INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Design and Simulation of E-Vehicle Charging Substations with Load  
Flow and Performance Analysis  
1 Indrajeet Singh, 2 Vikas Sharma, 1 Sharad Kumar  
1 School of Engineering & Technology, Shri Venkateshwara University, Gajraula, U.P. India  
2 Department of Computer Applications, SRM Institute of Science and Technology, Delhi NCR Campus,  
Ghaziabad, U.P. India  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000107  
Received: 22 December 2025; Accepted: 31 December 2025; Published: 10 January 2026  
ABSTRACT  
The rapid adoption of electric vehicles (EVs) has created a pressing need for efficient, reliable, and scalable  
charging infrastructure integrated with existing power distribution networks. This paper presents the design  
and simulation of electric vehicle charging substations with a focus on load flow and performance analysis. A  
detailed substation model is developed considering typical EV charging loads, transformer ratings, feeder  
configurations, and protection constraints. Load flow analysis is carried out to evaluate key performance  
parameters such as voltage profile, power losses, loading conditions, and system stability under different  
charging scenarios. Simulation results demonstrate the impact of high EV penetration on distribution networks  
and highlight critical operational challenges, including voltage deviations and increased losses. The proposed  
design framework aids in optimizing substation capacity planning and operational efficiency, ensuring reliable  
power delivery to EV charging stations. The findings provide valuable insights for utilities, planners, and  
researchers involved in the development of sustainable EV charging infrastructure.  
Keywords—Electric Vehicles (EV), Charging Substation, Load Flow Analysis, Power Distribution System,  
Performance Analysis, Smart Grid, Power Losses.  
INTRODUCTION  
The global transition toward sustainable and low-carbon transportation has significantly accelerated the  
adoption of electric vehicles (EVs) in recent years. Governments, industries, and research communities are  
actively promoting EV deployment as an effective solution to reduce greenhouse gas emissions, dependence  
on fossil fuels, and urban air pollution. However, the large-scale integration of EVs into existing power  
systems presents new technical and operational challenges, particularly at the distribution level where EV  
charging infrastructure is directly connected. Among the critical components of this infrastructure are EV  
charging substations, which play a vital role in ensuring reliable, efficient, and safe power delivery to charging  
stations. Unlike conventional electrical loads, EV charging loads are highly dynamic, stochastic, and power-  
intensive. Fast and ultra-fast charging stations demand high power levels within short durations, leading to  
significant stress on transformers, feeders, and associated protection equipment. Uncoordinated charging can  
result in voltage fluctuations, increased system losses, transformer overloading, and degradation of overall  
power quality. These issues necessitate a systematic design and performance evaluation of EV charging  
substations to ensure compatibility with existing distribution networks while meeting the growing charging  
demand. Load flow analysis is a fundamental tool used in power system planning and operation to assess the  
steady-state performance of electrical networks. It provides critical information regarding bus voltages, power  
flows, line losses, and equipment loading under various operating conditions. In the context of EV charging  
substations, load flow studies are essential to analyze the impact of different charging scenarios, penetration  
levels, and substation configurations on the distribution system. Such analysis enables planners to identify  
potential constraints, optimize component sizing, and implement corrective measures to maintain network  
stability and reliability.  
www.ijltemas.in  
Page 1210  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
EV Charging Substation Design and Analysis Architecture  
The design of EV charging substations requires careful consideration of several technical factors, including  
transformer capacity selection, feeder arrangement, reactive power support, and protection coordination.  
Additionally, the integration of advanced power electronic interfaces used in EV chargers introduces  
harmonics and non-linear characteristics, further complicating substation performance. Simulation-based  
studies offer an effective approach to model these complexities and evaluate system behavior under realistic  
operating conditions without disturbing actual grid operations. Recent advancements in smart grid  
technologies, such as intelligent monitoring, automated control, and communication systems, have further  
enhanced the potential for efficient EV charging infrastructure. However, before implementing such advanced  
solutions, it is crucial to establish a robust baseline design supported by detailed load flow and performance  
analysis shown in Fig. 1. This helps utilities and stakeholders understand the operational limits of the system  
and develop strategies for future expansion, including the integration of renewable energy sources and energy  
storage systems. This paper focuses on the design and simulation of EV charging substations with an emphasis  
on load flow and performance analysis. A comprehensive substation model is developed to represent realistic  
EV charging loads and distribution network characteristics. Different operating scenarios are simulated to  
evaluate voltage profiles, power losses, and equipment loading. The analysis aims to identify key performance  
challenges and propose insights for optimal substation planning and operation. By providing a structured and  
analytical approach, this study contributes to the development of reliable and sustainable EV charging  
infrastructure, supporting the broader goals of smart transportation and resilient power systems.  
