Electric vehicle charging methods, devices, computer equipment, readable storage media, and program products

Abstract

This application relates to an electric vehicle charging method, apparatus, computer equipment, computer-readable storage medium, and computer program product. The method includes: acquiring generation-side data, load-side data, and power grid structure data, and determining first carbon factor distribution data; acquiring electric vehicle charging requests from electric vehicles to be charged, and generating a first objective function and second carbon factor distribution data; generating a second objective function based on the first and second carbon factor distribution data; acquiring a third objective function of charging station resource gain data and a fourth objective function of charging equipment utilization rate; determining target charging station information when the electric vehicles to be charged meet a first constraint condition, each charging station meets a second constraint condition, and the values ​​of the first, second, third, and fourth objective functions meet requirements, and sending this information to each electric vehicle. This method can improve resource utilization.

Description

Technical Field

[0001] This application relates to the field of electric vehicle charging technology, and in particular to an electric vehicle charging method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the development of electric vehicles, the demand for charging is also increasing. How to plan and implement electric vehicle charging can improve charging efficiency.

[0003] In related technologies, when an electric vehicle user finds that the battery is low and needs charging, they can simply move the vehicle to a charging station. However, this charging method cannot achieve planned charging or rational resource utilization. Summary of the Invention

[0004] Therefore, it is necessary to provide an electric vehicle charging method, device, computer equipment, computer-readable storage medium, and computer program product that can improve resource utilization in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for charging an electric vehicle, comprising:

[0006] Acquire generation-side data, load-side data, and power grid structure data, and determine the distribution data of the first carbon factor corresponding to the load side based on the generation-side data, load-side data, and power grid structure data;

[0007] Obtain data on electric vehicle charging requests and charging station distribution for electric vehicles awaiting charging, and

[0008] Based on the electric vehicle charging request and the charging station distribution data included in the load-side data, a first objective function corresponding to the resource transfer required for charging the electric vehicle to be charged is generated, as well as the second carbon factor distribution data corresponding to electric vehicle charging.

[0009] Based on the distribution data of the first carbon factor and the distribution data of the second carbon factor, a second objective function is generated;

[0010] Obtain the third objective function corresponding to the resource gain data of the charging station, and the fourth objective function corresponding to the utilization rate of each charging device in the charging station;

[0011] Determine the first preset constraint condition for the electric vehicle and the second preset constraint condition for the charging station;

[0012] Determine the target charging station information corresponding to each electric vehicle when the electric vehicle to be charged meets the first constraint condition, each charging station meets the second constraint condition, and the first objective function value is minimized, the second objective function value is minimized, the third objective function value is maximized, and the fourth objective function value is maximized.

[0013] Guidance information is generated based on the target charging station information and sent to each electric vehicle. The guidance information is used to guide the electric vehicles to the target charging station.

[0014] In one embodiment, acquiring generation-side data, load-side data, and power grid structure data, and determining the first carbon factor distribution data corresponding to the load side based on the generation-side data, load-side data, and power grid structure data, includes: acquiring the carbon emission factor of power generation equipment from the generation-side data; determining the root bus based on the power grid structure data, and determining the data of each first node connected to the root bus; determining the second node data downstream of each first node data, and the energy consumption data corresponding to the second node data, based on the load-side data; determining the weighted value corresponding to each first node data based on the energy consumption data of the second node data corresponding to each first node data; and determining the first carbon factor distribution data corresponding to each first node data based on the weighted value corresponding to each first node data and the carbon emission factor.

[0015] In one embodiment, determining the charging time required to fully charge an electric vehicle based on the electric vehicle charging request and charging station distribution data included in the load-side data includes: obtaining the electric vehicle's expected parking location and required battery level from the electric vehicle charging request; generating a charging station search area based on the expected parking location, and determining a target charging station based on the charging station search area and annual charging station distribution data; obtaining the charging power of the target charging station, and determining the expected charging completion time based on the charging power and required battery level; obtaining road condition data between the expected parking location and the target charging station, and calculating the travel time for the electric vehicle to move from the expected parking location to the target charging station based on the road condition data; determining the expected queuing time at the target charging station; and determining the charging time required to fully charge the electric vehicle based on the expected charging completion time, travel time, and expected queuing time.

[0016] In one embodiment, the first constraint includes a remaining battery power constraint for the electric vehicle to be charged. The electric vehicle to be charged satisfies the first constraint, which includes: the maximum travel distance that the electric vehicle to be charged can travel is greater than the distance that the electric vehicle to be charged can travel to each charging station; the maximum travel distance that the electric vehicle to be charged can travel is determined based on the remaining battery power of the electric vehicle to be charged.

[0017] In one embodiment, the second constraint includes the charging station capacity and the charging station load. Each charging station satisfies the second constraint, which includes: the number of electric vehicles charging in the charging station is less than the corresponding capacity of the charging station and the load of the charging station is less than or equal to the maximum allowable load.

[0018] Secondly, this application also provides an electric vehicle charging device, comprising:

[0019] The first data determination module is used to acquire generation-side data, load-side data and power grid structure data, and determine the first carbon factor distribution data corresponding to the load side based on the generation-side data, load-side data and power grid structure data;

[0020] The second data determination module is used to obtain the electric vehicle charging request of the electric vehicle to be charged, and generate a first objective function corresponding to the resource transfer required for charging the electric vehicle to be charged, and a second carbon factor distribution data corresponding to electric vehicle charging, based on the electric vehicle charging request and the charging station distribution data included in the load-side data.

