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Financial feasibility of electric vehicle charging stations in Thailand: An analysis of operational models and energy costs
(2025-07-01)
Chiradeja, Pathomthat
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Sottiyaphai, Chayanut
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Ngaopitakkul, Atthapol
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Ananwattanaporn, Santipont
Despite the increasing demand for electric vehicles (EVs) and the associated charging stations, the variances in EV charging station models and diversity of energy costs have not been effectively studied to date. Therefore, this study examines the financial viability of EV charging stations in Thailand by analysing the effects of operational models, charger counts, and fluctuating energy costs. The profitability, payback periods, and investment returns of various types of EV charging stations are evaluated by combining financial analysis tools and historical energy cost data. The results indicate that small-scale charging stations with 1–3 chargers demonstrate superior financial viability, achieving internal rates of return (IRR) of 24.18–39.86 % and payback periods ranging from 3 to 4 years, depending on the tariff model. By contrast, stations with more than three chargers experience extended payback periods, with some configurations failing to recover investment within the project duration owing to increased capital expenditures and operational costs. This study also emphasises the critical role of dynamic energy pricing in the financial modelling of charging stations. Electricity costs vary significantly between conventional and low-priority stations, with energy costs for conventional stations being up to 23 % higher. The financial feasibility of EV charging stations in Thailand presents competitive advantages in terms of investment attractiveness and return on capital compared with higher energy cost regions with fewer financial incentives. The findings have significant implications for station operators, investors, and policymakers, highlighting the need for strategic planning and adaptive pricing strategies in EV charging infrastructure development.
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Impact of charging infrastructure on willingness to pay for EV: Mediating role of driving experience and behavior intentions
(2025-04-01)
Ahmad, Sheraz
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Chaveesuk, Singha
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Chaiyasoonthorn, Wornchanok
Global primary transportation networks are affected by challenges associated with carbon emissions and rising oil prices. A growing population is opting for electric vehicles (EVs) because of their independent operation without reliance on gasoline and their lower pollution emissions. Establishing a charging infrastructure is necessary to stimulate consumer willingness to pay for EVs. Therefore, this study explored the impact of charging infrastructure on the willingness to pay, as well as the mediating role of driving experience and behavior intentions, using the theory of planned behavior. A quantitative methodology was employed, whereby data were gathered via a survey questionnaire distributed among Thai users residing in Bangkok, Thailand. Statistical analysis was conducted on data collected from 231 potential consumers using structural equation modeling and confirmatory factor analysis to validate the variables. This study revealed that charging infrastructure positively and significantly enhances the driving experience. The driving experience of an EV significantly affects behavior intentions, and positive behavior intentions significantly affect willingness to pay for an EV. Additionally, this study tested the sequential mediation of driving experience and behavior intentions, and the results revealed partial mediation. Furthermore, this study's findings indicate that the government and EV manufacturers should invest in developing charging infrastructure to enhance consumers' willingness to pay.
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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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Synthetic Inertia-Power Sharing in High Renewable Power Grids Through Vehicle-to-Grid Topology
(2024-01-01)
Kerdphol, Thongchart
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Surinkaew, Tossaporn
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Ngamroo, Issarachai
With the increasing integration of renewable energy sources (RESs), the overall inertia of the power system is expected to decline. The remaining inertia is crucial for regulating system frequency and mitigating excessive rates of change. The deployment of dispatchable loads, such as electric vehicles (EVs), offers a promising solution. This paper presents a synchronized inertia support framework utilizing a vehicle-to-grid (V2G) system through its bidirectional chargers. This concept is realized by integrating a large-scale energy storage system (ESS) composed of controllable EVs into an enhanced inertia emulation structure. The synthetic inertia control strategy has been refined to account for EV user convenience and synchronized state of charge (SOC) management, facilitating synchronized inertia power sharing. This approach enhances the grid's dynamic performance and resilience. Simulation results demonstrate that the proposed method effectively delivers rapid inertia support from the onboard ESS of EVs, improving frequency stability.
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Analysis Of Mutual Inductance Between Transmitter And Receiver Coils In Wireless Power Transfer System Of Electric Vehicle
(2023-01-01)
Zheng, Junlong
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Jettanasen, Chaiyan
In the electric vehicle wireless power transfer (WPT) system, the mutual inductance (M) between the transmitting and receiving coils is an important factor influencing overall system efficiency. The M is affected by various factors such as the physical structure of the coils diagram, the distance and relative position between the transmitting and receiving coils, and so on. Our work here has two outstanding contributions. First, the detailed mathematical model of the M was developed. Second, the three-dimensional spatial distribution diagram of the M was drawn using Python software, the maximum value of the M and its corresponding position coordinates were calculated. Then, the theoretical analysis of the M distribution was proven correct through experiments. The theoretical analysis and experimental verification of the M distribution provided a theoretical reference for the positioning requirements between the transmitting and receiving coils.in the electric vehicle WPT system.
