In order to accelerate the low-carbon development of the electric vehicle industry and adjust the energy composition of the transportation sector,research on the energy consumption of electric vehicles has become the current focus.This paper firstly analyzes the impact of weather factors,social factors,and road network line characteristics on road traffic speeds,and constructs an electric vehicle energy consumption model based on average speed prediction;secondly,a long-term and short-term memory neural network considering sample similarity is proposed in this paper.The average speed of the car is predicted,the energy consumption of the car is calculated,and the total power consumption of the electric vehicle per unit mileage is obtained by combining the energy consumption of the air-conditioning.Finally,based on the example analysis of the Hangzhou traffic road network,the results show that,compared with the traditional LSTM neural network and the BP neural network,the improved LSTM neural network has higher prediction accuracy and stronger generalization ability.
Electrical Measurement & Instrumentation
energy consumption prediction
urban road network
S-LSTM neural network