A charging optimization method and related device

Abstract

The application provides a charging optimization method and related equipment. In the application, a network device obtains arrival time and load data. The arrival time is used to indicate the time when a first vehicle to be charged arrives at a target charging station. The load data is used to indicate whether the vehicle can be charged at the target charging station during a night valley period. If the network device confirms that the target charging station can charge the vehicle during the night valley period, the network device can instruct the first vehicle to charge at the target charging station according to the arrival time. The network device can evaluate the charging behavior of the vehicle in combination with the scheduling of the vehicle operation and the load condition of the charging station itself, optimize the charging behavior of the vehicle in multiple dimensions, control the vehicle to charge as much as possible during the night valley period, and reduce the charging amount during the day period and the non-valley period at night, so as to reduce the power consumption cost.

Description

Technical Field

[0001] This application relates to the field of electrical engineering, and more particularly to a charging optimization method and related equipment. Background Technology

[0002] With the gradual development of the new energy industry, vehicle electrification is a growing trend. Most cities in China have a high degree of vehicle electrification, making electricity costs a major expense during vehicle operation. Industrial electricity in various regions typically uses time-of-use pricing, dividing electricity consumption into daytime, off-peak, and nighttime periods. Prices vary significantly across these periods. Generally, vehicles are preferentially charged during off-peak nighttime periods, avoiding charging during daytime and off-peak nighttime periods to reduce overall electricity costs.

[0003] Typically, vehicle operating companies can adjust various factors to reduce vehicle electricity costs and optimize vehicle charging. For example, they can plan the layout of charging stations. After determining the locations of each charging station, the company divides a city into regions based on demand, resulting in multiple Thiessen polygons, each containing one or more charging stations. First, the maximum power supply that each Thiessen polygon can provide is calculated, and the power demand of vehicles within each Thiessen polygon is calculated based on actual conditions. Then, the difference between the maximum power supply and the power demand for each Thiessen polygon is calculated. Finally, the Thiessen polygons are merged based on this difference to obtain a Thiessen polygon with balanced supply and demand. For Thiessen polygons that still cannot achieve supply and demand balance after merging, if supply is less than demand, charging stations within the Thiessen polygon can be expanded or new charging stations can be built. If supply exceeds demand, redundant charging stations within the Thiessen polygon can be removed. By planning the layout of charging stations, vehicle operating companies improve vehicle charging efficiency and thus reduce vehicle electricity costs.

[0004] In the above-described layout approach, vehicle operating companies reduce vehicle charging costs solely by planning the distribution of charging stations. However, besides charging station layout, vehicle charging costs are influenced by a variety of other factors. Traditional optimization methods are not effective in reducing vehicle charging costs, resulting in high charging costs. Summary of the Invention

[0005] This application provides a charging optimization method and related equipment. The network equipment can evaluate the charging behavior of vehicles by combining the vehicle operation schedule and the load status of the charging station itself, and optimize the charging behavior of vehicles according to multiple dimensions, so as to control the vehicles to charge during the off-peak hours at night and reduce the charging amount during the daytime and non-off-peak hours at night, thereby reducing electricity costs.

[0006] The first aspect of this application provides a charging optimization method, which includes: a network device acquiring arrival time and load data, wherein the arrival time indicates the time when a first vehicle to be charged arrives at a target charging station, and the arrival time belongs to a daytime period, a nighttime off-peak period, or a nighttime non-off-peak period; the load data indicates whether the target charging station can charge the vehicle during the nighttime off-peak period; the network device confirms, based on the load data, that the target charging station can charge the vehicle during the nighttime off-peak period; and the network device instructs the first vehicle to charge at the target charging station based on the arrival time.

[0007] In this application, the network device acquires arrival time and load data. The arrival time indicates the time when the first vehicle to be charged arrives at the target charging station, and the load data indicates whether the vehicle can be charged at the target charging station during the off-peak hours at night. If the network device confirms that the target charging station can charge the vehicle during the off-peak hours at night, the network device can instruct the first vehicle to charge at the target charging station based on the arrival time. The network device can evaluate the vehicle's charging behavior by combining the vehicle's operational scheduling and the charging station's own load status, and optimize the vehicle's charging behavior based on multiple dimensions to control the vehicle to charge during the off-peak hours at night as much as possible, reducing the charging amount during daytime and non-off-peak hours at night, thereby reducing electricity costs.

[0008] In one possible implementation of the first aspect, the network device instructs the first vehicle to charge at the target charging station based on the arrival time, comprising: the network device confirming that the arrival time belongs to a daytime period; the network device calculating a target charging amount for the first vehicle; and the network device instructing the first vehicle to charge at the target charging station according to the target charging amount.

[0009] In this possible implementation, if a bus runs out of power midway through its route and must recharge at a bus charging station, the network equipment first obtains the bus's vehicle data. The network equipment includes a charging prediction model and a power consumption prediction model. These models calculate the amount of electricity required for the remaining mileage of the bus's route based on the vehicle data. Then, the network equipment instructs bus A to charge at charging station B according to this calculated amount. In this possible implementation, during daytime hours, when charging prices are higher, the network equipment can control the amount of electricity the vehicle receives at the target charging station, thereby reducing the vehicle's charging costs.

[0010] In one possible implementation of the first aspect, the network device instructs the first vehicle to charge at the target charging station based on the arrival time, including: the network device confirming that the arrival time belongs to a daytime period; the network device instructing the first vehicle to suspend operation and postpone charging to a nighttime off-peak period; and the network device instructing a second vehicle to operate the mileage to be operated on the first vehicle's operating route.

