Electric vehicle energy supplementing method and device, terminal and medium

Abstract

This invention provides a method, device, terminal, and medium for electric vehicle recharging. The method includes: acquiring charging station grid data, charging station power supply data, vehicle owner demand data, vehicle status data, and weather and road condition data; the charging station grid data includes a predicted dynamic marginal carbon emission factor; based on the vehicle owner demand data, the vehicle status data, and the weather and road condition data, obtaining energy consumption per unit mile and total detour distance; based on the charging station power supply data and the vehicle status data, obtaining a charging power prediction curve; based on the predicted dynamic marginal carbon emission factor, the charging power prediction curve, energy consumption per unit mile, and total detour distance, obtaining the spatiotemporal marginal carbon total cost; and based on the spatiotemporal marginal carbon total cost, generating charging station data to allow vehicle owners to select charging stations from the available charging station data. This achieves green, safe, and efficient operation of the entire electric vehicle recharging chain.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging scheduling technology, specifically to an electric vehicle charging replenishment method, device, terminal, and medium. Background Technology

[0002] With the rapid growth in the number of electric vehicles, the total demand for charging has surged. At the same time, the increasing popularity of technologies such as 800V high-voltage fast charging and supercharging has significantly increased the charging power of single charging piles, placing higher demands on the power grid's supply capacity.

[0003] The lack of unified allocation of current charging demand easily leads to a series of problems. On the one hand, car owners often blindly search for charging stations, resulting in a large amount of ineffective mileage and additional energy waste, which contradicts the "dual-carbon" strategic goal. On the other hand, the distribution of power grid energy and charging stations is uneven. In core urban areas, the power grid capacity is limited but the number of charging stations is dense, while in suburban areas, the power grid capacity is abundant but the number of charging stations is scarce, resulting in ineffective resource matching. Most existing charging scheduling methods make simple recommendations based on single factors such as electricity price and distance, failing to comprehensively consider the impact of additional carbon emissions generated by vehicle detours. Summary of the Invention

[0004] In view of the shortcomings of existing technologies, this invention proposes a method, device, terminal and medium for electric vehicle recharging, aiming to achieve green, safe and efficient operation of the entire electric vehicle recharging chain.

[0005] In a first aspect, embodiments of this application provide a method for replenishing energy for an electric vehicle, including: The system acquires charging station grid data, charging station power supply data, vehicle owner demand data, vehicle status data, and weather and road condition data; the charging station grid data includes a predicted dynamic marginal carbon emission factor. Based on the owner's demand data, the vehicle status data, and the weather and road condition data, the energy consumption per unit mileage and the total detour distance are obtained. Based on the power supply data of the charging station and the vehicle status data, a charging power prediction curve is obtained; Based on the predicted dynamic marginal carbon emission factor, the predicted charging power curve, the energy consumption per unit mileage, and the total detour distance, the total spatiotemporal marginal carbon cost is obtained. Based on the spatiotemporal marginal total carbon cost, data on available charging stations is generated to allow car owners to select charging stations from this data.

[0006] Optionally, obtaining the energy consumption per unit mile and the total detour distance based on the vehicle owner's demand data, the vehicle status data, and the weather and road condition data includes: Based on the vehicle status data and the weather and road condition data, the energy consumption per unit mileage is obtained; The total detour distance is obtained based on the car owner's demand data and the vehicle status data.

[0007] Optionally, the weather and road condition data includes: weather data and road condition data; vehicle status data includes vehicle gross weight, speed, rolling resistance coefficient, air resistance coefficient, vehicle frontal area, and rotational mass conversion factor; based on the vehicle status data and the weather and road condition data, the energy consumption per unit mileage is obtained, including: The air density is obtained based on weather data; Based on road condition information, the road slope angle is obtained; The energy consumption per unit mileage is obtained based on air density, road slope angle, total vehicle mass, driving speed, rolling resistance coefficient, air resistance coefficient, vehicle frontal area, and rotational mass conversion factor.

[0008] Optionally, the energy consumption per unit mileage is obtained based on air density, road slope angle, total vehicle mass, driving speed, rolling resistance coefficient, air resistance coefficient, vehicle frontal area, and rotational mass conversion factor, including:

[0009] in, This represents the energy consumption per unit mileage of vehicle i under the current driving conditions. This refers to the overall efficiency of the power system, which is the overall efficiency from the battery to the wheels. The total mass of the vehicle; It is the acceleration due to gravity; This is the rolling resistance coefficient; The road slope angle; air density; This refers to the air drag coefficient; The vehicle's frontal area; Let be the speed of vehicle i at time t; This is the rotational mass conversion factor; Let be the acceleration of vehicle i at time t; Power of vehicle accessories.