LITERATURE REVIEW  
Pawar et al. [1] presented the design of a wireless EV charging station integrated with an automatic billing  
system, emphasizing user convenience and contactless energy transfer. Their work highlights the growing trend  
toward wireless charging and automated payment mechanisms, but it primarily focuses on charging technology  
and billing architecture rather than the impact of charging stations on power distribution networks or substation-  
level performance. Makuwatsine and Singh [2] investigated the design and simulation of an on-grid solar-  
powered EV charging station. Their study demonstrated the feasibility of integrating photovoltaic (PV) systems  
with grid-connected charging infrastructure to reduce dependency on conventional power sources. While the  
work addresses renewable integration and energy management, detailed load flow and substation performance  
analysis under varying EV penetration levels remains limited. Nagila et al. [3] focused on ultra-fast EV battery  
charging using PV sources combined with DC–DC converters. The authors analysed converter performance and  
charging efficiency, highlighting the technical challenges of fast charging. However, the study does not evaluate  
the broader distribution system impacts such as voltage variation, feeder congestion, or transformer loading  
caused by ultra-fast chargers. K. M et al. [4] explored the design and implementation of wireless charging coils  
for EVs, concentrating on coil geometry, efficiency, and power transfer characteristics. This work contributes to  
advancements in wireless charging hardware but does not address grid integration challenges or substation-level  
load flow considerations. Saritha et al. [5] proposed a smart monitoring system for DC power in EVs along with  
regenerative charging techniques. Their work emphasizes energy recovery and monitoring at the vehicle level,  
improving overall system efficiency. However, the scope is limited to DC-side monitoring and does not extend  
to analyzing distribution network performance under aggregated EV charging loads. Kowsalya et al. [6]  
www.ijltemas.in  
Page 1211  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
conducted an investigation into batteries and supercapacitors for hybrid EV applications. The study compared  
energy storage technologies in terms of efficiency, response time, and lifecycle. Although energy storage plays  
a crucial role in EV charging infrastructure, the work does not examine its influence on charging substations or  
distribution system load flow. Kavitha et al. [7] presented a retrospective review of solar-powered charging  
stations and EV technologies. Their survey highlighted the evolution of charging infrastructure and renewable  
integration trends. While the paper provides valuable insights into technological progress, it lacks quantitative  
performance analysis of charging stations within power distribution networks. Sivasankar et al. [8] discussed  
sustainable EV charging infrastructure by integrating solar energy and IoT-based monitoring. Their study  
focused on smart charging station architecture, real-time monitoring, and energy optimization. However, the  
impact of such charging stations on voltage stability, losses, and substation loading was not extensively  
analysed. Sivakumar et al. [9] introduced a UPI-based EV charging station aimed at simplifying payment and  
improving user accessibility. The work concentrates on digital payment integration and system usability,  
offering minimal discussion on electrical design, load flow, or performance evaluation of charging substations.  
Arulmozhi et al. [10] proposed an IoT-enabled EV charging system with battery monitoring and charge  
scheduling. Their approach improves charging efficiency and battery health through intelligent scheduling.  
Nevertheless, the study does not include a comprehensive assessment of distribution network constraints under  
large-scale EV charging scenarios. Senthil et al. [11] investigated solar-based wireless charging using inductive  
resistance for EVs. The research addressed renewable-powered wireless charging feasibility, but grid  
interaction, substation design, and system-level performance metrics were not considered. K. V et al. [12]  
focused on the design and implementation of a common EV charging station suitable for public use. Their work  
discussed basic electrical design and charging station layout; however, detailed simulation-based load flow  
analysis and performance evaluation under different EV penetration levels were not included. N. K. K et al. [13]  
developed a solar-powered EV charging station integrated with IoT for monitoring and control. The study  
highlighted sustainability and remote monitoring benefits but did not analyze the effects of charging demand  
variability on distribution substations. Kavin et al. [14] examined dynamic EV charging using wireless power  
transfer, emphasizing continuous charging while vehicles are in motion. Although innovative, the work remains  
focused on wireless power transfer mechanisms rather than the supporting power infrastructure and substation  
performance. Finally, Nair and Sujith [15] presented a comparative path planning analysis for recommending  
EV charging stations, addressing optimal location and routing strategies. While useful for infrastructure  
planning from a transportation perspective, the study does not consider electrical network constraints or load  
flow impacts.  
