KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 10 of 27

Some of the metrics are blocked by your
consent settings
SOC-Dependent Soft Current Limiting for Second-Life Lithium-Ion Batteries in Off-Grid Photovoltaic Battery Energy Storage Systems
(2026-04-01)
Wang, Hongyan
;
Chiradeja, Pathomthat
;
Ngaopitakkul, Atthapol
;
Yoomak, Suntiti
The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework.
Some of the metrics are blocked by your
consent settings
An approach to energy conservation in lighting systems using luminaire-based sensor for automatic dimming
(2025-12-01)
Jettanasen, Chaiyan
;
Thongsuk, Surakit
;
Sottiyaphai, Chayanut
;
Songsukthawan, Panapong
;
Chiradeja, Pathomthat
Lighting systems account for a significant proportion of energy consumption in buildings. Therefore, energy conservation within these systems can greatly enhance overall building energy efficiency. This study proposes a control strategy for LED lamps by adjusting lighting intensity and improving the performance of electric luminaires. The approach involves implementing an automated dimming system that adapts lighting intensity based on surrounding light levels. A system comprising an ambient light sensor, microcontroller, power supply module, dimming controller, and lamp was developed. The sensors measure brightness within a specified range in real time, and the microcontroller analyzes and compares this data against the brightness settings for specific areas. The designed control system was tested in a laboratory setup to demonstrate its effectiveness in a controlled environment. Results showed a 75.65% reduction in power consumption compared to standard lamps in a simulated environment, highlighting its potential for significant energy savings in buildings and its contribution to environmental sustainability.
Some of the metrics are blocked by your
consent settings
Development and Analysis of a Fast-Charge EV-Charging Station Model for Power Quality Assessment in Distribution Systems
(2025-09-01)
Chiradeja, Pathomthat
;
Yoomak, Suntiti
;
Srisuksai, Panu
;
Klomjit, Jittiphong
;
Ngaopitakkul, Atthapol
With the rapid rise in electric vehicle (EV) adoption, the deployment of EV charging infrastructure—particularly fast-charging stations—has expanded significantly to meet growing energy demands. While fast charging offers the advantage of reduced charging time and improved user convenience, it imposes considerable stress on existing power distribution systems due to its high power and current requirements. This study investigated the impact of EV fast charging on power quality within Thailand’s distribution network, emphasizing compliance with accepted standards such as IEEE Std 519-2014. We developed a control-oriented EV-charging station model in power systems computer-aided design and electromagnetic transients, including DC (PSCAD/EMTDC), which integrates grid-side vector control with DC fast-charging (CC/CV) behavior. Active/reactive power setpoints were mapped onto (Formula presented.) current references via Park’s transformation and regulated by proportional integral (PI) controllers with sinusoidal pulse-width modulation (SPWM) to command the voltage source converter (VSC) switches. The model enabled dynamic studies across battery state-of-charge and staggered charging schedules while monitoring voltage, current, and total harmonic distortion (THD) at both transformer sides, charger AC terminals, and DC adapters. Across all scenarios, the developed control achieved grid-current THDi of <5% and voltage THD of <1.5%, thereby meeting IEEE 519-2014 limits. These quantitative results show that the proposed, implementation-ready approach maintains acceptable power quality under diverse fast-charging patterns and provides actionable guidance for planning and scaling EV fast-charging infrastructure in Thailand’s urban networks.
Some of the metrics are blocked by your
consent settings
Analysis of Investment Feasibility for EV Charging Stations in Residential Buildings
(2025-09-01)
Chiradeja, Pathomthat
;
Yoomak, Suntiti
;
Sottiyaphai, Chayanut
;
Ngaopitakkul, Atthapol
;
Klomjit, Jittiphong
This study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors.
Some of the metrics are blocked by your
consent settings
Financial feasibility of electric vehicle charging stations in Thailand: An analysis of operational models and energy costs
(2025-07-01)
Chiradeja, Pathomthat
;
Sottiyaphai, Chayanut
;
Ngaopitakkul, Atthapol
;
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.
Some of the metrics are blocked by your
consent settings
Evaluating Effects of Electric Vehicle Chargers on Residential Power Infrastructure
(2025-06-01)
Chiradeja, Pathomthat
;
Chuadmee, Orawan
;
Ananwattanaporn, Santipont
;
Sottiyaphai, Chayanut
;
Ngaopitakkul, Atthapol
This study investigated the impact of electric vehicle (EV) chargers on residential electrical systems through a real-world case study in a condominium located in Bangkok, Thailand. A two-week field measurement was conducted to analyze load profiles, current and voltage behavior, phase symmetry, and harmonic distortion during EV charger operation. The results show that single-phase charging dominated usage patterns, leading to phase imbalance and significant neutral current flow. Voltage unbalance was quantified using the maximum deviation method, with an average value of 0.535 percent and a peak of 2.18 percent observed during charging activity. A harmonic distortion analysis revealed a substantial increase in current total harmonic distortion (THD) during active charging, with values rising to between 15 and 20 percent. These findings highlight nonlinear loading effects that may reduce power quality and pose risks to electrical equipment and system stability. In retrofitted electrical infrastructures, these effects are often exacerbated by design limitations and the absence of coordinated load management. This study’s findings offer practical insights for engineers, facility managers, and policymakers in designing EV-ready residential systems that are both efficient and resilient.
