Charging scheduling method and device for electric vehicle and electric vehicle

Abstract

This application discloses a charging scheduling method, apparatus, and electric vehicle for electric vehicles, relating to the field of intelligent transportation scheduling technology. The method includes: determining the multi-dimensional estimated cost of charging an electric vehicle at various charging stations via various charging devices, wherein the multi-dimensional estimated cost includes estimated charging electricity cost and estimated charging time cost. The estimated charging electricity cost is related to the electricity price during the estimated charging period, and the estimated charging time cost is related to the estimated travel time of the electric vehicle to the charging station and the estimated waiting time for charging after arriving at the charging station; based on the principle of minimizing multi-dimensional estimated cost, determining a target charging strategy for the electric vehicle, wherein the target charging strategy includes a target charging station, a target charging device, and a target charging period; and controlling the electric vehicle to charge according to the target charging strategy. This application can reduce the charging cost of electric vehicles.

Description

Technical Field

[0001] This application relates to the field of intelligent traffic scheduling technology, specifically to a charging scheduling method, device, and electric vehicle for electric vehicles. Background Technology

[0002] With the accelerated electrification of construction vehicles, electric vehicles (pump trucks, mixer trucks, and dump trucks) have been widely used in the construction machinery industry. Electric vehicles are characterized by large battery capacity, long charging times (typically 1-2 hours), and strong operational continuity (requiring frequent trips between charging stations and construction sites). Currently, charging scheduling for electric vehicles mainly relies on manual experience, with drivers deciding which charging station to go to based on their experience and preferences; drivers typically tend to choose familiar charging stations. However, this existing technology, which only selects charging stations based on driver experience, does not consider charging costs, potentially leading to higher charging costs for electric vehicles. Summary of the Invention

[0003] The purpose of this application is to provide a charging scheduling method, device, electric vehicle, and storage medium for electric vehicles, in order to solve the problem of high charging costs for electric vehicles in the prior art.

[0004] To achieve the above objectives, the first aspect of this application provides a charging scheduling method for electric vehicles, comprising:

[0005] Determine the multidimensional estimated cost of charging an electric vehicle at each charging station through each charging device. The multidimensional estimated cost includes the estimated electricity cost and the estimated charging time cost. The estimated electricity cost is related to the electricity price during the estimated charging period, and the estimated charging time cost is related to the estimated travel time of the electric vehicle to the charging station and the estimated waiting time after arriving at the charging station and waiting for the charging device to charge. Based on the principle of minimizing multidimensional projected costs, the target charging strategy for the electric vehicle to be charged is determined according to the multidimensional projected costs. The target charging strategy includes the target charging station, the target charging device in the target charging station, and the target charging time period. Control the electric vehicles to be charged to charge according to the target charging strategy.

[0006] In this application embodiment, the multidimensional estimated cost also includes at least one of the following: estimated travel energy consumption cost, which relates to the cost of electricity consumed by the electric vehicle to be charged on its journey to the charging station; and estimated next-day risk cost, which relates to the time sequence between the estimated end time of charging of the electric vehicle to be charged and the start time of operation of the electric vehicle to be charged on the next day.

[0007] In this embodiment of the application, determining the multidimensional estimated cost of charging an electric vehicle to be charged through each charging device in each charging station includes: obtaining the charging urgency level of the electric vehicle to be charged, wherein the charging urgency level includes an immediate charging level; when the charging urgency level of the electric vehicle to be charged is an immediate charging level, determining the multidimensional estimated cost of charging the electric vehicle to be charged through each charging device in each charging station according to a first cost determination method, wherein the first cost determination method is a cost determination method that takes the current time period as the estimated charging time period and takes the estimated start time of charging of each charging device in each charging station with respect to the electric vehicle to be charged as the time reference.

[0008] In this embodiment of the application, determining the multidimensional estimated cost of charging an electric vehicle through each charging device at each charging station includes: obtaining the charging urgency level of the electric vehicle, wherein the charging urgency level includes a flexible charging level; when the charging urgency level of the electric vehicle is a flexible charging level, determining the multidimensional estimated cost of charging the electric vehicle through each charging device at each charging station according to a first cost determination method and a second cost determination method; wherein the first cost determination method is a cost determination method that uses the current time period as the estimated charging time period and the estimated start time of charging of each charging device at each charging station with respect to the electric vehicle as the time base; the second cost determination method is a cost determination method that uses the next low electricity price period after the current time period as the estimated charging time period and postpones the estimated start time of charging of each charging device at each charging station with respect to the electric vehicle to be charged to the start time of the next low electricity price period, using the postponed estimated start time of charging as the time base.

[0009] In this embodiment of the application, the determination of the expected start time of charging of each charging device in each charging station with respect to the electric vehicle to be charged includes: obtaining the idle time of all charging devices in all charging stations; determining the expected arrival time of the electric vehicle to be charged at each charging station; and determining the later of the expected arrival time and the idle time to obtain the expected start time of charging of each charging device in each charging station with respect to the electric vehicle to be charged.

[0010] In this embodiment, determining the charging urgency level of the electric vehicle to be charged includes: obtaining the current remaining battery percentage of the electric vehicle to be charged; if the current remaining battery percentage is lower than a first preset safety threshold, determining the charging urgency level of the electric vehicle to be charged as an immediate charging level; if the current remaining battery percentage is higher than or equal to the first preset safety threshold and lower than a second preset safety threshold, determining the charging urgency level of the electric vehicle to be charged as a flexible charging level, wherein the second preset safety threshold is higher than the first preset safety threshold, and the second preset safety threshold is the sum of the first preset safety threshold and a preset buffer threshold.

