Automatic driving taxi charging pile selection method

Abstract

The invention discloses an automatic driving taxi charging pile selection method, and the method comprises the steps of building a charging station traffic information environment model, which comprises a charging pile state information matrix and a charging station queuing matrix, for an automatic driving taxi needing to be charged; based on the queuing matrix, establishing a charging station queuing model used for predicting how many charging automobiles possibly arrive at the charging station in each time period; calculating the total time cost and the total mileage cost of the self-driving taxi needing to be charged to each charging station for charging; constructing a cost objective function of automatic driving rental charging and constraint conditions of the objective function; and according to a solving result of the cost objective function under the constraint condition, enabling the automatic driving taxi to select a charging station with the minimum cost to go for charging. According to the invention, Internet of Vehicles data in the big data era are integrated and efficiently utilized, modeling of the charging station selection strategy of the automatic driving charging taxi is completed, and the optimal charging station strategy for selecting estimation can be provided for the taxi in real time.

Description

technical field [0001] The invention relates to the fields of computers and transportation, in particular to a method for selecting a charging pile for an automatic driving taxi. Background technique [0002] The global energy shortage and environmental problems are becoming more and more serious, and the high dependence on oil around the world has exacerbated a series of problems. In recent years, with the development of battery technology, the progress of the electric vehicle industry has been promoted. At the same time, the increasingly powerful artificial intelligence and the vigorous development of the mobile Internet have given birth to large-scale data analysis application scenarios such as the Internet of Vehicles. [0003] With the maturity of Internet of Vehicles technology, research on self-driving charging vehicles has also made many qualitative leaps. A host of taxi companies have also joined the race for self-driving vehicles. The paper "Research on Charging ...

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Problems solved by technology

The paper "Research on Charging Behavior of Pure Electric Network Taxi" analyzes the driver's charging behavior, but when automatic driving is applied to the operation of charging taxis, a problem that cannot be ignored is: how to control the charging behavior of the vehicle without human intervention? The system independently selects the appropriate charging station? Obviously, in a traditional taxi with a driver, the driver can decide when to charge and which charging station to charge based on his own experience, but in a self-driving taxi, it becomes a problem that needs to be solved

[0004] Many existing studies on self-driving charging taxis, such as the papers "Electric Taxi Charging Station Site Planning Based on Improved PSO Algorithm", "Electric Taxi Charging Demand Analysis Based on Trajectory Data", etc., often default to the taxi charging tradition. The "nearest selection" principle or how to determine the location of the charging station according to the analysis of the driver's walking track data; The relationship changes, and the planned charging station only plays an auxiliary role in the charging selection of taxis. In a more practical situation, it is still necessary to choose according to the current supply and demand relationship.

[0005] At present, the data collection capability in the era of big data is very powerful, and we can obtain a large amount of data in real time. According to the current research, various scholars have formulated various selection methods by analyzing data, such as "A Management Method for Electric Energy Supply of Pure Electric Taxi" , "A Recommended Charging Strategy for Electric Taxi Considering Time and Electricity Price", etc., but their considerations are relatively small, such as limiting the charging time, not considering the delay caused by queuing, and not considering how long it takes to pick up passengers again after charging , it is necessary to re-arrange the location of charging stations globally; from the perspective of commercial taxi companies, only by completing charging as soon as possible and starting to serve the next order can maximize the benefits

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