[0010] Optionally, the driver's demand data includes destination data; the vehicle status data includes the vehicle's current location; and the total detour distance is obtained based on the driver's demand data and the vehicle status data. ,include: Calculate the first distance traveled from the vehicle's current location to station j; Calculate the second driving distance from station j to the destination; Calculate the third distance traveled from the vehicle's current location to its destination; The total detour distance is obtained based on the first driving distance, the second driving distance, and the third driving distance.

[0011] Optionally, the charging station power supply data includes the rated power of the charging pile; the vehicle status data includes: the vehicle's maximum allowable power, total battery energy, initial remaining charge, target remaining charge, initial remaining charge at power reduction, minimum sustaining power, and battery temperature discount factor; based on the charging station power supply data and the vehicle status data, a charging power prediction curve is obtained, including: The actual usable peak power is obtained based on the rated power of the charging pile, the maximum allowable power of the vehicle, and the battery temperature discount factor. Based on the remaining charge at the start of charging, the remaining charge at the start of power reduction, and the total battery energy, the amount of charge required for the constant power stage is obtained. Based on the remaining charge at the start of charging, the remaining charge at the start of power reduction, the target remaining charge, and the total battery energy, the amount of charge required for the degradation stage is obtained. The duration of the constant power phase is obtained based on the amount of electricity required to be charged during the constant power phase and the actual available peak power. The duration of the decay phase is obtained based on the amount of electricity required to be charged during the decay phase, the actual available peak power, and the minimum sustaining power. The charging power prediction curve is obtained based on the actual available peak power, minimum sustaining power, constant power stage duration, and constant power stage duration.

[0012] Optionally, based on the predicted dynamic marginal carbon emission factor, the charging power prediction curve, the energy consumption per unit mileage, and the total detour distance, the spatiotemporal marginal total carbon cost is obtained, including: ; in, The total spatiotemporal marginal carbon cost incurred by selecting charging station j for vehicle i; The current moment; The estimated time when the vehicle will arrive at charging station j; This represents the energy consumption per unit mileage of vehicle i under the current driving conditions. Carbon emission factor per kilometer over the entire life cycle of vehicle i; This represents the total detour distance from the current location to station j and then to the destination; The expected start time for charging; The expected end time of charging; For vehicle i at time The charging power; For the power grid node where station j is located at time... The predicted dynamic marginal carbon emission factor.

[0013] Secondly, embodiments of this application provide an electric vehicle refueling device, comprising: The data acquisition module is used to acquire charging station grid data, charging station power supply data, vehicle owner demand data, vehicle status data, and weather and road condition data; the charging station grid data includes the predicted dynamic marginal carbon emission factor. The mileage distance determination module is used to obtain the energy consumption per unit mileage and the total detour distance based on the vehicle owner's demand data, the vehicle status data, and the weather and road condition data. The power prediction module is used to obtain a charging power prediction curve based on the power supply data of the charging station and the vehicle status data. The total carbon cost determination module is used to obtain the spatiotemporal total carbon cost based on the predicted dynamic marginal carbon emission factor, the charging power prediction curve, the energy consumption per unit mileage, and the total detour distance. The rechargeable station output module is used to generate rechargeable station data based on the spatiotemporal marginal total carbon cost, so that car owners can select a charging station from the rechargeable station data.

[0014] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the electric vehicle refueling method as described in any of the first aspects above.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the electric vehicle refueling method as described in any one of the first aspects above.

[0016] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the electric vehicle refueling method described in any one of the first aspects.

