Research And Implementation Of Charging Station Recommendation System Based On Improved GS Algorithm

With the increasingly severe global energy crisis,environmental protection and energy conservation have become an important strategy for governments and enterprises.As an emerging clean energy source,electric vehicles are considered an important choice for future vehicle development.However,with the popularity of electric vehicles,the issue of "mileage anxiety" caused by electric vehicle charging has gradually emerged.Due to the current inability to compare the range of electric vehicles with traditional internal combustion engine vehicles,the charging problem of electric vehicles has become one of the bottlenecks restricting their popularization and promotion.In practical use,many electric vehicle users face problems such as inconvenient charging,long charging time,and insufficient charging stations during driving.Therefore,"mileage anxiety" has become a key issue affecting the experience and satisfaction of electric vehicle users.To address this issue,based on the Internet of Things technology,real-time collection of data information on electric vehicles and charging stations,including the location,electricity level,charging demand,as well as the location of charging stations,number of charging stations,charging speed,etc.Calculate the distance cost,charging time cost,and price cost of an electric vehicle to a charging station based on the above data.Electric vehicles with different battery levels have different charging needs,and are classified into Level 1,Level 2,and Level 3 warnings based on their remaining battery level.For electric vehicles with different warning levels,we recommend different charging schemes to meet the charging needs of users.Among them,the development of recommended solutions is based on calculations from multiple perspectives such as distance cost,charging time cost,and price cost.Through fuzzy optimization algorithm,comprehensive ratings of users for different charging stations are obtained,and the preference ranking of an electric vehicle for a charging station is obtained.For charging stations,in order to minimize the time available for idle charging stations within the station and obtain a ranking of their preference for electric vehicles.Based on the two preference rankings mentioned above,this article introduces the GS algorithm and improves the classic GS algorithm.In the recommendation system,the number of idle charging stations in the charging station should be considered.If a charging station has multiple idle charging stations,it can be recommended for multiple electric vehicles at the same time.For charging stations with high queuing pressure,electric vehicles are less recommended or not recommended to enter the station for charging,thereby improving charging efficiency and user experience.In practical applications,the recommendation system for electric vehicle charging stations based on Internet of Things technology proposed in this article has high efficiency and accuracy,and can effectively solve the problem of "mileage anxiety".In the experiment,this article compared three methods: shortest distance,shortest charging time,GS algorithm,and improved GS algorithm.It was found that the improved GS algorithm can achieve the optimal match between the vehicle and the charging station,further improving charging efficiency and user satisfaction,and ensuring the throughput of the charging station.