[0011] In this possible implementation, if a bus runs out of power midway through its route, it must recharge at a bus charging station. To reduce daytime charging load at these stations, the network equipment can control buses to delay charging so they can charge during off-peak hours at night. Furthermore, the network equipment can instruct another bus to cover the remaining mileage of the route. This possible implementation reduces daytime charging load by adjusting bus schedules, effectively lowering bus charging costs.

[0012] In one possible implementation of the first aspect, the network device instructs the first vehicle to charge at the target charging station based on the arrival time, including: the network device confirming that the arrival time belongs to a non-off-peak period at night; and the network device instructing the first vehicle to postpone charging to the off-peak period at night.

[0013] In one possible implementation, if a bus arrives at a bus charging station during off-peak hours at night, the network equipment can control the bus to queue for charging during off-peak hours at night. In this possible implementation, by adjusting the charging time of the bus, the charging amount at the bus charging station during off-peak hours at night is reduced, further reducing the charging cost of the bus.

[0014] A second aspect of this application provides a charging optimization method, comprising: a network device acquiring off-peak utilization rate and off-peak percentage, wherein the off-peak utilization rate represents the ratio between the charging amount of a target charging station and the maximum charging amount of the target charging station during the off-peak period at night, and the off-peak percentage represents the ratio between the charging amount of the target charging station during the off-peak period at night and the total charging amount of the target charging station; and the network device generating a charging strategy based on the off-peak utilization rate and the off-peak percentage.

[0015] In this application, the network device acquires the off-peak utilization rate and off-peak percentage. The off-peak utilization rate represents the ratio between the charging amount of the target charging station and its maximum charging amount during the off-peak hours at night. The off-peak percentage represents the ratio between the charging amount of the target charging station during the off-peak hours at night and its total charging amount. The network device then generates a charging strategy based on these two metrics. The network device can evaluate the charging behavior of charging stations by considering their off-peak utilization and optimize it based on multiple dimensions. This aims to control the charging of vehicles at the charging station to occur primarily during the off-peak hours at night, reducing charging during daytime and non-off-peak hours at night, thereby lowering electricity costs.

[0016] In one possible implementation of the second aspect, the network device generates a charging strategy based on the off-peak utilization rate and the off-peak percentage, including: the network device confirming that the off-peak utilization rate is lower than a first threshold; and the network device instructing an increase in the number of vehicles charging at the target charging station during the nighttime off-peak period.

[0017] In this possible implementation, the network device confirms that the off-peak utilization rate of the bus charging station is below a first threshold, indicating an oversupply of charging piles. Therefore, the network device can instruct an increase in the number of vehicles charging at the target charging station during the off-peak nighttime hours. Furthermore, if the network device confirms that the off-peak electricity ratio at the bus charging station is below a second threshold, this could be due to fewer buses charging at the station during the off-peak nighttime hours, or it could be due to excessive charging during the daytime hours. Therefore, the charging volume at the bus charging station during the daytime hours can be reduced. In this possible implementation, increasing the number of vehicles charging at the target charging station during the off-peak nighttime hours improves the off-peak utilization rate of the target charging station, allowing idle charging piles to be effectively utilized during the off-peak nighttime hours, thus increasing the overall utilization rate of the target charging station and reducing vehicle charging costs. Reducing the charging volume at the target charging station during the daytime hours encourages vehicles to charge during the off-peak nighttime hours, further reducing vehicle charging costs.

[0018] In one possible implementation of the second aspect, the method further includes: the network device confirming that the off-peak electricity ratio is below a second threshold; and the network device instructing the target charging station to reduce the amount of charging during the daytime.

[0019] In this application, the network device confirms that the off-peak utilization rate of the bus charging station is lower than a first threshold, indicating a surplus of charging piles at the station. Therefore, the network device can instruct an increase in the number of vehicles charging at the target charging station during the off-peak hours at night. Furthermore, the network device confirms that the off-peak electricity ratio at the bus charging station is higher than a second threshold, allowing the network device to confirm that the number of bus trips charging at the bus station is reasonable. In this possible implementation, the charging station increases the number of vehicles charging at the target charging station during the off-peak hours at night, improving the off-peak utilization rate of the target charging station. This ensures that idle charging piles at the target charging station can be effectively utilized during the off-peak hours at night, thereby increasing the overall utilization of the target charging station and reducing vehicle charging costs.

[0020] In one possible implementation of the second aspect, the network device generates a charging strategy based on the off-peak utilization rate and the off-peak percentage, including: the network device confirming that the off-peak utilization rate is higher than a first threshold and the network device confirming that the off-peak percentage is lower than a first threshold; and the network device instructing an increase in the number of charging piles in the target charging station.

[0021] In this application, the network equipment confirms that the off-peak utilization rate is higher than a first threshold, and the network equipment confirms that the off-peak percentage is lower than the first threshold, indicating that the charging piles at the bus charging station are insufficient. Therefore, the number of charging piles in the bus charging station can be increased. In this possible implementation, in areas where charging is more frequent, the utilization rate of the target charging station is higher. When the off-peak utilization rate is higher and the off-peak percentage is lower, the number of charging piles in the target charging station can be increased so that vehicles can charge more efficiently at the target charging station, thus improving the charging efficiency of vehicles at the target charging station.

[0022] A third aspect of this application provides a network device including at least one processor and a memory. The processor is coupled to the memory. The memory stores instructions, and the processor executes the instructions, which, when executed by the processor, cause the network device to perform the methods of the first aspect or any possible implementation thereof, or cause the network device to perform the methods of the second aspect or any possible implementation thereof.

