Research and Application of Range Anxiety in Electric Vehicle Drivers

This paper studied the influence of various factors on range anxiety of electric vehicle drivers, and proposed a prediction model of anxiety range based on historical driving data of electric vehicle drivers. The model USES the particle swarm algorithm to optimize the BP neural network that PSO - BP neural network, BP neural network for predicting model of slow convergence speed, and easy to fall into local optimum problem, BP neural network are optimized by the particle swarm optimization (PSO) algorithm to update of all the weight and improve the connection layer in the forecast for the current range of anxiety in the electric car driver, Based on data and experience, the influencing factors that are positively correlated with driver's range anxiety are selected from real data to extract the influencing factors of driver's range anxiety. The predicted anxiety mileage can directly show the current driver's range anxiety, and can also be differentiated by a specific algorithm, which can provide theoretical and data support for future related applications.