[0021] The function generation module is used to generate a second objective function based on the first carbon factor distribution data and the second carbon factor distribution data;

[0022] The function acquisition module is used to acquire the third objective function corresponding to the resource gain data of the charging station, and the fourth objective function corresponding to the utilization rate of each charging device in the charging station.

[0023] The constraint determination module is used to determine the preset first constraint condition for the electric vehicle and the preset second constraint condition for the charging station.

[0024] The charging station determination module is used to determine the target charging station information corresponding to each electric vehicle when the electric vehicle to be charged meets the first constraint condition, each charging station meets the second constraint condition, and the first objective function value is minimized, the second objective function value is minimized, the third objective function value is maximized, and the fourth objective function value is maximized.

[0025] The guidance information generation module is used to generate guidance information based on the target charging station information and send it to each electric vehicle. The guidance information is used to guide the electric vehicles to move to the target charging station.

[0026] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method embodiments.

[0027] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method embodiments.

[0028] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method embodiments.

[0029] The aforementioned electric vehicle charging methods, devices, computer equipment, computer-readable storage media, and computer program products, when determining target charging stations, consider the resource consumption needs of electric vehicle charging users, the carbon emissions generated by electric vehicles during charging, and the resource revenue data of the charging station. This ensures that the determined target charging stations can reduce carbon emissions, reduce electric vehicle user resource consumption, increase charging station resource revenue, and improve the rational allocation of resources. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A diagram illustrating the application environment of an electric vehicle charging method in one embodiment;

[0032] Figure 2 This is a flowchart illustrating an electric vehicle charging method in one embodiment;

[0033] Figure 3 This is a schematic diagram of the structure of a virtual node in one embodiment;

[0034] Figure 4 This is a schematic diagram of the structure of a virtual node in another embodiment;

[0035] Figure 5 This is a structural block diagram of an electric vehicle charging device in one embodiment;

[0036] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0037] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0038] The electric vehicle charging method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server. Server 104 acquires generation-side data, load-side data, and power grid structure data. Based on these data, it determines the first carbon factor distribution data corresponding to the load side. Server 104 also acquires electric vehicle charging requests uploaded by terminal 102. Based on the charging requests and the charging station distribution data included in the load-side data, it generates a first objective function corresponding to the resource transfer required for charging the electric vehicle, and second carbon factor distribution data corresponding to electric vehicle charging. Based on the first and second carbon factor distribution data, it generates a second objective function. It acquires a third objective function corresponding to the charging station resource gain data, and a fourth objective function corresponding to the utilization rate of each charging device at the charging station. It determines a preset first constraint condition for the electric vehicle and a preset second constraint condition for the charging station. It determines the target charging station information for each electric vehicle when the electric vehicle meets the first constraint condition, each charging station meets the second constraint condition, and the first, second, third, and fourth objective function values ​​are minimized. Based on the target charging station information, it generates guidance information and sends it to each electric vehicle. This guidance information guides the electric vehicle to the target charging station. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0039] In one exemplary embodiment, such as Figure 2 As shown, an electric vehicle charging method is provided, which is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 202 to 206. Wherein:

[0040] Step 202: Obtain generation-side data, load-side data, and power grid structure data, and determine the first carbon factor distribution data corresponding to the load side based on the generation-side data, load-side data, and power grid structure data.

[0041] Among them, the first carbon factor distribution data is used to characterize the carbon emission responsibility that the load side should bear.

[0042] It is understandable that any power generation equipment will generate carbon emissions during the power generation process. Since the electricity generated by the power generation equipment is used for load-side applications, it can be considered that the carbon emissions generated by the power generation equipment are for load-side applications. Therefore, the carbon emissions generated by the power generation equipment during the power generation process can be attributed to the load side so that carbon emissions can be quantified.

[0043] For example, the method for quantifying the carbon emission responsibility of load-side units can be as shown in formula (1):

[0044]

[0045] Among them, EPU corresponds to production equipment, ECU corresponds to application energy unit, and P i,t r is used to characterize the output or power consumption of an energy-consuming unit. i,t Carbon emission factors used to characterize energy production / use.

[0046] Optionally, the power system can be divided into a distribution network and a transmission network. The transmission network is a power transmission network that connects power plants, substations, or substations, and is mainly responsible for transmitting electrical energy. The distribution network is a power grid that receives electrical energy from the transmission network or regional power plants and distributes it locally or in stages according to voltage to various users through distribution facilities.

[0047] In one embodiment, the line connecting the transmission network to the distribution network can be used as the main bus, while the busbars connected to each distribution network on the main bus can be used as branch busbars.

[0048] Understandably, when electricity is transferred from upstream to downstream, carbon emission responsibility is assigned to the power generation equipment. For components connected to the main bus, the injected and outflowing power of the bus can be... and P out exemplarily and P out The calculation formulas can be found in formulas (2) and (3).

[0049]

[0050] P out =∑P B (3)

[0051]

[0052] in, Used to characterize the power output of generators connected to the main bus; if no generator is connected, the value is zero.

[0053] P B Used to characterize the power flowing into the node, Used to characterize the upstream node of this node. The carbon density is used to characterize the energy generated by the distributed generators connected to the node, and l is used to characterize all the transmission lines connecting the node.

[0054] CIF (carbon density coefficient) used to characterize each upstream component.

[0055] The CIF (Carbon Intensity Factor) is used to characterize DER (Distributed Energy Resources) located on the bus.

[0056] The CIF (carbon density factor) is used to characterize the electrical energy delivered from the main bus to downstream components.

