CN117635220A - An electric taxi charging cost optimization method and system - Google Patents

Abstract

The invention discloses a method and a system for optimizing charging cost of an electric taxi, and relates to the technical field of electric car charging, comprising the following steps: acquiring a set of charging piles of the electric taxis and the rentable platform, and constructing a renting model of the charging piles of the electric taxis according to service request response relation of the charging piles of the electric taxis and the rentable platform; based on the electric taxi charging pile lease model, constructing a charging cost model according to the charging completion time of an electric taxi charging task; if the lease time of any charging pile in the charging cost model meets the maximum lease time, a charging cost optimization model is built by taking the minimum charging cost of the electric taxi as an objective function; and determining a charging distribution strategy of the electric taxis according to the charging cost optimization model, and optimizing the charging cost of the electric taxis. The invention saves the construction cost of the charging pile, preferentially meets the charging requirement of the electric taxi, improves the charging efficiency of the electric taxi, and reduces the charging cost under the constraint of the maximum lease time of the charging pile.

Description

A method and system for optimizing charging cost of electric taxis

Technical Field

The present invention relates to the technical field of electric vehicle charging, and in particular to a method and system for optimizing the charging cost of an electric taxi.

Background Art

In order to promote the development of the new energy vehicle industry and reduce carbon emissions and air pollution, traditional fuel taxis in many provinces and cities will be replaced by electric taxis. However, the charging problem has always restricted the promotion of electric taxis. Although the public charging infrastructure is becoming more and more complete, with the growing demand for charging of household electric vehicles, more and more electric taxis need to compete with household electric vehicles for the use of public charging piles, which affects the effective operation time of electric taxis and reduces operating income. The construction of dedicated charging stations is the most direct way to solve the problem of charging electric taxis. However, the high construction cost leads to a small number of dedicated charging stations, which is difficult to meet the charging needs of electric taxis with high temporal and spatial randomness.

Existing charging cost optimization mainly optimizes charging delay or charging cost through centralized methods or distributed game theory methods, optimizes charging cost by optimizing charging power based on real-time electricity prices, etc., or studies the location of charging stations based on trajectory analysis. However, there is currently no charging cost optimization for the charging pile rental model of electric taxis.

Summary of the invention

In view of the above existing problems, the present invention is proposed.

Therefore, the present invention provides an electric taxi charging cost optimization method and system to solve the problem that the existing charging cost optimization method cannot optimize the charging cost for the charging pile rental model of the electric taxi.

In order to solve the above technical problems, the present invention provides the following technical solutions:

In a first aspect, an embodiment of the present invention provides a method for optimizing charging costs of an electric taxi, comprising:

Obtain a collection of electric taxis and charging piles on the rentable platform, and build an electric taxi charging pile rental model based on the service request response relationship between the electric taxis and the charging piles on the rentable platform;

Based on the electric taxi charging pile rental model, a charging cost model is constructed according to the charging completion time of the electric taxi charging task;

If the rental time of any charging pile in the charging cost model meets the maximum rental time, a charging cost optimization model is established with the minimum charging cost of the electric taxi as the objective function;

The charging allocation strategy for electric taxis is determined according to the charging cost optimization model to optimize the charging cost of electric taxis.

As a preferred solution of the electric taxi charging cost optimization method of the present invention, wherein: according to the service request response relationship between the electric taxi and the charging piles on the rentable platform, an electric taxi charging pile rental model is constructed, including:

Electric taxi Submitting a charging task to the charging pile rental platform is expressed as:

;

in, For electric taxis location, For electric taxis The charging needs is the battery capacity;

Charging Station Submitting your own information to the rental platform is represented by:

;

in, For charging pile The base rental price per rental period, For charging pile The unit electricity price, For charging pile location, For charging pile The maximum lease time.

As a preferred solution of the method for optimizing the charging cost of electric taxis according to the present invention, a charging cost model is constructed according to the charging completion time of the charging task of the electric taxi, including:

make Indicates charging pile The charging sequence is Charging task , then the charging pile Middle The charging completion time of a charging task is expressed as:

;

in, For charging pile The completion time of the non-taxi charging task being executed, that is, the earliest charging start time for the electric taxi to start charging, For charging tasks Arrival time, For charging tasks At the charging station The actual charging time, For charging pile Middle The charging completion time of each charging task;

Charging Station Rental time The maximum completion time of the charging task assigned to the charging pile is expressed as:

;

in, For charging tasks The binary decision variables assigned, For charging tasks At the charging station The charging completion time on To allocate a charging station A collection of charging tasks, For charging pile Middle The charging completion time of a charging task.