[0023] A fourth aspect of this application provides a computer-readable storage medium storing a program that causes the network device to perform the method of the first aspect or any possible implementation thereof, or causes the network device to perform the method of the second aspect or any possible implementation thereof.

[0024] The fifth aspect of this application provides a computer program product storing one or more computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the processor executes the method of the first aspect or any possible implementation thereof, or the processor executes the method of the second aspect or any possible implementation thereof.

[0025] The sixth aspect of this application provides a chip including a processor and a communication interface, wherein the processor is coupled to the communication interface, and the processor is configured to read instructions to execute the method of the first aspect or any possible implementation thereof, or to execute the method of the second aspect or any possible implementation thereof.

[0026] The seventh aspect of this application is a network system comprising the network device described in the first aspect or any possible implementation thereof, or the system comprising the network device described in the second aspect or any possible implementation thereof. Attached Figure Description

[0027] Figure 1 A schematic diagram of the structure of a network system provided in this application;

[0028] Figure 2 This application provides another schematic diagram of the structure of a network system;

[0029] Figure 3 A schematic diagram illustrating an application of the charging optimization method provided in this application;

[0030] Figure 4 A schematic diagram of charging prices provided in this application;

[0031] Figure 5 A schematic diagram of a charging strategy provided in this application;

[0032] Figure 6 A schematic diagram illustrating an application of another charging optimization method provided in this application;

[0033] Figure 7 A schematic diagram of another charging strategy provided in this application;

[0034] Figure 8 Another schematic diagram of another charging strategy provided in this application;

[0035] Figure 9 A schematic diagram of the structure of a network device provided in this application;

[0036] Figure 10 A schematic diagram of another network device provided in this application;

[0037] Figure 11 A schematic diagram of the structure of another network device provided in this application. Detailed Implementation

[0038] The examples provided in this application are described below with reference to the accompanying drawings. Obviously, the described examples are only a part of the examples in this application, and not all of them. Those skilled in the art will understand that, with the development of technology and the emergence of new scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0039] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the examples described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0040] With the gradual development of the new energy industry, vehicle electrification is a growing trend. Most cities in China have a high degree of vehicle electrification, making electricity costs a major expense during vehicle operation. Industrial electricity in various regions typically uses time-of-use pricing, dividing electricity consumption into daytime, off-peak, and nighttime periods. Prices vary significantly across these periods. Generally, vehicles are preferentially charged during off-peak nighttime periods, avoiding charging during daytime and off-peak nighttime periods to reduce overall electricity costs.

[0041] Typically, vehicle operating companies can adjust various factors to reduce vehicle electricity costs and optimize vehicle charging. For example, they can plan the layout of charging stations. After determining the locations of each charging station, the company divides a city into regions based on demand, resulting in multiple Thiessen polygons, each containing one or more charging stations. First, the maximum power supply that each Thiessen polygon can provide is calculated, and the power demand of vehicles within each Thiessen polygon is calculated based on actual conditions. Then, the difference between the maximum power supply and the power demand for each Thiessen polygon is calculated. Finally, the Thiessen polygons are merged based on this difference to obtain a Thiessen polygon with balanced supply and demand. For Thiessen polygons that still cannot achieve supply and demand balance after merging, if supply is less than demand, charging stations within the Thiessen polygon can be expanded or new charging stations can be built. If supply exceeds demand, redundant charging stations within the Thiessen polygon can be removed. By planning the layout of charging stations, vehicle operating companies improve vehicle charging efficiency and thus reduce vehicle electricity costs.

[0042] In the above-described layout approach, vehicle operating companies reduce vehicle charging costs solely by planning the distribution of charging stations. However, besides charging station layout, vehicle charging costs are influenced by a variety of other factors. Traditional optimization methods are not effective in reducing vehicle charging costs, resulting in high charging costs.

[0043] To address the problems in the aforementioned solutions, this application provides a charging optimization method, network equipment, and network system. The network equipment can evaluate vehicle charging behavior by combining vehicle operation scheduling and the charging station's own load status, and optimize vehicle charging behavior based on multiple dimensions. This controls vehicles to charge as much as possible during off-peak hours at night, reducing charging during daytime and off-peak hours at night, thereby reducing electricity costs.

[0044] The network system, charging optimization method, and network equipment provided in this application will be described below with reference to the accompanying drawings.

[0045] The following examples, in conjunction with the accompanying drawings, will first introduce the network system provided in this application.

[0046] Figure 1 This is a schematic diagram of the structure of a network system provided in this application.

[0047] Please see Figure 1 The network system provided in this application includes a network device 101, a scheduling and management device 102, an intelligent scheduling device 103, and a data collection device 104. The network device 101, the scheduling and management device 102, the intelligent scheduling device 103, and the data collection device 104 can communicate with each other.

[0048] Figure 2 This is a schematic diagram of another network system structure provided in this application.

[0049] Please see Figure 2 In this application, the dispatch management device 102 is the dispatch management console of the vehicle operation company, with a visual and operable interface. Dispatchers of each fleet can generate shift plans and real-time dispatch instructions through the dispatch management device. Optionally, dispatchers can dispatch vehicles either through the intelligent dispatch device 103 or manually through the dispatch management device 102; the specific method is not limited here.

[0050] In this application, network device 101 can obtain charging instructions through an algorithm, which are used to instruct the vehicle to charge. Optionally, the charging instructions may include vehicle number, charging station number, charging pile number, charging start time, charging end time, and / or charging level. The charging instructions may also include other parameters, which are not limited here.