[0017] In this embodiment, charging station grid data, charging station power supply data, vehicle owner demand data, vehicle status data, and weather and road condition data are acquired. The charging station grid data includes a predicted dynamic marginal carbon emission factor. Based on the vehicle owner demand data, vehicle status data, and weather and road condition data, energy consumption per unit mile and total detour distance are obtained. Based on the charging station power supply data and vehicle status data, a charging power prediction curve is obtained. Based on the predicted dynamic marginal carbon emission factor, charging power prediction curve, energy consumption per unit mile, and total detour distance, the spatiotemporal marginal carbon cost is obtained. Based on the spatiotemporal marginal carbon cost, available charging station data is generated to allow vehicle owners to select charging stations from the available charging station data. This achieves green, safe, and efficient operation of the entire electric vehicle charging chain. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the electric vehicle recharging method provided in this application; Figure 2 This is a schematic diagram of the structure of the electric vehicle refueling device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0021] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0022] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0023] Figure 1 The diagram illustrates a first embodiment of the electric vehicle recharging method provided in this application. It is provided as an example and not a limitation; the method can be applied to the aforementioned electric vehicle recharging device. Figure 1 As shown, the method may include: S10, acquire the power grid data where the charging station is located, the power supply data of the charging station, the owner's demand data, the vehicle status data, and the weather and road condition data; the power grid data of the charging station includes the predicted dynamic marginal carbon emission factor; To achieve green, safe, and efficient operation of the entire electric vehicle (EV) charging chain, the EV charging device acquires data from the charging station's power grid, power supply, vehicle owner demand, vehicle status, and weather and road conditions. The charging station's power grid data refers to the data from the power grid where charging station j is located; this data includes the predicted dynamic marginal carbon emission factor of the power grid where the charging station is located. .

[0024] S20, based on the owner's demand data, the vehicle status data, and the weather and road condition data, the energy consumption per unit mileage and the total detour distance are obtained; After acquiring data on the owner's needs, vehicle status, and weather and road conditions, the electric vehicle charging device calculates the energy consumption per unit mileage based on these data. Total detour distance .

[0025] As one implementation method, obtaining the energy consumption per unit mileage and the total detour distance based on the vehicle owner's demand data, the vehicle status data, and the weather and road condition data may include: A1. Based on the vehicle status data and the weather and road condition data, the energy consumption per unit mileage is obtained. After acquiring vehicle status data and weather / road condition data, the electric vehicle refueling device calculates the energy consumption per unit mileage based on the vehicle status data and the weather / road condition data. .

[0026] As one implementation method, weather and road condition data includes weather data and road condition data; vehicle status data includes vehicle gross vehicle weight. Driving speed Rolling resistance coefficient air drag coefficient Vehicle frontal area and rotational mass conversion factor Based on the vehicle status data and the weather and road condition data, the energy consumption per unit mileage is obtained, including: B1, based on weather data, obtains the air density; After acquiring weather data, the electric vehicle charging device calculates the air density based on that data. .

[0027] B2, based on road condition information, obtain the road slope angle; After acquiring road condition information, the electric vehicle charging device determines the road slope angle based on that information. .

[0028] B3, based on air density, road slope angle, total vehicle mass, driving speed, rolling resistance coefficient, air resistance coefficient, vehicle frontal area, and rotational mass conversion factor, yields the energy consumption per unit mileage.

[0029] Electric vehicle refueling devices obtained air density and road slope angle and obtained the total mass of the vehicle Driving speed Rolling resistance coefficient air drag coefficient Vehicle frontal area and rotational mass conversion factor Then, based on air density Road slope angle Total vehicle weight Driving speed Rolling resistance coefficient air drag coefficient Vehicle frontal area and rotational mass conversion factor The energy consumption per unit distance was obtained. .

[0030] As one implementation method, the energy consumption per unit mileage can be obtained based on air density, road slope angle, total vehicle mass, driving speed, rolling resistance coefficient, air resistance coefficient, vehicle frontal area, and rotational mass conversion factor. This can include:

[0031] in, This represents the energy consumption per unit mileage of vehicle i under the current driving conditions. This refers to the overall efficiency of the power system, which is the overall efficiency from the battery to the wheels. The total mass of the vehicle; It is the acceleration due to gravity; This is the rolling resistance coefficient; The road slope angle; air density; This refers to the air drag coefficient; The vehicle's frontal area; Let be the speed of vehicle i at time t; This is the rotational mass conversion factor; Let be the acceleration of vehicle i at time t; Power of vehicle accessories.

[0032] A2. Based on the owner's demand data and the vehicle status data, the total detour distance is obtained.

[0033] As one implementation method, the driver's demand data includes destination data; the vehicle status data includes the vehicle's current location; based on the driver's demand data and the vehicle status data, the total detour distance is obtained, specifically including: C1, calculate the first distance traveled from the vehicle's current location to station j; After acquiring the vehicle's current location data, the electric vehicle charging device calculates the first travel distance from the current location to station j. .

