Kittiratsatcha, Supat
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Preferred name
Kittiratsatcha, Supat
Alternative Name
Kittiratsatcha, S.
Main Affiliation
Email
supat.ki@kmitl.ac.th
12 results
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Item type:Publication, Tractive force estimation for hybrid PM-electromagnetic suspension system maglev train prototype(2020-07-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Online Parameters Identification for an IPMSM Based on Extended Kalman Filter(2024-01-01); This paper introduces an online identification of d-q axis apparent inductance in an interior permanent magnet synchronous machines (IPMSM) using extended Kalman filter (EKF). The primary objective is to achieve accurate tracking of apparent inductances, which are nonlinear and varies under different operating conditions. The apparent inductances are crucial for calculating electromagnetic torque. A nonlinear IPMSM model based on finite element analysis (FEA) is employed for simulations. The effectiveness of the proposed approach is demonstrated through MATLAB/Simulink simulations. To verify the efficiency of real-world application, two machines with different conditions were investigated under various operating scenarios, with the results compared to steady state analysis to ensure the accuracy of the proposed estimation method. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Standalone Real-Time State of Charge Estimator Using the Long Short-Term Memory Artificial Neural Network(2024-01-01); Precise monitoring of the State-of-Charge (SoC) ensures optimal Lithium-ion (Li-ion) battery utilization, avoiding overcharging and deep discharging. Furthermore, it contributes to maximizing battery enhancement and longevity. However, a Li-ion battery's behavior is non-linear and varies under several conditions, making direct SoC measurement impossible. Currently, machine learning techniques are extensively used to estimate SoC; unfortunately, these models are typically executed in software only and cannot be realistically implemented in hardware. Consequently, this article proposes an approach for estimating SoC using a Recurrent Neural Network (RNN) with an improved Long Short-Term Memory (LSTM) cell. The proposed network was trained and tested on MATLAB using datasets of driving profiles for Electric Vehicles (EVs), which included battery voltage, current, and temperature while charging and discharging under various temperature conditions. Afterwards, the trained model was transferred to STM32-Cube-AI for validation and deployment of the proposed network for hardware implementation. Thanks to the STM32 microcontroller's performance, the network operates smoothly on a microcontroller and can estimate the SoC accurately and rapidly. Although Li-ion batteries have different characteristics at each temperature condition, the LSTM can predict the remaining SoC for each time step within a millisecond. Moreover, the experimental results show the proposed approach's quick convergence to the ground truth, substantiated by Root-Mean-Square-Errors (RMSEs) of around 4.5%, 2%, and 1.9% at 0 °C, 25 °C, and 45 °C, respectively. As a result, the proposed LSTM can be practically implemented for a real-time application with accuracy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of Converter and Control System for Variable Speed Permanent Magnet Synchronous Generator in Small Hydro Power Plant Model(2022-01-01); In micro and small hydro power plants, the flow and head of the water fluctuate seasonally. The torque-speed characteristic of hydro turbine requires variable speed operation of generator to always obtain maximum power generation. This paper presents variable speed control of a permanent magnet synchronous generator and maximum power point tracking for small hydro power plant. The INC-like maximum power point tracking algorithm is proposed and implemented. The experimental results validate the proposed algorithm. However the speed fluctuations are observed and have to be addressed in future study. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of Extended Kalman Filter and Long Short-Term Memory Neural Network for State of Charge Estimation of Lithium-Ion Battery(2023-01-01); The state of charge (SoC) estimation for lithium-ion batteries is an essential function of the battery management system (BMS) for ensuring reliable operation of electric vehicles. The nonlinear behavior of the battery makes estimating of SoC a challenging task. In this paper, SoC estimation based on model-based and data-driven methodology are implemented and compared. In the case of model-based estimation, static OCV-SOC test, AC impedance measurement and system identification technique are utilized to obtain accurate third order equivalent circuit model of the battery. Subsequently, SoC was estimated using Extended Kalman Filter (EKF). In the case of data-driven estimation, long short-Term memory recurrent neural network (LSTM-RNN) is adopted. The Input that fed into the network are terminal voltage and current, while output is SoC. The training set is WLTP Class 3 driving cycle, and the test set is WLTP Class 2 driving cycle. The performance of state estimation based on EKF and LSTM-RNN is evaluated through the RMSE. Considering only the accuracy of estimation, the SoC estimation using EKF is more accurate than LSTM-RNN, demonstrating the effectiveness of model-based state estimation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fast Approach of Open Circuit Voltage Estimation for Li-ion Battery Based on OCV Error Compensation(2020-11-24); As in a modern world, usages of rechargeable battery become sixth subsistence factor of human life from small appliances such as smart phone up to large industry scale like electric vehicle transportation. Hence, knowing the accurate remaining capacity or State of Charge (SOC) of the battery is a necessary parameter. Open Circuit Voltage (OCV) is an important indicator to show battery SOC. Unfortunately, we cannot measure OCV directly from the terminal of battery. To predict OCV after the battery has been charge or discharge, this process takes a very long resting time in order to let the reaction of chemistry inside the battery reach its steady state. To get rid of waste resting time, this paper proposes an easy and fast approach to estimate battery OCV by using OCV error compensation method. The Simulation and Experimental results show the proposed method is easy, fast and effective for OCV estimation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Accurate Assessment of Moisture Content and Degree of Polymerization in Power Transformers via Dielectric Response Sensing(2023-10-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A number of RC pairs consideration of electrical equivalent circuit model of li-ion battery(2020-07-01); In 20th century, handheld electronic appliances have changed our live style and make things as comfortable as never had before. Of course, the main important part of those devices is the battery. Li-ion battery is the most used not only in those small appliances such as smart phone, tablet, laptop, etc., but also in high power application like in electric vehicle or backup system for power grid. To fully utilize the battery for those applications, a suitable electrical equivalent circuit model must be selected for precise estimation of battery voltage and remaining capacity. Thevenin-based equivalent circuit model with various number of RC pairs has been used in many literatures which give different accuracy of voltages results. This paper proposed the consideration for number of RC pairs for equivalent circuit model using curve fitting method and the goodness of fit results base on the values of root mean square error at several state of charge of battery. The results show good indicator between simple circuit model with low accuracy or complex circuit model with higher accuracy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A New Tapped Inductor Buck Converter with Large Step-Down Voltage Conversion(2023-01-01); This paper presents a new DC-DC converter with large step-down voltage conversion. The proposed converter is obtained by cascading an input circuit of a Quadratic Buck Converter (QBC) to a Tapped Inductor Buck Converter (TIBC). It uses only one active switch and provides a wider voltage conversion range than other well-known step-down converters. In the paper, the operation of the proposed converter is described. Circuit analysis is performed to derive the voltage gain and key current and voltage equations. The prototype circuit operating with the input voltage of 150 V, the output voltage of 5 V, the load current of 10 A and the switching frequency of 100 kHz is implemented. Experimental results show that the prototype converter exhibits good output voltage regulation and achieves the 30-to-1 voltage step-down operation with a maximum efficiency of 82%. In addition, measurement results confirm that the converter operation is consistent with the theoretical analysis. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Output power control using artificial neural network for switched reluctance generator(2021-01-01);