An optimization method and device for a charging strategy and a trip pricing strategy of a charging station

Abstract

The present invention provides an optimization method and device for a charging strategy and a trip pricing strategy of a charging station, relating to the technical field of electric vehicles, including obtaining a site charging strategy and a regional pricing strategy, and determining a plurality of basic parameters; constructing an optimization model based on the plurality of basic parameters to obtain a target programming model; adjusting the basic parameters based on a preset constraint function in the target programming model to obtain target parameters, where the target parameters are parameters that satisfy each constraint function in the target programming model; and correspondingly adjusting the charging strategy and the pricing strategy based on the target parameters. By dynamically adjusting the thresholds of the charging upper limit and the lower limit, and combining this charging strategy with dynamic trip pricing, the present invention effectively transfers the charging activities during the operation period to the night, reduces the pressure on the power grid system, and reduces the occurrence of extreme pricing such as ultra-high prices or extremely low prices, ensuring the supply-demand balance of vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and more particularly, to an optimization method and device for a charging strategy and a trip pricing strategy of a charging station. Background Art

[0002] The current charging management method for self-driving electric shared vehicles (SAEV) largely depends on the accurate prediction of travel demand and road congestion. However, these influencing factors are often random in reality. Due to the uncertainty of the position of the vehicle after completing an order and its remaining battery power, the pre-set vehicle charging location allocation plan cannot be effectively implemented in the actual operation of self-driving electric shared vehicles. Summary of the Invention

[0003] The purpose of the present invention is to provide an optimization method and device for a charging strategy and a trip pricing strategy of a charging station to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows:

[0004] In a first aspect, the present application provides an optimization method for a charging strategy and a trip pricing strategy of a charging station, including:

[0005] Obtain a site charging strategy and a regional pricing strategy, and determine a plurality of basic parameters. The site charging strategy is used to characterize the charging upper and lower limits of a vehicle at different charging stations, and the regional pricing strategy is used to characterize the operating price setting of a vehicle in different operating regions;

[0006] Construct an optimization model based on the plurality of basic parameters to obtain a target programming model;

[0007] Adjust the basic parameters based on the preset constraint functions in the target programming model to obtain target parameters, where the target parameters are parameters that satisfy each constraint function in the target programming model;

[0008] Correspondingly adjust the charging strategy and the pricing strategy based on the target parameters.

[0009] In a second aspect, the present application also provides an optimization device for a charging strategy and a trip pricing strategy of a charging station, including:

[0010] A first acquisition unit, configured to obtain a site charging strategy and a regional pricing strategy, and determine a plurality of basic parameters. The site charging strategy is used to characterize the charging upper and lower limits of a vehicle at different charging stations, and the regional pricing strategy is used to characterize the operating price setting of a vehicle in different operating regions;

[0011] A first construction unit, configured to construct an optimization model based on the plurality of basic parameters to obtain a target programming model;

[0012] A first adjustment unit for adjusting the basic parameters based on a preset constraint function in the target programming model to obtain target parameters, where the target parameters are parameters that satisfy each constraint function in the target programming model;

[0013] A second adjustment unit for correspondingly adjusting the charging strategy and the pricing strategy based on the target parameters.

[0014] In a third aspect, the present application further provides an optimization device for a charging station charging strategy and a trip pricing strategy, including:

[0015] A memory for storing a computer program;

[0016] A processor for implementing the steps of the optimization method for the charging station charging strategy and the trip pricing strategy when executing the computer program.

[0017] In a fourth aspect, the present application further provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned optimization method based on the charging station charging strategy and the trip pricing strategy are implemented.

[0018] The beneficial effects of the present invention are as follows:

[0019] By dynamically adjusting the thresholds of the charging upper limit and the charging lower limit, and combining this charging strategy with dynamic trip pricing, the present invention effectively transfers the charging activities during the operation period to the night, reduces the pressure on the power grid system, and reduces the occurrence of extreme pricing such as ultra-high prices or extremely low prices, ensuring the supply-demand balance of vehicles.