Some of the metrics are blocked by your
consent settings
Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
(2025-03-01)
Chiradeja, Pathomthat
;
Liang, Yijuan
;
Jettanasen, Chaiyan
Sign language plays a crucial role in bridging communication barriers within the Deaf community. Recognizing sign language sentences remains a significant challenge due to their complex structure, variations in signing styles, and temporal dynamics. This study introduces an innovative sign language sentence recognition (SLSR) approach using Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) specifically developed for single-handed wearable glove devices. The system relies on sensor data from a glove with six-axis inertial sensors and five-finger curvature sensors. The proposed HGE-ACN framework integrates graph-based embeddings to capture dynamic spatial–temporal relationships in motion and curvature data. At the same time, the Adaptive Convolutional Networks extract robust glove-based features to handle variations in signing speed, transitions between gestures, and individual signer styles. The lightweight design enables real-time processing and enhances recognition accuracy, making it suitable for practical use. Extensive experiments demonstrate that HGE-ACN achieves superior accuracy and computational efficiency compared to existing glove-based recognition methods. The system maintains robustness under various conditions, including inconsistent signing speeds and environmental noise. This work has promising applications in real-time assistive tools, educational technologies, and human–computer interaction systems, facilitating more inclusive and accessible communication platforms for the deaf and hard-of-hearing communities. Future work will explore multi-lingual sign language recognition and real-world deployment across diverse environments.
Some of the metrics are blocked by your
consent settings
Exploration of Sign Language Recognition Methods Based on Improved YOLOv5s
(2025-03-01)
Li, Xiaohua
;
Jettanasen, Chaiyan
;
Chiradeja, Pathomthat
Gesture is a natural and intuitive means of interpersonal communication. Sign language recognition has become a hot topic in scientific research, holding significant importance and research value in fields such as deep learning, human–computer interaction, and pattern recognition. The sign language recognition process needs to ensure real-time performance and ease of deployment. Based on these two requirements, this paper proposes an improved YOLOv5s-based sign language recognition algorithm. Firstly, the lightweight concept from ShuffleNetV2 was applied to achieve lightweight characteristics and improve the model’s deployability. The specific improvements are as follows: The algorithm achieved model size reduction by removing the Focus layer, using the ShuffleNetv2 algorithm, and then channel pruning YOLOv5 at the head of the neck layer. All the convolutional layers and the cross-stage partial bottleneck layer with three convolutional layers in the backbone network were replaced with ShuffleBlock, the spatial pyramid pooling layer and a subsequent cross-stage partial bottleneck layer structure with three convolutional layers were removed, and the cross-stage partial bottleneck layer module with three convolutional layers in the detection header section was replaced with a depth-separable convolutional module. Experimental results show that the parameters of the improved YOLOv5 algorithm decreased from 7.2 M to 0.72 M, and the inference speed decreased from 3.3 ms to 1.1 ms.
Some of the metrics are blocked by your
consent settings
Impact of Rising Number of Electric Vehicle Charging Points on Electrical Utility
(2025-01-01)
Chiradeja, Pathomthat
;
Lertwanitrot, Praikanok
;
Songsukthawan, Panapong
;
Yoomak, Suntiti
;
Ngaopitakkul, Atthapol
This paper presents an investigation of the impact of a rising number of electric-vehicle charging points on an interconnected distribution system. Using the PSCAD 4.6 software, this study simulates an electric-vehicle charging station consisting of a charging unit with 2 DC charging and a maximum power of 100 kW. The station receives power from a 23 kV substation through a step-down transformer at 400 V and provides power through the charging unit. The unit regulates the voltage level at 500 V and has a maximum charging capacity of 50 kW each. In addition, the study replicates an actual distribution system of the Provincial Electricity Authority (PEA) in the section spanning the Si Samrong to Sawankhalok substations in Sukhothai Province, Thailand. The effects of the following study conditions on the power quality are observed in the simulation: 1) variable location of charging panels, 2) variable number of charging panels, and 3) additional sets of charging panels in the distribution system. The results show that the charging-panel installation location and number of charging panels are significant factors that affect the power quality, including the voltage level. Therefore, optimizing the design of EV charging stations should consider the location and number of charging panels as important factors.
Some of the metrics are blocked by your
consent settings
Evaluating the Financial Dynamics of Electric Vehicle Charging Stations in Thailand: Implications of Energy Cost Variability
(2025-01-01)
Chiradeja, Pathomthat
;
Sottiyaphai, Chayanut
;
Ananwattanaporn, Santipont
;
Ngaopitakkul, Atthapol
This study examines the financial impact of energy costs on electric vehicle charging stations by comparing financial models with fixed and variable energy pricing structures. Through an in-depth financial simulation, key metrics such as Internal Rate of Return, Net Present Value, and payback period were calculated to evaluate the profitability and risk associated with each model. Findings reveal that charging stations with fixed energy costs generally experience a higher Internal Rate of Return at 14.55% and a Net Present Value of 1,711,781, with a payback period of four years. In comparison, stations with variable energy costs exhibit a lower Internal Rate of Return at 7.28% and a Net Present Value of 1,347,781, although the payback period remains consistent at four years. These results demonstrate that fixed energy costs enhance investment predictability and profitability, while variable costs increase exposure to energy price fluctuations, which raises financial risk but may capture greater returns under favorable market conditions. The analysis highlights the critical importance of accounting for energy cost variability in financial planning to maintain sustainable, profitable operations. This research provides essential insights for investors and policymakers to optimize electric vehicle charging infrastructure investments, supporting effective and profitable operations.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results