[0034] C2, calculate the second driving distance from station j to the destination; After acquiring destination data, the electric vehicle charging device calculates the second driving distance from station j to the destination. .

[0035] C3 calculates the third distance traveled from the vehicle's current location to its destination; After acquiring the vehicle's current location data and destination data, the electric vehicle charging device calculates the third travel distance from the vehicle's current location to the destination. .

[0036] C4, the total detour distance is obtained based on the first driving distance, the second driving distance, and the third driving distance.

[0037] The electric vehicle charging device achieved its first driving distance. Second driving distance and the third driving distance Then, based on the first, second, and third driving distances, the total detour distance is obtained. Where station j is charging station j.

[0038] Of which, the total detour distance .

[0039] S30: Based on the power supply data of the charging station and the vehicle status data, a charging power prediction curve is obtained.

[0040] After acquiring the power supply data of the charging station and the vehicle status data, the electric vehicle charging device obtains a charging power prediction curve based on the power supply data of the charging station and the vehicle status data.

[0041] In one implementation, the charging station power supply data includes the rated power of the charging piles. Vehicle status data includes: maximum permissible power of the vehicle. Total battery energy Remaining battery power at the start of charging Target remaining power Remaining power at the start of power reduction Minimum sustaining power and battery temperature discount factor Based on the power supply data of the charging station and the vehicle status data, a charging power prediction curve is obtained, specifically including: D1 is the actual usable peak power obtained based on the rated power of the charging pile, the maximum allowable power of the vehicle, and the battery temperature discount factor. The electric vehicle charging device obtains power based on the rated power of the charging pile. Maximum permissible power of the vehicle and battery temperature discount factor Then, based on the rated power of the charging pile Maximum permissible power of the vehicle and battery temperature discount factor To obtain the actual usable peak power .

[0042] in, ; D2, based on the remaining charge at the start of charging, the remaining charge at the start of power reduction, and the total battery energy, the charge required for the constant power stage is obtained; The electric vehicle charging device obtains the remaining power at the start of charging. The remaining power at the start of the power reduction and total battery energy Then, based on the remaining power at the start of charging... The remaining power at the start of the power reduction and total battery energy To achieve constant power, the required charge amount is needed. .

[0043] During the constant power phase, electricity needs to be charged. : ; in, The amount of electricity required for the constant power phase is expressed in units of [missing information]. ; The remaining power is at the start of power reduction; The remaining battery level at the start of charging; This refers to the total energy of the battery. like ,but It then directly enters the attenuation phase.

[0044] D3, based on the remaining charge at the start of charging, the remaining charge at the start of power reduction, the target remaining charge, and the total battery energy, the charge required for the degradation stage is obtained; The electric vehicle charging device obtains the remaining power at the start of charging. The remaining power at the start of the power reduction Target remaining power and total battery energy Then, based on the remaining power at the start of charging... The remaining power at the start of the power reduction Target remaining power and total battery energy During the decay phase, electricity needs to be charged. .

[0045] During the decay phase, electricity needs to be charged. : ; in, The amount of electricity required during the degradation phase is measured in units of [units missing]. ; The target remaining battery level; The remaining power is at the start of power reduction; The remaining battery level at the start of charging; This refers to the total energy of the battery. like ,but Constant power throughout the entire process.

[0046] D4, based on the amount of electricity required to charge during the constant power phase and the actual available peak power, yields the duration of the constant power phase; Electric vehicle charging devices need to be charged when they reach a constant power level. and actual available peak power Afterwards, the amount of electricity required to charge during the constant power phase... Actual available peak power The duration of the constant power phase was obtained. .

[0047] Among them, the duration of the constant power phase : ; in, Duration of the constant power phase; The power needs to be charged during the constant power phase; This represents the actual available peak power.

[0048] D5, based on the amount of electricity required for the decay phase, the actual available peak power, and the minimum sustaining power, yields the duration of the decay phase; Electric vehicle charging devices need to be recharged during the depletion phase. Actual available peak power and minimum sustaining power Afterwards, charge the battery according to the required amount during the decay phase. Actual available peak power and minimum sustaining power The duration of the decay phase is obtained. .

[0049] Approximation for the attenuation phase: The power in this phase is approximated by the actual available peak power. Linear decay to minimum sustaining power The average power is The duration of the decay phase : ; in, This refers to the duration of the decay phase. During the decay phase, electricity needs to be added. This represents the actual available peak power. This is the minimum sustaining power.