[0020] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will become obvious from the specification, or can be understood by implementing the embodiments of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic diagram of the operation process of the autonomous driving electric shared car system described in the embodiments of the present invention;

[0023] Figure 2It is a flow chart of the optimization method of the charging station charging strategy and the trip pricing strategy described in the embodiment of the present invention;

[0024] Figure 3 It is a schematic diagram of the structure of the optimization device for the charging station charging strategy and the trip pricing strategy described in the embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of the optimization device for the charging station charging strategy and the trip pricing strategy described in the embodiment of the present invention.

[0026] Markings in the figure:

[0027] 10. First acquisition unit; 20. First construction unit; 30. First adjustment unit; 40. Second adjustment unit; 800. Optimization device for charging station charging strategy and trip pricing strategy; 801. Processor; 802. Memory; 803. Multimedia component; 804. I / O interface; 805. Communication component. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0030] Embodiment 1:

[0031] As urban mobility undergoes a transformation phase of automation, electrification, and shared mobility integration, the shared autonomous electric vehicle industry has great potential as a pioneer of future smart city transportation systems. The operating process of the autonomous electric shared vehicle system discussed in this application is as follows: Figure 1As shown, it can be seen from the figure that the user sends travel information to the system control center through the mobile application, and the system will allocate a nearby idle autonomous electric shared car to pick up the user. For the sake of convenience of description, hereinafter the autonomous electric shared car will be simply referred to as the vehicle.

[0032] The departure place and destination of User 1 are in different regions. After Vehicle 1 picks up User 1, it first arrives at the starting point of the link connecting the two regions, passes through the link to the end point of the link in the destination region, and finally delivers to the destination. The destination of User 2 is a certain place within the departure region, and Vehicle 2 can directly send the user to the destination without passing through the link. For users whose ride demands have been met, they will exit the autonomous electric shared car system after arriving at the destination, such as Figure 1 User 1 and User 2 in Figure 1 . However, when the user's waiting willingness is exceeded but there is still no matching vehicle, the user will also leave the system, such as

[0033] User 4 in Figure 1 . When the vehicle sends the user to the destination, it will automatically judge the charging situation. If the battery power does not meet the charging lower limit of the current region, it needs to go to the charging station in the current region for charging, such as Figure 1 Car 2 in

[0034] . If the vehicle's battery is fully charged, it will enter the waiting matching sequence, such as Figure 1 Car 3 in

[0034] After the car in the waiting sequence receives a new order, it will pick up the user according to the address on the order. For example, after Car 1 in the figure sends User 1 to the destination and receives the order of User 3, it will go to the departure place of User 3. If the car in the waiting sequence does not receive any order, it needs to wait in place all the time.

[0035] This embodiment provides an optimization method for the charging strategy of the charging station and the trip pricing strategy.

[0036] Referring to Figure 2 , the figure shows that this method includes Step S10, Step S20, Step S30 and Step S40.

[0037] Step S10. Obtain the charging strategy of the station and the pricing strategy of the region, and determine a plurality of basic parameters. The charging strategy of the station is used to characterize the charging upper and lower limits of the vehicle at different charging stations, and the pricing strategy of the region is used to characterize the operating price setting of the vehicle in different operating regions;

[0038] Specifically, the charging strategy for stations dynamically adjusts the charging upper and lower limits for different regions at different time periods. After each vehicle completes an order-taking task, it needs to conduct a charging assessment, that is, compare the relationship between the current battery level and the currently set charging lower limit in the region where it is located. Only vehicles with a battery level exceeding this lower limit can enter the order waiting queue. If the battery level is lower than this lower limit, the vehicle will be guided to a charging station for charging.

[0039] The regional pricing strategy dynamically adjusts the operating unit price for different regions at different time periods. During certain time periods, the supply and demand of vehicles are unbalanced, which will seriously affect the operating stability of the autonomous electric shared vehicle system. Therefore, by increasing or decreasing the operating profit and the number of vehicles in the fleet, the problem of supply and demand imbalance can be properly addressed to ensure the efficient travel of users.

