Optimization of Charging Strategies for New Energy Vehicles Based on Reinforcement Learning Algorithms

With the popularization of new energy vehicles (NEVs) and the increasing severity of traffic congestion, charging difficulties have become a concern for people. Effective management and optimization of the NEV charging process have become increasingly important. This not only concerns the safety and stable operation of the power grid, but also directly affects the efficiency of road traffic, the utilization of renewable energy, and the charging experience and cost for users. The charging behavior of NEVs is a complex process that involves multiple considerations, such as grid load balancing, availability and efficiency of charging stations, user charging needs, and electricity prices. To address these issues, this paper proposes a NEV charging optimization strategy based on reinforcement learning (RL) algorithm, which can handle high-dimensional and complex environments and effectively deal with randomness and uncertainty factors. This strategy can not only reduce the load fluctuation of the power grid, improve the safety and stability of the power grid, but also reduce the charging time and cost of users, improve charging efficiency and user satisfaction. Meanwhile, by combining renewable energy, this strategy can also promote sustainable development, reduce reliance on traditional energy, and improve the utilization rate of renewable energy.