[0011] In this embodiment of the application, there are multiple electric vehicles to be charged, and the charging scheduling method further includes: obtaining the charging urgency level and the current remaining power percentage of each electric vehicle to be charged; based on a preset sorting strategy, prioritizing the multiple electric vehicles to be charged according to the charging urgency level and the current remaining power percentage, wherein the preset sorting strategy is that the sorting priority of the charging urgency level is higher than the sorting priority of the current remaining power percentage, and when the charging urgency levels are the same, they are sorted according to the current remaining power percentage; for the sorted multiple electric vehicles to be charged, a target charging strategy is determined sequentially to realize the charging scheduling of the multiple electric vehicles to be charged.

[0012] In this embodiment of the application, the method further includes: after determining the target charging strategy, performing resource locking on the target charging device in the target charging strategy regarding the target charging period.

[0013] In this embodiment of the application, the target charging strategy further includes a target charging percentage; the determination of the target charging percentage includes: when the target charging period is a low-voltage period, the target charging percentage is a first preset charging percentage; when the target charging period is a non-low-voltage period, the target charging percentage is a second preset charging percentage, wherein the second preset charging percentage is less than the first preset charging percentage.

[0014] In this embodiment of the application, the estimated charging time cost is the product of the sum of the estimated driving time and the estimated waiting time and the preset time value coefficient.

[0015] In this embodiment of the application, the estimated charging time cost is also related to the estimated charging time of the charging device. The estimated charging time cost is the product of the sum of the estimated driving time, the estimated waiting time and the estimated charging time and the preset time value coefficient.

[0016] In this embodiment of the application, the expected charging cost is the difference between the expected charging cost for the expected charging period and the reference charging cost.

[0017] A second aspect of this application provides a charging scheduling apparatus for electric vehicles, comprising: a memory configured to store instructions; and a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement the charging scheduling method for electric vehicles described above.

[0018] A third aspect of this application provides an electric vehicle, including: the charging scheduling device for an electric vehicle as described above.

[0019] A fourth aspect of this application provides a machine-readable storage medium storing instructions that cause a machine to execute the charging scheduling method for electric vehicles described above.

[0020] The above technical solution considers both the estimated electricity cost and the estimated charging time cost of electric vehicles. Based on the principle of minimizing both the estimated electricity cost and the estimated charging time cost, it determines the target charging station, the target charging device at the target charging station, and the target charging time period for the electric vehicle to be charged. In addition to considering the electricity cost, it also takes into account the travel time and waiting time of the electric vehicle to the charging station, thus monetizing time and treating charging resources as dynamically occupied resources over time. This determines the charging strategy with the lowest comprehensive cost (including money and time) for the electric vehicle to be charged, greatly reducing the charging cost of electric vehicles.

[0021] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The illustration shows a schematic flowchart of a charging scheduling method for electric vehicles according to an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0024] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0025] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0026] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0027] Figure 1 The illustration schematically shows a flowchart of a charging scheduling method for electric vehicles according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a charging scheduling method for electric vehicles. Taking the application of the charging scheduling method to a processor as an example, the charging scheduling method may include the following steps.

[0028] Step S102: Determine the multi-dimensional estimated cost of charging the electric vehicle to be charged through each charging device in each charging station. The multi-dimensional estimated cost includes the estimated charging electricity cost and the estimated charging time cost. The estimated charging electricity cost is related to the electricity price during the estimated charging period. The estimated charging time cost is related to the estimated travel time of the electric vehicle to be charged to the charging station and the estimated waiting time for the charging device to charge after arriving at the charging station.

[0029] Step S104: Based on the principle of minimizing multidimensional estimated costs, determine the target charging strategy for the electric vehicle to be charged according to the multidimensional estimated costs. The target charging strategy includes the target charging station, the target charging device in the target charging station, and the target charging time period.

[0030] Step S106: Control the electric vehicle to be charged to charge according to the target charging strategy.

[0031] It can be understood that the electric vehicle to be charged is an electric vehicle that needs charging, such as electric pump trucks, electric mixer trucks, and electric dump trucks. Multidimensional estimated costs refer to the estimated cost values ​​across different dimensions. Estimated charging electricity cost is the estimated electricity cost for the electric vehicle to be charged via the charging device (e.g., charging gun) at the charging station. This cost is related to the electricity price during the estimated charging period, which is the expected charging time. Understandably, the electricity price will fluctuate depending on the charging period. Estimated charging time cost is the time cost required for the electric vehicle to be charged via the charging device (e.g., charging gun) at the charging station. This cost is related to the estimated travel time of the electric vehicle to the charging station and the estimated waiting time after arrival. The principle of minimizing multidimensional estimated costs is the principle of minimizing the sum of estimated charging electricity cost and estimated charging time cost. The target charging strategy is the charging strategy for the electric vehicle to be charged, determined based on the multidimensional estimated costs. Specifically, it may include the target charging station, the target charging device within the target charging station, and the target charging period.

[0032] Specifically, the processor can determine the multi-dimensional estimated cost of charging the electric vehicle at each charging station using each charging device. This multi-dimensional estimated cost includes the estimated electricity cost and the estimated charging time cost. The estimated electricity cost is related to the electricity price during the estimated charging period, while the estimated charging time cost is related to the estimated travel time of the electric vehicle to the charging station and the estimated waiting time after arrival. Then, based on the principle of minimizing the multi-dimensional estimated cost, the processor can determine the target charging strategy for the electric vehicle. This target charging strategy includes the target charging station, the target charging device at the target charging station, and the target charging period. Finally, the processor can control the electric vehicle to charge according to the target charging strategy.