[0040] Step S20. Construct an optimization model based on multiple basic parameters to obtain a target programming model;

[0041] Specifically, the target programming model aims to maximize the net profit during the operating cycle under the dynamic pricing and charging threshold strategies.

[0042] Specifically, step S20 specifically includes step S21, step S22, and step S23:

[0043] Step S21. Construct an objective function based on vehicle operating income, charging station operating costs, and charging station fixed costs;

[0044] Specifically, the objective function is:

[0045]

[0046] Specifically, Z is the operating net profit; C op is the charging station operating cost; C fix is the charging station fixed cost; p u is the payment fee of user u; u is the user index; U is the set of users.

[0047] Specifically, step S21 specifically includes step S211, step S212, step S213, step S214, step S215, step S216, step S217, step S218, step S219, step S2110, step S2111, step S2112, and step S2113:

[0048] Step S211. Obtain the charging time, charging location, and corresponding charging amount of the vehicle during operation;

[0049] Step S212. Determine the charging unit price of the charging station where the vehicle is located based on the charging time and charging location;

[0050] Specifically, based on the charging location and charging time during vehicle operation, the charging station where the vehicle is located and the corresponding charging unit price at the time of charging are determined.

[0051] Step S213. Calculate the product of the charging unit price and the corresponding charging quantity, and perform a summation calculation to obtain the total daytime charging cost.

[0052] Step S214. Calculate the sum of all charging quantities to obtain the total daytime charging quantity.

[0053] Step S215. Obtain the vehicle battery capacity, the battery power at the end of vehicle operation, and the total energy consumption during vehicle night dispatching, and calculate to obtain the total vehicle night charging quantity.

[0054] Specifically, after the vehicle completes daytime operation, all vehicles will return to the charging station in the current area for charging, and the fully charged vehicles will be relocated to the assigned area for daytime operation. To ensure that the large-scale directional movement of vehicles does not cause road congestion, it is selected to be carried out when the traffic flow is lower than a certain condition. Therefore, the total vehicle night charging quantity is the sum of the charging quantity required for the vehicle to return to the charging station after completing the last order and the energy consumed during the night relocation process.

[0055] Specifically, step S215 specifically includes step S2151, step S2152, step S2153, step S2154, step S2155, step S2156, and step S2157:

[0056] Step S2151. Obtain the unit energy consumption cost, battery capacity, and the battery power at the end of vehicle operation for the vehicles migrating at night between operating areas.

[0057] Step S2152. Based on the operating area where the vehicle is located at the end of operation, determine the total number of vehicles that need to be transferred out and the total number of vehicles that need to be received within each operating area, and use them as the first quantity and the second quantity respectively.

[0058] Step S2153. Determine the minimum value from the first quantity and the second quantity as the target quantity.

[0059] Step S2154. Calculate the product of the target quantity and the unit energy consumption cost to obtain the sixth result.

[0060] Step S2155. Calculate the difference between the set threshold and the battery power at the end of vehicle operation to obtain the seventh result.

[0061] Step S2156. Calculate the product of the seventh result and the battery capacity to obtain the eighth result.

[0062] Step S2157. Calculate the sum of the eighth result and the sixth result to obtain the total vehicle night charging quantity.

[0063] Specifically, the formula for the total night charging amount of the vehicle is:

[0064]

[0065] Among them, TC night is the total night charging amount of the vehicle; cb h is the battery capacity of the vehicle h; SOC h (τ end ) is the power of the vehicle h at time t end ; t end is the end time of vehicle operation; TC rel is the total energy consumption cost of the night-shifted vehicles between operating regions; h is the vehicle index; H is the vehicle set;

[0066] In this application, since the vehicles are homogeneous and each vehicle does not have a fixed initial charging station, the night vehicle relocation problem can essentially be regarded as a balanced transportation problem.