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A Comparative Study on the Parameter Identification of an Equivalent Circuit Model for an Li-ion Battery Based on Different Discharge Tests
(2022-03-01)
Poopanya, Piyawong
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Sivalertporn, Kanchana
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Phophongviwat, Teeraphon
An effective model of battery performance is important for battery management systems to control the state of battery and cell balancing. The second-order equivalent circuit model of a lithium-ion battery is studied in the present paper. The identification methods that include the multiple linear regression (MLR), exponential curve fitting (ECF) and Simulink design optimization tool (SDOT), were used to determine the model parameters. The aim of this paper is to compare the validity of the three proposed algorithms, which vary in complexity. The open circuit voltage was measured based on the pulse discharge test. The voltage response was collected for every 10% SOC in the interval between 0–100% SOC. The battery voltages calculated from the estimated parameters under the constant current discharge test and dynamic discharge tests for electric vehicles (ISO and WLTP) were compared to the experimental data. The mean absolute error and root mean square error were calculated to analyze the accuracy of the three proposed estimators. Overall, SDOT provides the best fit with high accuracy, but requires a heavy computation burden. The accuracy of the three methods under the constant current discharge test is high compared to other experiments, due to the nonlinear behavior at a low SOC. For the ISO and WLTP dynamic tests, the errors of MLR are close to that of SDOT, but have less computing time. Therefore, MLR is probably more suitable for EV use than SDOT.
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Barriers to electric vehicle adoption in Thailand
(2021-11-01)
Kongklaew, Chanwit
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Phoungthong, Khamphe
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Prabpayak, Chanwit
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Chowdhury, Md Shahariar
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Khan, Imran
Electric vehicles (EVs) are considered to be a solution for sustainable transportation. EVs can reduce fossil fuel consumption, greenhouse gas emissions, and the negative impacts of climate change and global warming, as well as help improve air quality. However, EV adoption in Thailand is quite low. Against this backdrop, this study investigates barriers and motivators for EV adoption and their public perception in Thailand. A total of 454 responses were collected through an online questionnaire. The results indicate that the top three concerns of respondents about EVs are public infrastructure and vehicle performance in terms of charge range and battery life. Respondents with more than five years of driving experience in the age range of 26–35 years old could be key targets for early EV adoption.
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The implementation of fundamental harmonic approximation technique on electric vehicle wireless charger
(2021-05-19)
Pairindra, Worapong
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Khemmook, Panya
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Khomfoi, Surin
This paper presents the electric vehicle (EV) wireless charger by using the practical design frequency converter. A 500 W LLC series resonant full-bridge converter for demonstrating prototype with the standard charging frequency range 85 kHz to 100 kHz is connected to the transmission coils with 10 cm air gap and performed a power transfer from the source to the EV batteries. The resonant converter with the Fundamental Harmonic Approximation (FHA) technique is chosen in this proposed paper for transferring the highly magnetic coupling between the transmitter side to the pickup side. The overall system is designed by MATLAB/Simulink program, and the experimental result efficiency can be measured out by approximately 80%.
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Battery sizing for electric vehicles based on real driving patterns in Thailand
(2019-06-01)
Duangsrikaew, Bongkotchaporn
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Mongkoltanatas, Jiravan
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Benyajati, Chi Na
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Karin, Preecha
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Hanamura, Katsunori
The rising population in suburban areas have led to an increasing demand for commuter buses. Coupled with a desire to reduce pollution from the daily routine of traveling and transportation, electric vehicles have become more interesting as an alternative placement for internal combustion engine vehicles. However, in comparison to those conventional vehicles, electric vehicles have an issue of limited driving range. One of the main challenges in designing electric vehicles (EVs) is to estimate the size and power of energy storage system, i.e., battery pack, for any specific application. Reliable information on energy consumption of vehicle of interest is therefore necessary for a successful EV implementation in terms of both performance and cost. However, energy consumption usually depends on several factors such as traffic conditions, driving cycle, velocities, road topology, etc. This paper presents an energy consumption analysis of electric vehicle in three different route types i.e., closed-area, inter-city, and local feeder operated by campus tram and shuttle bus. The driving data of NGV campus trams operating in a university located in suburban Bangkok and that of shuttle buses operating between local areas and en route to the city were collected and the corresponding representative driving cycles for each route were generated. The purpose of this study was to carry out a battery sizing based on the fulfilment of power requirements from the representative real driving pattern in Thailand. The real driving cycle data i.e., velocity and vehicle global position were collected through a GPS-based piece of equipment, VBOX. Three campus driving data types were gathered to achieve a suitable dimensioning of battery systems for electrified university public buses.
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Simultaneous control of frequency fluctuation and battery SOC in a smart grid using LFC and EV controllers based on optimal MIMO-MPC
(2017-03-01)
Pahasa, Jonglak
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Ngamroo, Issarachai
This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model- based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.

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