[0033] The above technical solution considers both the estimated electricity cost and the estimated charging time cost of electric vehicles. Based on the principle of minimizing both the estimated electricity cost and the estimated charging time cost, it determines the target charging station, the target charging device at the target charging station, and the target charging time period for the electric vehicle to be charged. In addition to considering the electricity cost, it also takes into account the travel time and waiting time of the electric vehicle to the charging station, thus monetizing time and treating charging resources as dynamically occupied resources over time. This determines the charging strategy with the lowest comprehensive cost (including money and time) for the electric vehicle to be charged, greatly reducing the charging cost of electric vehicles.

[0034] In one embodiment, the multidimensional projected cost may further include at least one of the following: projected travel energy cost, relating to the cost of electricity consumed by the electric vehicle to be charged on its journey to the charging station; and projected next-day risk cost, relating to the time sequence between the projected end time of charging of the electric vehicle to be charged and the start time of operation of the electric vehicle to be charged on the next day.

[0035] It is understandable that the estimated energy cost of the journey is the estimated cost of electricity consumed by the electric vehicle on its way to the charging station. The estimated next-day risk cost, also known as the estimated overnight risk cost, is related to the time sequence between the estimated end time of charging of the electric vehicle and the start time of operation of the electric vehicle the next day. The start time of operation is the time when the electric vehicle begins operation the next day. It is understood that if the estimated end time of charging of the electric vehicle is later than the start time of operation of the electric vehicle the next day (e.g., 6:00 AM), it is determined that there is an estimated next-day risk cost; otherwise, it is determined that there is no estimated next-day risk cost.

[0036] Understandably, in one embodiment, when the multidimensional estimated cost also includes estimated travel energy consumption cost, the principle of minimizing the multidimensional estimated cost can be the principle of minimizing the sum of estimated charging electricity cost, estimated charging time cost, and estimated travel energy consumption cost. In one embodiment, when the multidimensional estimated cost also includes estimated next-day risk cost, the principle of minimizing the multidimensional estimated cost can be the principle of minimizing the sum of estimated charging electricity cost, estimated charging time cost, and estimated next-day risk cost. In one embodiment, when the multidimensional estimated cost also includes estimated travel energy consumption cost and estimated next-day risk cost, the principle of minimizing the multidimensional estimated cost can be the principle of minimizing the sum of estimated charging electricity cost, estimated charging time cost, estimated travel energy consumption cost, and estimated next-day risk cost.

[0037] In this application embodiment, the estimated travel energy consumption cost is introduced, which takes into account the cost of electricity consumed by the vehicle on the way to the charging station, and avoids consuming too much travel electricity in order to go to a distant "low-price station"; the estimated next day risk cost is introduced, which can avoid the situation where the vehicle cannot be used the next morning due to the desire to charge at night at a low price.

[0038] In one embodiment, determining the multidimensional estimated cost of charging an electric vehicle at each charging station using each charging device includes: obtaining the charging urgency level of the electric vehicle, wherein the charging urgency level includes an immediate charging level; and, if the charging urgency level of the electric vehicle is an immediate charging level, determining the multidimensional estimated cost of charging the electric vehicle at each charging station using each charging device according to a first cost determination method, wherein the first cost determination method is a cost determination method that uses the current time period as the estimated charging time period and the estimated start time of charging of each charging device at each charging station with respect to the electric vehicle as the time base.

[0039] It is understandable that the urgency of charging for different electric vehicles may be the same or different. Therefore, charging urgency levels are set, which can include an immediate charging level, indicating that the vehicle's battery level is critical and immediate charging is required. Regarding the first cost determination method, it can be understood as a cost determination method for charging as early as possible. That is, the expected charging period is clearly defined as the current period, regardless of the electricity price during the current period, i.e., charging is not delayed. The multi-dimensional expected cost is calculated based on the expected start time of charging for each charging device at each charging station with respect to the electric vehicle. The expected start time of charging for each charging device at each charging station is different and needs to be determined based on the current operating status of each charging device at each charging station and the time when the electric vehicle arrives at the charging station. The current operating status of the charging device can be active or idle.

[0040] Specifically, the processor can obtain the charging urgency level of the electric vehicle to be charged, and if the charging urgency level of the electric vehicle to be charged is the immediate charging level, it can determine the multi-dimensional estimated cost of charging the electric vehicle to be charged through each charging device in each charging station according to the first cost determination method. For example, if there are 3 charging stations and 8 charging devices in each charging station, then 3×8=24 multi-dimensional estimated costs (including estimated charging electricity cost and estimated charging time cost) can be obtained according to the first cost determination method.

[0041] In this embodiment of the application, when calculating the multidimensional estimated cost of charging an electric vehicle through each charging device at each charging station, it is necessary to first obtain the charging urgency level of the electric vehicle and select a matching cost determination method based on the charging urgency level. When the charging urgency level is the immediate charging level, the cost determination method adopts the current time period as the estimated charging time period and the estimated start time of charging of each charging device at each charging station for the electric vehicle as the time base. That is, the strategy of immediate charging in the current time period is adopted, which prioritizes the urgency of the actual charging needs of the electric vehicle, rather than taking the electricity price at different times as the primary consideration. This method is more applicable and the charging scheduling is more reasonable.