[0050] D6, based on the actual available peak power, minimum sustaining power, constant power stage duration, and constant power stage duration, obtains the charging power prediction curve.

[0051] Electric vehicle refueling devices have achieved actual usable peak power. Minimum sustaining power Duration of constant power phase and the duration of the constant power phase Then, based on the actual available peak power Minimum sustaining power Duration of constant power phase and the duration of the constant power phase The charging power prediction curve is obtained. .

[0052] Let the starting time of charging be t=0, then the predicted change of charging power with time τ is: ; in, For the charging power prediction curve; This represents the actual available peak power. Minimum sustaining power (near full charge); Duration of the constant power phase; This refers to the duration of the decay phase.

[0053] S40, based on the predicted dynamic marginal carbon emission factor Charging power prediction curve Energy consumption per unit distance Total detour distance Thus, the total carbon cost at the spatiotemporal margin is obtained; Electric vehicle refueling devices have obtained predicted dynamic marginal carbon emission factors. Charging power prediction curve Energy consumption per unit distance Total detour distance Subsequently, based on the predicted dynamic marginal carbon emission factor... Charging power prediction curve Energy consumption per unit distance Total detour distance Thus, the total spatial and temporal marginal carbon cost is obtained.

[0054] As one implementation method, based on the predicted dynamic marginal carbon emission factor, the charging power prediction curve, the energy consumption per unit mileage, and the total detour distance, the spatiotemporal marginal total carbon cost is obtained, including: ; in, The total spatiotemporal marginal carbon cost incurred by selecting charging station j for vehicle i; The current moment; The estimated time when the vehicle will arrive at charging station j; This represents the energy consumption per unit mileage of vehicle i under the current driving conditions. Carbon emission factor per kilometer over the entire life cycle of vehicle i; This represents the total detour distance from the current location to station j and then to the destination; The expected start time for charging; The expected end time of charging; For vehicle i at time The charging power; For the power grid node where station j is located at time... The predicted dynamic marginal carbon emission factor.

[0055] in, ;in, The shortest travel time from the vehicle's current location to charging station j; where, if the station has available charging stations and no conflicting reservations, the estimated start time of charging is [the shortest travel time]. The estimated time for the vehicle to arrive at charging station j ; Expected end time of charging ;in, ;in, This indicates the current state of battery charge. The target state of charge; For vehicle i at time The charging power. This represents the total energy of the battery.

[0056] S50 generates charging station data based on the spatiotemporal marginal total carbon cost, so that car owners can select charging stations from the charging station data; After obtaining the total spatiotemporal marginal carbon cost, the electric vehicle charging device generates charging station data based on the total spatiotemporal marginal carbon cost, so that vehicle owners can select charging stations from the charging station data.

[0057] In other words, after obtaining the total spatiotemporal marginal carbon cost, the electric vehicle charging device sorts the total spatiotemporal marginal carbon cost from smallest to largest to generate charging station data, so that car owners can select charging stations from the charging station data.

[0058] In summary, this application acquires charging station grid data, charging station power supply data, vehicle owner demand data, vehicle status data, and weather and road condition data. The charging station grid data includes a predicted dynamic marginal carbon emission factor. Based on the vehicle owner demand data, vehicle status data, and weather and road condition data, it obtains energy consumption per unit mile and total detour distance. Based on the charging station power supply data and vehicle status data, it obtains a charging power prediction curve. Based on the predicted dynamic marginal carbon emission factor, charging power prediction curve, energy consumption per unit mile, and total detour distance, it obtains the spatiotemporal marginal carbon total cost. Based on the spatiotemporal marginal carbon total cost, it generates charging station data, allowing vehicle owners to select charging stations from this data. This achieves green, safe, and efficient operation of the entire electric vehicle charging chain.

[0059] This application, by acquiring predicted dynamic marginal carbon emission factors, vehicle energy consumption per unit mileage, total detour distance, and charging power prediction curves, constructs for the first time a spatiotemporal marginal total carbon cost model that simultaneously incorporates carbon emissions from driving and detours and carbon emissions during the charging process. This shifts carbon emission assessment from static and localized to dynamic and comprehensive, providing a scientific quantitative basis for truly achieving "green charging." In particular, dividing the charging power prediction curve into a constant power stage and a decay stage, and combining it with the dynamic marginal carbon emission factors of grid nodes for integral calculation, significantly improves the prediction accuracy of charging carbon emissions.

