Method for predicting vehicle introduction number and charging and swapping station scale based on battery-swapping heavy truck

Abstract

The invention provides a method for predicting the number of introduced vehicles and the scale of a battery charging and swapping station based on a battery-swapping heavy truck, and the method comprises the steps: setting the model of the introduced battery-swapping heavy truck, and determining the operation parameters of the battery-swapping heavy truck; calculating a daily total transportation volume demand and a charging demand of the introduced battery-replacing heavy truck based on the operation parameters of the battery-replacing heavy truck, and constructing a vehicle number prediction model and a charging demand model of the introduced battery-replacing heavy truck; constructing a charging and battery swap station scale prediction model based on the number of operation times of the battery swap heavy truck, the model for predicting the number of the introduced battery swap heavy truck vehicles and the charging demand model; and based on the charging and swapping station scale prediction model, obtaining a dynamic relationship between charging and swapping equipment parameters and swapping workshop parameters of the charging and swapping station, so that an investment operator can carry out reasonable investment scale prediction and planning. The method is suitable for various application scenes, provides a decision basis for an operator who needs to replace or introduce a battery replacement heavy truck and an operator of a battery charging and replacing station, and helps investors to formulate an investment scale more scientifically.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system energy storage planning, and relates to a capacity configuration method, in particular to a method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks. Background Art

[0002] In the context of global climate governance, carbon neutrality has become a key benchmark. Only by achieving net-zero global greenhouse gas emissions can global warming be effectively controlled. Driven by the global "carbon neutrality" movement, the automotive industry's transition to new energy is rapidly advancing. China is the world's largest car owner, with heavy-duty gasoline trucks being a key driver of pollution emissions annually. Faced with the increasingly prominent energy crisis and environmental issues of recent years, electrification plays a crucial role in achieving my country's carbon neutrality goals, while also presenting new challenges and opportunities for the global automotive industry.

[0003] Against the backdrop of accelerating the formation of a new development pattern with the domestic circulation as the main body and the domestic and international dual circulations mutually reinforcing each other, reducing dependence on foreign oil and natural gas resources will become an important task to ensure national energy security and guarantee the "power source" of high-quality development. The development of fuel vehicle electrification and the expansion of charging and swapping industry models have a strong driving force for reducing oil and gas consumption, protecting the environment, and expanding domestic demand in the electricity market. Under the demand for the development of the national green transportation industry, the electrification of heavy-duty trucks, with its zero emissions, low noise and high efficiency, can meet the needs of clean, quiet and efficient logistics and transportation. In addition, with the rapid development of electric vehicle technology, the performance of the three-electric system has also been significantly improved, which also provides corresponding technical support for the electrification of heavy-duty trucks.

[0004] While battery-swap heavy-duty trucks currently offer the advantages of high operating efficiency and low overall costs, the construction of battery-swap stations still faces challenges such as difficult site selection and power distribution access, as well as difficulties in promoting shared and intelligent battery swapping. The scale and design of battery-swap stations are complex, and operating costs are high. Furthermore, due to the current lack of standardized battery standards, infrastructure construction is fragmented, resulting in many companies using power batteries from different manufacturers, specifications, shapes, and sizes, making them incompatible. This forces each company to build its own battery-swap stations, which are subject to numerous infrastructure constraints. Furthermore, the application scenarios for battery-swap heavy-duty trucks are limited, making their widespread promotion in the future difficult.

[0005] Therefore, in the existing technology, the construction of battery-swap heavy-duty trucks and battery-swap stations is unable to accurately determine the demand and construction scale of battery-swap stations due to the different needs of each battery-swap station and the different models of battery-swap heavy-duty trucks. As a result, during the construction of battery-swap stations, there are problems such as the inability to accurately and reasonably design the investment scale decision of the battery-swap stations according to user needs. Summary of the Invention

[0006] In view of the shortcomings of the existing technology mentioned above, the purpose of this application is to provide a method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swap heavy-duty trucks, which is used to solve the problem in the existing technology that the construction of battery-swap heavy-duty trucks and battery-swap stations is unable to accurately determine the demand and construction scale of battery-swap stations due to the different needs of each battery-swap station and the different models of battery-swap heavy-duty trucks, thereby resulting in the inability to accurately design the investment scale decision of the battery-swap station according to user needs during the construction of the battery-swap station.

[0007] To achieve the above-mentioned objectives and other related objectives, on the first aspect, the present application provides a method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swap heavy-duty trucks, comprising the following steps: setting the model of the introduced battery-swap heavy-duty truck and determining the operating parameters of the battery-swap heavy-duty truck; calculating the daily total transportation demand and charging demand of the introduced battery-swap heavy-duty truck based on the operating parameters of the battery-swap heavy-duty truck, and constructing a vehicle number prediction model and a charging demand model for the introduced battery-swap heavy-duty truck; constructing a charging and swapping station scale prediction model based on the number of battery-swap heavy-duty truck operations and the vehicle number prediction model and charging demand model for the introduced battery-swap heavy-duty truck; based on the charging and swapping station scale prediction model, obtaining the dynamic relationship between the charging and swapping equipment parameters of the charging and swapping station and the battery swapping workshop parameters, so as to provide investors and operators with reasonable investment scale prediction and planning.

[0008] In an implementation method of the first aspect, the daily total transport demand and charging demand of the introduced battery-swap heavy-duty trucks are calculated based on the operating parameters of the battery-swap heavy-duty trucks, and a vehicle quantity prediction model and a charging demand model for the introduced battery-swap heavy-duty trucks are constructed, including the following steps: based on the operating parameters of the battery-swap heavy-duty trucks, the vehicle's mileage under the rated load is calculated; the battery-swap heavy-duty truck operating scenarios are analyzed, the daily total transport demand and charging demand of the introduced battery-swap heavy-duty trucks are calculated, and a vehicle quantity prediction model and a charging demand model for the introduced or replaced battery-swap heavy-duty trucks are obtained.

[0009] In one implementation of the first aspect, the operating parameters of the battery-swap heavy-duty truck include: vehicle battery capacity, power consumption per 100 kilometers under rated load, vehicle rated load, and battery charge and discharge depth; the calculation formula for the vehicle's mileage under rated load is:

[0010]

[0011] Among them, k highest Indicates the upper limit depth of battery charge and discharge, unit: %; k lowest Indicates the lower limit depth of battery charge and discharge, unit: %; q B Indicates vehicle battery capacity, unit: kWh; q car Indicates the power consumption per 100 kilometers under rated load, unit: kWh / 100km; Dcar Indicates the mileage of the vehicle under rated load, unit: km.