As a preferred solution of the method for optimizing the charging cost of electric taxis described in the present invention, wherein:

Also includes: the charging pile The electricity cost is ,in, For charging pile The unit electricity price, For charging pile Total charge capacity;

Total charging capacity of the charging pile It is expressed as:

;

in, is the unit moving energy consumption, For electric taxis To the charging station The shortest distance, Gather for charging tasks;

The charging cost model is expressed as:

;

in, is a monotonically increasing concave function that characterizes the length of the rental period. For charging pile total cost on .

As a preferred solution of the method for optimizing the charging cost of electric taxis according to the present invention, establishing a charging cost optimization model includes formalizing the problem of minimizing the charging cost of electric taxis under the constraint of the maximum rental time of charging piles, and the charging cost optimization model is expressed as:

;

in, A collection of charging stations for rent.

As a preferred solution of the method for optimizing the charging cost of electric taxis described in the present invention, the charging allocation strategy of electric taxis is determined according to the charging cost optimization model to optimize the charging cost of electric taxis, including:

For any , initialize the charging task set ;

Initialize the unassigned charging task set , rental charging pile collection And the charging task allocation matrix ;

When there is at least one charging task in the charging task set, for any ,based on Constructing an extended set of charging tasks ; The charging pile with the minimum average marginal cost in the extended charging task set is expressed as:

;

in, For any charging station A collection of charging tasks, For any charging station An extended set of charging tasks, For any charging station The charging task set is The charging cost, For any charging station The extended set of charging tasks is Charging cost;

Update charging task set , rental charging pile collection And the collection of unassigned charging tasks , until there is no charging task;

When there is no charging task, for any , ,make ; Output charging task allocation matrix .

As a preferred solution of the method for optimizing the charging cost of electric taxis described in the present invention, wherein: ,based on Constructing an extended set of charging tasks ,include:

Initialize the number of new charging tasks , charging pile No. The extended task set after adding the charging task , the index of the extended set with the minimum average marginal charging cost , the current unassigned charging task set ;

According to the charging completion time of the charging task of the charging pile and the charging pile rental time is the maximum completion time of the charging task assigned to the charging pile, the current charging pile rental time is calculated as:

;

If the current rental time of the charging pile is not greater than the maximum rental time of the charging pile and there is at least one charging task, the charging task with the shortest charging completion time is calculated based on the charging completion time of the charging pile charging task as follows:

;

in, For any charging task At the charging station Charging completion time on

If the electric taxi with the shortest charging completion time can reach the charging pile and the current rental time after adding the charging task is not greater than the maximum rental time, the number of new charging tasks will be updated. , charging task extension set 、Current charging station rental time And the collection of unassigned charging tasks , otherwise directly update the unassigned charging task set , until the current charging pile rental time is greater than the maximum charging pile rental time or there is no charging task;

If the current rental time of the charging pile is greater than the maximum rental time of the charging pile or there is no charging task, the extended set index of the charging task with the minimum average marginal cost is calculated as: , output .

In a second aspect, the present invention provides an electric taxi charging cost optimization system, comprising:

The electric taxi charging pile rental model building module is used to obtain the electric taxi and the charging pile collection of the rental platform, and build the electric taxi charging pile rental model according to the service request response relationship between the electric taxi and the charging pile of the rental platform;

A charging cost model establishment module, which is used to construct a charging cost model based on the electric taxi charging pile rental model and the charging completion time of the electric taxi charging task; a charging cost optimization model establishment module, which is used to establish a charging cost optimization model with the minimum charging cost of the electric taxi as the objective function if the rental time of any charging pile in the charging cost model meets the maximum rental time;

The decision optimization module is used to determine the charging allocation strategy of the electric taxi according to the charging cost optimization model, and optimize the charging cost of the electric taxi.

In a third aspect, the present invention provides a computing device, comprising:

Memory and processor;

The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the electric taxi charging cost optimization method are implemented.

In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the electric taxi charging cost optimization method.

