Routing optimization of electric vehicles considering real-time ambient temperature

Due to the characteristics that the energy consumption and battery capacity of electric vehicle batteries change with the change of ambient temperature, the problem of electric vehicle path under real-time ambient temperature is discussed, and a vehicle path optimization model with soft time window is constructed with the goal of optimizing comprehensive costs such as electric vehicle fixed cost, driving cost, power replenishment fee and time window penalty fee. Combined with relevant literature, the hybrid genetic annealing algorithm was used to solve the example. Finally, taking the temperature change in Beijing in May as an example, the cost analysis of cities with environmental temperature range is carried out. The results show that after considering the change of battery capacity with ambient temperature, the charging time and charging cost are better than when the battery capacity is not considered.