[0067] The number of vehicles to be dispatched from region m and the number of vehicles to be received in region n are represented by the following formula:

[0068]

[0069] Among them, a m is the number of vehicles to be dispatched from region m; b n is the number of vehicles that region n needs to receive; is the number of vehicles in region m after the daytime operation period; is the initial number of vehicles in region m; is the number of vehicles in region n after the daytime operation period; is the initial number of vehicles in region n.

[0070] Use x mn to represent the number of vehicles dispatched from region m to region n, and use w mn to represent the energy consumption cost of dispatching from region m to region n. In order to minimize the total energy consumption cost of the night-shifted vehicles, it is necessary to minimize the energy consumption of the night dispatching. The corresponding objective function is:

[0071] minTC rel = w mn *x mn

[0072] Among them, TC rel is the total energy consumption cost of the night-shifted vehicles between operating regions; w mn is the energy consumption cost of dispatching from region m to region n; x mn is the number of vehicles dispatched from region m to region n;

[0073] The corresponding constraint functions include: indicating that the number of vehicles scheduled in area m is equal to the number of vehicles to be transferred out of area m, where Z1 = {a1, a2, a3... a m}, Z1 is the set of areas for re-transfer, and a m is the number of vehicles to be transferred out of area m; indicating that the number of vehicles scheduled to area n is equal to the number of vehicles to be received in area n, where Z2 = {b1, b2, b3... b n}, Z2 is the set of areas to receive vehicles, and b n is the number of vehicles to be received in area n.

[0074] Step S216. Calculate the sum of the preset unit battery degradation cost and the night charging unit price to obtain a first result;

[0075] Step S217. Calculate the product of the first result and the total night charging amount of the vehicle to obtain a second result;

[0076] Step S218. Calculate the product of the unit battery degradation cost and the total day charging amount to obtain a third result;

[0077] Step S219. Calculate the sum of the total day charging cost, the second result, and the third result to obtain the charging station operation cost;

[0078] Specifically, the charging station operation cost calculation formula is:

[0079]

[0080] where C op is the charging station operation cost; TCC i is the total day charging cost of charging station i; c bd is the unit battery degradation cost; TC i is the total day charging amount of charging station i; TC night is the total night charging amount; c nc is the night charging unit price; i is the charging station index; S is the set of charging stations.

[0081] Step S2110. Obtain the number of charging station parking spaces, the number of vehicles, the unit parking space cost, and the unit vehicle cost;

[0082] Step S2111. Calculate the product of the number of charging station parking spaces and the unit parking space cost to obtain a fourth result;

[0083] Step S2112. Calculate the product of the number of vehicles and the unit vehicle cost to obtain a fifth result;

[0084] Step S2113. Calculate the sum of the fourth result and the fifth result to obtain the fixed cost of the charging station;

[0085] Specifically, the calculation formula for the fixed cost of the charging station is:

[0086]

[0087] where, C fix is the fixed cost of the charging station; c fs is the cost per vehicle; FS is the number of vehicles; c cs is the cost per parking space; cs i is the number of parking spaces of charging station i; i is the charging station index; S is the set of charging stations.

[0088] Step S22. Construct constraint functions based on the basic parameters to obtain multiple objective constraint functions;

[0089] Specifically, Step S22 specifically includes Step S221, Step S222, and Step S223:

[0090] Step S221. Construct multiple first constraint functions based on the operation price of the vehicle position and the preset price threshold range of each operation area;

[0091] Step S222. Construct multiple second constraint functions based on the charging upper and lower limits of each charging station and the preset charging range;

[0092] Step S223. Obtain multiple objective constraint functions based on multiple first constraint functions and multiple second constraint functions;

[0093] Specifically, the operation price at any moment needs to be within a certain range, and the corresponding constraint function expression is as follows:

[0094] pr min ≤pr(t)≤pr max

[0095] where, pr(t) is the unit operation price at time t; pr min is the lower limit of the unit operation price; pr max is the upper limit of the unit operation price.