PROPOSED METHODOLOGY  
The proposed methodology presents a structured and systematic framework for the design, simulation, and  
performance evaluation of electric vehicle (EV) charging substations integrated with power distribution  
networks. The methodology is organized into well-defined phases to ensure accurate system modeling,  
controlled simulation, comprehensive load flow analysis, and reliable performance assessment under various  
EV charging scenarios. The following steps outline the complete methodological approach adopted in this  
study.  
1. Selection and Modeling of EV Charging Substation System: The methodology begins with the selection  
of a representative distribution network integrated with an EV charging substation. A standard test distribution  
system or a practical radial/meshed distribution feeder is considered to reflect real-world operating conditions.  
The EV charging substation is modelled by incorporating key components such as distribution transformers,  
feeders, circuit breakers, protection devices, and EV charging units. Different types of EV chargers (slow, fast,  
and rapid chargers) are represented using appropriate load models based on their power ratings and charging  
characteristics. Accurate electrical parameters, including line impedance, transformer ratings, and load demand  
profiles, are defined to establish a realistic baseline system for analysis.  
2. Characterization of EV Charging Load Scenarios: In the next phase, various EV charging scenarios are  
identified to capture the dynamic nature of EV load demand. These scenarios include normal charging  
conditions, peak-hour charging, high EV penetration levels, and simultaneous fast-charging events. Time-  
dependent and aggregated load models are developed to reflect realistic charging behavior. This step enables  
www.ijltemas.in  
Page 1212  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
the assessment of the impact of different EV adoption levels on substation performance and distribution  
network operation.  
3. Load Flow Analysis Framework: Load flow analysis is formulated as the core analytical tool to evaluate  
the steady-state performance of the EV charging substation system. Appropriate load flow algorithms, such as  
Newton–Raphson or Gauss–Seidel methods, are selected based on system size and convergence requirements.  
The analysis focuses on determining bus voltage magnitudes, active and reactive power flows, line losses, and  
transformer loading under each charging scenario. This phase provides insights into voltage regulation issues,  
feeder congestion, and potential overloading conditions caused by EV charging demand.  
4. Simulation Implementation and Parameter Configuration: The complete system model is implemented  
using simulation tools such as MATLAB/Simulink, ETAP, or other power system analysis software. System  
parameters, including transformer tap settings, feeder capacities, and load distribution, are configured based on  
standard practices and utility guidelines. Simulation runs are carried out for each defined EV charging scenario  
to observe system behavior under varying operating conditions. This controlled simulation environment  
ensures repeatability and accuracy of results.  
5. Performance Evaluation Metrics: System performance is evaluated using key technical metrics such as  
voltage deviation, total real and reactive power losses, transformer utilization factor, feeder loading  
percentage, and overall system efficiency. The results obtained from load flow simulations are systematically  
recorded for each scenario. These metrics enable quantitative assessment of the impact of EV charging  
substations on distribution network performance and help identify critical operating limits.  
6. Comparative and Sensitivity Analysis: A comparative analysis is conducted by evaluating system  
performance with and without EV charging loads, as well as under different levels of EV penetration.  
Sensitivity analysis is performed by varying key parameters such as charger ratings, number of charging  
points, and transformer capacity. This step helps determine the robustness of the substation design and  
highlights parameters that significantly influence system performance.  
7. Validation and Planning Insights: Finally, the results are validated through additional simulation runs and  
consistency checks to ensure reliability of the findings. The analysed outcomes are used to derive practical  
insights for optimal EV charging substation planning, capacity sizing, and operational strategies. The proposed  
methodology provides a comprehensive framework for utilities and researchers to assess EV charging  
infrastructure impacts and supports the development of efficient, reliable, and scalable EV charging  
substations for future smart grid applications.  