[0012] In an implementation of the first aspect, the battery-swap heavy-duty truck operation scenario includes: battery-swap heavy-duty trucks replacing vehicles and battery-swap heavy-duty trucks introducing vehicles; analyzing the battery-swap heavy-duty truck operation scenario, calculating the daily total transport demand for introducing battery-swap heavy-duty trucks, and obtaining a vehicle number prediction model for introducing or replacing battery-swap heavy-duty trucks, including the following steps: when the vehicle operated by the battery-swap heavy-duty truck is a replacement vehicle, the annual total transport volume of the replacement vehicle should be kept at least equal to the original transport volume, and the daily transport volume to be achieved is calculated; the number of battery-swap heavy-duty trucks is calculated based on the daily transport volume to be achieved; the annual total transport volume calculation formula for replacement vehicles is:

[0013] W EX =W1N1Q1+W2N2Q2+…+W n N n Q n

[0014] Among them, W n Indicates the load capacity of different vehicle types replaced by the operation plan; N n Indicates the number of annual operations; Q n Indicates the number of vehicles in operation; when the battery-swap heavy-duty trucks are newly introduced, it is necessary to analyze and formulate the total annual transport capacity demand, and calculate the daily transport volume to be achieved; the calculation formula for the daily transport volume to be achieved is:

[0015]

[0016] Among them, W in Indicates the total annual transport capacity demand; n r.day Indicates the total number of operating days per year;

[0017] The calculation formula for the number of battery-swap heavy trucks is:

[0018]

[0019] Where W represents the daily transport volume to be achieved; W car represents the rated load of the vehicle; n represents the number of daily operations of the battery-swap heavy-duty truck.

[0020] In an implementation of the first aspect, based on the number of times battery-swap heavy-duty trucks are operated and the prediction model for the number of battery-swap heavy-duty trucks introduced and the charging demand model, a charging and swapping station scale prediction model is constructed, including the following steps: according to different operating scenarios, the operating distance of battery-swap heavy-duty trucks and the total number of battery swaps required for battery-swap heavy-duty trucks per day are calculated; based on the total number of battery swaps required for battery-swap heavy-duty trucks per day, the operating parameters of the charging and swapping station are calculated to obtain a charging and swapping station scale prediction model.

[0021] In one implementation of the first aspect, according to different operating scenarios, calculating the running distance of the battery-swap heavy truck and the total number of battery swaps required per day for the battery-swap heavy truck includes the following steps: When the battery-swap heavy truck is a one-way transport: the running distance of the battery-swap heavy truck is: n*D tr The formula for calculating the number of battery swaps per day for a heavy-duty truck with battery swaps during one-way transportation is:

[0022]

[0023] The calculation formula for the daily battery swapping times of a heavy-duty truck during total one-way transportation within a certain period of time is:

[0024] N=Q*N single

[0025] Among them, D tr represents the one-way distance of the vehicle; n represents the number of daily operations of the battery-swap heavy truck; D car Indicates the mileage of the vehicle under rated load, unit: km; Q indicates the number of battery-swapped heavy-duty trucks;

[0026] When the battery-swap heavy truck is used for two-way transportation: the actual running distance of the battery-swap heavy truck is: 2*n*D tr The calculation formula for the number of battery swaps per day for a heavy-duty truck with battery swapping during two-way transportation is:

[0027]

[0028] The formula for calculating the daily battery swapping times of a heavy-duty truck during total two-way transportation within a certain period of time is:

[0029] N=Q*N return

[0030] Among them, D tr represents the one-way distance of the vehicle; n represents the number of daily operations of the battery-swap heavy truck; D car It indicates the mileage of the vehicle under rated load, unit: km; Q indicates the number of battery-swap heavy-duty trucks.

[0031] In one implementation of the first aspect, the operating parameters of the charging and swapping station include: the total daily charging demand of the charging and swapping station, the number of chargers, the number of battery swapping workshops, the time of a single battery swap, and the charging time of a single battery; the calculation formula for the total daily charging demand of the charging and swapping station is:

[0032] q D =Q*N*(k highest -k lowest )q B

[0033] Among them, k highest Indicates the upper limit depth of battery charge and discharge, unit: %; k lowestIndicates the lower limit depth of battery charge and discharge, unit: %; q B Indicates the vehicle battery capacity, unit: kWh; Q represents the number of battery-swap heavy-duty trucks; N represents the number of battery-swap heavy-duty trucks per day during the total two-way transportation within a certain time period;

[0034] The calculation formula for charging time of a single battery is:

[0035]

[0036] Among them, T CH Indicates the charging time of a single battery, unit: min; P CH Indicates the charging power of the charger, unit: kWh / min; k highest Indicates the upper limit depth of battery charge and discharge, unit: %; k lowest Indicates the lower limit depth of battery charge and discharge, unit: %; q B Indicates the vehicle battery capacity, unit: kWh.

[0037] In an implementation of the first aspect, based on the charging and swapping station scale prediction model, obtaining the dynamic relationship between the charging and swapping equipment parameters of the charging and swapping station and the battery swapping workshop parameters includes the following steps: calculating the number of charging and swapping workshops based on the charging and swapping station operating parameters; calculating the number of charging services that a single charging and swapping station can provide per day based on the number of charging and swapping workshops and the number of chargers, and obtaining the dynamic relationship between the charging and swapping equipment parameters of the charging and swapping station and the battery swapping workshop parameters; making a judgment based on the number of charging services that a single charging and swapping station can provide per day, and obtaining the final number of battery swapping workshops and the number of chargers owned by the charging and swapping station.

[0038] In an implementation of the first aspect, the calculation formula for the number of charging and swapping workshops is:

[0039]

[0040] Among them, N CH Indicates the number of chargers owned by the charging and swapping station; T EX Indicates the single battery replacement time, unit: min;

[0041] The calculation formula for the number of charging services that a single charging and swapping station can provide per day is:

[0042]

[0043] Among them, T CH Indicates the charging time of a single battery, unit: min; T EX Indicates the time for a single battery replacement, unit: min; N CH Indicates the number of chargers owned by the charging and swapping station; N EX Number of charging and swapping workshops.

[0044] In an implementation of the first aspect, judging based on the number of charging services that a single charging and swapping station can provide per day, obtaining the final number of battery swapping workshops and the number of chargers owned by the charging and swapping station includes the following steps: when the number of charging services that a single charging and swapping station can provide per day is greater than or equal to the total number of battery swaps per day for heavy-duty battery swapping trucks during transportation within a certain time period, output the number of chargers owned by the charging and swapping station and the number of battery swapping workshops; when the number of charging services that a single charging and swapping station can provide per day is less than the total number of battery swaps per day for heavy-duty battery swapping trucks during transportation within a certain time period, return to the number of chargers, and repeat the above steps until the result is satisfied.