[0051] In this application, the intelligent dispatching device 103 can obtain the next day's vehicle schedule through an algorithm. Optionally, the next day's schedule may include the vehicle number, driver number, route number, and / or departure time. Other parameters may also be included in the schedule, which are not limited here. Furthermore, the intelligent dispatching device 103 can also perform real-time vehicle dispatching. Due to various reasons such as driver absence, vehicle malfunction, or road congestion, the intelligent dispatching device can generate real-time dispatching instructions to adjust the vehicle's travel plan. Optionally, the real-time dispatching instructions may include the vehicle number, driver number, route number, and / or departure time. Other parameters may also be included, which are not limited here.

[0052] In this application, the data collection device 104 can aggregate datasets from various relevant devices of the vehicle operating company. The data collection device 104 may include vehicle data and charging station data. Optionally, the vehicle data may include vehicle location data, vehicle charging data, vehicle power consumption data, and / or vehicle shift data. The vehicle data may also include other data, which are not limited here. The charging station data may include charging pile load data, etc. The vehicle data and load data originate from the charging station, on-board equipment, and dispatch management system. The on-board equipment may include GPS and CAN bus, and may also include other types of equipment, which are not limited here.

[0053] In this application, optionally, network device 101 and scheduling management device 102 can be the same device, that is, the device can have the functions of both network device 101 and scheduling management device 102. Similarly, network device 101 and intelligent scheduling device 103 can be the same device, network device 101 and data collection device 104 can be the same device, and network device 101, scheduling management device 102, intelligent scheduling device 103 and data collection device 104 can be the same device; no specific limitation is made here.

[0054] based on Figure 1 and Figure 2 The network system described herein introduces the charging optimization method provided in this application.

[0055] Figure 3 This is a schematic diagram illustrating an application of the charging optimization method provided in this application.

[0056] Please see Figure 3 An example of the charging optimization method provided in this application includes steps 201 to 203. Below, in conjunction with... Figure 3 This will illustrate the charging optimization method provided in this application.

[0057] 201. Network devices acquire arrival time and load data.

[0058] In this application, the arrival time is used to indicate the time when the first vehicle to be charged arrives at the target charging station. The arrival time falls within the daytime period, the nighttime off-peak period, or the nighttime non-off-peak period. The nighttime off-peak period can also be called the low-peak period, while the daytime and nighttime non-off-peak periods include peak and off-peak periods.

[0059] In this application, load data is used to indicate whether vehicles can be charged at the target charging station during off-peak hours at night. Typically, the target charging station sets a maximum charging capacity for each day to prevent overloading. If the target charging station reaches its maximum charging capacity during the daytime and non-off-peak hours at night, it cannot continue charging vehicles during the off-peak hours at night. Conversely, if the target charging station does not reach its maximum charging capacity during the daytime and non-off-peak hours at night, it can continue charging vehicles during the off-peak hours at night.

[0060] 202. The network equipment confirms, based on load data, that the target charging station can charge vehicles during off-peak hours at night.

[0061] In this application, if the network device determines, based on load data, that the target charging station can charge the vehicle during the off-peak hours at night, the network device can instruct the first vehicle to charge at the target charging station based on the arrival time. If the network device determines, based on load data, that the target charging station cannot charge the vehicle during the off-peak hours at night, the network device can instruct the first vehicle to charge at another charging station.

[0062] 203. The network equipment instructs the first vehicle to charge at the target charging station based on the arrival time.

[0063] Figure 4 This is a schematic diagram of a charging price provided for this application.

[0064] Please see Figure 4 In this application, electricity prices vary significantly across different time periods. Generally, electricity prices are higher during daytime and off-peak hours (nighttime), and lower during off-peak hours (nighttime). For example, the charging price during the daytime is 1.7 yuan / kWh, during off-peak hours it is 1.1 yuan / kWh, and during off-peak hours it is 0.5 yuan / kWh. The network equipment instructs the first vehicle to charge at the target charging station based on its arrival time. The purpose is to control the charging of vehicles to occur as much as possible during off-peak hours (nighttime), reducing the charging amount during daytime and off-peak hours (nighttime), thereby lowering electricity costs.

[0065] It is understandable that the optional daytime, off-peak nighttime, and off-peak nighttime electricity consumption periods can be divided in various ways, and the charging price can also be set in different ways. Specific details are not limited here.

[0066] In this application, the network device acquires arrival time and load data. The arrival time indicates the time when the first vehicle to be charged arrives at the target charging station, and the load data indicates whether the vehicle can be charged at the target charging station during the off-peak hours at night. If the network device confirms that the target charging station can charge the vehicle during the off-peak hours at night, the network device can instruct the first vehicle to charge at the target charging station based on the arrival time. The network device can evaluate the vehicle's charging behavior by combining the vehicle's operational scheduling and the charging station's own load status, and optimize the vehicle's charging behavior based on multiple dimensions to control the vehicle to charge during the off-peak hours at night as much as possible, reducing the charging amount during daytime and non-off-peak hours at night, thereby reducing electricity costs.

[0067] In this application, when describing step 203 above, the network device instructs the first vehicle to charge at the target charging station according to the arrival time. The network device can generate different strategies for different scenarios. The following will describe in detail a variety of specific implementation methods with reference to the accompanying drawings.

[0068] Figure 5 A schematic diagram of a charging strategy provided in this application.

[0069] Please see Figure 5 In this application, the charging optimization method provided by this application is illustrated by taking the first vehicle as a bus and the target charging station as a bus charging station as an example. Optionally, the first vehicle can also be other types of vehicles, and the target charging station can also be other types of charging stations. No specific limitation is made here.