[0042] In one embodiment, determining the multidimensional estimated cost of charging an electric vehicle at each charging station using each charging device includes: obtaining the charging urgency level of the electric vehicle, wherein the charging urgency level includes a flexible charging level; and, if the charging urgency level of the electric vehicle is a flexible charging level, determining the multidimensional estimated cost of charging the electric vehicle at each charging station using each charging device according to a first cost determination method and a second cost determination method. The first cost determination method uses the current time period as the estimated charging period and the estimated start time of charging for the electric vehicle at each charging station relative to the electric vehicle as the time base. The second cost determination method uses the next low-electricity-price period following the current time period as the estimated charging period and postpones the estimated start time of charging for the electric vehicle at each charging station to the start time of the next low-electricity-price period, using the postponed estimated start time of charging as the time base.

[0043] It is understandable that the charging urgency level can include a flexible charging level. The flexible charging level indicates that the vehicle's battery level is low, charging is recommended, but there is time to wait and choose; that is, immediate charging is not required, and its charging urgency level is lower than the immediate charging level. Regarding the second cost determination method, it can be understood as a cost determination method based on the lowest available price. This means the expected charging period is the next lowest electricity price period after the current period, prioritizing electricity prices across different periods. Charging can be delayed, and the expected start time for each charging unit at each charging station for the electric vehicle to be charged is postponed to the start time of the next lowest electricity price period. The multi-dimensional expected cost is calculated based on the postponed expected start time. The next lowest electricity price period is the period after the current period with an electricity price lower than the current period.

[0044] Specifically, the processor can obtain the charging urgency level of the electric vehicle to be charged, and when the charging urgency level of the electric vehicle to be charged is the flexible charging level, it can determine the multi-dimensional estimated cost of charging the electric vehicle to be charged through each charging device in each charging station according to the first cost determination method and the second cost determination method respectively. For example, if there are 3 charging stations and 8 charging devices in each charging station, then according to the first cost determination method, 3×8=24 multi-dimensional estimated costs can be obtained (including estimated charging electricity cost and estimated charging time cost), and according to the second cost determination method, 3×8=24 multi-dimensional estimated costs can also be obtained, that is, a total of 48 multi-dimensional estimated costs can be obtained.

[0045] In this embodiment of the application, when calculating the multidimensional estimated cost of charging an electric vehicle at each charging station through each charging device, it is necessary to first obtain the charging urgency level of the electric vehicle and select a matching cost determination method based on the charging urgency level. When the charging urgency level is the flexible charging level, two different cost determination methods can be used to select the target charging strategy for the electric vehicle, thereby significantly reducing operating costs without affecting the normal operation of the electric vehicle. This satisfies the charging needs of the electric vehicle and achieves the goal of minimizing charging costs.

[0046] In one embodiment, determining the expected start time of charging for each charging device at each charging station with respect to the electric vehicle to be charged includes: obtaining the idle time of all charging devices at all charging stations; determining the expected arrival time of the electric vehicle to be charged at each charging station; and determining the later of the expected arrival time and the idle time to obtain the expected start time of charging for each charging device at each charging station with respect to the electric vehicle to be charged.

[0047] It can be understood that if a charging device (such as a charging gun) is currently idle, the idle time is the current time; if the charging device is currently working, the idle time is the end time of its current operation (i.e., the estimated end time of charging). The estimated arrival time of the electric vehicle to be charged at each charging station is the current time plus the travel time to the charging station. The estimated start time of charging for the electric vehicle at each charging station is the start time of charging for the electric vehicle at each charging station, specifically the later of the estimated arrival time of the electric vehicle at each charging station and the idle time of the charging device.

[0048] In one embodiment, determining the charging urgency level of the electric vehicle to be charged includes: obtaining the current remaining battery percentage of the electric vehicle to be charged; determining the charging urgency level of the electric vehicle to be charged as an immediate charging level if the current remaining battery percentage is lower than a first preset safety threshold; and determining the charging urgency level of the electric vehicle to be charged as a flexible charging level if the current remaining battery percentage is higher than or equal to the first preset safety threshold and lower than a second preset safety threshold, wherein the second preset safety threshold is higher than the first preset safety threshold, and the second preset safety threshold is the sum of the first preset safety threshold and a preset buffer threshold.

[0049] It is understandable that the current remaining battery percentage, i.e., the current State of Charge (SOC), can be obtained through the vehicle terminal. Both the first and second preset safety thresholds are pre-set battery safety thresholds. The second preset safety threshold is higher than the first preset safety threshold. The second preset safety threshold is the sum of the first preset safety threshold and a preset buffer threshold, which is a pre-set buffer level, for example, 15%.

[0050] In one embodiment, the first preset safety threshold is (α×Esafe_base) / battery capacity, and Esafe_base=max(energy consumption of the nearest station, 0.2×battery capacity), where, under normal circumstances, the energy consumption of the nearest station is less than (0.2×battery capacity), and α is a safety redundancy coefficient (e.g., 1.6-1.8) to ensure that the vehicle has sufficient power to cope with unexpected detours or congestion.

[0051] In some embodiments, the charging urgency level may also include a no-charging level. Specifically, if the current remaining battery percentage of the electric vehicle to be charged is higher than or equal to a second preset safety threshold, the charging urgency level of the electric vehicle to be charged is determined to be a no-charging level, which means that the vehicle has sufficient battery power and does not need to participate in charging scheduling.