[0045] On the second aspect, the present application provides a system for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swap heavy-duty trucks, including: an acquisition module for setting the model of the introduced battery-swap heavy-duty truck and determining the operating parameters of the battery-swap heavy-duty truck; a quantity calculation module for calculating the daily total transportation demand and charging demand of the introduced battery-swap heavy-duty trucks based on the operating parameters of the battery-swap heavy-duty trucks, and constructing a vehicle quantity prediction model and a charging demand model for the introduced battery-swap heavy-duty trucks; a scale calculation module for constructing a charging and swapping station scale prediction model based on the number of battery-swap heavy-duty truck operations and the vehicle quantity prediction model and charging demand model for the introduced battery-swap heavy-duty trucks; a prediction and planning module for obtaining the dynamic relationship between the charging and swapping equipment parameters and the battery swapping workshop parameters of the charging and swapping station based on the charging and swapping station scale prediction model, so as to provide investors and operators with reasonable investment scale prediction and planning.

[0046] Finally, the present application provides a device for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations, comprising: a processor and a memory. The memory is used to store a computer program; the processor is connected to the memory and is used to execute the computer program stored in the memory, so that the device for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations executes the method for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations.

[0047] As mentioned above, the method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks of the present invention has the following features:

[0048] Beneficial effects:

[0049] This application provides a method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swap heavy-duty trucks. By analyzing the operating parameters, operating scenarios and operating methods of battery-swap heavy-duty trucks, a calculation of the demand for the introduction of battery-swap heavy-duty trucks is established based on the application scenarios. At the same time, various influencing factors of the operation of battery-swap heavy-duty trucks are parameterized, and a charging and swapping station scale prediction model is built in combination with the vehicle introduction parameters. The establishment of this model will provide an estimation basis for operators who need to replace or introduce battery-swap heavy-duty trucks and operators of charging and swapping stations. This application is applicable to a variety of application scenarios and will provide a decision-making basis for operators who need to replace or introduce battery-swap heavy-duty trucks and operators of charging and swapping stations, which will help investors to formulate investment scales more scientifically. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Shown is a flow chart of a method for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations in one embodiment of the present invention.

[0051] Figure 2 It shows a flow chart of S12 in the method for predicting the number of vehicles introduced based on battery-swap heavy trucks and the scale of charging and swapping stations of the present invention.

[0052] Figure 3 It shows a schematic diagram of the process flow for predicting the number of vehicles to be replaced or introduced by operators in various operation scenarios according to the present invention.

[0053] Figure 4 It shows a flow chart of S13 in the method for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations of the present invention.

[0054] Figure 5 Shown is a schematic diagram of the process flow for predicting the number of service times and electricity consumption demand of operator charging and swapping stations in various operating scenarios according to the present invention.

[0055] Figure 6 It shows a flow chart of S14 in the method for predicting the number of vehicles introduced based on battery-swap heavy trucks and the scale of charging and swapping stations of the present invention.

[0056] Figure 7 Shown is a schematic diagram of the operator's charging and swapping station scale prediction process in various operating scenarios, based on the number of vehicles introduced and the charging and swapping station scale prediction method of the present invention.

[0057] Figure 8 Shown is a schematic diagram of the principle structure of a system for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations in one embodiment of the present invention.

[0058] Figure 9 Shown is a schematic diagram of the principle structure of a device for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations in one embodiment of the present invention.

[0059] Component number description

[0060] 81 Get Module

[0061] 82 Quantity calculation module

[0062] 83 Scale calculation module

[0063] 84 Forecasting and Planning Module

[0064] 91 processors

[0065] 92 Memory

[0066] Steps S11 to S14 DETAILED DESCRIPTION

[0067] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0068] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0069] The following will describe in detail the method for predicting the number of vehicles introduced based on battery swapping heavy trucks and the scale of charging and swapping stations provided in the embodiments of the present application in conjunction with the accompanying drawings. This application is used to solve the problem in the prior art that the construction of battery swapping heavy trucks and battery swapping stations is different due to the different needs of each battery swapping station and the different models of battery swapping heavy trucks, which makes it impossible to accurately determine the demand and construction scale of battery swapping stations, and thus causes problems in the battery swapping station construction process, such as the inability to accurately and reasonably design the battery swapping station investment scale decision according to user needs.

[0070] This application analyzes the operational parameters, scenarios, and methods of battery-swap heavy-duty trucks, establishes a demand calculation for battery-swap heavy-duty trucks based on these scenarios, and parameterizes the various factors influencing battery-swap heavy-duty truck operations. Combined with vehicle introduction parameters, this application builds a charging and swap station scale prediction model. This model will provide an estimation basis for operators who need to replace or introduce battery-swap heavy-duty trucks, as well as charging and swap station operators.

[0071] See also Figure 1 , which shows a flow chart of the method for predicting the number of vehicles introduced based on battery swapping heavy trucks and the scale of charging and swapping stations in one embodiment of the present invention. Figure 1 As shown, this embodiment provides a method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks.

[0072] The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks specifically includes the following steps:

[0073] S11, set the model of the battery-swap heavy-duty truck to be introduced and determine the operating parameters of the battery-swap heavy-duty truck.

[0074] In this embodiment, the model of the battery-swap heavy-duty truck introduced is set according to user needs, and the operating parameters of the battery-swap heavy-duty truck are determined based on the model of the battery-swap heavy-duty truck. The operating parameters of the battery-swap heavy-duty truck include but are not limited to: vehicle battery capacity, power consumption per 100 kilometers under rated load, vehicle rated load, battery charge and discharge depth, etc.

[0075] S12, based on the battery-swap heavy truck operation parameters, calculate the daily total transport demand and charging demand of the battery-swap heavy trucks, and build a vehicle quantity prediction model and a charging demand model for the battery-swap heavy trucks. Figure 2 and Figure 3 , respectively showing the flow chart of S12 in the method for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations of the present invention and the flow chart of the prediction of the number of vehicles replaced or introduced by operators in various operating scenarios of the present invention. Figure 2 and Figure 3 As shown, the S12 includes the following steps:

[0076] S121, based on the operating parameters of the battery-swap heavy-duty truck, calculate the vehicle's mileage under rated load.