[0070] Scene 1:

[0071] The network equipment confirms, based on load data, that vehicles can charge at the target charging station during off-peak hours at night, and that the arrival time falls within daytime hours.

[0072] In this application, in scenario one, bus charging station B has sufficient power to charge buses during off-peak hours at night. If bus A arrives at bus charging station B during the daytime, the network device can generate multiple strategies based on the actual situation. Two strategies are illustrated below.

[0073] Strategy 1:

[0074] The network device calculates the target charging amount for the first vehicle and instructs the first vehicle to charge at the target charging station according to the target charging amount.

[0075] In this application, if bus A runs out of power midway through its operating route C and must recharge at bus charging station B, the network device first obtains the vehicle data of bus A. The network device includes a charging prediction model and a power consumption prediction model. Based on the vehicle data, the charging prediction model and the power consumption prediction model calculate the amount of electricity (D) required for the remaining mileage of bus A on its operating route C. Then, the network device instructs bus A to recharge at charging station B according to the required amount of electricity (D). In this possible implementation, during daytime hours, due to higher charging prices, the network device can control the amount of electricity the vehicle charges at the target charging station, thereby reducing the vehicle's charging costs.

[0076] In this application, the data collection device stores vehicle data, which may include static data and dynamic data. Static data is data with a low update frequency and may include weather, holiday information, power consumption data, charging data, vehicle number and / or route number. Static data may also include other data, which are not limited here.

[0077] It is understandable that, optionally, the update frequency of static data can be once a day, once a week, or other frequencies, but no specific restrictions are imposed here.

[0078] In this application, dynamic data refers to data that is updated frequently. Dynamic data may include the real-time battery level of bus A, the real-time location of bus A, and / or the scheduling plan of bus A. Dynamic data may also include other data, which are not limited here.

[0079] It is understandable that, optionally, the update frequency of dynamic data can be once per minute, the update frequency of static data can be once per hour, and the update frequency of dynamic data can also be other frequencies, which are not limited here.

[0080] Strategy Two:

[0081] The network equipment instructs the first vehicle to postpone charging to the off-peak period at night and suspend operation, and instructs the second vehicle to operate the mileage that the first vehicle was not operating on.

[0082] In this application, if bus A runs out of power midway through its route C, it must recharge at bus charging station B. To reduce the charging load at bus charging station B during the daytime, the network equipment can control bus A to delay its charging so that it can charge during off-peak hours at night. Furthermore, the network equipment can instruct bus E to take over the remaining mileage of route C for bus A. This possible implementation reduces the daytime charging load at bus charging station B by adjusting bus schedules, effectively lowering the charging costs for buses.

[0083] Scene 2:

[0084] The network equipment confirms, based on load data, that the vehicle can charge at the target charging station during the off-peak hours at night, and confirms that the arrival time falls within the non-off-peak hours at night.

[0085] Strategy 3: The network equipment instructs the first vehicle to postpone charging until the off-peak hours at night.

[0086] In this application, if bus A arrives at bus charging station B during off-peak hours at night, the network equipment can control bus A to queue for charging during off-peak hours at night. In this possible implementation, by adjusting the charging time of bus A, the charging amount at bus charging station B during off-peak hours at night is reduced, further reducing the charging cost of buses.

[0087] Scene 3:

[0088] The network equipment determines, based on load data, that vehicles cannot charge at the target charging station during off-peak hours at night.

[0089] Strategy 4: The network equipment instructs the first vehicle to go to another charging station to charge.

[0090] In this application, because the charging volume of bus charging station B exceeds its maximum charging capacity during daytime and / or non-off-peak nighttime periods, the bus charging station is unable to charge buses during off-peak nighttime periods. The network equipment can instruct bus A to charge at other charging stations. In this possible implementation, when bus charging station B is overloaded, bus A is promptly notified to charge at other charging stations, saving bus A's charging time and improving its charging efficiency.

[0091] Optionally, the network device can instruct the bus to charge at the nearest bus charging station B, or it can instruct the bus to charge at a bus charging station with available charging piles. The network device can also instruct the bus to charge at other charging stations as needed, but this is not limited here.

[0092] In this application, the network device acquires arrival time and load data. The arrival time indicates the time when the first vehicle to be charged arrives at the target charging station, and the load data indicates whether the vehicle can be charged at the target charging station during the off-peak hours at night. If the network device confirms that the target charging station can charge the vehicle during the off-peak hours at night, the network device can instruct the first vehicle to charge at the target charging station based on the arrival time. The network device can evaluate the vehicle's charging behavior by combining the vehicle's operational scheduling and the charging station's own load status, and optimize the vehicle's charging behavior based on multiple dimensions to control the vehicle to charge during the off-peak hours at night as much as possible, reducing the charging amount during daytime and non-off-peak hours at night, thereby reducing electricity costs.

[0093] based on Figure 1 and Figure 2 The described network system introduces the charging optimization method provided in this application.

[0094] Figure 6 This is a schematic diagram illustrating an application of another charging optimization method provided in this application.

[0095] Please see Figure 6 An example of the charging optimization method provided in this application includes steps 301 and 302. The following is in conjunction with... Figure 6 This application illustrates another charging optimization method provided.

[0096] 301. Utilization rate and percentage of off-peak periods for network equipment.

[0097] In this application, the off-peak utilization rate is used to represent the ratio between the charging amount of the target charging station and the maximum charging amount of the target charging station during the off-peak period at night, and the off-peak electricity ratio is used to represent the ratio between the charging amount of the target charging station during the off-peak period at night and the total charging amount of the target charging station.

[0098] 302. Network devices generate charging strategies based on off-peak utilization rate and off-peak percentage.