[0060] This application generates and ranks data on available charging stations based on the calculated spatiotemporal marginal total carbon cost, guiding car owners to proactively choose the charging option with the lowest carbon emissions. This method effectively avoids vehicles crowding into high-carbon emission nodes or taking excessively long detours to charging stations, reducing energy waste and additional carbon emissions caused by blindly searching for charging stations. At the same time, it alleviates local grid load pressure and achieves synergy between user charging behavior and the "dual-carbon" strategic goal.

[0061] This community fully considers the vehicle's own status, the charging station's power supply capacity, and dynamic road conditions, generating customized charging power prediction curves and total carbon cost assessments for each vehicle, each trip, and each moment. This application accurately fits the real charging process by calculating the actual available peak power, the duration of the constant power phase, and the duration of the decay phase. When vehicle status, weather conditions, road conditions, or grid carbon emission factors change, the system can update the assessment results in real time, ensuring that the recommended charging stations are always the optimal low-carbon choice under the current spatiotemporal conditions.

[0062] For those consistent with the above, please refer to Figure 2 , Figure 2 This application provides a schematic diagram of the structure of an electric vehicle refueling device. Figure 2 As shown, it includes: The data acquisition module 201 is used to acquire charging station power grid data, charging station power supply data, vehicle owner demand data, vehicle status data, and weather and road condition data; the charging station power grid data includes the predicted dynamic marginal carbon emission factor. The mileage distance determination module 202 is used to obtain the energy consumption per unit mileage and the total detour distance based on the vehicle owner's demand data, the vehicle status data and the weather and road condition data. The power prediction module 203 is used to obtain a charging power prediction curve based on the power supply data of the charging station and the vehicle status data. The total carbon cost determination module 204 is used to obtain the spatiotemporal total carbon cost based on the predicted dynamic marginal carbon emission factor, the charging power prediction curve, the energy consumption per unit mileage, and the total detour distance. The rechargeable station output module 205 is used to generate rechargeable station data based on the spatiotemporal marginal total carbon cost, so that vehicle owners can select a charging station from the rechargeable station data.

[0063] like Figure 3 As shown, this application embodiment also provides a terminal device 2, which includes: at least one processor 20, a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor. The processor 20 and the memory 21 are connected. When the processor 20 executes the computer program 22, it implements the steps in the embodiments of the HIC value prediction method or the vehicle data augmentation method.

[0064] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the electric vehicle refueling methods described in the above method embodiments.

[0065] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the electric vehicle refueling methods described in the above method embodiments.

[0066] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable storage media cannot be electrical carrier signals or telecommunication signals.

[0067] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0068] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0069] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0070] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method for replenishing energy for an electric vehicle, characterized in that, include: Acquire charging station power grid data, charging station power supply data, vehicle owner demand data, vehicle status data, and weather and road condition data; The power grid data for the charging station includes a predicted dynamic marginal carbon emission factor. Based on the owner's demand data, the vehicle status data, and the weather and road condition data, the energy consumption per unit mileage and the total detour distance are obtained. Based on the power supply data of the charging station and the vehicle status data, a charging power prediction curve is obtained; Based on the predicted dynamic marginal carbon emission factor, the predicted charging power curve, the energy consumption per unit mileage, and the total detour distance, the total spatiotemporal marginal carbon cost is obtained. Based on the spatiotemporal marginal total carbon cost, data on available charging stations is generated to allow car owners to select charging stations from this data.

2. The electric vehicle refueling method according to claim 1, characterized in that, The step of obtaining energy consumption per unit mileage and total detour distance based on the vehicle owner's demand data, the vehicle status data, and the weather and road condition data includes: Based on the vehicle status data and the weather and road condition data, the energy consumption per unit mileage is obtained; The total detour distance is obtained based on the car owner's demand data and the vehicle status data.

3. The electric vehicle refueling method according to claim 2, characterized in that, Weather and road condition data include: weather data and road condition data; vehicle status data includes vehicle gross weight, speed, rolling resistance coefficient, air resistance coefficient, vehicle frontal area, and rotational mass conversion factor; based on the vehicle status data and the weather and road condition data, energy consumption per unit mileage is obtained, including: The air density is obtained based on weather data; Based on road condition information, the road slope angle is obtained; The energy consumption per unit mileage is obtained based on air density, road slope angle, total vehicle mass, driving speed, rolling resistance coefficient, air resistance coefficient, vehicle frontal area, and rotational mass conversion factor.