[0052] In one embodiment, the number of electric vehicles to be charged is multiple, and the charging scheduling method further includes: obtaining the charging urgency level and the current remaining battery percentage of each electric vehicle to be charged; prioritizing the multiple electric vehicles to be charged according to the charging urgency level and the current remaining battery percentage based on a preset sorting strategy, wherein the preset sorting strategy prioritizes the charging urgency level over the current remaining battery percentage, and sorts the vehicles according to the current remaining battery percentage when the charging urgency levels are the same; and determining target charging strategies for the sorted electric vehicles to be charged in sequence to achieve charging scheduling for the multiple electric vehicles to be charged.

[0053] It is understandable that when multiple electric vehicles need to be prioritized for charging, they are first sorted according to their vehicle level, i.e., the charging urgency level. That is, electric vehicles with the immediate charging level are placed before those with the flexible charging level. Then, they are sorted according to the current remaining battery percentage. Under the same charging urgency level, the lower the current remaining battery percentage, the higher the priority for allocation. Priority allocation means placing them in the front position to prevent low-battery vehicles from running out of power while waiting in line.

[0054] In this embodiment of the application, when there are multiple electric vehicles waiting to be charged, the charging order of the multiple electric vehicles waiting to be charged is prioritized according to the charging urgency level and the current remaining power percentage of the electric vehicles waiting to be charged. The charging strategy of the vehicles is determined in turn according to the sorting result. The global sorting of all electric vehicles waiting to be charged solves the problem of charging resource competition.

[0055] In one embodiment, the method further includes: after determining the target charging strategy, locking resources for the target charging device in the target charging strategy regarding the target charging period. Furthermore, the idle time (i.e., the expected charging end time) of the target charging device can be updated, thereby affecting the resource allocation for subsequent vehicles.

[0056] In one embodiment, the target charging strategy further includes a target charging percentage; the determination of the target charging percentage includes: when the target charging period is a low-voltage period, the target charging percentage is a first preset charging percentage; when the target charging period is a non-low-voltage period, the target charging percentage is a second preset charging percentage, wherein the second preset charging percentage is less than the first preset charging percentage.

[0057] It is understood that off-peak electricity hours, also known as low-price periods, refer to the time when electricity demand in the power system is lowest and the grid load is lightest, for example, 22:00 to 8:00 the next day. Non-off-peak electricity hours are the periods of the day other than off-peak hours. The first preset charging percentage is a pre-set percentage of charging capacity, for example, 100%. The second preset charging percentage is a pre-set percentage of charging capacity that is lower than the first preset percentage, for example, 80%.

[0058] In this embodiment of the application, the target charging percentage is dynamically adjusted according to the target charging period, which can balance economy and time efficiency.

[0059] In one embodiment, the estimated charging time cost is the product of the sum of the estimated driving time and the estimated waiting time.

[0060] It is understandable that the preset time value coefficient is a pre-set time value coefficient, the unit of which can be yuan / minute. It converts time into money and reflects the opportunity cost caused by the downtime of construction vehicles.

[0061] Specifically, the estimated charging time cost = (estimated driving time + estimated waiting time) × preset time value coefficient.

[0062] In one embodiment, the estimated charging time cost is also related to the estimated charging time of the charging device, which is the product of the sum of the estimated travel time, the estimated waiting time, and the estimated charging time, and a preset time value coefficient.

[0063] It is understandable that the estimated charging time is the estimated time it will take for the charging device to charge the electric vehicle to be charged.

[0064] Specifically, the estimated charging time cost = (estimated driving time + estimated waiting time + estimated charging time) × preset time value coefficient.

[0065] In one embodiment, the expected charging cost is the difference between the expected charging cost for the expected charging period and the reference charging cost.

[0066] It is understood that the reference charging cost is a predetermined charging cost used as a benchmark, such as the average reference charging cost. The difference between the estimated charging cost and the "reference charging cost" is calculated. If off-peak electricity is used, this value is negative (reward); if peak electricity is used, this value is positive (penalty). Furthermore, the estimated charging cost may include integral calculations for charging across different tariff periods.

[0067] In this embodiment of the application, the expected charging cost is determined as the difference between the expected charging cost and the reference charging cost during the expected charging period. This reduces the amount of calculation and more intuitively reflects the gap and direction between the expected charging cost and the reference charging cost.

[0068] In existing technologies, vehicle or fleet charging dispatch mainly relies on the following methods: Manual experience-based dispatch: The fleet leader or driver decides which charging station to go to based on experience, usually preferring the closest or most familiar station. Simple proximity / lowest price recommendation: Existing navigation or charging apps typically only recommend based on "currently closest" or "currently lowest electricity price," without considering subsequent price changes or actual queue conditions at the station.

[0069] The aforementioned existing technologies suffer from the following drawbacks: Uneven resource utilization and congestion (clustering effect): Existing technologies often lead to multiple vehicles simultaneously converging on the same "theoretically optimal" charging station (such as the station with the lowest current electricity price), resulting in severe queues at that station while other stations remain idle. This not only increases vehicle waiting costs but also reduces the utilization rate of charging piles. Low operational efficiency: Electric vehicles have high time sensitivity requirements. Ignoring queuing time can cause vehicles to remain at charging stations for extended periods, delaying transportation tasks and even causing material shortages at construction sites. Inefficient cost control: Existing technologies often overlook the "time value" and "time-of-use electricity price differences." For example, waiting an hour to save a few dollars in electricity costs is not worthwhile for high-value construction vehicles; or, when electricity prices are about to shift from peak to off-peak, vehicles are not advised to wait briefly to take advantage of lower prices.

