Electric vehicles (EVs) are becoming increasingly popular as an efficient transportation solution but they also present unique challenges for energy management. Bi-directional charging (BDC) is a solution that allows EVs to not only consume energy from the grid but also supply energy back to the grid. This facilitates vehicle-to-load (V2L) integration, where EVs can act as mobile power sources for homes, buildings, and the grid. V2L enables better energy management by utilizing EVs as a flexible resource to balance grid demand and supply in the proposed system. This is achieved through intelligent coordination between the EVs, charging stations, and the grid, using smart meters and communication networks. Integration of BDC and V2L also enables EVs to provide backup power during grid outages, reduce the need for costly grid infrastructure, and support renewable energy integration. BDC with V2L integration is a promising approach for optimal energy management in EVs and can play a significant role in the future of sustainable transportation and energy systems. The proposed model reached 95.13% charging efficiency, 95.03% energy management, 95.69% power rating, 96.28% voltage support and 87.99% temperature management.

Robotic grasping based on monocular vision remains challenging due to perception uncertainty, pixel-to-world mapping errors, and a tight per-frame budget. This paper presents a lightweight vision-based multi-object robotic grasping framework that integrates object detection, polynomial pixel-to-world mapping, and rule-based grasp execution into a unified pipeline. Object detection is carried out with a You Only Look Once version 13 (YOLOv13) detector, into which lightweight structural modules are inserted for the tabletop grasping task. To enable accurate perception-to-action mapping, a polynomial calibration model is introduced to transform image-space detections into planar world-space coordinates, effectively correcting geometric distortions caused by preprocessing. Based on the mapped targets, a rule-based grasp planning strategy is developed to generate physically feasible grasp poses for a Universal Robots 5 (UR5) robotic arm without relying on depth sensors or learning-based grasp prediction. The proposed framework is implemented and validated in CoppeliaSim. Quantitative results show end-to-end grasp success rising from 86% with the center-point baseline to 94% with the proposed strategy on the same 50 paired placements, while mapping and planning together add about 1 ms per frame. The gain comes from enforcing execution feasibility on an already sufficient map, not from a more accurate mapping model.