This paper presents the design and simulation-based evaluation of a 225 kW grid-interactive bidirectional fast DC charger for electric vehicles operating in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The proposed charger is developed in MATLAB/Simscape and integrates a two-level PWM AC/DC converter, a bidirectional DC/DC stage, an LCL-filtered grid interface, a regulated 1000 V DC-link, and a lithium-ion battery pack operating under CC and CC–CV control. The battery model and supervisory state-of-charge signal are reused from a previously validated electro-thermal and EKF-based battery framework and are embedded here within a charger-level architecture to study the coupled behavior of the battery, converter, and grid interface. Under nominal operation, the charger achieves near-unity power factor and low grid-current total harmonic distortion of 0.55% in G2V mode and 0.26% in V2G mode. The study also examines charger behavior under bidirectional mode transitions and disturbed-grid conditions including source-harmonic injection, voltage sag, frequency deviation, and reactive-power command tests. The results show stable bidirectional operation, preserved DC-link regulation, and acceptable current quality under both nominal and non-ideal conditions, providing a charger-level simulation reference for future real-time and experimental validation.

The paper proposes a detailed analysis of electric vehicle (EV) battery modelling, state estimation, and charging infrastructure in an integrated MATLAB Simscape framework. As EV penetration is increasing at a rapid rate, sound battery management and coordination of charging are the primary challenges. The analysis combines sophisticated battery modelling, Extended Kalman Filter (EKF)-based estimation, and high-speed DC charging in a single framework. At the pack level, a precise 96s46p pack arrangement with Molicel INR-21700 P45B cells was simulated to embody electro-thermal behavior, resistance characteristics, and degradation patterns. EKF algorithms were implemented for State of Charge (SOC) and State of Health (SOH) estimation with an average error margin less than 1.8% that exhibited high accuracy, nonlinear stability, and steady thermal profiles at 45 °C. At the charging system level, an ultra-fast DC charging/discharging mode both grid-to-vehicle (G2V) and V2G modes. Simulation results confirmed stable performance for different conditions, proving the proposed control strategy. Unlike isolated analyses of existing work, this study integrates modelling, estimation, and charging control. The work aligns with the United Nations Sustainable Development Goals on affordable clean energy, sustainable cities, and climate action. The results encourage both industrial practice and research, and this study is a major contribution towards sustainable and intelligent EV systems.

BOOLE is a four-face educational platform integrating analog, combinational-logic, and sequential-logic activities with optional AI-assisted component identification and datasheet support. The system was developed through requirements translation, circuit simulation, two-layer PCB design, mechanical review, fabrication, assembly, functional verification, and iterative refinement. A Raspberry Pi 5, Camera Module 3 NoIR, and touchscreen support image capture and local interaction, while an Arduino Mega provides deterministic control of the physical learning faces. Segmented power energizes only the selected face and activity, and removable boards improve maintenance and fault isolation. Hardware demonstrations reproduced the intended voltage-divider, diode threshold/polarity, counter, and sequential-logic states. Ten one-versus-rest classifiers were fine-tuned from a pretrained ViT-Base model using 2000 original photographs, with 200 images for each of ten categories. The dataset was partitioned class-wise into mutually exclusive 80/10/10 training, validation, and final-test sets before augmentation, which was applied only to training data. Final-test accuracy ranged from 91.0% to 99.5%, with precision, recall, F1-score, specificity, balanced accuracy, and confusion matrices also evaluated. A 73-student pilot produced 89–96% positive (Yes) responses across six binary survey items, providing preliminary evidence of learner-perceived effectiveness, engagement, usability, and theory-to-practice support. Overall, BOOLE demonstrates a feasible, serviceable architecture for progressive electronics education.