[0099] In this application, electricity prices vary significantly across different time periods. Typically, electricity prices are higher during daytime and off-peak hours at night, and lower during off-peak hours at night. Network equipment can formulate charging strategies based on off-peak utilization and the proportion of off-peak periods. The purpose of these charging strategies is to adjust the frequency of vehicles assigned to target charging stations and the number of charging piles within those stations. This aims to control vehicle charging to occur primarily during off-peak hours at night, reducing charging during daytime and off-peak hours at night, thereby lowering electricity costs.

[0100] In this application, the network device acquires the off-peak utilization rate and off-peak percentage. The off-peak utilization rate represents the ratio between the charging amount of the target charging station and its maximum charging amount during the off-peak hours at night. The off-peak percentage represents the ratio between the charging amount of the target charging station during the off-peak hours at night and its total charging amount. The network device then generates a charging strategy based on these two metrics. The network device can evaluate the charging behavior of charging stations by considering their off-peak utilization and optimize it based on multiple dimensions. This aims to control the charging of vehicles at the charging station to occur primarily during the off-peak hours at night, reducing charging during daytime and non-off-peak hours at night, thereby lowering electricity costs.

[0101] Figure 7 A schematic diagram of another charging strategy provided in this application.

[0102] Please see Figure 7 In this application, the charging optimization method provided by this application is illustrated by taking the first vehicle as a bus and the target charging station as a bus charging station as an example. Optionally, the first vehicle can also be other types of vehicles, and the target charging station can also be other types of charging stations. No specific limitation is made here.

[0103] Figure 8 Another schematic diagram of another charging strategy provided in this application.

[0104] In this application, when describing step 302 above, the network device generates a charging strategy based on the off-peak utilization rate and the proportion of off-peak periods. The network device can generate different strategies for different scenarios. The following will combine... Figure 7 , Figure 8 This section details several specific implementation methods.

[0105] Scene 1:

[0106] like Figure 7 , Figure 8As shown in Region 1, the network device confirms that the off-peak utilization rate of the charging station in Region 1 is lower than the first threshold, and the off-peak electricity ratio of the charging station in Region 1 is lower than the second threshold.

[0107] Strategy 1: The network device instructs the number of vehicles charging at the target charging station during off-peak hours at night to increase, and the network device instructs the target charging station to reduce the charging volume during daytime hours.

[0108] In this application, the network device confirms that the off-peak utilization rate of bus charging station A is lower than a first threshold, indicating an oversupply of charging piles at bus charging station A. Therefore, the network device can instruct an increase in the number of vehicles charging at the target charging station during the off-peak nighttime hours. Furthermore, the network device confirms that the off-peak electricity ratio of the bus charging station is lower than a second threshold. This could be due to fewer buses charging at bus charging station A during the off-peak nighttime hours, or it could be because bus charging station A generates excessive charging during the daytime hours. Therefore, the charging volume at the bus charging station during the daytime hours can be reduced. In this possible implementation, the charging station increases the number of vehicles charging at the target charging station during the off-peak nighttime hours, improving the off-peak utilization rate of the target charging station. This allows idle charging piles at the target charging station to be effectively utilized during the off-peak nighttime hours, increasing the utilization rate of the target charging station and reducing vehicle charging costs. Reducing the charging volume at the target charging station during the daytime hours encourages vehicles to charge during the off-peak nighttime hours, further reducing vehicle charging costs.

[0109] Scene 2:

[0110] like Figure 7 , Figure 8 As shown in region 2, network equipment confirms that the utilization rate during off-peak hours is lower than the first threshold, and the proportion of off-peak electricity is higher than the second threshold.

[0111] Strategy 2: Increase the number of vehicles charging at the target charging stations using network equipment.

[0112] In this application, the network device confirms that the off-peak utilization rate of bus charging station A is lower than a first threshold, indicating that there is a surplus of charging piles at bus charging station A. Therefore, the network device can instruct an increase in the number of vehicles charging at the target charging station during the off-peak hours at night. Furthermore, the network device confirms that the off-peak electricity ratio of the bus charging station is higher than a second threshold, allowing the network device to confirm that the number of bus trips charging at bus station A is reasonable. In this possible implementation, the charging station increases the number of vehicles charging at the target charging station during the off-peak hours at night, improving the off-peak utilization rate of the target charging station. This ensures that idle charging piles at the target charging station can be effectively utilized during the off-peak hours at night, thereby increasing the utilization rate of the target charging station and reducing vehicle charging costs.

[0113] Scene 3:

[0114] like Figure 7 , Figure 8 As shown in region 3, the network device confirms that the utilization rate during off-peak periods is higher than the first threshold, and the network device confirms that the proportion of off-peak periods is lower than the first threshold.

[0115] Strategy 3: Instruct network devices to increase the number of charging piles in the target charging station.

[0116] In this application, the network device confirms that the off-peak utilization rate of bus charging station A is higher than a first threshold, and the network device confirms that the off-peak percentage is lower than the first threshold. Therefore, it can be concluded that bus charging station A has insufficient charging piles. Consequently, the number of charging piles in bus charging station A can be increased. In this possible implementation, in areas with frequent charging, the utilization rate of the target charging station is higher. When the off-peak utilization rate is high and the off-peak percentage is low, the number of charging piles in the target charging station can be increased, allowing vehicles to charge more efficiently at the target charging station and improving the charging efficiency of vehicles at the target charging station.

[0117] In this application, optionally, the first threshold and the second threshold can be determined based on the actual charging situation. The first threshold can be 80%, 50%, or other values, which are not limited here. Similarly, the second threshold can be 90%, 40%, or other values, which are not limited here.