[0057] Understandably, as power flow is transmitted through various components of the power system, emission factors can be weighted and averaged at each node based on the principle of equal distribution, thus yielding the emission responsibility to be borne by the load side. Specifically in the distribution network, due to its unidirectional power flow and radial nature, nodes with the same carbon emission responsibility, along with their subordinate components, can be considered as a single node in carbon responsibility allocation; this node can be called a virtual node (VB). By decoupling the distribution network—characterized by a large number of nodes and diverse, complex components—into a connection of virtual nodes, the computational burden is effectively reduced, thereby shifting the calculation of load-side carbon emission responsibility from the transmission network side to the distribution network side.

[0058] In an optional embodiment, acquiring generation-side data, load-side data, and power grid structure data, and determining the first carbon factor distribution data corresponding to the load side based on the generation-side data, load-side data, and power grid structure data, includes: acquiring the carbon emission factor of power generation equipment from the generation-side data; determining the root bus based on the power grid structure data, and determining the data of each first node connected to the root bus; determining the second node data downstream of each first node data, and the energy consumption data corresponding to the second node data, based on the load-side data; determining the weighted value corresponding to each first node data based on the energy consumption data of the second node data corresponding to each first node data; and determining the first carbon factor distribution data corresponding to each first node data based on the weighted value corresponding to each first node data and the carbon emission factor.

[0059] Among them, the busbar is used to characterize the busbar that connects the transmission network to the distribution network.

[0060] Optionally, when determining the distribution data of the first carbon factor, the data can be determined based on the power grid structure data, specifically the root bus, and then the data of all the first nodes on the root bus can be determined.

[0061] The first node data is used to determine the sub-buses connected to the root bus.

[0062] For example, the distribution data of the first carbon factor can be calculated and determined by taking the mother line as a whole.

[0063] In one embodiment, after determining the first node data, the sub-busbar can be determined based on the first node data, and after determining the sub-busbar, all loads connected to the sub-busbar can be determined based on the load-side data to determine the second node data.

[0064] Optionally, after determining the first node data and the second node data, the power consumption data of each of the first node data and the second node data is determined, thereby determining the power consumption corresponding to each sub-bus.

[0065] In an optional embodiment, after determining the power consumption corresponding to each sub-bus, the power consumption ratio between each sub-bus is determined according to the power consumption corresponding to each sub-bus, the weighting value is determined according to the ratio, and the responsibility for carbon factor on the power generation side is distributed according to the weighting value.

[0066] For example, see Figure 3 Assuming the node is determined based on its connection relationships, node b k b i b i+1 b i+ b n B′ i , And B i If all nodes are on the same parent-child line, then b can be... k b i b i+1 b i+ b n B′ i , And B i As a whole, it can be counted as the first virtual node VB1, and b is determined. k b i b i+1 b i+ b n B′ i , And B i The power consumption of each of these nodes will be b k b i b i+1 bi+ b n B′ i , And B i The energy consumption of each of these nodes is added together to obtain the energy consumption E1 of the first virtual node VB1. Optionally, other virtual nodes are determined, for example, including node B. j The second virtual node VB2, including B k The third virtual node VB3 is included, and the power consumption E2 and E3 corresponding to VB2 and VB3 are determined respectively.

[0067] In one embodiment, the ratios between E1, E2, and E3 are calculated, weight values ​​are determined based on these ratios, and carbon factor data is allocated according to these weights to obtain the first carbon factor distribution data. For example, assuming the ratios between E1, E2, and E3 are 2:1:3, when allocating carbon factor data, the first virtual node VB1 should be allocated two copies of carbon factor data. Similarly, the second virtual node VB2 should be allocated one copy, and the third virtual node VB3 should be allocated three copies. The first carbon factor distribution data corresponding to each virtual node is determined based on the carbon factor data allocated to each node.

[0068] In one embodiment, the virtual nodes can be further decoupled to determine the carbon factor distribution data corresponding to the sub-virtual nodes under each virtual node, so as to more accurately determine the carbon factor distribution data corresponding to each sub-virtual node.

[0069] For example, such as Figure 4 As shown, sub-virtual nodes can be formed based on the functions of each node in the parent-child network. Assuming that b1 and b2 perform the same or related functions, b1 and b2 will form sub-virtual node B1. Similarly, b3 and b4 will form sub-virtual node B2, and b5, b6 and b7 will form virtual node B3. The energy consumption of B1, B2 and B3 will be determined, and the weighting will be determined based on the determined energy consumption. The carbon factor distribution data of B1, B2 and B3 will be determined based on the determined weighting.

[0070] In one embodiment, when calculating the node carbon factor distribution data, the nodes can be updated. Optionally, the latest power grid structure data can be obtained, the power grid structure data can be traversed to determine whether a new node should be connected, and the node can be assigned to the corresponding virtual node according to the node's connection and function, and the first carbon factor distribution data of each virtual node can be recalculated.

[0071] Step 204: Obtain the electric vehicle charging request and the charging station distribution data included in the load-side data of the electric vehicle to be charged, and generate the first objective function corresponding to the resource transfer required for charging the electric vehicle to be charged, and the second carbon factor distribution data corresponding to electric vehicle charging, based on the electric vehicle charging request and the charging station distribution data included in the load-side data.

[0072] The electric vehicle charging request can be initiated by the electric vehicle that needs charging. Optionally, the charging request can be initiated by the electric vehicle that needs charging and sent to the server, or it can be initiated by a terminal connected to the electric vehicle that needs charging and sent to the server.