[0077] In this embodiment, the vehicle's mileage under the rated load is calculated by introducing parameters of the battery-swap heavy-duty truck, such as the vehicle's battery capacity, power consumption per 100 kilometers under the rated load, the vehicle's rated load, and the battery charge and discharge depth. The battery charge and discharge depth is generally set with upper and lower charge and discharge limits, such as 90% for the upper limit and 10% for the lower limit. These values ​​can be set based on actual conditions.

[0078] Specifically, the calculation formula for the vehicle's mileage under rated load is:

[0079]

[0080] Among them, k highest Indicates the upper limit depth of battery charge and discharge, unit: %; klowest Indicates the lower limit depth of battery charge and discharge, unit: %; q B Indicates vehicle battery capacity, unit: kWh; q car Indicates the power consumption per 100 kilometers under rated load, unit: kWh / 100km; D car Indicates the mileage of the vehicle under rated load, unit: km.

[0081] S122, analyze the operation scenarios of battery-swap heavy-duty trucks, calculate the daily total transportation demand and charging demand of the battery-swap heavy-duty trucks, and obtain a vehicle quantity prediction model and charging demand model for introducing or replacing battery-swap heavy-duty trucks.

[0082] In this embodiment, the analysis of battery-swap heavy-duty truck operation scenarios includes two situations: analyzing the operator's daily total transport capacity demand for replacing or introducing vehicles. Based on the different battery-swap heavy-duty truck operation scenarios, the demand for operational capacity in the operation scenarios is analyzed to calculate the total transport capacity of the replaced vehicles or the total new transport capacity demand, thereby obtaining the total daily transport capacity required. Combined with the daily operation frequency of battery-swap heavy-duty trucks and the rated load capacity corresponding to different models of battery-swap heavy-duty trucks, the introduction or replacement volume of battery-swap heavy-duty trucks can be calculated.

[0083] Specifically, the battery-swap heavy-duty truck operation scenarios are divided into two situations: battery-swap heavy-duty trucks replacing vehicles and battery-swap heavy-duty trucks introducing vehicles.

[0084] (1) When the vehicles used in battery-swap heavy-duty truck operations are replacement vehicles, the total annual transport volume of the replacement vehicles should be kept at least equal to the original transport volume, and the daily transport volume to be achieved should be calculated; the number of battery-swap heavy-duty trucks should be calculated based on the daily transport volume to be achieved.

[0085] Therefore, the total annual transport volume of replacement vehicles is calculated as follows:

[0086] W EX =W1N1Q1+W2N2Q2+…+W n N n Q n

[0087] Among them, W n Indicates the load capacity of different vehicle types replaced by the operation plan; N n Indicates the number of annual operations; Q n Indicates the number of holdings.

[0088] Based on the total annual transport volume of replacement vehicles in the above formula, the daily transport volume to be achieved can be calculated as:

[0089]

[0090] Among them, W EX Indicates the total annual transport capacity demand; n r.dayIndicates the total number of operating days per year.

[0091] (2) When the battery-swap heavy-duty trucks are newly introduced, it is necessary to analyze and formulate the total annual transport capacity demand and calculate the daily transport volume required.

[0092] Therefore, the formula for calculating the daily transport volume required for the newly introduced vehicles is:

[0093]

[0094] Among them, W in Indicates the total annual transport capacity demand; n r.day Indicates the total number of operating days per year.

[0095] The calculation formula for the number of newly introduced battery-swap heavy-duty trucks is:

[0096]

[0097] Where W represents the daily transport volume to be achieved; W car represents the rated load of the vehicle; n represents the number of daily operations of the battery-swap heavy-duty truck. When calculating, it is necessary to consider whether the vehicle is transporting in one direction or in a round trip.

[0098] It should be noted that n r.day The input must be based on the actual number of operating days throughout the year.

[0099] S13, based on the number of battery swap heavy trucks running and the vehicle number prediction model and charging demand model, a charging station scale prediction model is constructed. Figure 4 and Figure 5 , respectively showing the flow chart of S13 in the method for predicting the number of vehicles introduced based on battery swapping heavy trucks and the scale of charging and swapping stations of the present invention and the flow chart of the operator's charging and swapping station service times and power demand prediction process under various operating scenarios of the present invention. Figure 4 and Figure 5 As shown, the S13 includes the following steps:

[0100] S131, based on different operating scenarios, calculate the operating distance of the battery-swap heavy-duty truck and the total number of battery swaps required per day.

[0101] In this embodiment, different operating scenarios are analyzed, and the number of battery swaps per day is calculated based on known parameters such as the transportation distance, rated mileage, and daily operation times of battery swap heavy-duty trucks. The number of battery swaps per day needs to consider whether it is one-way transportation or round-trip transportation. The total number of charging times required per day for the introduced or replaced battery swap heavy-duty trucks is then calculated based on the number of introduced or replaced battery swap heavy-duty trucks.

[0102] Specifically, the operation scenarios can be divided into two types: unit transport by battery-swap heavy-duty trucks and round-trip transport by battery-swap heavy-duty trucks. That is, when transporting by battery-swap heavy-duty trucks, it is necessary to consider the situation where the vehicle is fully loaded at the destination and then returns empty.

[0103] (1) When the battery-swap heavy truck is used for one-way transportation:

[0104] The running distance of battery-swap heavy truck is: n*D tr ;

[0105] The calculation formula for the number of battery swaps per day for a heavy-duty truck with battery swapping during one-way transportation is:

[0106]

[0107] The calculation formula for the daily battery swapping times of a heavy-duty truck during total one-way transportation within a certain period of time is:

[0108] N=Q*N single

[0109] Among them, D tr represents the one-way distance of the vehicle; n represents the number of daily operations of the battery-swap heavy truck; D car It indicates the mileage of the vehicle under rated load, unit: km; Q indicates the number of battery-swap heavy-duty trucks.

[0110] It should be noted that the above calculation process is illustrated using a single battery-swap heavy-duty truck as an example.

[0111] (2) When the battery-swap heavy truck is used for two-way transportation:

[0112] The actual operating distance of a battery-swap heavy truck is: 2*n*D tr ;

[0113] The calculation formula for the number of battery swaps per day for a heavy-duty truck with battery swapping during two-way transportation is:

[0114]

[0115] The formula for calculating the daily battery swapping times of a heavy-duty truck during total two-way transportation within a certain period of time is:

[0116] N=Q*N return

[0117] Among them, D tr represents the one-way distance of the vehicle; n represents the number of daily operations of the battery-swap heavy truck; D car It indicates the mileage of the vehicle under rated load, unit: km; Q indicates the number of battery-swap heavy-duty trucks.