[0096] The upper and lower limits of the charging range should be within a certain range, and the corresponding constraint function expression is as follows:

[0097]

[0098] where, SOC min is the lower limit of battery charging; SOC max is the upper limit of battery charging; is the lower limit of the charging range in area j at time t; is the upper limit of the charging range for area j at time t.

[0099] Under normal circumstances, it is considered that the upper and lower limits of the set charging range are integers, which is convenient for model calculation. The corresponding constraint function expressions are as follows:

[0100]

[0101] Among them, is the lower limit of the charging range for area j at time t; is the upper limit of the charging range for area j at time t; Z + is a positive integer.

[0102] Step S23. Based on the objective function and multiple objective constraint functions, construct an objective optimization model;

[0103] Specifically, the objective function and multiple constraint functions together constitute the objective optimization model.

[0104] Step S30. Based on the constraint functions preset in the goal programming model, adjust the basic parameters to obtain the target parameters, where the target parameters are the parameters that satisfy each constraint function in the goal programming model;

[0105] Step S40. Correspondingly adjust the charging strategy and pricing strategy based on the target parameters;

[0106] Specifically, in this application, the Alternating Synchronous Perturbation Stochastic Approximation Genetic Algorithm (ASPSA-GA) is used to solve the above objective optimization model, the Synchronous Perturbation Stochastic Approximation Algorithm is used to optimize the pricing strategy, and the Genetic Algorithm is used to optimize the charging threshold of the charging station, allowing the two processes to alternate, so as to obtain the optimal strategy in different time periods and maximize the benefits.

[0107] Embodiment 2:

[0108] As Figure 3 shown, this embodiment provides an optimization device for the charging strategy of a charging station and the itinerary pricing strategy. The device includes:

[0109] The first acquisition unit 10 is used to acquire the site charging strategy and the regional pricing strategy, and determine a plurality of basic parameters. The site charging strategy is used to characterize the charging upper and lower limits of vehicles at different charging stations, and the regional pricing strategy is used to characterize the operating price setting of vehicles in different operating regions;

[0110] The first construction unit 20 is used to construct an optimization model based on a plurality of basic parameters to obtain a goal programming model;

[0111] The first adjustment unit 30 is configured to adjust the basic parameters based on the preset constraint functions in the target programming model to obtain target parameters, where the target parameters are the parameters that satisfy the respective constraint functions in the target programming model;

[0112] The second adjustment unit 40 is configured to correspondingly adjust the charging strategy and the pricing strategy based on the target parameters.

[0113] In a specific implementation manner disclosed in the present application, the first construction unit 20 includes:

[0114] The second construction unit is configured to construct an objective function based on the vehicle operation revenue, the charging station operation cost, and the charging station fixed cost;

[0115] The third construction unit is configured to construct constraint functions based on the basic parameters to obtain a plurality of target constraint functions;

[0116] The fourth construction unit is configured to construct a target optimization model based on the objective function and the plurality of target constraint functions.

[0117] In a specific implementation manner disclosed in the present application, the second construction unit includes:

[0118] The second acquisition unit is configured to acquire the charging time, the charging location, and the corresponding charging amount of the vehicle during operation;

[0119] The first determination unit is configured to determine the charging unit price of the charging station where it is located based on the charging time and the charging location;

[0120] The first calculation unit is configured to calculate the product of the charging unit price and the corresponding charging amount, and perform a summation calculation to obtain the total daytime charging cost;

[0121] The second calculation unit is configured to calculate the sum of all charging amounts to obtain the total daytime charging amount;

[0122] The third acquisition unit is configured to acquire the vehicle battery capacity, the power of the vehicle at the end of operation, and the total nighttime dispatching energy consumption of the vehicle, and calculate to obtain the total nighttime charging amount of the vehicle;

[0123] The third calculation unit is configured to calculate the sum of the preset unit battery degradation cost and the nighttime charging unit price to obtain a first result;

[0124] The fourth calculation unit is configured to calculate the product of the first result and the total nighttime charging amount of the vehicle to obtain a second result;

[0125] The fifth calculation unit is configured to calculate the product of the unit battery degradation cost and the total daytime charging amount to obtain a third result;

[0126] The sixth calculation unit is configured to calculate the sum of the total daytime charging cost, the second result, and the third result to obtain the charging station operation cost.