[0073] In one embodiment, after receiving a charging request, the server marks and stores the charging request so that it can be retrieved from the storage space when processing is required.

[0074] Optionally, the charging request can be marked by setting a tag. Optionally, the tag can be the vehicle identifier of the electric vehicle that initiated the charging request, or other tags, as long as they can distinguish different charging requests, and there is no limitation on this.

[0075] In one embodiment, the charging request may include, but is not limited to, the electric vehicle's expected parking location, the electric vehicle's expected charging time, the electric vehicle's current remaining battery power, the electric vehicle's required battery power, and the electric vehicle's battery capacity, so that the server can obtain the necessary data from the charging request for processing.

[0076] The expected parking location for electric vehicles refers to the location where the electric vehicles will be parked during the expected parking time. Optionally, the expected parking location for electric vehicles can be used to determine the target charging station.

[0077] The estimated charging time for an electric vehicle is the desired charging time. Optionally, the estimated charging time can be selected by the electric vehicle user as needed, for example, the user sets a specific time period for charging. Alternatively, the estimated charging time can also be automatically generated by the electric vehicle's control system based on the vehicle's current remaining battery power. For example, the electric vehicle's control system determines the estimated mobility time of the electric vehicle based on its current remaining battery power, and determines the estimated charging time based on the estimated mobility time and the current time.

[0078] The current remaining battery power of an electric vehicle is used to represent the current available battery power of the electric vehicle. Optionally, the current remaining battery power of the electric vehicle can be automatically obtained by the electric vehicle's control system and added to the charging request.

[0079] The required charge amount for an electric vehicle is used to characterize the amount of charge needed by the electric vehicle that initiated the charging request. Optionally, the charge level of the electric vehicle after being fully charged can be determined based on the required charge amount and the current remaining charge level of the electric vehicle.

[0080] The electric vehicle battery capacity is used to characterize the maximum capacity of the electric vehicle battery. Optionally, the electric vehicle battery capacity can be used to determine whether the required power of the electric vehicle is reasonable. For example, when the sum of the current remaining power of the electric vehicle and the required power of the electric vehicle is greater than the battery capacity, it indicates that the required power of the electric vehicle is unreasonable. Unreasonable feedback information can be generated to the terminal so that the electric vehicle user can adjust the required power of the electric vehicle according to the unreasonable feedback information. Alternatively, the difference between the battery capacity and the current remaining power of the electric vehicle can be determined using the battery capacity and the current remaining power of the electric vehicle, and the required power of the electric vehicle can be adjusted using the obtained difference.

[0081] Charging station distribution data is used to characterize the distribution of charging stations and can be used to confirm the location of a target charging station, for example, the location information of the charging station.

[0082] Understandably, the resource transfer involved in the electric vehicle charging process mainly includes two aspects: time allocation and resource consumption required to fully charge the vehicle. Therefore, a first objective function corresponding to the resource transfer required for electric vehicle charging can be generated based on these two aspects. In one embodiment, the charging time required to fully charge the electric vehicle can be determined based on the electric vehicle charging request and charging station distribution data; the resource data corresponding to the required charge can be determined based on the electric vehicle charging request; and the first objective function is obtained by weighted summing of the electric vehicle charging time and the electric vehicle resource data.

[0083] Optionally, the charging time for an electric vehicle to fully charge the required amount of electricity may include the travel time of the electric vehicle to the charging station, the waiting time at the charging station, and the time to fully charge the required amount of electricity.

[0084] In one embodiment, a method for determining the charging time required to fully charge an electric vehicle may include: obtaining information from an electric vehicle charging request, including the electric vehicle's expected parking location and the required battery level; generating a charging station search area based on the expected parking location, and determining a target charging station based on the search area and annual charging station distribution data; obtaining the charging power of the target charging station, and determining the expected charging completion time based on the charging power and the required battery level; obtaining road condition data between the expected parking location and the target charging station, and calculating the travel time for the electric vehicle to move from the expected parking location to the target charging station based on the road condition data; determining the expected queuing time at the target charging station; and determining the charging time required to fully charge the electric vehicle based on the expected charging completion time, travel time, and expected queuing time.

[0085] Optionally, after receiving a charging request, the server can obtain the expected parking location of the electric vehicle from the charging request, generate a charging station search area based on the expected parking location, and determine the target charging station using the generated charging station search area and charging station distribution data. Optionally, there can be one or more target charging stations. Optionally, each charging station includes one or more charging stations.

[0086] In one embodiment, the charging station search area can be determined based on the expected parking location of the electric vehicle and the search range.

[0087] Optionally, the search range can be determined by the movable distance of the electric vehicle. For example, the remaining battery power of the electric vehicle at its expected parking location is determined, and the theoretical maximum distance the electric vehicle can move from that location is determined based on the remaining battery power; this theoretical maximum distance is then used as the search range.

[0088] Understandably, the travel time of an electric vehicle from its intended parking location to the target charging station can include the time the electric vehicle spends on the road. This travel time can include waiting time at traffic lights, travel time when there are no other vehicles obstructing the road, and travel time when there are other vehicles obstructing the road. Since traffic lights and the presence or absence of other vehicles on the road are probabilistic, the travel time of an electric vehicle from its intended parking location to the target charging station can be determined statistically.

[0089] In one embodiment, road network information between the expected parking location of the electric vehicle and the target charging station can be determined. Based on this road network information, traffic conditions on the roads between the expected parking location and the target charging station during the expected travel time can be collected. For example, the expected travel time can be determined based on the expected charging time of the electric vehicle and the theoretical travel time it would take to move from the expected parking location to the target charging station.