[0118] S132, calculating the operating parameters of the charging and swapping station based on the total number of battery swapping times required per day for the battery swapping heavy trucks, and obtaining a charging and swapping station scale prediction model.

[0119] In this embodiment, the charging time of a single battery is calculated based on data such as the total number of battery swaps required per day for a battery-swap heavy truck and the mileage of the vehicle under the rated load.

[0120] The charging and swapping station operating parameters include: the total daily charging demand of the charging and swapping station, the number of chargers, the number of battery swapping workshops, the time required for a single battery swap, and the charging time of a single battery. The number of charging and swapping station services provided is affected by the number of chargers owned by the charging and swapping station, the number of battery swapping workshops, the time required for a single battery swap, and the charging time of a single battery. The number of chargers owned by the charging and swapping station represents the maximum number of batteries that can be charged simultaneously at the station; the charging time of a single battery depends on the charging power of the chargers.

[0121] Specifically, the calculation formula for the total daily charging demand of the charging and swapping station is:

[0122] q D =Q*N*(k highest -k lowest )q B

[0123] Among them, k highest Indicates the upper limit depth of battery charge and discharge, unit: %; k lowest Indicates the lower limit depth of battery charge and discharge, unit: %; q B It represents the vehicle battery capacity, unit: kWh; Q represents the number of battery-swap heavy-duty trucks; N represents the number of battery-swap heavy-duty trucks that swap batteries per day during total two-way transportation within a certain time period.

[0124] The calculation formula for charging time of a single battery is:

[0125]

[0126] Among them, T CH Indicates the charging time of a single battery, unit: min; P CH Indicates the charging power of the charger, unit: kWh / min; k highest Indicates the upper limit depth of battery charge and discharge, unit: %; k lowest Indicates the lower limit depth of battery charge and discharge, unit: %; q B Indicates the vehicle battery capacity, unit: kWh.

[0127] S14, based on the charging and swapping station scale prediction model, obtain the dynamic relationship between the charging and swapping equipment parameters and the battery swapping workshop parameters of the charging and swapping station, so as to provide investors with reasonable investment scale prediction and planning. Figure 6 and Figure 7, respectively showing the flow chart of S14 in the method for predicting the number of vehicles introduced based on battery swapping heavy trucks and the scale of charging and swapping stations of the present invention and the flow chart of the operator's charging and swapping station scale prediction process in a variety of operating scenarios in the method for predicting the number of vehicles introduced based on battery swapping heavy trucks and the scale of charging and swapping stations of the present invention. Figure 6 and Figure 7 As shown, the S14 includes the following steps:

[0128] S141, calculating the number of charging and swapping workshops based on the operating parameters of the charging and swapping station.

[0129] In this embodiment, the number of battery swapping workshops is calculated based on the standard parameters of the charging and swapping station and data such as the number of chargers.

[0130] The calculation formula for the number of charging and swapping workshops is:

[0131]

[0132] Among them, N CH Indicates the number of chargers owned by the charging and swapping station; T EX Indicates the time for a single battery replacement, unit: min.

[0133] S142, based on the number of charging and swapping workshops and the number of chargers, calculate the number of charging services that a single charging and swapping station can provide per day, and obtain the dynamic relationship between the charging and swapping equipment parameters of the charging and swapping station and the parameters of the swapping workshop.

[0134] In this embodiment, based on the number of battery swapping workshops and the charging time of a single battery, a dynamic relationship curve between the charging and swapping equipment data of the charging and swapping station and the battery swapping workshop data is established to calculate the number of charging services that a single charging and swapping station can provide per day.

[0135] Specifically, the calculation formula for the number of charging services that a single charging and swapping station can provide per day is:

[0136]

[0137] Among them, T CH Indicates the charging time of a single battery, unit: min; T EX Indicates the time for a single battery replacement, unit: min; N CH Indicates the number of chargers owned by the charging and swapping station; N EX Number of charging and swapping workshops.

[0138] S143, judging based on the number of charging services that a single charging and swapping station can provide per day, and obtaining the final number of battery swapping workshops and the number of chargers owned by the charging and swapping station.

[0139] In this embodiment, when a single charging and swapping station can provide charging services more than or equal to the total number of battery swaps per day for heavy trucks that can be used for battery swapping during transportation within a certain period of time, the number of chargers and battery swapping workshops owned by the charging and swapping station is output;

[0140] When the number of charging services that a single charging and swapping station can provide per day is less than the total number of battery swapping heavy trucks that can be used for transportation during a certain period of time, the number of chargers is returned and the above steps are executed repeatedly until the result is output.

[0141] Specifically, substitute the number greater than or equal to 0 in the total number of charging times N required for the total number of battery-swapped heavy trucks introduced or replaced by battery-swapped heavy trucks per day into the number of chargers owned by the charging and swapping station N. CH , and then calculate the number of battery swap workshops N EX Substitute into the number of charging services N that can be provided by the single charging and swapping station per day service In the calculation formula, it is compared with the total number of charging times N required per day for the total number of battery-swap heavy-duty trucks introduced or replaced by battery-swap heavy-duty trucks in this period.

[0142] When a single charging and swapping station can provide charging services N times a day service When the number of battery replacements per day for heavy trucks is greater than or equal to the total number of battery replacements during a certain period of time, that is, when N service -When N≧0, output N CH 、N EX On the contrary, if N is not satisfied service -When N≧0, loop until the condition is met and then output the result.

[0143] The method of this application is explained below using the common battery-swap pure electric motor new energy traction heavy-duty truck 6×4 as an example.

[0144] First, determine the battery-swap pure electric new energy traction heavy-duty truck 6×4 type, and determine its battery capacity, vehicle rated load, power consumption per 100 kilometers under rated load and other data based on this model, and calculate the vehicle's mileage under rated load.

[0145] For example: The battery-swap pure electric motor new energy traction heavy-duty truck 6×4 uses lithium iron phosphate batteries, the vehicle's total power is 513kWh, and the cargo capacity can reach more than 35 tons.

[0146] Then, by parameterizing the battery-swap heavy-duty truck models, operating parameters, operating scenarios, and operating methods, combined with vehicle introduction parameters, a charging and swap station scale prediction model was built.

[0147] Specifically, the operator analyzes the vehicle's operational scenarios, calculates the total transport volume required for replacement or introduction of vehicles, converts this into the daily transport volume required, and then calculates the total number of battery-swap heavy-duty trucks introduced or replaced. The vehicle's operating distance is input to calculate the total daily charging demand for the vehicles introduced during the current period. The number of chargers and battery-swap workshops at the charging and swapping stations is then calculated. This analysis then determines the total number of daily charging cycles required for the battery-swap heavy-duty trucks introduced or replaced during the current period.