Compared with the prior art, the present invention has the following beneficial effects: the present invention constructs an electric taxi charging pile rental system based on the charging demand of electric taxis and the rentable charging piles. By renting widely distributed public charging piles as special charging piles for electric taxis for a short period of time, it can save the high initial construction cost and give priority to meeting the charging demand of electric taxis. A charging cost model based on charging completion time is established, an electric taxi charging allocation algorithm based on completion time is proposed, and a charging allocation plan under the electric taxi charging pile rental system is determined, which improves the charging efficiency of electric taxis and reduces the charging cost of electric taxis under the constraint of the maximum rental time of charging piles.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

FIG1 is a schematic diagram of a method flow of an electric taxi charging cost optimization method and system according to an embodiment of the present invention;

FIG2 is a schematic diagram of an electric taxi charging pile rental system according to an electric taxi charging cost optimization method and system according to an embodiment of the present invention;

FIG3 is a flow chart of a task allocation method of an electric taxi charging cost optimization method and system according to an embodiment of the present invention;

FIG4 is a flowchart of constructing a charging task extension set of a method and system for optimizing charging cost of an electric taxi according to an embodiment of the present invention;

FIG5 is a comparison diagram of total charging costs under different numbers of charging tasks for an electric taxi charging cost optimization method and system according to an embodiment of the present invention;

FIG6 is a comparison diagram of total charging costs under different maximum rental time intervals of a method and system for optimizing charging costs of electric taxis according to an embodiment of the present invention;

FIG7 is a comparison diagram of total charging costs under different electricity price ranges for an electric taxi charging cost optimization method and system according to an embodiment of the present invention;

FIG8 is a comparison diagram of total charging costs under different electricity price interval lengths for an electric taxi charging cost optimization method and system according to an embodiment of the present invention;

FIG9 is a comparison diagram of total charging costs under different benchmark rental price ranges for an electric taxi charging cost optimization method and system according to an embodiment of the present invention;

FIG10 is a comparison chart of total charging costs under different base rental price interval lengths for an electric taxi charging cost optimization method and system according to an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

Example 1

1 to 4 , which are an embodiment of the present invention, provide a method for optimizing the charging cost of an electric taxi, including:

S1: Obtain a set of electric taxis and charging piles on the rentable platform, and build an electric taxi charging pile rental model based on the service request response relationship between the electric taxis and the charging piles on the rentable platform;

In the embodiment of the present invention, Indicates a collection of electric taxis, Indicates a collection of charging stations that can be rented;

Furthermore, according to the service request response relationship between electric taxis and charging piles on the rental platform, an electric taxi charging pile rental model is constructed, including:

Electric taxi Submitting a charging task to the charging pile rental platform is expressed as:

;

in, For electric taxis location, For electric taxis The charging needs is the battery capacity;

Charging Station Submitting your own information to the rental platform is represented by:

;

in, For charging pile The base rental price per rental period, For charging pile The unit electricity price, For charging pile location, For charging pile The maximum lease time.

S2: Based on the electric taxi charging pile rental model, a charging cost model is constructed according to the charging completion time of the electric taxi charging task;

It should be noted that the total charging cost of a charging pile consists of rental cost and electricity cost; the rental cost of a charging pile is related to the rental time and the benchmark rental price.

In the embodiment of the present invention, Indicates charging pile Rental time, charging pile The rental cost is ,in is a monotonically increasing concave function that characterizes the length of the rental period, and , , the function is used in the embodiment of the present invention ;

make Indicates charging task The arrival time of electric taxis Arrival at the charging station The moving time is For electric taxis The average moving speed of

make Indicates charging task At the charging station The actual charging time is For charging tasks The actual charge capacity, is the unit movement energy consumption, It's an electric taxi To the charging station The shortest distance, It is a charging station Constant charging power;

make Indicates charging task At the charging station The charging completion time on Not only the arrival time of the charging task and actual charging time It is also related to the charging completion time of other charging tasks that are waiting for charging or are being charged in the service queue of the charging pile;

make Indicates that it is assigned to a charging pile A collection of charging tasks, Assign binary decision variables to charging tasks. If the charging task Assigned to charging station ,but ,otherwise .

Furthermore, a charging cost model is constructed based on the charging completion time of the electric taxi charging task, including:

make Indicates charging pile The charging sequence is Charging task , then the charging pile Middle The charging completion time of a charging task is expressed as:

;

in, For charging pile The completion time of the non-taxi charging task being executed, that is, the earliest charging start time for the electric taxi to start charging, For charging tasks Arrival time, For charging tasks At the charging station The actual charging time, For charging pile Middle The charging completion time of each charging task;

Charging Station Rental time The maximum completion time of the charging task assigned to the charging pile is expressed as:

;

in, For charging tasks The binary decision variables assigned, For charging tasks At the charging station The charging completion time on To allocate a charging station A collection of charging tasks, For charging pile Middle The charging completion time of a charging task.