[0118] In this application, the network device acquires the off-peak utilization rate and off-peak percentage. The off-peak utilization rate represents the ratio between the charging amount of the target charging station and its maximum charging amount during the off-peak hours at night. The off-peak percentage represents the ratio between the charging amount of the target charging station during the off-peak hours at night and its total charging amount. The network device then generates a charging strategy based on these two metrics. The network device can evaluate the charging behavior of charging stations by considering their off-peak utilization and optimize it based on multiple dimensions. This aims to control the charging of vehicles at the charging station to occur primarily during the off-peak hours at night, reducing charging during daytime and non-off-peak hours at night, thereby lowering electricity costs.

[0119] The above example provides a different implementation of a charging optimization method. Below is a network device 40, such as... Figure 9 As shown, the network device 40 is used to perform the steps executed by the network device in the above example. For details on these steps and their corresponding beneficial effects, please refer to the corresponding examples above; they will not be repeated here. The network device 40 includes:

[0120] The acquisition unit 401 is used to acquire arrival time and load data. The arrival time is used to indicate the time when the first vehicle to be charged arrives at the target charging station. The arrival time belongs to the daytime period, the nighttime off-peak period, or the nighttime non-off-peak period. The load data is used to indicate whether the target charging station can charge the vehicle during the nighttime off-peak period.

[0121] Confirmation unit 402 is used to confirm, based on the load data, that the target charging station can charge vehicles during the nighttime off-peak period;

[0122] Indication unit 403 is used to instruct the first vehicle to charge at the target charging station according to the arrival time.

[0123] In one possible implementation,

[0124] The confirmation unit 402 is used to confirm that the arrival time belongs to the daytime period;

[0125] The calculation unit is used to calculate the target charging amount of the first vehicle;

[0126] The indicating unit 403 is used to instruct the first vehicle to charge at the target charging station according to the target charging amount.

[0127] In one possible implementation,

[0128] The confirmation unit 402 is used to confirm that the arrival time belongs to the daytime period;

[0129] The instruction unit 403 is used to instruct the first vehicle to suspend operation and postpone charging to the off-peak period at night.

[0130] The indicating unit 403 is used to indicate the mileage to be operated by the second vehicle on the operating route of the first vehicle.

[0131] In one possible implementation,

[0132] The confirmation unit 402 is used to confirm that the arrival time belongs to the non-valley period of night;

[0133] The indicating unit 403 is used to instruct the first vehicle to postpone charging until the nighttime off-peak period.

[0134] It should be noted that the information interaction and execution process between the modules of the network device 40 mentioned above are based on the same concept as the method example of this application, and the execution steps are consistent with the detailed content of the method steps mentioned above. Please refer to the description in the method example mentioned above.

[0135] The above examples provide a different implementation of network device 40. Below is a network device 50, such as... Figure 10 As shown, the network device 50 is used to perform the steps executed by the network device in the above example. For details on these steps and their corresponding beneficial effects, please refer to the corresponding examples above; they will not be repeated here. The network device 50 includes:

[0136] The acquisition unit 501 is used to acquire the off-peak utilization rate and the off-peak proportion. The off-peak utilization rate is used to represent the ratio between the charging amount of the target charging station and the maximum charging amount of the target charging station during the off-peak period at night. The off-peak proportion is used to represent the ratio between the charging amount of the target charging station during the off-peak period at night and the total charging amount of the target charging station.

[0137] The generation unit 502 is used to generate a charging strategy based on the valley period utilization rate and the valley period proportion.

[0138] In one possible implementation,

[0139] A confirmation unit is used to confirm that the valley period utilization rate is lower than a first threshold.

[0140] An indicator unit is used to indicate an increase in the number of vehicles charging at the target charging station during the nighttime off-peak period.

[0141] In one possible implementation,

[0142] The confirmation unit is also used to confirm that the off-peak electricity ratio is lower than the second threshold.

[0143] The indicating unit is also used to instruct the target charging station to reduce the amount of charging during the daytime.

[0144] In one possible implementation,

[0145] The confirmation unit is used to confirm that the valley period utilization rate is higher than a first threshold, and the network device confirms that the valley period proportion is lower than a first threshold.

[0146] The indicating unit is used to indicate an increase in the number of charging piles in the target charging station.

[0147] The above examples provide a different implementation of network device 50. Below, a network device 60 is provided, such as... Figure 11 As shown, the network device 60 is used to perform the steps performed by the network device in the above example. For details on the execution steps and the corresponding beneficial effects, please refer to the corresponding examples above. They will not be repeated here.

[0148] See Figure 11The diagram shown illustrates the structure of a network device 60 provided in this application. The network device 60 includes a processor 602, a communication interface 603, and a memory 601. Optionally, a bus 604 may be included. The communication interface 603, processor 602, and memory 601 can be interconnected via the bus 604. The bus 604 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 11 The symbol is represented by a single thick line, but this does not indicate that there is only one bus or one type of bus. This network device 60 can achieve... Figure 9 or Figure 10 The example illustrates the functionality of any of the network devices. Processor 602 and communication interface 603 can perform the corresponding operations of the network devices in the above method examples.

[0149] The following is combined with Figure 11 A detailed introduction to each component of a network device:

[0150] The memory 601 may be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, used to store program code, configuration files, or other content that can implement the method of this application.

[0151] The processor 602 is the control center of the controller. It can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the examples provided in this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0152] Communication interface 603 is used for communication with other network devices.