[0148] For example, consider a park requiring an additional annual capacity of 1 million tons, operating 365 days a year, with a 24-hour charging and swapping station operating at a battery depth of 20% to 80%. The number of vehicles introduced during this phase should be no fewer than 27, with three or more daily trips required. Assuming a 100km transport distance and a full-load outbound and empty-load return pattern, the required daily charging and swapping times total 90, for a total daily charge capacity of 743.58MWh. Assuming a charging and swapping station takes 40 minutes to fully charge a battery and 5 minutes to swap, the station requires a battery swapping workshop equipped with at least three chargers. Therefore, a charging and swapping station for 6×4 battery-swappable, pure electric new energy traction heavy-duty trucks in this park requires a battery swapping workshop equipped with at least three chargers.

[0149] The method provided in this application for predicting the number of vehicles introduced based on battery-swap heavy-duty trucks and the scale of charging and swapping stations can establish a calculation of the demand for the introduction of battery-swap heavy-duty trucks based on the application scenario by analyzing the operating parameters, operating scenarios, and operating methods of battery-swap heavy-duty trucks. At the same time, it parameterizes the various influencing factors of the battery-swap heavy-duty truck operation and builds a charging and swapping station scale prediction model based on the vehicle introduction parameters. This application is applicable to a variety of application scenarios and will provide a decision-making basis for operators who need to replace or introduce battery-swap heavy-duty trucks and charging and swapping station operators, helping investors to formulate investment scales more scientifically.

[0150] The protection scope of the method for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations described in the embodiment of the present application is not limited to the order of execution of the steps listed in this embodiment. All solutions implemented by adding, subtracting, or replacing steps in the existing technology based on the principles of the present application are included in the protection scope of the present application.

[0151] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the following is achieved: Figure 1 The method for predicting the number of vehicles introduced based on battery-swap heavy trucks and the scale of charging and swapping stations.

[0152] At any possible level of technical detail combination, the present application may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present application.

[0153] Computer-readable storage media can be a tangible device that can hold and store the instructions used by the instruction execution device. Computer-readable storage media can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. Computer-readable storage media used herein is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (for example, light pulses by fiber optic cables), or electrical signals transmitted by wires.

[0154] The computer-readable program described herein can be downloaded to each computing / processing device from a computer-readable storage medium, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device. The computer program instructions for performing the operation of this application can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as "C" language or similar programming languages. Computer readable program instructions can be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a computer, or entirely on a computer or server. In the case of a computer, the computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be personalized by utilizing the state information of the computer readable program instructions, and the electronic circuit can execute the computer readable program instructions, thereby realizing various aspects of the present application.

[0155] An embodiment of the present application also provides a system for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks. The system for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks can implement the method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks described in the present application. However, the implementation device of the method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks described in the present application includes but is not limited to the structure of the system for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the scope of protection of the present application.

[0156] The following will describe in detail the vehicle introduction number and charging and swapping station scale prediction system based on battery swapping heavy trucks provided by this embodiment with reference to the diagrams.

[0157] This embodiment provides a system for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks, including:

[0158] See also Figure 8 , which shows a schematic diagram of the principle structure of a system for predicting the number of vehicles introduced based on battery swapping heavy trucks and the scale of charging and swapping stations in one embodiment of the present invention. Figure 8 As shown, the vehicle introduction number and charging and swapping station scale prediction system based on battery swapping heavy trucks includes: an acquisition module 81, a quantity calculation module 82, a scale calculation module 83, and a prediction and planning module 84.

[0159] The acquisition module 81 is used to set the model of the battery-swap heavy-duty truck to be introduced and determine the operating parameters of the battery-swap heavy-duty truck.

[0160] In this embodiment, the model of the battery-swap heavy-duty truck introduced is set according to user needs, and the operating parameters of the battery-swap heavy-duty truck are determined based on the model of the battery-swap heavy-duty truck. The operating parameters of the battery-swap heavy-duty truck include but are not limited to: vehicle battery capacity, power consumption per 100 kilometers under rated load, vehicle rated load, battery charge and discharge depth, etc.

[0161] The quantity calculation module 82 is connected to the acquisition module 81, and is used to calculate the daily total transportation demand and charging demand of the introduced battery-swap heavy-duty trucks based on the operating parameters of the battery-swap heavy-duty trucks, and to construct a vehicle quantity prediction model and a charging demand model for the introduced battery-swap heavy-duty trucks.

[0162] In this embodiment, the mileage of the vehicle under rated load is calculated based on the operating parameters of the battery-swap heavy-duty truck. The battery-swap heavy-duty truck operation scenario is analyzed, and the total daily transportation demand for introducing battery-swap heavy-duty trucks is calculated to obtain a prediction model for the number of battery-swap heavy-duty trucks to be introduced or replaced.

[0163] In this embodiment, the vehicle's mileage under the rated load is calculated by introducing parameters of the battery-swap heavy-duty truck, such as the vehicle's battery capacity, power consumption per 100 kilometers under the rated load, the vehicle's rated load, and the battery charge and discharge depth. The battery charge and discharge depth is generally set with upper and lower charge and discharge limits, such as 90% for the upper limit and 10% for the lower limit. These values ​​can be set based on actual conditions.

[0164] Analyzing battery-swap heavy-duty truck operating scenarios involves two scenarios: analyzing the total daily transport capacity demand of operators replacing or introducing vehicles. Based on the different battery-swap heavy-duty truck operating scenarios, the transport capacity demand in these scenarios is analyzed to calculate the total transport capacity of the replaced vehicles or the total new transport capacity demand, thereby deriving the total daily transport capacity required. Combined with data such as the daily operation frequency of battery-swap heavy-duty trucks and the rated load capacity corresponding to different battery-swap heavy-duty truck models, the introduction or replacement volume of battery-swap heavy-duty trucks can be calculated.

[0165] The scale calculation module 83 is used to build a charging and swapping station scale prediction model based on the number of battery-swapping heavy-duty truck operations and the introduction of battery-swapping heavy-duty truck vehicle quantity prediction model and charging demand model.

[0166] In this embodiment, the operating distance of the battery-swapped heavy truck and the total number of battery swaps required per day are calculated according to different operating scenarios. Based on the total number of battery swaps required per day for the battery-swapped heavy truck, the operating parameters of the charging and swapping station are calculated to obtain a charging and swapping station scale prediction model.