4. The electric vehicle refueling method according to claim 3, characterized in that, Based on air density, road slope angle, total vehicle mass, driving speed, rolling resistance coefficient, air resistance coefficient, vehicle frontal area, and rotational mass conversion factor, the energy consumption per unit mileage is obtained, including: in, This represents the energy consumption per unit mileage of vehicle i under the current driving conditions. This refers to the overall efficiency of the power system, which is the overall efficiency from the battery to the wheels. The total mass of the vehicle; It is the acceleration due to gravity; This is the rolling resistance coefficient; The road slope angle; air density; This refers to the air drag coefficient; The vehicle's frontal area; Let be the speed of vehicle i at time t; This is the rotational mass conversion factor; Let be the acceleration of vehicle i at time t; Power of vehicle accessories.

5. The electric vehicle refueling method according to claim 2, characterized in that, The vehicle owner's demand data includes destination data; The vehicle status data includes the vehicle's current location; Based on the vehicle owner's demand data and the vehicle status data, the total detour distance is obtained, including: Calculate the first distance traveled from the vehicle's current location to station j; Calculate the second driving distance from station j to the destination; Calculate the third distance traveled from the vehicle's current location to its destination; The total detour distance is obtained based on the first driving distance, the second driving distance, and the third driving distance.

6. The electric vehicle refueling method according to claim 1, characterized in that, The charging station power supply data includes the rated power of the charging pile; the vehicle status data includes: the vehicle's maximum allowable power, total battery energy, initial remaining charge, target remaining charge, initial remaining charge at power reduction, minimum sustaining power, and battery temperature discount factor; based on the charging station power supply data and the vehicle status data, a charging power prediction curve is obtained, including: The actual usable peak power is obtained based on the rated power of the charging pile, the maximum allowable power of the vehicle, and the battery temperature discount factor. Based on the remaining charge at the start of charging, the remaining charge at the start of power reduction, and the total battery energy, the amount of charge required for the constant power stage is obtained. Based on the remaining charge at the start of charging, the remaining charge at the start of power reduction, the target remaining charge, and the total battery energy, the amount of charge required for the degradation stage is obtained. The duration of the constant power phase is obtained based on the amount of electricity required to be charged during the constant power phase and the actual available peak power. The duration of the decay phase is obtained based on the amount of electricity required to be charged during the decay phase, the actual available peak power, and the minimum sustaining power. The charging power prediction curve is obtained based on the actual available peak power, minimum sustaining power, constant power stage duration, and constant power stage duration.

7. The electric vehicle refueling method according to claim 1, characterized in that, Based on the predicted dynamic marginal carbon emission factor, the charging power prediction curve, the energy consumption per unit mileage, and the total detour distance, the spatiotemporal marginal total carbon cost is obtained, including: ； in, The total spatiotemporal marginal carbon cost incurred by selecting charging station j for vehicle i; The current moment; The estimated time when the vehicle will arrive at charging station j; This represents the energy consumption per unit mileage of vehicle i under the current driving conditions. Carbon emission factor per kilometer over the entire life cycle of vehicle i; This represents the total detour distance from the current location to station j and then to the destination; The expected start time for charging; The expected end time of charging; For vehicle i at time The charging power; For the power grid node where station j is located at time... The predicted dynamic marginal carbon emission factor.

8. An electric vehicle refueling device, characterized in that, include: The data acquisition module is used to acquire charging station grid data, charging station power supply data, vehicle owner demand data, vehicle status data, and weather and road condition data; the charging station grid data includes the predicted dynamic marginal carbon emission factor. The mileage distance determination module is used to obtain the energy consumption per unit mileage and the total detour distance based on the vehicle owner's demand data, the vehicle status data, and the weather and road condition data. The power prediction module is used to obtain a charging power prediction curve based on the power supply data of the charging station and the vehicle status data. The total carbon cost determination module is used to obtain the spatiotemporal total carbon cost based on the predicted dynamic marginal carbon emission factor, the charging power prediction curve, the energy consumption per unit mileage, and the total detour distance. The rechargeable station output module is used to generate rechargeable station data based on the spatiotemporal marginal total carbon cost, so that car owners can select a charging station from the rechargeable station data.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the electric vehicle refueling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the electric vehicle refueling method as described in any one of claims 1 to 7.

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