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Implementation of Bang-Bang Charge Control for an LLC Resonant Converter for Battery Charger
(2025-01-01)
Potjanatkomol, Natkamol
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Polmai, Sompob
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Wiangtong, Theerayod
This paper presents the design of a 2-kW full-bridge LLC resonant converter for battery charger operating in a range of DC input voltages of 380-420 V and output voltage of 60 V. The bang-bang charge control technique, whose controller adjusts the threshold voltage of the bang-bang comparators, is adopted for controlling the converter switching frequency. This control method provides fast single-pole control-to-output transfer function resulting in simplicity of controller design. In this paper the design procedure of LLC converter resonant circuit is presented and a prototype is built. The discrete-time PI compensator for constant voltage (CV) and constant current (CC) control is implemented using STM32 microcontroller. The simulation and experimental results show satisfactory control characteristics.
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Power and Phase Current Balancing Technique for Bidirectional Cascade Three-Phase CLLLC Resonant Converters
(2025-01-01)
Kowi, Prachaya
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Polmai, Sompob
This paper presents an interleaved configuration of bidirectional cascade three-phase CLLLC resonant converters (BCTRC) topology. The proposed approach reduces the high-voltage transformer turns ratio, minimizes input/output current ripple, and improves current sharing across MOSFETs and power balance among converters, thereby lowering device stress and extending the system's lifespan. In practice, component tolerances lead to resonant parameter mismatches, resulting in phase current imbalance and unequal high-side voltage sharing. To overcome this, a fundamental voltage control (FVC) method is employed for power balancing, while trigonometric current balancing (TCB) is used to equalize phase currents. Simulation results confirm that the combination of FVC and TCB achieves the lowest unbalanced coefficient (U<inf>f</inf>) decreasing from 8.36% to 2.93% and reduces the power sharing error (PSE), decreasing from 9.78% to 0.24% in the forward case. In the reverse case U<inf>f</inf> decreases from 9.37% to 1.62%, while PSE is reduced from 9.66% to 0.58%. Furthermore, the proposed method equalizes the high-side voltages and maintains zero voltage switching (ZVS) under both forward and reverse power flow conditions.
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ACCURATE STATE OF CHARGE ESTIMATION OF LITHIUM-ION BATTERY USING RECURRENT AND NON-RECURRENT NEURAL NETWORKS FOR WLTP DRIVING PROFILES
(2024-12-01)
Praisan, Akkarawat
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Polmai, Sompob
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Kittiratsatcha, Supat
Estimating the state of charge (SoC) of a battery is essential to maximize its performance and ensure reliable operation and battery life. Nowadays, many countries are increasingly adopting electric vehicles (EVs) with lithium-ion batteries due to their high specific energy and long service life. This paper presents a method for estimating the state of charge of lithium-ion batteries using artificial neural networks, specifically the Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), through a data-driven approach. The training and testing of the networks are conducted using recorded datasets of the battery, based on the WLTP driving profiles class 2 and class 3. These driving profiles are specifically designed for testing electric vehicles, thereby enhancing the realism of the state of charge estimation by the network. In terms of the analytical aspect, the FNN was able to train the network faster due to its simpler structure, requiring less computation. On the other hand, the LSTM demonstrated more accurate SoC estimation with fewer response oscillations, thanks to its ability to learn and adapt network parameters internally.
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A Standalone Real-Time State of Charge Estimator Using the Long Short-Term Memory Artificial Neural Network
(2024-01-01)
Praisan, Akkarawat
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Polmai, Sompob
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Kittiratsatcha, Supat
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.
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Online Parameters Identification for an IPMSM Based on Extended Kalman Filter
(2024-01-01)
Treebubpha, Khanaphot
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Polmai, Sompob
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Kittiratsatcha, Supat
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.
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Technical Assessment of Reusing Retired Electric Vehicle Lithium-Ion Batteries in Thailand
(2023-06-01)
Phophongviwat, Teeraphon
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Polmai, Sompob
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Maneeinn, Chaitouch
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Hongesombut, Komsan
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Sivalertporn, Kanchana
A rapid growth in electric vehicles has led to a massive number of retired batteries in the transportation sector after 8–10 years of use. However, retired batteries retain over 60% of their original capacity and can be employed in less demanding electric vehicles or stationary energy storage systems. As a result, the management of end-of-life electric vehicles has received increased attention globally over the last decade due to their environmental and economic benefits. This work presents knowledge and technology for retired electric vehicle batteries that are applicable to the Thai context, with a particular focus on a case study of a retired lithium-ion battery from the Nissan X-Trail Hybrid car. The disassembled battery modules are designed for remanufacturing in small electric vehicles and repurposing in energy storage systems. The retired batteries were tested in a laboratory under high C-rate conditions (10C, 20C, and 30C) to examine the limitations of the batteries’ ability to deliver high current to electric vehicles during the driving operation. In addition, the electric motorcycle conversion has also been studied by converting the gasoline engine to an electric battery system. Finally, the prototypes were tested both in the laboratory and in real-world use. The findings of this study will serve as a guideline for the sorting and assessment of retired lithium-ion batteries from electric vehicles, as well as demonstrate the technical feasibility of reusing retired batteries in Thailand.
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Comparison of Extended Kalman Filter and Long Short-Term Memory Neural Network for State of Charge Estimation of Lithium-Ion Battery
(2023-01-01)
Rupanwong, Kritayod
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Kittiratsatcha, Supat
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Polmai, Sompob
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.
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Development of Converter and Control System for Variable Speed Permanent Magnet Synchronous Generator in Small Hydro Power Plant Model
(2022-01-01)
Thanajitr, Chatchaphong
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Polmai, Sompob
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Kittiratsatcha, Supat
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.
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Stabilization of power system using improved virtual inertia of virtual synchronous generator
(2022-01-01)
Poungdokmai, Aditap
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Polmai, Sompob
The renewable energy source (RES) based distributed generation (DG) has grown up rapidly that causing a reduction inertia and instability of power system. To support the increased RES, grid connected inverters require improved control schemes. Therefore, Virtual synchronous generator is getting attention. VSG is a control scheme applied for distributed generating (DG) that controls virtual rotational synchronous generator and enhances stability of power system. However, VSG has some issues when grid frequency fluctuations occur, which can cause the steady-state active power output deviation. It can be solved by analyzing closed loop transfer function of power output. The steady-state power deviation is caused by damping coefficient in the swing equation of the VSG. Thus, power deviation is decreased by set damping coefficients to zero and adding differential compensation to control dynamic characteristic instead of damping coefficients. By adding differential compensation, we can control dynamic characteristics without steady-state power output deviation in weak grid.
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Fast Approach of Open Circuit Voltage Estimation for Li-ion Battery Based on OCV Error Compensation
(2020-11-24)
Kamrueng, Chairat
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Kittiratsatcha, Supat
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Polmai, Sompob
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.

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