[0070] To address the aforementioned issues, a specific embodiment of this application provides a charging scheduling method for electric vehicles. The core of this method is to treat charging resources (such as charging guns) as resources that are dynamically occupied over time. By simulating the resource competition process of the entire fleet, the method allocates the charging scheme with the lowest overall cost (including time, money, and risk) to each vehicle.

[0071] This application also provides an intelligent charging scheduling system, which mainly consists of four parts: vehicle terminal, charging pile / station system, cloud scheduling platform and user terminal. Each part interacts with data through a wireless communication network.

[0072] Vehicle-mounted terminal: Installed on the electric vehicle, it is responsible for collecting real-time vehicle operating data, including battery management system (BMS) data (such as current SoC, SOH, and battery temperature), vehicle positioning data (GPS / BeiDou coordinates), driving speed, and energy consumption data. This data is uploaded to the cloud dispatch platform in real-time via 4G / 5G networks, and the terminal also receives dispatch instructions from the platform.

[0073] Charging station / station system: Deployed at each charging station, responsible for real-time monitoring of the physical status (idle / charging / faulty), output power, charging progress, and estimated remaining time of each charging gun. The station gateway uploads the charging gun status data to the cloud and can receive reservation locking commands from the cloud.

[0074] Cloud-based scheduling platform (core server): Serving as the "brain" of the system, it is deployed on a cloud server. It receives real-time data from vehicle-mounted terminals and station systems, and, combined with a built-in Geographic Information System (GIS) and time-of-use pricing database, runs the core scheduling algorithm of this application. It is responsible for generating the optimal charging scheduling plan and distributing it to each terminal via API interface.

[0075] User terminals (Driver App / Fleet Management Web): Driver App: Displays the navigation to the recommended charging stations, the estimated queuing time, and the estimated cost to the driver, and supports one-click navigation. Fleet Management Web: Allows fleet dispatchers to monitor the battery status of the entire fleet, review dispatch suggestions, and calculate operating costs.

[0076] The core processes are as follows: The processing flow of the embodiments of this application mainly includes four stages: data perception, vehicle stratification and sorting, global greedy allocation, and result output.

[0077] Step 1: Data perception and preprocessing The system uses the above hardware architecture to collect the following data in real time: Vehicle status (from on-vehicle terminal): vehicle ID, current state of charge (SoC), battery capacity, current location, no-load energy consumption.

[0078] Station status (from charging station side): station location, charging gun list, current status of each gun (idle / occupied), estimated release time, maximum power.

[0079] Electricity price policy: time-of-use electricity price table (peak / shoulder / flat / valley and corresponding prices).

[0080] Environmental parameters: time value coefficient, risk penalty coefficient, safety power redundancy coefficient, etc.

[0081] Step 2: Vehicle safety stratification (Vehicle Classification) To ensure safety and distinguish between priorities, the present invention constructs a "safe power model" to classify vehicles into three categories: Calculate the safety threshold (SoCsafe): Esafe_base = max(energy consumption of the nearest station, 0.2 × battery capacity) SoCsafe = (α × Esafe_base) / battery capacity Where α is the safety redundancy coefficient (such as 1.6 - 1.8) to ensure that the vehicle has enough power to handle unexpected detours or congestion.

[0082] Stratification logic: RED (Must-charge state): SoC < SoCsafe. The vehicle's battery power is critical and it must be charged immediately.

[0083] YELLOW (Flexible state): SoCsafe ≤ SoC < SoCsafe + Δ (Δ is the buffer zone, such as 15%). The vehicle's battery power is low, and it is recommended to charge, but there is some room for waiting / selection.

[0084] GREEN (No charging required) SoC≥SoCsafe+Δ. Vehicle has sufficient battery power and does not participate in dispatching.

[0085] Step 3: Global Priority Sort To resolve resource contention, this invention globally sorts all vehicles awaiting charging (RED + YELLOW) to determine the allocation order: First priority: Vehicle class. RED vehicles have absolute priority over YELLOW vehicles.

[0086] Second priority: Current SoC. Within the same priority level, vehicles with lower SoCs are allocated priority to prevent low-battery vehicles from running out of power while queuing.

[0087] Step 4: Global Greedy Allocation This invention maintains a global "gun position availability schedule" to simulate the process of resources being continuously occupied.

[0088] Initialization: Record the NextFreeTimej of each charging gun j (the current time if it is currently idle, and the estimated end time if it is occupied).

[0089] Assign sequentially: For each car i, in the sorted order: Iterate through all charging guns j at all stations.

[0090] Calculation time points: Arrival time Tarrive = Tnow + travel time.

[0091] The estimated start time for charging is Tstart = max(Tarrive, NextFreeTimej).

[0092] Strategy generation (calculate the cost of both strategy A and strategy B for YELLOW vehicles, and calculate the cost of only strategy A for RED vehicles): Strategy A (Charge as early as possible): Utilize the earliest Tstart to charge immediately.

[0093] Strategy B (Wait for Low Prices): If the current electricity price period is high and there is a low electricity price period within an acceptable waiting range, try to postpone Tstart until the start of the low price period.

[0094] Cost assessment: Calculate the total cost Ctotal of this allocation scheme using a multidimensional cost function.

[0095] Optimal choice: Select the option with the minimum Ctotal (site + gun position + time).

[0096] Resource locking: Update NextFreeTimej of the charging gun position j to Tend (estimated charging end time), thereby affecting the allocation of subsequent vehicles.