[0090] Alternatively, the time it takes for an electric vehicle to travel on a road can be determined using statistical methods and traffic flow theory, as shown in formula (5):

[0091]

[0092] in, It is road e ij Travel time when there are no vehicles; n ij It is the current road reachability; c ij It refers to the road's traffic capacity.

[0093] The travel time for an electric vehicle to move from its intended parking location to the target charging station can be the sum of the travel times along all roads between the intended parking location and the target charging station. For example, if the electric vehicle travels along roads (paths) during its journey from its intended parking location to the target charging station, then... ij and e jh Therefore, the driving time of the electric vehicle can be expressed by formula (6):

[0094]

[0095] Among them, T id (t) is used to characterize the path e of the electric vehicle starting from its expected parking location. ij and e jh The time spent getting to the charging station CS.

[0096] Understandably, after an electric vehicle moves to a target charging station, there may already be vehicles charging but not yet finished charging, as well as vehicles waiting to be charged. Therefore, the time required for an electric vehicle to fully charge includes both the waiting time and the time required for the vehicle to charge itself. Optionally, the waiting time may include the time required for vehicles already charging but not yet finished charging at the target charging station to complete charging, and the time required for vehicles waiting to finish charging.

[0097] Understandably, the number of vehicles waiting to charge at a charging station is uncertain, so probabilistic statistical methods can be used to determine the queuing time. Optionally, the queuing time conforms to the M / G / K model of queuing theory.

[0098] For example, the expected queuing time can be determined as shown in formula (7):

[0099]

[0100] Among them, T iw (t) is used to characterize the expected queuing time, μ is the expected charging time, the variance σ follows a normal distribution, and ρ is the expected charging time. j =λ j μ, k j λ is used to characterize the number of charging piles in the j-th charging station. j Used to characterize the average arrival rate of electric vehicles within a charging station.

[0101] In one embodiment, assuming that the charging power of the electric vehicle is constant during the charging process, the charging time can be as shown in formula (8):

[0102]

[0103] Among them, E ie E is the expected charging amount for electric vehicles. ih P is the remaining battery power of the electric vehicle. j It refers to the charging power.

[0104] In an alternative embodiment, the driving time, queuing time, and charging time can be added together to obtain the electric vehicle charging time, as shown in formula (9):

[0105] T i (t)=T id (t)+T iw (t)+T ic (9)

[0106] In one embodiment, determining the resource data corresponding to the amount of electricity required to fully charge an electric vehicle may include: obtaining the product attribute value corresponding to the target charging station; and determining the resource data corresponding to the amount of electricity required to fully charge the electric vehicle based on the product attribute value and the required amount of electricity of the electric vehicle.

[0107] Among them, the product attribute value is used to characterize the attribute value of the service provided by the charging station, such as the charging electricity price provided by the charging station.

[0108] Optionally, based on the product attribute values ​​and the required power of the electric vehicle, the resource data corresponding to the power required to fully charge the electric vehicle can be determined as shown in formula (10):

[0109] C t =λ j (t)·(E ie -E ih (10)

[0110] Among them, C t λ is used to characterize the resource data corresponding to the required power to fully charge the battery. j (t) is used to characterize the product attribute value of charging station j.

[0111] Understandably, different electric vehicle users pay different levels of attention to charging time and the resource data corresponding to the required amount of electricity to fully charge. Therefore, we can set weight coefficients for charging time and resource data respectively to generate a first objective function for different electric vehicle users.

[0112] For example, the generated first objective function can be as shown in formula (11):

[0113] F1=[α i ζT i (t)+β i C i (t)]x ij (11)

[0114] Where, α i β is a weighting coefficient used to characterize the cost of charging time for electric vehicle users. i The weighting coefficients used to characterize the time cost of charging for electric vehicle users; ζ represents the cost per unit time and is the coefficient that converts time consumption into cost; x ij It is a variable between 0 and 1. If an electric vehicle comes to the charging station, it is assigned the value 1, and otherwise it is assigned the value 0.

[0115] It is understandable that electric vehicles also generate carbon emissions during the charging process, for example as shown in formula (12):

[0116]

[0117] Among them, P i Used to characterize charging power The carbon density coefficient is used to characterize the carbon density of a vehicle during charging.

[0118] Step 206: Generate a second objective function based on the first carbon factor distribution data and the second carbon factor distribution data.

[0119] Optionally, the first carbon factor distribution data can be determined using carbon factor data from the power generation side and the weight values ​​corresponding to the virtual nodes, for example:

[0120] E = Ei,t / W (13)

[0121] Where W is used to represent the weight value.

[0122] In one embodiment, the second objective function can be as shown in equation (14):

[0123]

[0124] Step 208: Obtain the third objective function corresponding to the resource gain data of the charging station, and the fourth objective function corresponding to the utilization rate of each charging device in the charging station.

[0125] Among them, the charging station resource gain data is used to characterize the increased revenue after the charging station provides services.

[0126] In one embodiment, the third objective function corresponding to the charging station resource gain data can be expressed as formula (15):

[0127]

[0128] Among them, P RE,t P is used to characterize renewable energy output. rel,t P is used to characterize the battery discharge capacity. BL,t Used to characterize the base load P is used to characterize the charging power of electric vehicles. ave The value is used to characterize the average power, i is used to characterize the electric vehicle number, t is used to characterize the time, and T is used to characterize the optimization period.