Furthermore, it also includes: charging piles The electricity cost is ,in, For charging pile The unit electricity price, For charging pile Total charge capacity;

Total charging capacity of the charging pile It is expressed as:

;

in, is the unit moving energy consumption, For electric taxis To the charging station The shortest distance, Gather for charging tasks;

The charging cost model is expressed as:

;

in, is a monotonically increasing concave function that characterizes the length of the rental period, For charging pile The total cost on .

S3: If the rental time of any charging pile in the charging cost model meets the maximum rental time, a charging cost optimization model is established with the minimum charging cost of the electric taxi as the objective function;

Furthermore, the establishment of a charging cost optimization model includes: formalizing the problem of minimizing the charging cost of electric taxis under the constraint of the maximum rental time of charging piles. The charging cost optimization model is expressed as:

;

in, A collection of charging stations for rent.

It should be noted that the constraints of the charging cost optimization model in the embodiment of the present invention are to ensure that the rental time of any charging pile cannot exceed its maximum rental time, that any electric taxi has sufficient power to reach the assigned charging pile, and that each charging task can be assigned to and only to one charging pile.

S4: Determine the charging allocation strategy of the electric taxi according to the charging cost optimization model, and optimize the charging cost of the electric taxi;

Furthermore, the charging allocation strategy of electric taxis is determined according to the charging cost optimization model to optimize the charging cost of electric taxis, including:

For any , initialize the charging task set ;

Initialize the unassigned charging task set , rental charging pile collection And the charging task allocation matrix ;

When there is at least one charging task in the charging task set, When, for any ,based on Constructing an extended set of charging tasks ; The charging pile with the minimum average marginal cost in the extended set of charging tasks is expressed as: ;

in, For any charging station A collection of charging tasks, For any charging station An extended set of charging tasks, For any charging station The charging task set is The charging cost, For any charging station The extended set of charging tasks is Charging cost;

Update charging task set , rental charging pile collection And the collection of unassigned charging tasks , until there is no charging task. ;

when When, for any , ,make ; Output charging task allocation matrix .

Furthermore, for any ,based on Constructing an extended set of charging tasks ,include:

Initialize the number of new charging tasks , charging pile No. The extended task set after adding the charging task , the index of the extended set with the minimum average marginal charging cost , the current unassigned charging task set ;

According to the charging completion time of the charging task of the charging pile and the charging pile rental time is the maximum completion time of the charging task assigned to the charging pile, the current charging pile rental time is calculated as:

;

If the current rental time of the charging pile is not greater than the maximum rental time of the charging pile and there is at least one charging task, the charging task with the shortest charging completion time is calculated based on the charging completion time of the charging pile charging task as follows:

;

in, For any charging task At the charging station Charging completion time on

If the electric taxi with the shortest charging completion time can reach the charging pile and the current rental time after the new charging task is added is not greater than the maximum rental time, and , then update the number of new charging tasks , charging task extension set 、Current charging station rental time And the collection of unassigned charging tasks , otherwise directly update the unassigned charging task set Until the current charging pile rental time is greater than the maximum charging pile rental time or there is no charging task. or ;

like or , then the calculation of the extended set index of charging tasks with the minimum average marginal cost is expressed as: , output .

The above is a schematic scheme of an electric taxi charging cost optimization method of this embodiment. It should be noted that the technical scheme of this electric taxi charging cost optimization system and the technical scheme of the above electric taxi charging cost optimization method belong to the same concept. For details not described in detail in the technical scheme of the electric taxi charging cost optimization system in this embodiment, please refer to the description of the technical scheme of the above electric taxi charging cost optimization method.

In this embodiment, a system for optimizing charging costs of electric taxis includes:

The electric taxi charging pile rental model building module is used to obtain the electric taxi and the charging pile collection of the rental platform, and build the electric taxi charging pile rental model according to the service request response relationship between the electric taxi and the charging pile of the rental platform;

A charging cost model building module is used to build a charging cost model based on the electric taxi charging pile rental model and the charging completion time of the electric taxi charging task;

A charging cost optimization model establishment module is used to establish a charging cost optimization model with the minimum charging cost of electric taxis as the objective function if the rental time of any charging pile in the charging cost model meets the maximum rental time;

The decision optimization module is used to determine the charging allocation strategy of electric taxis according to the charging cost optimization model and optimize the charging cost of electric taxis.

This embodiment also provides a computing device, which is applicable to the case of the method for optimizing the charging cost of electric taxis, and includes:

Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for optimizing the charging cost of electric taxis as proposed in the above embodiment.

This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing the charging cost of an electric taxi as proposed in the above embodiment is implemented.