[0127] In a specific embodiment disclosed in the present application, the second construction unit includes:

[0128] A fourth acquisition unit, configured to acquire the number of charging station parking spaces, the number of vehicles, the cost per unit parking space, and the cost per unit vehicle;

[0129] A seventh calculation unit, configured to calculate the product of the number of parked vehicles at the charging station and the cost per unit parking space to obtain a fourth result;

[0130] An eighth calculation unit, configured to calculate the product of the number of vehicles and the cost per unit vehicle to obtain a fifth result;

[0131] A ninth calculation unit, configured to calculate the sum of the fourth result and the fifth result to obtain the fixed cost of the charging station.

[0132] In a specific embodiment disclosed in the present application, the third construction unit includes:

[0133] A fifth construction unit, configured to construct a plurality of first constraint functions based on the vehicle position operation price and the preset price threshold range of each operation area;

[0134] A sixth construction unit, configured to construct a plurality of second constraint functions based on the charging upper and lower limits of each charging station and the preset charging range;

[0135] An obtaining unit, configured to obtain a plurality of target constraint functions based on the plurality of first constraint functions and the plurality of second constraint functions.

[0136] In a specific embodiment disclosed in the present application, the third acquisition unit includes:

[0137] A fifth acquisition unit, configured to acquire the unit energy consumption cost, the battery capacity, and the power of the vehicle at the end of operation of the vehicles migrating at night between each operation area;

[0138] A second determination unit, configured to determine, based on the operation area where the vehicle is located at the end of operation, the total number of vehicles to be transferred out and the total number of vehicles to be received within each operation area, respectively, as a first quantity and a second quantity;

[0139] A third determination unit, configured to determine the minimum value from the first quantity and the second quantity as the target quantity;

[0140] A tenth calculation unit, configured to calculate the product of the target quantity and the unit energy consumption cost to obtain a sixth result;

[0141] An eleventh calculation unit, configured to calculate the difference between the set threshold and the power of the vehicle at the end of operation to obtain a seventh result;

[0142] The twelfth calculation unit is configured to calculate the product of the seventh result and the battery capacity to obtain an eighth result;

[0143] The thirteenth calculation unit is configured to calculate the sum of the eighth result and the sixth result to obtain the total vehicle night charging amount.

[0144] It should be noted that regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0145] Embodiment 3:

[0146] Corresponding to the above method embodiment, an optimization device for a charging station charging strategy and a trip pricing strategy is further provided in this embodiment. An optimization device for a charging station charging strategy and a trip pricing strategy described below can be correspondingly referred to the optimization method for a charging station charging strategy and a trip pricing strategy described above.

[0147] Figure 4 is a block diagram of an optimization device 800 for a charging station charging strategy and a trip pricing strategy shown according to an exemplary embodiment. As Figure 4 shown, the optimization device 800 for a charging station charging strategy and a trip pricing strategy may include: a processor 801, a memory 802. The optimization device 800 for a charging station charging strategy and a trip pricing strategy may further include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0148] Among them, the processor 801 is used to control the overall operation of the optimization device 800 for the charging station charging strategy and the trip pricing strategy, so as to complete all or part of the steps in the above-mentioned optimization method for the charging station charging strategy and the trip pricing strategy. The memory 802 is used to store various types of data to support the operation of the optimization device 800 for the charging station charging strategy and the trip pricing strategy. These data may include, for example, instructions for any application or method operating on the optimization device 800 for the charging station charging strategy and the trip pricing strategy, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. The multimedia component 803 may include a screen and an audio component. Among them, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signals may be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the optimization device 800 for the charging station charging strategy and the trip pricing strategy and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, and an NFC module.