[0129] It is understandable that when the utilization rate of each charging device in a charging station is maximized, the resource gain data of the charging station is generally maximized. Therefore, the resource gain data of the charging station can also be determined based on the utilization rate of each charging device in the charging station. For example, the fourth objective function corresponding to the utilization rate of each charging device in the charging station can be expressed by formula (16):

[0130]

[0131] Where N represents the total number of electric vehicles in the region, M represents the number of charging stations, and P i Used to characterize the charging power of electric vehicles.

[0132] Step 210: Determine the preset first constraint condition for the electric vehicle and the preset second constraint condition for the charging station.

[0133] Step 212: Determine the target charging station information corresponding to each electric vehicle when the electric vehicle to be charged meets the first constraint condition, each charging station meets the second constraint condition, and the first objective function value is minimized, the second objective function value is minimized, the third objective function value is maximized, and the fourth objective function value is maximized.

[0134] In one embodiment, the first objective function, the second objective function, the third objective function, and the fourth objective function can be weighted and summed to obtain a total function, and the total function can be minimized to determine the target charging station information that meets the requirements.

[0135] For example, the total function can be shown in formula (17):

[0136]

[0137] Among them, γ1, γ2, γ3, and γ4 are used to characterize the optimization weight coefficients, where γ1+γ2+γ3+γ4=1.

[0138] In one embodiment, the first constraint includes a remaining capacity constraint for the electric vehicle to be charged. The electric vehicle to be charged satisfies the first constraint by: the maximum travel distance that the electric vehicle to be charged can travel is greater than the distance that the electric vehicle to be charged can travel to each charging station; the maximum travel distance that the electric vehicle to be charged can travel is determined based on the remaining battery power of the electric vehicle to be charged.

[0139] Optionally, the current remaining battery power of the electric vehicle can be obtained, and the estimated remaining battery power when the electric vehicle is expected to be parked can be determined based on the current remaining battery power. The maximum driving distance of the electric vehicle can be determined based on the estimated remaining battery power.

[0140] For example, if the current location of the electric vehicle is the same as the expected parking location, then the current remaining battery power of the electric vehicle is the expected remaining battery power. If the current location of the electric vehicle is not the same as the expected parking location, the movement path between the current location and the expected parking location can be determined. Based on the movement path and the moving speed of the electric vehicle, the battery power consumed by the electric vehicle to travel from the current location to the expected parking location can be determined. Then, the expected remaining battery power is determined by combining the current remaining battery power and the consumed battery power. Optionally, the driving speed of the electric vehicle when determining the expected remaining battery power can be the average driving speed of the electric vehicle.

[0141] In one embodiment, after determining the expected remaining battery power, the maximum travel distance of the electric vehicle can be determined based on the expected remaining battery power and the vehicle's speed. Optionally, when determining the maximum travel distance, the vehicle's speed can be either the minimum permissible speed or the vehicle's average speed.

[0142] In one embodiment, the distance the electric vehicle travels to the charging station can be determined based on the remaining battery power and the electric vehicle's speed. Optionally, the speed of the electric vehicle used to determine the distance to the charging station can be either the minimum permissible speed or the average speed. It is understood that, to ensure the feasibility of comparison, the speed of the electric vehicle used to calculate the maximum travel distance should be consistent with the speed used to determine the distance to the charging station. For example, if the maximum travel distance is determined based on the minimum permissible speed, then the speed of the electric vehicle used to determine the distance to the charging station should also be the minimum speed; if the maximum travel distance is determined based on the average speed, then the speed of the electric vehicle used to determine the distance to the charging station should also be the average speed.

[0143] For example, the relationship between the maximum driving distance of an electric vehicle and the distance between the electric vehicle and the charging station can be expressed as shown in formula (18):

[0144]

[0145] L1 is used to characterize the maximum driving distance of an electric vehicle. Used to represent the distance traveled to a charging station.

[0146] Optionally, once the maximum driving distance of the electric vehicle and the distance from the electric vehicle to the charging station are determined, the charging stations can be initially screened by excluding those whose distance from the electric vehicle to the charging station is greater than the maximum driving distance of the electric vehicle, thus obtaining a preliminary list of charging stations.

[0147] In one embodiment, the second constraint includes the charging station capacity and the charging station load. Each charging station satisfies the second constraint by: the number of electric vehicles charging in the charging station being less than the corresponding capacity of the charging station and the charging station load being less than or equal to the maximum allowable load.

[0148] The capacitor of a charging station is used to characterize the maximum electrical force that the charging station can handle under safe operating conditions, and is usually expressed in units of power or energy.

[0149] The load of a charging station is used to characterize the components connected across the power source in a circuit. Optionally, the load of a charging station can be the electrical energy consumed by the charging station machines when charging electric vehicles.

[0150] In one embodiment, when determining a target charging station, the target charging station can be one with available charging piles, so that the electric vehicle can be charged after moving to the target charging station, effectively improving charging efficiency.

[0151] Optionally, from the initial screening of charging stations, it can be determined whether each initial screening charging station still has usable charging piles. If not, it will be excluded; if so, it will be retained, and the retained charging stations will be used as target charging stations.

[0152] For example, the relationship between the capacity of the target charging station and the number of electric vehicles at the charging station can be expressed as formula (19):

[0153]

[0154] Among them, K j (t) is used to characterize the capacity of the charging station, x ij ε is used to characterize the number of electric vehicles at a charging station. j (t) is used to characterize the number of electric vehicles expected at the charging station at time t.

[0155] In one embodiment, when further screening the initial screening charging stations, charging stations that can still accommodate electric vehicles can be selected from the initial screening charging stations, and the selected charging stations are set as second screening charging stations.