The storage medium proposed in this embodiment and the method for optimizing the charging cost of electric taxis proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform the methods of various embodiments of the present invention.

Example 2

1 to 4, which are an embodiment of the present invention, in which a method for optimizing the charging cost of an electric taxi is provided by the present invention, with the goal of minimizing the charging cost, comprising the following steps:

In this embodiment, Indicates a collection of electric taxis, Represents a collection of charging piles.

Electric taxi Submit charging tasks to the charging pile rental platform It is expressed as:

;

in, For electric taxis location, For electric taxis The charging needs is the battery capacity.

In this embodiment, the positions of the electric taxis are , , , , , ;

The battery capacities are 65, 75, 75, 80, 85, and 70 kWh respectively. The charging requirements for electric taxis are The moving energy consumption of electric taxis are 0.15, 0.16, 0.17, 0.2, 0.25 and 0.18 kWh/km respectively, and the average moving speeds are 39, 39, 42, 45, 48 and 51 km/h respectively.

The charging pile submits its own information to the rental platform It is expressed as:

;

in, For charging pile The base rental price per rental time. For charging pile The unit electricity price, For charging pile location, For charging pile The maximum lease time.

In this embodiment, the base rental prices per unit rental time are 0.3, 0.4, 0.5, and 0.6 yuan/minute, respectively; the unit electricity prices are 0.8, 0.9, 0.85, and 1.0 yuan/kWh, respectively; the maximum rental time is 60, 70, 80, and 75 minutes, respectively; and the locations of the charging piles are , , , The charging power of the charging piles are 120, 140, 130 and 140 kilowatts respectively , and the completion time of the non-taxi charging tasks being executed is 3, 5, 2 and 5 minutes respectively.

Function that characterizes the length of the rental period , They are 0.98, 0.95, 0.97 and 0.95 respectively.

In this embodiment, for any , initialize the charging task set ;

Initialize the unassigned charging task set , rental charging pile collection And the charging task allocation matrix ;

For any ,based on Constructing an extended set of charging tasks ;

Select charging station , initialize the number of new charging tasks , charging pile No. The extended task set after adding the charging task , the index of the extended set with the minimum average marginal charging cost , the current unassigned charging task set ;

According to the charging completion time of the charging task of the charging pile and the charging pile rental time is the maximum completion time of the charging task assigned to the charging pile, the current charging pile rental time is calculated as:

;

in, For charging tasks At the charging station The charging completion time on For charging pile The current rental time of

like and , then the charging task with the shortest charging completion time is calculated based on the charging completion time of the charging pile charging task as follows:

;

in, For any charging task At the charging station Charging completion time on

If the electric taxi with the shortest charging completion time can reach the charging pile and the current rental time after the new charging task is added is not greater than the maximum rental time, that is and , then update the number of new charging tasks , update the charging task extension set , update the current charging pile rental time ; Otherwise update the unassigned charging task set , until or ; therefore we get .

The calculation of the extended set index of the charging task with the minimum average marginal cost is expressed as: ; Output .

therefore , the same goes for charging piles Performing the same process, the result is ;

Calculate the charging pile with the smallest average marginal cost:

;

renew Charging task collection , update the rental charging station collection , update the unassigned charging task set ;until ;

The final result is , , , , the corresponding charging cost is: 371.937 yuan;

when When, for any , ,make ; Output charging task allocation matrix .

Example 3

5 to 10 , which are an embodiment of the present invention, in order to better verify and illustrate the technical effects of the method of the present invention, this embodiment designs three algorithms and conducts actual comparative tests with the charging allocation algorithm based on completion time of the present invention to verify the real effects of the method of the present invention, specifically including:

(1) Minimum charging travel time algorithm: This algorithm selects the charging task and the available charging pile with the smallest travel time each time until all charging tasks are assigned. The available charging pile refers to the charging pile that can complete the charging task within its maximum rental time.

(2) Earliest charging start time algorithm: This algorithm selects the available charging pile with the smallest charging start time and matches it with the charging task each time, and updates the status of the charging pile until all charging tasks are assigned and the iteration terminates.

(3) Minimum charging cost charging pile algorithm: In each iteration of this algorithm, charging tasks are first assigned to each charging pile in the ascending order of the charging completion time of the unassigned tasks until no more can be assigned; then the charging pile with the lowest charging cost is selected and matched with the corresponding charging task until all charging tasks are assigned and the iteration terminates.