[0149] In an exemplary embodiment, the optimization device 800 for the charging station charging strategy and the trip pricing strategy can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above-mentioned optimization method for the charging station charging strategy and the trip pricing strategy.

[0150] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned optimization method for the charging station charging strategy and the trip pricing strategy are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above program instructions can be executed by the processor 801 of the optimization device 800 for the charging station charging strategy and the trip pricing strategy to complete the above-mentioned optimization method for the charging station charging strategy and the trip pricing strategy.

[0151] Embodiment 4:

[0152] Corresponding to the above method embodiment, in this embodiment, a readable storage medium is further provided. A readable storage medium described below can be correspondingly referred to with an optimization method for a charging station charging strategy and a trip pricing strategy described above.

[0153] A readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the optimization method for the charging station charging strategy and the trip pricing strategy in the above method embodiment are implemented.

[0154] The readable storage medium can specifically be various readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc that can store program codes.

[0155] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0156] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or replacements, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for optimizing charging station charging strategy and trip pricing strategy, characterized in that: include: Obtain the site charging strategy and regional pricing strategy, and determine multiple basic parameters. The site charging strategy is the upper and lower limits of charging for different regions in different time periods. The upper and lower limits are dynamically adjusted. The upper and lower limits are used to evaluate the charging after each vehicle completes the order task, and compare the current battery power with the current charging lower limit set in the region. When the battery power of the vehicle exceeds the lower limit, it enters the order waiting queue. If the battery power is lower than the lower limit, the vehicle will be guided to the charging station for charging. The regional pricing strategy is used to characterize the operating price setting of the vehicle in different operating areas; Build an optimization model based on multiple basic parameters to obtain a target planning model; Based on the constraint functions preset in the target programming model, the basic parameters are adjusted to obtain target parameters, where the target parameters are parameters that satisfy the constraint functions in the target programming model; Adjusting the charging strategy and the pricing strategy accordingly based on the target parameter; The optimization model is constructed based on multiple basic parameters to obtain a target planning model, including: constructing a target function based on vehicle operating income, charging station operating costs and charging station fixed costs.

2. The optimization method of charging station charging strategy and trip pricing strategy according to claim 1 is characterized in that ,Build an optimization model based on multiple basic parameters to obtain the target planning model, including: Constructing a constraint function based on the basic parameters to obtain a plurality of objective constraint functions; Based on the objective function and multiple objective constraint functions, a target optimization model is constructed.

3. The optimization method of charging station charging strategy and trip pricing strategy according to claim 2 is characterized in that ,Based on the vehicle operating income, charging station operating cost and charging station fixed cost, the objective function is constructed,including: Obtain the charging time, charging location and corresponding charging amount of the vehicle during operation; Determine a charging unit price of the charging station based on the charging time and the charging location; Calculate the product of the charging unit price and the corresponding charging amount, and sum them up to obtain the total daytime charging cost; Calculate the sum of all charging amounts to get the total daily charging amount; Obtain the vehicle battery capacity, the amount of electricity when the vehicle ends operation, and the total energy consumption of the vehicle during nighttime dispatch, and calculate the total amount of charge of the vehicle at night; Calculate the sum of the preset unit battery degradation cost and the nighttime charging unit price to obtain a first result; Calculate the product of the first result and the total nighttime charge amount of the vehicle to obtain a second result; calculating a product of the unit battery degradation cost and the total daily charge amount to obtain a third result; The sum of the total daytime charging cost, the second result and the third result is calculated to obtain the charging station operation cost.

4. The method for optimizing charging station charging strategy and trip pricing strategy according to claim 2, characterized in that ,Based on the vehicle operating income, charging station operating cost and charging station fixed cost, the objective function is constructed,including: Obtain the number of parking spaces, the number of vehicles, the unit parking space cost, and the unit vehicle cost of the charging station; Calculating the product of the number of parking spaces at the charging station and the unit parking space cost to obtain a fourth result; calculating the product of the number of vehicles and the unit vehicle cost to obtain a fifth result; The sum of the fourth result and the fifth result is calculated to obtain the fixed cost of the charging station.

