<![CDATA[The transition to electric vehicles (EVs) has intensified the need for efficient vehicle-to-grid (V2G) and grid-to-vehicle (G2V) systems. Bidirectional wireless power transfer (BWPT) presents a seamless and intelligent approach to energy exchange, particularly under dynamic tariff and grid demand conditions. This study aims to model and simulate a Python-based rule-driven BWPT system to evaluate energy efficiency and economic performance in V2G/G2V applications. A synthetic dataset representing grid demand and time-of-use (TOU) pricing over seven days was used to simulate real-world operating conditions. The model incorporates state-of-charge (SoC) dynamics, bidirectional power control logic, and profit calculation using a 15-minute resolution over 672 time steps. The simulation achieved a total energy exchange of 122.8 kWh and a cumulative net profit of ?536.67, with daily profits averaging ?76.6. SoC levels were effectively maintained between 20% and 90%, and power flows adapted accurately to tariff variations. The study confirms the feasibility of a lightweight, reproducible BWPT model capable of delivering optimized energy management and economic returns. The simulation approach offers strong potential for academic, research, and pre-deployment evaluation of intelligent charging systems.]]>
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