Research On The Accessibility Of Electric Vehicles Based On Driving Styles And Future Driving Conditions

With the improvement of people’s awareness of environmental protection,electric vehicles become more and more people’s choice because of their advantages of less pollution.Although the sales volume of electric vehicles in China has been increasing in recent years,"range anxiety" is an important factor affecting consumers’ purchase desire.Therefore,it’s very important for the promotion of electric vehicles and the improvement of user experience to solve this travel problem.Starting from the energy consumption of electric vehicles,this paper studies the influence of driving style and working conditions on energy consumption,judgment of electric vehicle accessibility and Urban Accessibility map.Firstly,the original data is processed to filter and construct the required feature data.In order to facilitate the analysis,the driving data is divided into single trip data,and the SOC and mileage of the whole vehicle are interpolated to get a more accurate expression.The single trip data is divided into 150s condition segments,and the characteristics of average speed,average acceleration and energy consumption are constructed.Then,the energy consumption of electric vehicles under different driving styles and speeds is studied.Aiming at the problem of driving style recognition,the K-means clustering algorithm is used to cluster the segment data of driving conditions,and four kinds of traffic conditions are clustered.Then,three kinds of driving styles,radical,general and conservative,are obtained under each kind of traffic conditions,which makes up for the shortcoming of ignoring the influence of traffic conditions in previous driving style research.The results show that the energy consumption of electric vehicles is affected by driving style and traffic conditions,mainly by traffic conditions.In order to get a more detailed relationship of energy consumption,driving conditions are not divided into four categories according to traffic conditions,but according to the average speed,so as to get the energy consumption of electric vehicles under different driving styles and speeds.Finally,the accessibility of electric vehicles is studied and the accessibility map is generated.Through the path planning API of Gaode map,the navigation information of the navigation route is obtained,including the distance and time consumption of each road section,and the average speed is calculated,so as to obtain the energy consumption of each road section.The total power consumption of the navigation route is calculated and compared with the current available power of the vehicle to judge whether it can be reached.The driving data of 10 drivers are tested to prove the accuracy of accessibility judgment.Taking Shenzhen City as an example,the accessibility map of Shenzhen city is generated by grid method.Accessibility is a better indicator than driving range to alleviate users’ mileage anxiety,which solves two disadvantages of driving range:first,driving range can’t reflect the complex and changeable energy consumption of travel routes,so there must be errors in the result.Second,the driving range will not change with different routes.Therefore,accessibility map can better enhance the user’s confidence and ease the mileage anxiety.