5. An optimization device for charging station charging strategy and trip pricing strategy, characterized in that: include: The first acquisition unit is used to acquire the site charging strategy and the regional pricing strategy, and determine a plurality of basic parameters. The site charging strategy is the upper and lower limits of charging for different regions in different time periods. The upper and lower limits of charging are dynamically adjusted. The upper and lower limits of charging are used to evaluate charging after each vehicle completes the order task, and compare the current battery power with the current charging lower limit set in the region. When the battery power of the vehicle exceeds the lower limit, the vehicle enters the order waiting queue. If the battery power is lower than the lower limit, the vehicle will be guided to the charging station for charging. The regional pricing strategy is used to characterize the operating price setting of the vehicle in different operating areas; A first construction unit is used to construct an optimization model based on multiple basic parameters to obtain a target planning model; A first adjustment unit, configured to adjust the basic parameters based on a constraint function preset in the target programming model to obtain a target parameter, wherein the target parameter is a parameter that satisfies each constraint function in the target programming model; A second adjustment unit, configured to adjust the charging strategy and the pricing strategy accordingly based on the target parameter; The first construction unit includes a second construction unit, which is used to construct an objective function based on vehicle operating income, charging station operating costs and charging station fixed costs.

6. The device for optimizing charging station charging strategy and trip pricing strategy according to claim 5, characterized in that: The first building block comprises: A third construction unit is used to construct a constraint function based on the basic parameters to obtain a plurality of target constraint functions; The fourth construction unit is used to construct a target optimization model based on the target function and multiple target constraint functions.

7. The device for optimizing charging station charging strategy and trip pricing strategy according to claim 6, characterized in that: The second building block comprises: A second acquisition unit is used to acquire the charging time, charging location and corresponding charging amount of the vehicle during operation; A first determining unit, configured to determine a charging unit price of a charging station based on the charging time and the charging location; A first calculation unit is used to calculate the product of the charging unit price and the corresponding charging amount, and perform sum calculation to obtain a total daytime charging cost; The second calculation unit is used to calculate the sum of all charging amounts to obtain the total daily charging amount; The third acquisition unit is used to obtain the vehicle battery capacity, the power of the vehicle when the operation ends, and the total energy consumption of the vehicle during nighttime dispatch, and calculate the total charging amount of the vehicle at night; A third calculation unit, used to calculate the sum of a preset unit battery degradation cost and a nighttime charging unit price to obtain a first result; a fourth calculation unit, configured to calculate a product of the first result and the total nighttime charge amount of the vehicle to obtain a second result; a fifth calculation unit, configured to calculate a product of the unit battery degradation cost and the total daily charge amount to obtain a third result; The sixth calculation unit is used to calculate the sum of the total daytime charging cost, the second result and the third result to obtain the charging station operation cost.

8. The device for optimizing charging station charging strategy and trip pricing strategy according to claim 6, characterized in that: The second building block comprises: A fourth acquisition unit, used to acquire the number of parking spaces, the number of vehicles, the unit parking space cost and the unit vehicle cost of the charging station; a seventh calculation unit, configured to calculate the product of the number of parking spaces at the charging station and the unit parking space cost to obtain a fourth result; an eighth calculation unit, configured to calculate the product of the number of vehicles and the unit vehicle cost to obtain a fifth result; The ninth calculation unit is used to calculate the sum of the fourth result and the fifth result to obtain the fixed cost of the charging station.

9. An optimization device for charging station charging strategy and trip pricing strategy, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the method for optimizing the charging station charging strategy and the trip pricing strategy as claimed in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for optimizing the charging station charging strategy and the trip pricing strategy as claimed in any one of claims 1 to 4.

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