Phophongviwat, Teeraphon
    Studying the comparison of the performance of battery packing between screw welding technique and laser beam welding technique, both battery packing techniques have different advantages and disadvantages to different manufacture and assembling. The research is testing the performance of using battery packing in both techniques by choosing the Lithium iron phosphate(LFP) battery manufactured and distributed in the present market. This research will compare the life cycle testing, which tests the battery life performance, testing DC internal resistance of the battery, and comparing the battery's temperature increase while the battery discharged. To collect the performance testing data and choose the most suitable for application use with suitable battery packing technique.
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    State of Health Estimation of LFP Batteries Using DC Internal Resistance and Neural Network
    (2022-01-01)
    Peanjad, Pannawat
    ;
    Manee-Inn, Chaitouch
    ;
    Khomfoi, Surin
    Studying the state of health estimation of lithium-ion phosphate batteries (LFP) using an Artificial neural network (ANN). This research examines the relationship between DC internal resistance and the state of health (SoH) of batteries. The advantage of DC internal resistance measurement is that it does not require battery removal from the system. Analysis of degradation patterns in the application of several cycles. Then apply the previously studied relationship to train the ANN to design and test the model with other battery packs. As a result, the error value is acceptable. ( MAE = 9.06%, MSE = 1.23% and RMSE = 11.11% ). Thus, this ANN Model can assist in the early detection of a potential battery failure due to battery degradation.