[0156] Optionally, the load on charging station nodes can also be limited.

[0157] Among them, node load is used to characterize the actual operating load of the station.

[0158] In one embodiment, the condition that the node load satisfies can be as shown in formula (20):

[0159]

[0160] in, Used to characterize the load at charging station nodes. Used to characterize the maximum allowable load of a power station, the base value depends on the design capacity.

[0161] In one embodiment, the second-selected charging stations are further screened using the node load requirements to obtain the third-selected charging stations.

[0162] In one embodiment, the target charging station is determined based on the charging stations obtained after the third screening.

[0163] Step 214: Generate guidance information based on the target charging station information and send it to each electric vehicle.

[0164] The guidance information is used to guide electric vehicles to the target charging station.

[0165] The guidance information is used to guide electric vehicles to the target charging station.

[0166] Optionally, the guidance information includes, but is not limited to, navigation information, target charging station information, etc.

[0167] In one embodiment, after receiving guidance information, an electric vehicle can move to the target charging station according to the navigation information included in the guidance information to achieve charging.

[0168] In the above-mentioned electric vehicle charging method, the resource consumption needs of electric vehicle users, the carbon factors generated by electric vehicles during the charging process, and the resource revenue data of the charging station are considered when determining the target charging station. This enables the determined target charging station to reduce carbon emissions, reduce the resource consumption of electric vehicle users, increase the resource revenue of the charging station, and improve the rational allocation of resources.

[0169] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0170] Based on the same inventive concept, this application also provides an electric vehicle charging device for implementing the electric vehicle charging method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the electric vehicle charging device provided below can be found in the limitations of the electric vehicle charging method described above, and will not be repeated here.

[0171] In one exemplary embodiment, such as Figure 5 As shown, an electric vehicle charging device 300 is provided, including: a first data determination module 302, a second data determination module 304, a function generation module 306, a function acquisition module 308, a constraint condition determination module 310, a charging station determination module 312, and a guidance information generation module 314, wherein:

[0172] The first data determination module 302 is used to acquire power generation side data, load side data and power grid structure data, and determine the first carbon factor distribution data corresponding to the load side based on the power generation side data, load side data and power grid structure data.

[0173] The second data determination module 304 is used to obtain the electric vehicle charging request of the electric vehicle to be charged, and generate a first objective function corresponding to the resource transfer required for charging the electric vehicle to be charged, and a second carbon factor distribution data corresponding to the charging of the electric vehicle, based on the charging request of the electric vehicle and the charging station distribution data included in the load-side data.

[0174] The function generation module 306 is used to generate a second objective function based on the first carbon factor distribution data and the second carbon factor distribution data.

[0175] The function acquisition module 308 is used to acquire the third objective function corresponding to the resource gain data of the charging station, and the fourth objective function corresponding to the utilization rate of each charging device in the charging station.

[0176] The constraint determination module 310 is used to determine the preset first constraint condition for the electric vehicle and the preset second constraint condition for the charging station.

[0177] The charging station determination module 312 is used to determine the target charging station information corresponding to each electric vehicle when the electric vehicle to be charged meets the first constraint condition, each charging station meets the second constraint condition, and the first objective function value is minimized, the second objective function value is minimized, the third objective function value is maximized, and the fourth objective function value is maximized.

[0178] The guidance information generation module 314 is used to generate guidance information based on the target charging station information and send it to each electric vehicle. The guidance information is used to guide the electric vehicles to move to the target charging station.

[0179] In one embodiment, the first data determination module is further configured to: obtain the carbon emission factor of power generation equipment from power generation side data; determine the root bus based on power grid structure data, and determine the data of each first node connected to the root bus; determine the second node data downstream of each first node data and the power consumption data corresponding to the second node data based on load side data; determine the weighted value corresponding to each first node data based on the power consumption data of the second node data corresponding to each first node data; and determine the first carbon factor distribution data corresponding to each first node data based on the weighted value corresponding to each first node data and the carbon emission factor.

[0180] In one embodiment, the function generation module is further configured to: obtain the electric vehicle's expected parking location and required battery power carried in the electric vehicle charging request; generate a charging station search area based on the expected parking location, and determine a target charging station based on the charging station search area and annual charging station distribution data; obtain the charging power of the target charging station, and determine the expected charging completion time of the electric vehicle based on the charging power and required battery power; obtain road condition data between the expected parking location and the target charging station, and calculate the travel time for the electric vehicle to move from the expected parking location to the target charging station based on the road condition data; determine the expected queuing time at the target charging station; and determine the charging time for the electric vehicle to fully charge the required battery power based on the expected charging completion time, travel time, and expected queuing time.

[0181] In one embodiment, the first constraint includes a remaining battery power constraint for the electric vehicle to be charged. The electric vehicle to be charged satisfies the first constraint, which includes: the maximum travel distance that the electric vehicle to be charged can travel is greater than the distance that the electric vehicle to be charged can travel to each charging station; the maximum travel distance that the electric vehicle to be charged can travel is determined based on the remaining battery power of the electric vehicle to be charged.

[0182] In one embodiment, the second constraint includes the charging station capacity and the charging station load. Each charging station satisfies the second constraint, which includes: the number of electric vehicles charging in the charging station is less than the capacity corresponding to the charging station and the load of the charging station is less than or equal to the maximum allowable load.

[0183] Each module in the aforementioned electric vehicle charging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0184] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a method for charging an electric vehicle.

[0185] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0186] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method embodiments.

[0187] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method embodiments.