[0167] In this embodiment, different operating scenarios are analyzed, and the number of battery swaps per day is calculated based on known parameters such as the transportation distance, rated mileage, and daily operation times of battery swap heavy-duty trucks. The number of battery swaps per day needs to consider whether it is one-way transportation or round-trip transportation. The total number of charging times required per day for the introduced or replaced battery swap heavy-duty trucks is then calculated based on the number of introduced or replaced battery swap heavy-duty trucks.

[0168] Specifically, the operation scenarios can be divided into two types: unit transport by battery-swap heavy-duty trucks and round-trip transport by battery-swap heavy-duty trucks. That is, when transporting by battery-swap heavy-duty trucks, it is necessary to consider the situation where the vehicle is fully loaded at the destination and then returns empty.

[0169] In this embodiment, the charging time of a single battery is calculated based on data such as the total number of battery swaps required per day for a battery-swap heavy truck and the mileage of the vehicle under the rated load.

[0170] The charging and swapping station operating parameters include: the total daily charging demand of the charging and swapping station, the number of chargers, the number of battery swapping workshops, the time required for a single battery swap, and the charging time of a single battery. The number of charging and swapping station services provided is affected by the number of chargers owned by the charging and swapping station, the number of battery swapping workshops, the time required for a single battery swap, and the charging time of a single battery. The number of chargers owned by the charging and swapping station represents the maximum number of batteries that can be charged simultaneously at the station; the charging time of a single battery depends on the charging power of the chargers.

[0171] The prediction and planning calculation module 84 is used to obtain the dynamic relationship between the parameters of the charging and swapping equipment and the parameters of the battery swapping workshops at the charging and swapping station based on the charging and swapping station scale prediction model, so that investors and operators can make reasonable investment scale predictions and plans. The number of charging services that a single charging and swapping station can provide per day is calculated based on the number of charging and swapping workshops and the number of chargers, and the dynamic relationship between the parameters of the charging and swapping equipment and the parameters of the battery swapping workshops at the charging and swapping station is obtained. The final number of battery swapping workshops and the number of chargers owned by the charging and swapping station are determined based on the number of charging services that a single charging and swapping station can provide per day.

[0172] In this embodiment, the number of battery swapping shops is calculated based on the standard parameters of the charging and swapping station and the number of chargers. Based on the number of battery swapping shops and the charging time of a single battery, a dynamic relationship curve between the charging and swapping equipment data at the charging and swapping station and the battery swapping shop data is established to calculate the number of charging services that a single charging and swapping station can provide per day.

[0173] When the number of charging services that a single charging and swapping station can provide per day is greater than or equal to the total number of battery swaps per day for heavy-duty battery swap trucks during transportation within a certain time period, the number of chargers and battery swap workshops owned by the charging and swapping station will be output; when the number of charging services that a single charging and swapping station can provide per day is less than the total number of battery swaps per day for heavy-duty battery swap trucks during transportation within a certain time period, the number of chargers will be returned and the above steps will be executed repeatedly until the result is output.

[0174] Specifically, substitute the number greater than or equal to 0 in the total number of charging times N required for the total number of battery-swapped heavy trucks introduced or replaced by battery-swapped heavy trucks per day into the number of chargers owned by the charging and swapping station N. CH , and then calculate the number of charging and swapping workshops N EX Substitute into the number of charging services N that can be provided by the single charging and swapping station per day service In the calculation formula, it is compared with the total number of charging times N required per day for the total number of battery-swap heavy-duty trucks introduced or replaced by battery-swap heavy-duty trucks in this period.

[0175] When a single charging and swapping station can provide charging services N times a day service When the number of battery replacements per day for heavy trucks is greater than or equal to the total number of battery replacements during a certain period of time, that is, when N service -When N≧0, output N CH 、N EX On the contrary, if N is not satisfied service -When N≧0, loop until the condition is met and then output the result.

[0176] A prediction model for the number of vehicles introduced based on battery-swap heavy-duty trucks and the scale of charging and swapping stations is built. A prediction system for the number of vehicles introduced based on battery-swap heavy-duty trucks and the scale of charging and swapping stations is established. For different battery-swap heavy-duty truck models and various application scenarios and operating methods of heavy-duty trucks, a prediction model for the number of vehicles replaced or introduced by operators under various operating scenarios is established. By parameterizing the battery-swap heavy-duty truck models, operating parameters, operating scenarios and operating methods, and combining the vehicle introduction parameters, a prediction model for the scale of charging and swapping stations is built. This application can be applied to a variety of application scenarios, and will provide a decision-making basis for operators who need to replace or introduce battery-swap heavy-duty trucks and operators of charging and swapping stations, helping investors to formulate investment scales more scientifically.

[0177] It should be understood that the division of the modules in the above system is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a single physical entity or physically separated. Furthermore, these modules may be implemented entirely in software called by a processing element, or entirely in hardware. Alternatively, some modules may be implemented in software called by a processing element, while others may be implemented in hardware. For example, module x may be a separate processing element, or integrated into a chip in the above system. Furthermore, it may be stored in the form of program code in the memory of the above system, called by a processing element in the system to perform the functions of module x. The implementation of other modules is similar. Furthermore, these modules may be fully or partially integrated or implemented independently. The processing element described herein may be an integrated circuit with signal processing capabilities. During implementation, the steps of the above method or the modules above may be performed by hardware integrated logic circuits in the processor element or by software instructions.

[0178] The above modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code through a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0179] See also Figure 9 , which shows a schematic diagram of the principle structure of a device for predicting the number of vehicles introduced based on battery-swapped heavy trucks and the scale of charging and swapping stations in one embodiment of the present invention. Figure 9As shown, this embodiment provides a device for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks, and the device for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks includes: a processor 91 and a memory 92; the memory 92 is used to store computer programs; the processor 91 is connected to the memory 92, and is used to execute the computer program stored in the memory 92, so that the device for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks performs each step of the method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swapped heavy trucks as described above.

