Intelligent Multi‐Mode <scp>DC</scp> Fast Charging System for Urban <scp>EV</scp> Infrastructure With Adaptive Control and Dynamic Load Management

ABSTRACT The rapid growth in the electric vehicle (EV) population necessitates the widespread deployment of charging infrastructure. However, establishing fully functional EV chargers at every required location is impractical due to resource and planning constraints. To address this, adaptive charging facilities offer a flexible alternative, particularly suited for space and power constrained environments such as urban roadsides, hotels, and parking lots. This study proposes the development of a DC Adaptive Charging Facility (DCACF), designed to meet three critical objectives: cost‐effectiveness, energy efficiency, and user accessibility. The system operates in three intelligent charging modes: fixed price mode (FPM), fixed SOC mode (FSM), and advanced distance mode (ADM). In FPM, the charger delivers energy based on a prepaid monetary value; in FSM and ADM, it supplies the amount of energy required to achieve a target state of charge (SOC) or driving range, respectively. Smart charging strategies are implemented for each mode, and an intelligent controller manages system dynamics to ensure safe and reliable operation. A comprehensive SIL‐based validation using the OPAL‐RT simulator demonstrates that the proposed adaptive charging system achieves 98% accuracy in cut‐off control. Mode‐wise analysis highlights the cost‐saving potential of partial, need‐based charging under dynamic tariff conditions, thereby demonstrating the system's suitability for real‐world urban deployment.

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Publication Details

Journal
Engineering Reports
Published
2026-02-01
DOI
https://doi.org/10.1002/eng2.70631
Primary Topic
Electric Vehicles and Infrastructure
Type
article
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article

Intelligent Multi‐Mode DC Fast Charging System for Urban EV Infrastructure With Adaptive Control and Dynamic Load Management

M. Monica Subashini, V. Sumathi
Engineering Reports
Electric Vehicles and Infrastructure
article

Intelligent Multi‐Mode DC Fast Charging System for Urban EV Infrastructure With Adaptive Control and Dynamic Load Management

M. Monica Subashini, V. Sumathi
article en

Abstract

ABSTRACT The rapid growth in the electric vehicle (EV) population necessitates the widespread deployment of charging infrastructure. However, establishing fully functional EV chargers at every required location is impractical due to resource and planning constraints. To address this, adaptive charging facilities offer a flexible alternative, particularly suited for space and power constrained environments such as urban roadsides, hotels, and parking lots. This study proposes the development of a DC Adaptive Charging Facility (DCACF), designed to meet three critical objectives: cost‐effectiveness, energy efficiency, and user accessibility. The system operates in three intelligent charging modes: fixed price mode (FPM), fixed SOC mode (FSM), and advanced distance mode (ADM). In FPM, the charger delivers energy based on a prepaid monetary value; in FSM and ADM, it supplies the amount of energy required to achieve a target state of charge (SOC) or driving range, respectively. Smart charging strategies are implemented for each mode, and an intelligent controller manages system dynamics to ensure safe and reliable operation. A comprehensive SIL‐based validation using the OPAL‐RT simulator demonstrates that the proposed adaptive charging system achieves 98% accuracy in cut‐off control. Mode‐wise analysis highlights the cost‐saving potential of partial, need‐based charging under dynamic tariff conditions, thereby demonstrating the system's suitability for real‐world urban deployment.

Engineering ReportsVol. 8(2)
Vellore Institute of Technology University (IN)
Openalex Percentile: Top 61%
Electric Vehicles and Infrastructure
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Intelligent Multi‐Mode <scp>DC</scp> Fast Charging System for Urban <scp>EV</scp> Infrastructure With Adaptive Control and Dynamic Load Management — M. Monica Subashini, V. Sumathi · Engineering Reports (2026) | TGRS Research Map | TGRS