[0188] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the above-described method embodiments.

[0189] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0190] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0191] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0192] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for charging an electric vehicle, characterized in that, The method includes: Acquire generation-side data, load-side data, and power grid structure data, and determine the first carbon factor distribution data corresponding to the load side based on the generation-side data, the load-side data, and the power grid structure data; Obtain the electric vehicle charging request of the electric vehicle to be charged, and generate a first objective function corresponding to the resource transfer required for charging the electric vehicle to be charged, and a second carbon factor distribution data corresponding to the charging of the electric vehicle, based on the electric vehicle charging request and the charging station distribution data included in the load-side data. A second objective function is generated based on the first carbon factor distribution data and the second carbon factor distribution data; Obtain the third objective function corresponding to the resource gain data of the charging station, and the fourth objective function corresponding to the utilization rate of each charging device in the charging station; Determine the first preset constraint condition for the electric vehicle and the second preset constraint condition for the charging station; Determine the target charging station information corresponding to each electric vehicle when the electric vehicle to be charged meets the first constraint condition, each charging station meets the second constraint condition, and the first objective function value is minimized, the second objective function value is minimized, the third objective function value is maximized, and the fourth objective function value is maximized; Guidance information is generated based on the target charging station information and sent to each electric vehicle. The guidance information is used to guide the electric vehicles to move to the target charging station.

2. The method according to claim 1, characterized in that, The step of acquiring generation-side data, load-side data, and power grid structure data, and determining the first carbon factor distribution data corresponding to the load side based on the generation-side data, the load-side data, and the power grid structure data, includes: The carbon emission factor of power generation equipment is obtained from the power generation side data; Based on the power grid structure data, determine the root busbar and the data of each first node connected to the root busbar; Based on the load-side data, determine the second node data downstream of each first node data, and the corresponding power consumption data of the second node data; Based on the energy consumption data of the second node data corresponding to each first node data, determine the weighting value corresponding to each first node data. Based on the weighted value corresponding to each first node data and the carbon emission factor, the distribution data of the first carbon factor corresponding to each first node data is determined.

3. The method according to claim 1, characterized in that, The step of generating a first objective function corresponding to the resource transfer required for charging the electric vehicle to be charged, based on the electric vehicle charging request and the charging station distribution data included in the load-side data, includes: Based on the electric vehicle charging request and the charging station distribution data, determine the charging time required for the electric vehicle to fully charge. Based on the electric vehicle charging request, determine the resource data corresponding to the amount of electricity required to fully charge the electric vehicle; The first objective function is obtained by weighted summation of the charging time and resource data of the electric vehicle.

4. The method according to claim 3, characterized in that, The step of determining the charging time required for the electric vehicle to fully charge based on the electric vehicle charging request and the charging station distribution data includes: The electric vehicle charging request includes the electric vehicle's expected parking location and the electric vehicle's required battery level. A charging station search area is generated based on the expected parking location of the electric vehicle, and a target charging station is determined based on the charging station search area and the charging station distribution data. The charging power of the target charging station is obtained, and the estimated charging completion time of the electric vehicle is determined based on the charging power and the required power. Obtain road condition data between the expected parking location of the electric vehicle and the target charging station, and calculate the travel time of the electric vehicle from the expected parking location to the target charging station based on the road condition data; Determine the estimated queuing time for the target charging station; The charging time required to fully charge the electric vehicle is determined based on the estimated charging completion time, the driving time, and the estimated queuing time.

5. The method according to claim 1, characterized in that, The first constraint includes a constraint on the remaining battery power of the electric vehicle to be charged, and the electric vehicle to be charged satisfies the first constraint, including: The maximum travel distance that the electric vehicle to be charged can travel is greater than the distance that the electric vehicle to be charged can travel to each of the charging stations; the maximum travel distance that the electric vehicle to be charged can travel is determined based on the remaining battery power of the electric vehicle to be charged.

6. The method according to claim 1, characterized in that, The second constraint includes charging station capacity and charging station load. Each charging station satisfies the second constraint in the following ways: The number of electric vehicles charging in the charging station is less than the capacity of the charging station, and the load of the charging station is less than or equal to the maximum allowable load.

7. An electric vehicle charging device, characterized in that, The device includes: The first data determination module is used to acquire power generation side data, load side data and power grid structure data, and determine the first carbon factor distribution data corresponding to the load side based on the power generation side data, the load side data and the power grid structure data; The second data determination module is used to obtain the electric vehicle charging request of the electric vehicle to be charged, and generate a first objective function corresponding to the resource transfer required for charging the electric vehicle to be charged, and a second carbon factor distribution data corresponding to the charging of the electric vehicle, based on the charging request of the electric vehicle and the charging station distribution data included in the load-side data. The function generation module is used to generate a second objective function based on the first carbon factor distribution data and the second carbon factor distribution data; The function acquisition module is used to acquire the third objective function corresponding to the resource gain data of the charging station, and the fourth objective function corresponding to the utilization rate of each charging device in the charging station. The constraint determination module is used to determine the preset first constraint condition for the electric vehicle and the preset second constraint condition for the charging station. The charging station determination module is used to determine the target charging station information corresponding to each electric vehicle when the electric vehicle to be charged meets the first constraint condition, each charging station meets the second constraint condition, and the first objective function value is minimized, the second objective function value is minimized, the third objective function value is maximized, and the fourth objective function value is maximized. The guidance information generation module is used to generate guidance information based on the target charging station information and send it to each electric vehicle. The guidance information is used to guide the electric vehicles to move to the target charging station.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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