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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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Accurate Assessment of Moisture Content and Degree of Polymerization in Power Transformers via Dielectric Response Sensing
(2023-10-01)
Kunakorn, Anantawat
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Pramualsingha, Sarawuth
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Yutthagowith, Peerawut
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Nimsanong, Phethai
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Kittiratsatcha, Supat
Power transformers are essential apparatuses used to transfer electrical energy from one voltage-level circuit to another. For reliable systems, preventive maintenance of the transformers is required to ensure good services of all mechanical, electrical, and insulation parts. Oil-immersed paper is most often used for transformer insulation. To ensure such good insulation performance and for assessing insulation conditions, advanced transformer sensing, monitoring, and effective assessment techniques are required. This paper introduces an effective technique for assessing the insulation conditions in power transformers, which are crucial for ensuring reliable energy transfer. The method utilizes advanced transformer sensing and monitoring, focusing on oil-immersed paper insulation commonly used in transformers. The technique employs dielectric response sensing, obtained from frequency-domain spectroscopy tests, to estimate degrees of polymerization (DP) and percentages of moisture content (PMCs) in the oil-immersed paper insulation. These parameters are well-known indicators of insulation performance. The approach is based on the weighted k-nearest neighbor regression, using a database of dielectric loss factors at low frequency and oil conductivities. To overcome limited data availability, linear interpolation and extrapolation techniques are applied to enlarge the database. Experimental verification and comparison with a previously developed method demonstrate the proposed technique’s superiority in accuracy and complexity. The maximum deviations of DP and PMC in the validation cases are 6.2% and 18.7%, respectively. In addition, to evaluate the validity of our proposed method in the case of a real power transformer, a comparative analysis of the DP and PMC values determined by the proposed method with those obtained through a previously developed and complicated approach was performed. The predicted results indicate that the DP and PMC values of the oil-immersed insulation fall within the ranges of 800 to 1000 and 1.5 to 2.0, respectively, which agree with the results determined by the complicated approach and closely align with real conditions. By offering a reliable and advanced means of assessing insulation conditions, this technique contributes to the preventive maintenance and overall efficiency of power transformers.
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Auxiliary Power Supply System with Parallel-Connected DC–AC Inverters for Low-Floor Light Rail Vehicle
(2023-07-01)
Kerdtuad, Paiwan
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Chaiamarit, Kunjana
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Kittiratsatcha, Supat
This research proposes a roof-mounted auxiliary power supply (APS) system for 600 VDC low-floor light rail vehicles (LRVs). The proposed APS system consists of five parallel-connected dc–ac inverter modules (modules 1–5). Inverter modules 1 and 2 are three-phase dc–ac inverters for the compressor motors of the air conditioning system, and inverter modules 3 and 4 are three-phase dc–ac inverters for the air pump motors of the air supply system. Inverter module 5 is a single-phase dc–ac inverter for the 220 VAC power supply of onboard electric loads. Simulations and experiments were carried out under variable load torques and output frequencies for modules 1–4 and under full and no resistive loads for module 5. The measured total input current and total input power of the proposed APS system under the full-load condition are 114.36 A and 68.84 kW. The total efficiency of the proposed APS system (modules 1–5) is 97.05%. The proposed APS system is suitable for 600 VDC low-floor LRVs. The novelty of this research lies in the use of five parallel-connected inverter modules, as opposed to the three-phase output transformer or isolated dc–dc converter in the early and conventional APS systems. Specifically, the proposed APS system requires neither a three-phase output transformer nor an isolated dc–dc converter.
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A New Tapped Inductor Buck Converter with Large Step-Down Voltage Conversion
(2023-01-01)
Trakuldit, Siripan
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Bunlaksananusorn, Chanin
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Kittiratsatcha, Supat
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.
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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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Output power control using artificial neural network for switched reluctance generator
(2021-01-01)
Kittiratsatcha, Supat
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Kerdtuad, Paiwan
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Bunlaksananusorn, Chanin
We propose an output power control of a variable-speed switched reluctance generator (SRG) by implementing an artificial neural network (ANN) in the control loop. In the high-speed operation with single pulse mode, the phase current waveform, and subsequently, the output power, depend on the conduction angles. The conduction angles, i.e., the turn-on and turn-off angles, can be determined by the proposed method using an ANN. A dynamic model of an SRG with eight stator poles and six rotor poles is used for simulation to obtain the output power profiles, which subsequently become the ANN training data. The inputs of the ANN are the reference value of the output power and the rotor speeds, while the outputs of the ANN are the turn-off and turn-on angles. The control algorithm is implemented by integrating the trained data into the dynamic model using MATLAB. The experimental setup of the SRG is implemented using a digital signal processor (DSP) to control the two-switches-per-phase drive system, which includes highly accurate phase current and dc-link voltage sensor circuits. The trained biases and weights of the ANN are also coded in the DSP. To validate the proposed method, comparisons are made between simulation and experimental results.
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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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