In this embodiment, a charging station dataset in a certain urban area is used, which includes 35 fast-charging public charging stations. Each fast-charging public charging station data includes the charging station ID, the charging station location, and the number of charging ports. A trajectory dataset of 4,000 electric taxis in a certain urban area on March 3, 2023 is used. Each trajectory data includes the electric taxi ID, GPS location, and recording time. The remaining power of the electric taxi is calculated based on its driving distance. When the remaining power drops to 20%, the electric taxi will have a charging demand. During the peak charging period, the electric taxi will submit a charging request to the rental platform to form a charging task. The rental platform publishes the charging task set submitted by the electric taxi to the public charging pile every 15 minutes and assigns the charging task.

Based on the basic parameters of electric taxis in a certain urban area, the battery capacity of electric taxis is set to follow a uniform distribution of 65 kWh to 85 kWh, the mobile energy consumption follows a uniform distribution of 0.15 kWh/km to 0.25 kWh/km, and the default parameter of the average mobile speed is 40 km/h to 60 km/h. When the remaining power drops to 20%, the electric taxi will have a charging demand, so the charging demand of the electric taxi is 80% of the battery capacity. Assuming that each charging station can rent 4 to 6 public charging piles, the maximum rental time of each charging pile is set to 60 minutes to 90 minutes by default, the completion time of the non-taxi charging task being executed is set to 0 minutes to 15 minutes by default, and the charging power parameter is 120 kW to 150 kW. Referring to the charging electricity price standard of electric vehicles in a certain urban area, the default value of the unit electricity price is set to 0.8 yuan/kWh to 1.2 yuan/kWh, and the default value of the benchmark rental price based on the rental time is set to 0.2 yuan/minute to 0.6 yuan/minute.

In this embodiment, the function Characterize the discount strategy of charging station operators based on the rental time scale. Based on the electric taxi trajectory data and charging demand analysis, the default value of the number of charging tasks on the rental platform is set to 150 for the test experiment. By changing the values of key parameters, the performance of the algorithm is observed, where each measurement value is averaged over 100 random topologies.

As shown in Figure 5, when the number of charging tasks for electric taxis in this embodiment increases from 50 to 150, the total charging cost of the charging allocation algorithm based on completion time is reduced by 4.75%, 13.06% and 14.15% respectively compared with the minimum charging cost charging pile algorithm, the minimum charging travel time algorithm and the earliest charging start time algorithm. As can be seen from Figure 5, since the minimum charging cost charging pile algorithm does not take into account the heterogeneity of the maximum rental time of the charging pile, the charging pile with the smallest charging cost and its task set have a small number of charging tasks that can be assigned because the maximum rental time of the charging pile itself is short, and the total charging amount is low. The minimum charging travel time algorithm tends to assign charging tasks to the charging pile with the closest travel time, and optimizes the total charging cost by reducing the mobile energy consumption of electric taxis and the charging pile rental time; the earliest charging start time algorithm focuses on the charging completion time of electric taxis, and optimizes the total charging cost by reducing the charging completion time of electric taxis; the earliest charging start time algorithm and the minimum charging travel time algorithm both ignore the difference between the electricity price and the benchmark rental price between charging piles, and cannot ensure the rational use of power resources and the economic benefits of users.

In this embodiment, the influence of the maximum rental time parameter of the charging pile on the total charging cost is further tested, and the maximum rental time of the charging pile is set to increase from [60,90] to [150,180]. As shown in Figure 6, as the maximum rental time increases, more charging tasks can be matched to charging piles with lower electricity prices and rental prices for the charging allocation algorithm based on completion time and the minimum charging cost charging pile algorithm, and their total charging cost decreases significantly with the increase of the maximum rental time. The minimum charging travel time algorithm optimizes the cost by optimizing the travel time of the electric taxi. When the location of the charging pile remains unchanged, it is impossible to match more charging tasks to charging piles with lower electricity prices and rental prices when the maximum rental time is increased. Therefore, its total charging cost has a slight downward trend, but no obvious change. When the maximum rental time continues to increase, the total charging cost of the earliest charging start time algorithm remains unchanged. Since the algorithm tends to focus on the charging completion time of the electric taxi, if any charging pile rented within a smaller maximum rental time range lacks the constraint of the rental time and is unavailable, the charging task allocation strategy will not change when the maximum rental time of the charging pile increases. As shown in Figure 6, the total charging cost of the charging allocation algorithm based on completion time is reduced by 4.84%, 11.93% and 15.26% respectively compared with the total charging cost of the minimum charging cost charging pile algorithm, the minimum charging travel time algorithm and the earliest charging start time algorithm.