RESULT & ANALYSIS  
This section presents the simulation results and detailed analysis of the designed EV charging substation  
integrated with a distribution network. Load flow studies were carried out under multiple EV charging  
scenarios to evaluate system performance in terms of voltage profile, power losses, and equipment loading.  
The simulations were performed using a standard distribution network model with an EV charging substation  
connected at a designated bus. The base system rating was considered as 11 kV/0.415 kV with a 1 MVA  
distribution transformer supplying conventional loads along with EV chargers.  
1. Description of Simulated Scenarios: To comprehensively analyze the behavior of the proposed EV  
charging substation and its impact on the distribution network, four distinct operating scenarios were  
considered in this study. The first scenario represents the base case, where no EV charging load is connected to  
the system, serving as a reference to evaluate normal network performance. The second scenario corresponds  
to normal EV charging conditions with approximately 30% EV penetration, reflecting early-stage or moderate  
adoption of electric vehicles. The third scenario models peak-hour charging conditions with 60% EV  
penetration, capturing periods of increased demand when many vehicles are charged simultaneously. The  
fourth and final scenario considers a high EV penetration level of 90% with fast charging facilities,  
representing a future-intensive charging environment with significant stress on the distribution system.  
Collectively, these scenarios reflect realistic levels of EV adoption and charging demand typically expected in  
www.ijltemas.in  
Page 1213  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
urban distribution networks and enable a thorough assessment of system performance under varying operating  
conditions.  
2. Voltage Profile Analysis: Voltage magnitude at critical buses was analyzed to assess voltage regulation  
performance. Table I summarizes the minimum and maximum bus voltages obtained from load flow  
simulations.  
Bus Voltage Profile Under Different EV Charging Scenarios  
Scenario  
Scenario 1  
Scenario 2  
Scenario 3  
Scenario 4  
Minimum Voltage (p.u.)  
0.997  
Maximum Voltage (p.u.)  
1.000  
0.998  
0.995  
0.991  
0.982  
0.964  
0.942  
In the base case, the voltage profile remains well within acceptable limits. With increasing EV penetration, a  
noticeable voltage drop is observed, particularly under peak and high fast-changing conditions. Scenario 4  
shows the minimum voltage approaching the lower permissible limit (0.95 p.u.), indicating the need for  
voltage regulation support such as reactive power compensation or on-load tap changers.  
Impact of EV Charging Scenarios on Bus Voltage Profile  
Fig. 2. showing minimum and maximum bus voltage levels under four different EV charging scenarios.  
Scenario 1 maintains voltages close to the nominal value of 1.0 p.u., while Scenarios 2, 3, and 4 show  
progressively lower minimum and maximum voltages. Scenario 4 exhibits the largest voltage drop, indicating  
increased voltage stress on the distribution network with higher EV charging impact.  
3. Power Loss Analysis: Total real and reactive power losses were calculated for each scenario to evaluate the  
efficiency of the distribution system.  
Total System Power Losses  
Scenario  
Scenario 1  
Real Power Loss (kW)  
Reactive Power Loss (kVAr)  
31.4  
44.2  
42.6  
58.9  
Scenario 2  
www.ijltemas.in  
Page 1214  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Scenario 3  
Scenario 4  
81.7  
63.8  
109.3  
87.6  
The results indicate a nonlinear increase in power losses with higher EV charging demand. Compared to the  
base case, real power losses increase by approximately 38% in Scenario 2 and over 150% in Scenario 4. This  
highlights the significant impact of fast and uncoordinated EV charging on system efficiency and emphasizes  
the importance of optimized substation and feeder design.  
Effect of EV Charging Scenarios on Real and Reactive Power Loss  
Fig. 3. showing real and reactive power losses under four different EV charging scenarios. Two bars are  
displayed for each scenario. The chart indicates that both real and reactive power losses increase significantly  
from Scenario 1 to Scenario 4, with Scenario 4 experiencing the highest losses, reflecting the increased loading  
impact of higher EV penetration on the distribution system.  
4. Transformer Loading Analysis: Transformer loading is a critical factor affecting reliability and asset  
lifespan. Table III presents the transformer loading levels under different scenarios.  