[0153] The processor 602 can perform the aforementioned... Figure 9 or Figure 10 The operation performed by any one of the network devices in the example shown will not be described in detail here.

[0154] It should be noted that the information interaction and execution process between the modules of the network device 60 mentioned above are based on the same concept as the method example of this application, and the execution steps are consistent with the detailed content of the method steps mentioned above. Please refer to the description in the method example mentioned above.

[0155] This application provides a chip including a processor and a communication interface, wherein the processor is coupled to the communication interface, and the processor is used to read instructions and execute the above-mentioned... Figures 3 to 8 The operations performed by the network device in the described embodiments.

[0156] This application provides a network system, which includes the above-mentioned... Figures 3 to 8 The network device described in the embodiments.

[0157] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned examples, and will not be repeated here.

[0158] In the examples provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus examples described above are merely illustrative; for instance, the division of 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 coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0159] 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 example, depending on actual needs.

[0160] Furthermore, the functional units in the various examples of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0161] 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, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various examples of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that different examples can be combined, and the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any combination, modification, equivalent substitution, or improvement made within the spirit and principles of the present invention should be included within the scope of protection of the present invention. The above examples are only used to illustrate the technical solutions of this application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing examples, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the examples in this application.

Claims

1. A charging optimization method, characterized in that, include: The network device acquires arrival time and load data. The arrival time is used to indicate the time when the first vehicle to be charged arrives at the target charging station. The arrival time belongs to the daytime period, the nighttime off-peak period, or the nighttime non-off-peak period. The load data is used to indicate whether the target charging station can charge the vehicle during the nighttime off-peak period. The network device confirms, based on the load data, that the target charging station can charge vehicles during the nighttime off-peak period; The network device instructs the first vehicle to charge at the target charging station according to the arrival time, wherein the network device is used to control the first vehicle to charge during the off-peak hours at night, and reduce the charging amount of the first vehicle during the daytime hours and the non-off-peak hours at night; The network device optimizes the charging behavior of the first vehicle by combining the vehicle operation schedule and the load status of the target charging station. If the charging amount of the target charging station does not reach the maximum charging amount set by the target charging station for a single day during the daytime and the non-off-peak hours at night, the network device instructs the target charging station to continue charging the first vehicle during the off-peak hours at night.

2. The charging optimization method according to claim 1, characterized in that, The network device instructs the first vehicle to charge at the target charging station according to the arrival time, including: The network device confirms that the arrival time falls during daytime. The network device calculates the target charging amount for the first vehicle, wherein the target charging amount is the amount of electricity that the first vehicle needs to consume for the remaining mileage on the operating route, calculated by the network device based on the charging prediction model and the power consumption prediction model. The network device instructs the first vehicle to charge at the target charging station according to the target charging amount.

3. The charging optimization method according to claim 1, characterized in that, The network device instructs the first vehicle to charge at the target charging station according to the arrival time, including: The network device confirms that the arrival time falls during daytime. The network device instructs the first vehicle to suspend operation and postpone charging until the off-peak hours at night; The network device instructs the second vehicle to operate the mileage to be operated on the route operated by the first vehicle.

4. The charging optimization method according to claim 1, characterized in that, The network device instructs the first vehicle to charge at the target charging station according to the arrival time, including: The network device confirms that the arrival time falls within a non-off-peak period at night. The network device instructs the first vehicle to postpone charging until the nighttime off-peak period.

5. A network device, characterized in that, include: The acquisition unit is used to acquire arrival time and load data. The arrival time is used to indicate the time when the first vehicle to be charged arrives at the target charging station. The arrival time belongs to the daytime period, the off-peak period at night, or the non-off-peak period at night. The load data is used to indicate whether the target charging station can charge the vehicle during the off-peak period at night. The confirmation unit is used to confirm, based on the load data, that the target charging station can charge vehicles during the nighttime off-peak period; An instruction unit is configured to instruct the first vehicle to charge at the target charging station according to the arrival time, wherein the network device is configured to control the first vehicle to charge during the off-peak hours at night, thereby reducing the amount of charging done by the first vehicle during the daytime hours and the non-off-peak hours at night; The network device is also used to optimize the charging behavior of the first vehicle by combining the vehicle operation schedule and the load status of the target charging station itself. If the charging amount of the target charging station does not reach the maximum charging amount that the target charging station will set for a single day during the daytime and the non-off-peak hours at night, the network device instructs the target charging station to continue charging the first vehicle during the off-peak hours at night.

6. The network device according to claim 5, characterized in that, The confirmation unit is used to confirm that the arrival time belongs to the daytime period; A calculation unit is used to calculate the target charging amount of the first vehicle, wherein the target charging amount is the amount of electricity that the first vehicle needs to consume for the remaining mileage on the operating route, calculated by the network device based on the charging prediction model and the power consumption prediction model. The indicating unit is used to instruct the first vehicle to charge at the target charging station according to the target charging amount.

7. The network device according to claim 5, characterized in that, The confirmation unit is used to confirm that the arrival time belongs to the daytime period; The instruction unit is used to instruct the first vehicle to suspend operation and postpone charging to the off-peak period at night; The indicating unit is used to indicate the mileage to be operated by the second vehicle on the operating route of the first vehicle.

8. The network device according to claim 5, characterized in that, The confirmation unit is used to confirm that the arrival time belongs to the non-valley period of night. The indicating unit is used to instruct the first vehicle to postpone charging until the nighttime off-peak period.

9. A network device, characterized in that, include: Processor and memory; The processor is configured to execute instructions stored in the memory to cause the network device to perform the method as described in any one of claims 1 to 4.

10. A network system comprising the network device of claim 9.

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