Taking into account the time-of-use electricity price mechanism adopted by the charging market, the unit electricity price of charging piles is increased from Increase to To test the total charging cost under different electricity price ranges. As shown in Figure 7, the total charging cost increases with the increase of the unit electricity price of the charging pile. Under different electricity price ranges, the total charging cost of the charging allocation algorithm based on completion time is reduced by 4.18%, 11.31% and 12.39% respectively compared with the minimum charging cost charging pile algorithm, the minimum charging travel time algorithm and the earliest charging start time algorithm. In addition, the impact of the unit electricity price heterogeneity between charging piles on this algorithm is tested by increasing the length of the unit electricity price interval. As the unit electricity price range changes from Gradually expand to the range , the interval length increases from 0.2 to 0.8. As shown in Figure 8, the total charging cost growth trend of the charging allocation algorithm based on completion time is slower than that of the minimum charging travel time algorithm and the earliest charging start time algorithm. When the electricity price range is When the charging allocation algorithm based on completion time has a total charging cost that is 16.15% and 17.49% lower than that of the minimum charging travel time algorithm and the earliest charging start time algorithm, respectively. This is because the minimum charging travel time algorithm and the earliest charging start time algorithm ignore the heterogeneity of the unit electricity price among charging piles, resulting in high charging costs.

In this embodiment, the total charging cost under different benchmark rental price ranges and heterogeneity of benchmark rental prices between charging piles is further tested. As shown in Figure 9, the total charging cost increases with the increase of the benchmark rental price. The total charging cost of the charging allocation algorithm based on completion time is reduced by 3.72%, 9.06% and 11.08% compared with the minimum charging cost charging pile algorithm, the minimum charging travel time algorithm and the earliest charging start time algorithm, respectively. When the length of the benchmark rental price interval increases, that is, the heterogeneity of the benchmark rental prices between charging piles increases, as shown in Figure 10, the total charging cost growth trend of the charging allocation algorithm based on completion time is slower than that of the minimum charging travel time algorithm and the earliest charging start time algorithm. When the benchmark rental price range is When the interval is , the total charging cost of the charging allocation algorithm based on completion time is reduced by 9.38% and 10.39% compared with the minimum charging travel time algorithm and the earliest charging start time algorithm. When the charging allocation algorithm based on completion time has a total charging cost that is reduced by 11.99% and 13.66% compared with the minimum charging travel time algorithm and the earliest charging start time algorithm, respectively. This is because the minimum charging travel time algorithm and the earliest charging start time algorithm ignore the heterogeneity of the benchmark rental prices among charging piles.

The method of the present invention rents widely distributed public charging piles as temporary dedicated charging piles for a short period of time according to the charging needs of electric taxis. With the help of widely distributed public charging piles, high construction costs can be saved; the charging needs of electric taxis generated at random times and locations can be met; and in the case of charging congestion, due to the payment of a certain rental fee, the public charging piles will be temporarily used as dedicated charging piles for electric taxis, giving priority to providing charging services for electric taxis; it can save high charging pile construction costs, and can give priority to meeting the charging needs of electric taxis, improve the charging efficiency of electric taxis, and reduce the charging cost of electric taxis under the constraint of the maximum rental time of the charging piles.

It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims (10)