[0097] Step 5: Dynamic target battery level Target battery level (SoCtarget): Dynamically adjusted based on time of day. Charges to 100% (full charge) during off-peak hours and to 80% (fast charge) during non-off-peak hours, balancing economy and time efficiency.

[0098] The multidimensional cost function is explained in detail below: Evaluate the merits of the solutions by minimizing the generalized total cost: Ctotal=Cenergy+Ctime+Cdetour+Crisk Electricity cost (Cenergy): Calculate the difference between the actual charging cost and the "average reference cost". If off-peak electricity is used for charging, this item is negative (reward); if peak electricity is used, this item is positive (penalty).

[0099] The formula includes integral calculations for the charging process spanning different tariff periods.

[0100] Time cost (Ctime): Ctime=(Ttravel+Tqueue+Tcharge)×βwait Among them, Ttravel is the estimated travel time, Tqueue is the estimated waiting time, Tcharge is the estimated charging time, and βwait is the time value coefficient (yuan / minute), which converts time into money and reflects the opportunity cost of the downtime of engineering vehicles.

[0101] Detour energy cost (Cdetour): Calculate the extra energy cost consumed by the vehicle while traveling to a charging station, avoiding excessive energy consumption on the way to a distant "low-cost station".

[0102] Risk Cost (Crisk): Overnight risk: If the expected charging end time exceeds the next morning cutoff time (e.g., 06:00), apply a severe linear penalty to ensure that the vehicle does not affect the early morning operation the following day.

[0103] In summary, the technical solutions provided in this application have the following key technical points: 1) Closed-loop hardware architecture for vehicle-pile-cloud collaboration: Real-time full perception of mobile vehicles and fixed resources is achieved through data fusion between vehicle terminals and station systems.

[0104] 2) Three-level vehicle hierarchical model based on safety thresholds: especially the SoCsafe dynamic calculation method that introduces return-to-station energy consumption and redundancy coefficient.

[0105] 3) Global Greedy Allocation Algorithm Based on Time-Axis Update: By maintaining the NextFreeTime state variable, the static allocation problem is transformed into a dynamic time slice filling problem, which effectively solves resource conflicts.

[0106] 4) Multidimensional cost function that includes time value: monetize time (queuing, charging, travel) and compare it with direct electricity costs and risk penalties in a unified dimension.

[0107] 5) Time-based arbitrage strategy for flexible demand: dynamically assess the feasibility of "time for money" for non-emergency vehicles (YELLOW).

[0108] Therefore, the technical solution of this application has the following advantages: 1) Eliminate congestion and achieve global optimization: In contrast, current technology for optimizing bicycle usage can easily lead to queues at popular stations.

[0109] Effect: This invention, through "time axis locking" and "sequential allocation", allows subsequent vehicles to "see" the time slices already occupied by preceding vehicles, and automatically diverts them to other suboptimal stations that do not require queuing, thereby maximizing the overall efficiency of the fleet.

[0110] 2) Balancing economy and timeliness: In contrast, existing technologies often only consider one aspect (either electricity price or distance).

[0111] Effect: By weighing Ctime and Cenergy, the system can automatically determine whether it is more worthwhile to spend more money to quickly charge and get back to work, or to wait in line a little longer to save on electricity costs.

[0112] 3) Intelligent time-based arbitrage strategy: In contrast, existing technologies are typically "plug and charge".

[0113] Effect: The "wait for low price" strategy for YELLOW vehicles can advise drivers to wait a little when electricity prices are at critical points (such as 30 minutes before peak hours), thereby significantly reducing operating costs.

[0114] 4) Ensure the safety of high-risk vehicles: In contrast, existing technology may cause vehicles with low battery levels to travel to remote, low-cost stations, resulting in them breaking down on the way.

[0115] Effect: RED-class vehicles have absolute priority in allocation, and the safe power model takes into account the energy consumption of returning to the station, ensuring that all vehicles "have power to return to".

[0116] 5) Operational risks are controllable: Effect: Introducing a deadline constraint for the next day's work prevents vehicles from being unable to work the following morning due to the desire for cheap overnight charging.

[0117] Furthermore, the technical solutions provided in this application can be applied to commercial vehicle fleets with fixed depots, strong operational regularity, cost sensitivity, and unified management, such as electric concrete mixer trucks, electric concrete pump trucks, electric dump trucks, port electric container trucks, and electric bus fleets. This application has low requirements for vehicle terminal hardware, requiring only vehicles to upload basic GPS and BMS (Battery Management System) data, with core calculations completed on a cloud-based dispatch platform.

[0118] In one embodiment, this application also provides a charging scheduling apparatus for electric vehicles, comprising: a memory configured to store instructions; and a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement the charging scheduling method for electric vehicles according to the above embodiments.

[0119] In one embodiment, this application also provides an electric vehicle, including: a charging scheduling device for an electric vehicle according to the above embodiments.

[0120] In one embodiment, this application also provides a machine-readable storage medium storing instructions for causing a machine to execute the charging scheduling method for an electric vehicle according to the above embodiments.

[0121] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0125] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0126] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0127] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0128] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0129] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A charging scheduling method for electric vehicles, characterized in that, include: Determine the multidimensional estimated cost of charging an electric vehicle at each charging station through each charging device. The multidimensional estimated cost includes the estimated charging electricity cost and the estimated charging time cost. The estimated charging electricity cost is related to the electricity price during the estimated charging period. The estimated charging time cost is related to the estimated travel time of the electric vehicle to the charging station and the estimated waiting time for the charging device to charge after arriving at the charging station. Based on the principle of minimizing multidimensional projected costs, a target charging strategy for the electric vehicle to be charged is determined according to the multidimensional projected costs. The target charging strategy includes a target charging station, a target charging device in the target charging station, and a target charging period. Control the electric vehicle to be charged to charge according to the target charging strategy.