Distribution Transformer Loading  
Scenario  
Scenario 1  
Scenario 2  
Scenario 3  
Scenario 4  
Transformer Loading (%)  
46  
63  
82  
96  
Under normal EV charging, the transformer operates within safe limits. However, during peak and high EV  
penetration scenarios, loading approaches the rated capacity. Scenario 4 indicates near-overloading conditions,  
which can accelerate transformer aging and increase the risk of thermal failure if sustained over long durations.  
www.ijltemas.in  
Page 1215  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Distribution Transformer Loading under EV Charging Scenarios  
Fig. 4. illustrating distribution transformer loading percentages under four EV charging scenarios. Transformer  
loading increases steadily from Scenario 1 (46%) to Scenario 4 (96%). Scenario 4 approaches the  
transformer’s rated capacity, indicating a high risk of overloading under heavy EV charging conditions.  
5. Feeder Loading and System Performance: Feeder loading levels were also monitored to identify  
congestion points within the network.  
Maximum Feeder Loading  
Scenario  
Scenario 1  
Scenario 2  
Scenario 3  
Scenario 4  
Maximum Feeder Loading (%)  
52  
68  
85  
93  
The results reveal that feeder congestion becomes critical at high EV penetration levels. While Scenarios 1 and  
2 operate comfortably, Scenarios 3 and 4 show feeder utilization nearing thermal limits, potentially leading to  
protection tripping and reliability concerns.  
Impact of EV Charging Scenarios on Feeder Loading  
www.ijltemas.in  
Page 1216  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Fig. 5. showing maximum feeder loading percentages under four EV charging scenarios. Feeder loading  
increases progressively from Scenario 1 (52%) to Scenario 4 (93%). Scenario 3 and Scenario 4 indicate heavy  
feeder loading, with Scenario 4 approaching critical operating limits, highlighting the need for feeder capacity  
enhancement or smart EV charging strategies.  
The simulation results clearly demonstrate that EV charging substations significantly influence the operational  
performance of distribution networks. Increased EV penetration leads to voltage degradation, higher system  
losses, and increased loading on transformers and feeders. While the system remains stable under moderate EV  
adoption, high penetration with fast charging necessitates network reinforcement, advanced voltage control,  
and proper capacity planning. The results validate the effectiveness of load flow analysis as a planning tool for  
EV charging infrastructure. The datasets and tabulated results provide actionable insights for utilities to  
determine optimal transformer sizing, feeder upgrades, and the integration of voltage support mechanisms.  
Overall, the proposed design and simulation framework supports reliable and efficient deployment of EV  
charging substations in future smart distribution networks.  
CONCLUSION  
This study presents the design and simulation of an EV charging substation integrated with a distribution  
network, with emphasis on load flow and performance analysis under varying EV penetration levels. The  
results demonstrate that while the system operates satisfactorily under low to moderate EV charging demand,  
high penetration and fast-charging scenarios significantly impact voltage profiles, power losses, and  
transformer and feeder loading. These findings highlight the necessity of proper substation planning, capacity  
sizing, and voltage regulation strategies to ensure reliable and efficient operation of EV charging  
infrastructure. Load flow analysis proved to be an effective tool for identifying critical operating limits and  
potential network constraints. As a future scope, the proposed work can be extended by incorporating  
coordinated and smart charging strategies, integration of renewable energy sources and energy storage  
systems, harmonic and power quality analysis, and real-time control using smart grid technologies to further  
enhance the resilience, sustainability, and scalability of EV charging substations.  
REFERENCES  
1. S. Pawar, S. Pathan, P. Chavan and R. Diware, "Wireless E-Vehicle Charging Station with Automatic  
Billing System," 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet  
of Things (ICoICI), Coimbatore, India, 2025, pp. 908-913, doi: 10.1109/ICoICI65217.2025.11254273.  
2. T. T. Makuwatsine and M. Singh, "Design and Simulation of on Grid Solar Powered Electric Vehicles  
Charging Station," 2024 International Conference on Computer, Electronics, Electrical Engineering &  
their Applications (IC2E3), Srinagar Garhwal, Uttarakhand, India, 2024, pp. 1-6, doi:  
10.1109/IC2E362166.2024.10826613.  
3. A. Nagila et al., "Ultra-Fast Charging E-Vehicle Batteries from PV using DC-DC Converter," 2022  
International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India, 2022,  
pp. 711-716, doi: 10.1109/ICECAA55415.2022.9936098.  