1. A method for optimizing the charging cost of an electric taxi, comprising: Obtain a collection of electric taxis and charging piles on the rentable platform, and build an electric taxi charging pile rental model based on the service request response relationship between the electric taxis and the charging piles on the rentable platform; Based on the electric taxi charging pile rental model, a charging cost model is constructed according to the charging completion time of the electric taxi charging task; If the rental time of any charging pile in the charging cost model meets the maximum rental time, a charging cost optimization model is established with the minimum charging cost of the electric taxi as the objective function; The charging allocation strategy for electric taxis is determined according to the charging cost optimization model to optimize the charging cost of electric taxis. 2. The method for optimizing the charging cost of an electric taxi according to claim 1, characterized in that, according to the service request response relationship between the electric taxi and the charging piles on the rentable platform, a charging pile rental model for an electric taxi is constructed, comprising: Electric taxi Submitting a charging task to the charging pile rental platform is expressed as: ; in, For electric taxis location, For electric taxis The charging needs is the battery capacity; Charging Station Submitting your own information to the rental platform is represented by: ; in, For charging pile The base rental price per rental period, For charging pile The unit electricity price, For charging pile location, For charging pile The maximum lease time. 3. The method for optimizing the charging cost of an electric taxi according to claim 1 or 2, characterized in that a charging cost model is constructed according to the charging completion time of the charging task of the electric taxi, comprising: make Indicates charging pile The charging sequence is Charging task , then the charging pile Middle The charging completion time of a charging task is expressed as: ; in, For charging pile The completion time of the non-taxi charging task being executed, that is, the earliest charging start time for the electric taxi to start charging, For charging tasks Arrival time, For charging tasks At the charging station The actual charging time, For charging pile Middle The charging completion time of each charging task; Charging Station Rental time The maximum completion time of the charging task assigned to the charging pile is expressed as: ; in, For charging tasks The binary decision variables assigned, For charging tasks At the charging station The charging completion time on To allocate a charging station A collection of charging tasks, For charging pile Middle The charging completion time of a charging task. 4. The method for optimizing the charging cost of an electric taxi as claimed in claim 3, characterized in that it also includes The charging pile The electricity cost is ,in, For charging pile The unit electricity price, For charging pile Total charge capacity; Total charging capacity of the charging pile It is expressed as: ; in, is the unit moving energy consumption, For electric taxis To the charging station The shortest distance, Gather for charging tasks; The charging cost model is expressed as: ; in, is a monotonically increasing concave function that characterizes the length of the rental period, For charging pile total cost on . 5. The method for optimizing the charging cost of an electric taxi as claimed in claim 4, wherein establishing a charging cost optimization model comprises formalizing the problem of minimizing the charging cost of an electric taxi under the constraint of the maximum rental time of a charging pile, and the charging cost optimization model is expressed as: ; in, A collection of charging stations for rent. 6. The method for optimizing the charging cost of an electric taxi according to claim 5, characterized in that the charging allocation strategy of the electric taxi is determined according to the charging cost optimization model to optimize the charging cost of the electric taxi, comprising: For any , initialize the charging task set ; Initialize the unassigned charging task set , rental charging pile collection And the charging task allocation matrix ; When there is at least one charging task in the charging task set, for any ,based on Constructing an extended set of charging tasks ; The charging pile with the minimum average marginal cost in the extended charging task set is expressed as: ; in, For any charging station A collection of charging tasks, For any charging station An extended set of charging tasks, For any charging station The charging task set is The charging cost, For any charging station The extended set of charging tasks is Charging cost; Update charging task set , rental charging pile collection And the collection of unassigned charging tasks , until there is no charging task; When there is no charging task, for any , ,make ; Output charging task allocation matrix . 7. The method for optimizing the charging cost of an electric taxi as claimed in claim 6, wherein for any ,based on Constructing an extended set of charging tasks ,include: Initialize the number of new charging tasks , charging pile No. The extended task set after adding the charging task , the index of the extended set with the minimum average marginal charging cost , the current unassigned charging task set ; According to the charging completion time of the charging task of the charging pile and the charging pile rental time is the maximum completion time of the charging task assigned to the charging pile, the current charging pile rental time is calculated as: ; If the current rental time of the charging pile is not greater than the maximum rental time of the charging pile and there is at least one charging task, the charging task with the shortest charging completion time is calculated based on the charging completion time of the charging pile charging task as follows: ; in, For any charging task At the charging station Charging completion time on If the electric taxi with the shortest charging completion time can reach the charging pile and the current rental time after adding the charging task is not greater than the maximum rental time, the number of new charging tasks will be updated. , charging task extension set 、Current charging station rental time And the collection of unassigned charging tasks , otherwise directly update the unassigned charging task set , until the current charging pile rental time is greater than the maximum charging pile rental time or there is no charging task; If the current rental time of the charging pile is greater than the maximum rental time of the charging pile or there is no charging task, the extended set index of the charging task with the minimum average marginal cost is calculated as: , output . 8. An electric taxi charging cost optimization system, characterized by comprising: The electric taxi charging pile rental model building module is used to obtain the electric taxi and the charging pile collection of the rental platform, and build the electric taxi charging pile rental model according to the service request response relationship between the electric taxi and the charging pile of the rental platform; A charging cost model establishment module, which is used to construct a charging cost model based on the electric taxi charging pile rental model and the charging completion time of the electric taxi charging task; a charging cost optimization model establishment module, which is used to establish a charging cost optimization model with the minimum charging cost of the electric taxi as the objective function if the rental time of any charging pile in the charging cost model meets the maximum rental time; The decision optimization module is used to determine the charging allocation strategy of the electric taxi according to the charging cost optimization model, and optimize the charging cost of the electric taxi. 9. An electronic device comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the electric taxi charging cost optimization method described in any one of claims 1 to 7 are implemented. 10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for optimizing the charging cost of an electric taxi as claimed in any one of claims 1 to 7.