2. The charging scheduling method according to claim 1, characterized in that, The multidimensional estimated cost also includes at least one of the following: The estimated energy cost for the journey is related to the cost of electricity consumed by the electric vehicle on its way to the charging station. The estimated risk cost for the next day is related to the time sequence between the estimated end time of charging for the electric vehicle to be charged and the start time of operation for the electric vehicle to be charged the next day.

3. The charging scheduling method according to claim 1, characterized in that, The multi-dimensional estimated cost of determining the charging cost of the electric vehicle to be charged by charging devices at each charging station includes: Obtain the charging urgency level of the electric vehicle to be charged, wherein the charging urgency level includes an immediate charging level; When the charging urgency level of the electric vehicle to be charged is the immediate charging level, the multi-dimensional estimated cost of charging the electric vehicle through each charging device in each charging station is determined according to the first cost determination method. The first cost determination method is a cost determination method that takes the current time period as the estimated charging time period and the estimated start time of charging of each charging device in each charging station with respect to the electric vehicle to be charged as the time reference.

4. The charging scheduling method according to claim 1, characterized in that, The multi-dimensional estimated cost of determining the charging cost of the electric vehicle to be charged by charging devices at each charging station includes: Obtain the charging urgency level of the electric vehicle to be charged, wherein the charging urgency level includes a flexible charging level; When the charging urgency level of the electric vehicle to be charged is the flexible charging level, the multi-dimensional estimated cost of charging the electric vehicle through each charging device in each charging station is determined according to the first cost determination method and the second cost determination method respectively. The first cost determination method is a cost determination method that takes the current time period as the estimated charging time period and takes the estimated start time of charging of each charging device in each charging station with respect to the electric vehicle to be charged as the time reference. The second cost determination method is a cost determination method that takes the next low electricity price period after the current time period as the estimated charging time period and postpones the estimated start time of charging of each charging device in each charging station with respect to the electric vehicle to be charged to the start time of the next low electricity price period, and takes the postponed estimated start time of charging as the time reference.

5. The charging scheduling method according to claim 3 or 4, characterized in that, The determination of the expected start time of charging for each charging device at each charging station with respect to the electric vehicle to be charged includes: Get the idle times of all charging devices at all charging stations; Determine the estimated arrival time of the electric vehicle to be charged at each of the charging stations; The later of the expected arrival time and the idle time is determined to obtain the expected start time of charging for each charging device at each charging station with respect to the electric vehicle to be charged.

6. The charging scheduling method according to claim 3 or 4, characterized in that, The determination of the charging urgency level of the electric vehicle to be charged includes: Obtain the current remaining battery percentage of the electric vehicle to be charged; If the current remaining battery percentage is lower than a first preset safety threshold, the charging urgency level of the electric vehicle to be charged is determined to be the immediate charging level. If the current remaining battery percentage is higher than or equal to a first preset safety threshold and lower than a second preset safety threshold, the charging urgency level of the electric vehicle to be charged is determined to be a flexible charging level, wherein the second preset safety threshold is higher than the first preset safety threshold, and the second preset safety threshold is the sum of the first preset safety threshold and the preset buffer threshold.

7. The charging scheduling method according to claim 1, characterized in that, The number of electric vehicles to be charged is multiple, and the charging scheduling method further includes: Obtain the charging urgency level of each of the electric vehicles to be charged and the current remaining battery percentage of each of the electric vehicles to be charged; Based on a preset sorting strategy, the charging urgency level and the current remaining battery percentage are used to prioritize the charging order of multiple electric vehicles to be charged. The preset sorting strategy prioritizes the charging urgency level over the current remaining battery percentage. If the charging urgency levels are the same, the vehicles are sorted according to the current remaining battery percentage. For the sorted electric vehicles to be charged, the target charging strategy is determined sequentially to achieve charging scheduling for the electric vehicles to be charged.

8. The charging scheduling method according to claim 1, characterized in that, Also includes: After determining the target charging strategy, resource locking is performed on the target charging device in the target charging strategy for the target charging period.

9. The charging scheduling method according to claim 1, characterized in that, The target charging strategy also includes a target charging percentage; the determination of the target charging percentage includes: If the target charging period is a low-electricity period, the target charging percentage is a first preset charging percentage. When the target charging period is a non-off-peak electricity period, the target charging capacity percentage is a second preset charging capacity percentage, wherein the second preset charging capacity percentage is less than the first preset charging capacity percentage.

10. The method according to claim 1, characterized in that, The estimated charging time cost is the product of the sum of the estimated driving time and the estimated waiting time and a preset time value coefficient.

11. The method according to claim 1, characterized in that, The estimated charging time cost is also related to the estimated charging time of the charging device. The estimated charging time cost is the product of the sum of the estimated driving time, the estimated waiting time, and the estimated charging time, and a preset time value coefficient.

12. The method according to claim 1, characterized in that, The estimated charging cost is the difference between the estimated charging cost for the expected charging period and the reference charging cost.

13. A charging scheduling device for electric vehicles, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the charging scheduling method for an electric vehicle according to any one of claims 1 to 12.

14. An electric vehicle, characterized in that, include: The charging scheduling device for electric vehicles according to claim 13.

15. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to execute the charging scheduling method for electric vehicles according to any one of claims 1 to 12.

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