4. K. M, T. B, S. Narayanan and V. P. M, "Design and Implementation of Wireless Charging Coil For E-  
Vehicle," 2025 6th International Conference for Emerging Technology (INCET), BELGAUM, India,  
2025, pp. 1-4, doi: 10.1109/INCET64471.2025.11140792.  
5. G. Saritha, S. Jayavardhini, G. Nandhini, G. V. P. Yuvanita, T. Saravanan and J. Surendiran, "Smart  
Monitoring of DC Power in E-Vehicle and Regenerative Charging Technique," 2023 International  
Conference on System, Computation, Automation and Networking (ICSCAN), PUDUCHERRY, India,  
2023, pp. 1-4, doi: 10.1109/ICSCAN58655.2023.10395388.  
6. M. Kowsalya, S. Elango, A. Elakya and R. Karthigayini, "Certain Investigation on Batteries and Super  
Capacitor for Hybrid E-Vehicle," 2023 3rd International Conference on Innovative Mechanisms for  
Industry  
Applications  
(ICIMIA),  
Bengaluru,  
India,  
2023,  
pp.  
1476-1479,  
doi:  
10.1109/ICIMIA60377.2023.10425843.  
7. D. Kavitha, B. Sharmila, M. S. Ramkumar, M. Sivaramkrishnan and M. Brindha, "A Retrospective of  
Solar-Powered Charging Stations and E-Vehicles," 2024 8th International Conference on Electronics,  
www.ijltemas.in  
Page 1217  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Communication and Aerospace Technology (ICECA), Coimbatore, India, 2024, pp. 131-135, doi:  
10.1109/ICECA63461.2024.10801145.  
8. C. Sivasankar, S. G, P. H, V. Arun and E. N. Ganesh, "Advancements in Sustainable Charging  
Infrastructure: Integrating Solar Energy and IoT for Smart E-Vehicle Charging Stations," 2023  
International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni,  
India, 2023, pp. 311-315, doi: 10.1109/ICSCNA58489.2023.10370592.  
9. V. Sivakumar, R. Pandiarajan, M. Kalyan, D. K. Reddy, P. G. Prasad and K. Pavan, "UPI-Based E-  
Vehicle Charging Station," 2025 International Conference on Advanced Computing Technologies  
(ICoACT), Sivalasi, India, 2025, pp. 1-5, doi: 10.1109/ICoACT63339.2025.11004807.  
10. S. Arulmozhi, M. Abiramavalli, M. B. D. Bhavaani and S. Loganayagi, "IoT enabled E-Vehicle  
Charging System with Battery Monitoring and Charge Scheduling," 2023 First International  
Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles (ICPEEV),  
Hyderabad, India, 2023, pp. 1-9, doi: 10.1109/ICPEEV58650.2023.10391930.  
11. S. R. Senthil S, A. M, S. M and U. A. A, "Solar based Wireless Charging using Inductive Resistance  
for E-Vehicle," 2023 Second International Conference on Electronics and Renewable Systems  
(ICEARS), Tuticorin, India, 2023, pp. 167-170, doi: 10.1109/ICEARS56392.2023.10085501  
12. K. V, M. K. R, N. V. K and H. S, "Design and Implementation of Common EV Charging Station,"  
2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore,  
India, 2023, pp. 1307-1313, doi: 10.1109/ICAIS56108.2023.10073755.  
13. N. K. K, P. Badrinath, S. Vickraman and G. Satheesan, "Charging Station for E-Vehicle using Solar  
with IoT," 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC),  
Bengaluru, India, 2022, pp. 716-721, doi: 10.1109/IIHC55949.2022.10060714.  
14. R. Kavin, D. Arvind, S. Dhanush, D. Ajay and K. Karpaganathan, "Dynamic EV Charging by Wireless  
Power Transfer," 2022 7th International Conference on Communication and Electronics Systems  
(ICCES), Coimbatore, India, 2022, pp. 100-105, doi: 10.1109/ICCES54183.2022.9835904.  
15. A. H. P. Nair and M. Sujith, "Comparative Path Planning Analysis for the Recommended E-Vehicle  
Charging Station," 2022 International Conference on Intelligent Innovations in Engineering and  
Technology (ICIIET), Coimbatore, India, 2022, pp. 238-244, doi:  
10.1109/ICIIET55458.2022.9967510.  
www.ijltemas.in  
Page 1218