Probability Modeling and Control Strategy Research for Electric Vehicle Charging and Discharging

In modern urban transportation, the use of electric vehicles has gained widespread attention. Time-of-use electricity pricing, as a common power pricing mechanism, plays a significant role in promoting the adoption and application of electric vehicles. This paper focuses on probability modeling and control strategies for electric vehicle charging and discharging under time-of-use pricing schemes to enhance usage efficiency and economic benefits. Our research involves analyzing different power pricing structures within time-of-use frameworks and developing corresponding probability models that incorporate the unique charging and discharging characteristics of electric vehicles. The implementation utilizes statistical modeling techniques with probability density functions to capture charging behavior patterns. Additionally, we design an effective control strategy featuring optimization algorithms that dynamically adjust charging schedules based on real-time electricity prices and vehicle state-of-charge levels. Key functions include load forecasting modules and cost-minimization algorithms that optimize charging cycles through linear programming approaches. Through analysis of real-world data and simulation experiments conducted using MATLAB/Simulink platforms, we demonstrate the practical value and effectiveness of the proposed control strategy, showing significant improvements in grid load balancing and user cost reduction.