[0180] Preferably, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0181] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0182] In summary, the method for predicting the number of vehicles introduced based on battery swapping heavy trucks and the scale of charging and swapping stations provided in this application has the following beneficial effects:

[0183] The present application provides a method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery-swap heavy-duty trucks, and establishes a prediction model for the number of vehicles replaced or introduced by operators in various operating scenarios. By parameterizing the types, operating parameters, operating scenarios, and operating methods of battery-swap heavy-duty trucks, combined with the vehicle introduction parameters, a charging and swapping station scale prediction model is built. That is: by analyzing the operating parameters, operating scenarios, and operating methods of battery-swap heavy-duty trucks, a calculation of the demand for the introduction of battery-swap heavy-duty trucks is established based on the application scenarios. At the same time, various influencing factors of the operation of battery-swap heavy-duty trucks are parameterized, and a charging and swapping station scale prediction model is built in combination with the vehicle introduction parameters. This application is applicable to a variety of application scenarios, and will provide a decision-making basis for operators who need to replace or introduce battery-swap heavy-duty trucks and charging and swapping station operators, and will help investors formulate investment scales more scientifically.

[0184] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks, characterized in that: The following steps are involved: Set the battery-swap heavy-duty truck models to be introduced and determine the operating parameters of the battery-swap heavy-duty trucks; Based on the operating parameters of the battery-swap heavy-duty trucks, the daily total transportation demand and charging demand of the battery-swap heavy-duty trucks are calculated, and a prediction model for the number of battery-swap heavy-duty trucks introduced and a charging demand model are constructed; Based on the number of battery-swap heavy truck operations and the battery-swap heavy truck quantity prediction model and charging demand model, a charging and swap station scale prediction model is constructed; Based on the charging and swapping station scale prediction model, the dynamic relationship between the charging and swapping equipment parameters and the battery swapping workshop parameters of the charging and swapping station is obtained to provide investors and operators with reasonable investment scale prediction and planning.

2. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 1 is characterized in that: Calculating the daily total transportation demand and charging demand of the battery-swap heavy trucks based on the operating parameters of the battery-swap heavy trucks, and constructing a vehicle quantity prediction model and a charging demand model for the battery-swap heavy trucks includes the following steps: Based on the operating parameters of the battery-swap heavy truck, the running mileage of the vehicle under rated load is calculated; Analyze the operation scenarios of battery-swap heavy-duty trucks, calculate the daily total transportation demand and charging demand of battery-swap heavy-duty trucks, and obtain the vehicle quantity prediction model and charging demand model for battery-swap heavy-duty trucks to be introduced.

3. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 2 is characterized in that: The battery-swap heavy-duty truck operating parameters include: vehicle battery capacity, power consumption per 100 kilometers under rated load, vehicle rated load, and battery charge and discharge depth; The calculation formula for the vehicle's mileage under rated load is: Among them, k highest Indicates the upper limit depth of battery charge and discharge, unit: %; k lowest Indicates the lower limit depth of battery charge and discharge, unit: %; q B Indicates the vehicle battery capacity, unit: kWh; q car Indicates the power consumption per 100 kilometers under rated load, unit: kWh / 100km; D car Indicates the mileage of the vehicle under rated load, unit: km.

4. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 2 is characterized in that: The battery-swap heavy truck operation scenarios include: battery-swap heavy truck replacement vehicles and battery-swap heavy truck introduction vehicles; analyzing the battery-swap heavy truck operation scenarios, calculating the daily total transportation demand for introducing battery-swap heavy trucks, and obtaining a vehicle quantity prediction model for introducing or replacing battery-swap heavy trucks includes the following steps: When the vehicle operated by the battery-swap heavy truck is a replacement vehicle, the total annual transport volume of the replacement vehicle should be kept at least equal to the original transport volume, and the daily transport volume to be achieved should be calculated; Calculate the number of battery-swap heavy trucks based on the daily transportation volume required; The total annual transport volume of replacement vehicles is calculated as follows: IN EX =W1N1Q1+W2N2Q2+…+W n N n Q n Among them, W n Indicates the load of different vehicle types replaced by the operation plan; N n Indicates the number of annual operations; Q n Indicates the number of holdings; When the battery-swap heavy-duty trucks are newly introduced, it is necessary to analyze and formulate the total annual transport capacity demand and calculate the daily transport volume required; The calculation formula for the daily transportation volume to be achieved is: Among them, W in Represents the total annual demand capacity; n r.day Indicates the total number of operating days per year; The calculation formula for the number of battery-swap heavy trucks is: Where W represents the daily transportation volume to be achieved; W car Represents the rated load of the vehicle; n represents the daily operation times of the battery-swap heavy-duty truck.

5. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 1 is characterized in that: Based on the number of battery swap heavy truck operations and the battery swap heavy truck quantity prediction model and charging demand model, building a charging and swap station scale prediction model includes the following steps: According to different operation scenarios, calculate the running distance of battery-swap heavy-duty trucks and the total number of battery-swap heavy-duty trucks required per day; Based on the total number of battery swapping times required per day for the battery swapping heavy trucks, the operating parameters of the charging and swapping station are calculated to obtain a charging and swapping station scale prediction model.

6. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 5 is characterized in that: According to different operating scenarios, the calculation of the operating distance of battery-swap heavy-duty trucks and the total number of battery swaps required per day for battery-swap heavy-duty trucks includes the following steps: When the battery-swap heavy truck is used for one-way transportation: The running distance of battery-swap heavy truck is: n*D tr ; The calculation formula for the number of battery replacements per day for a battery-swapping heavy truck in one-way transportation is: The calculation formula for the number of battery replacements per day for a battery-swapping heavy truck during total one-way transportation within a certain period of time is: N=Q*N single Among them, D tr represents the one-way distance of the vehicle; n represents the number of daily operations of the battery-swap heavy truck; D car It indicates the running mileage of the vehicle under rated load, in km; Q indicates the number of battery-swap heavy trucks; When the battery-swap heavy truck is used for two-way transportation: The actual running distance of battery-swap heavy trucks is: 2*n*D tr ; The calculation formula for the number of battery swaps per day for a battery swap heavy truck in two-way transportation is: The calculation formula for the number of battery swaps per day for a heavy-duty truck during total two-way transportation in a certain period of time is: N=Q*N return Among them, D tr represents the one-way distance of the vehicle; n represents the number of daily operations of the battery-swap heavy truck; D car It indicates the mileage of the vehicle under rated load, unit: km; Q indicates the number of battery-swap heavy-duty trucks.

7. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 5 is characterized in that: The charging and swapping station operation parameters include: the total daily charging demand of the charging and swapping station, the number of chargers, the number of battery swapping workshops, the time of a single battery swap, and the charging time of a single battery; The calculation formula for the total daily charging demand of the charging and swapping station is: q D =Q*N*(k highest -k lowest )q B Among them, k highest Indicates the upper limit depth of battery charge and discharge, unit: %; k lowest Indicates the lower limit depth of battery charge and discharge, unit: %; q B It represents the vehicle battery capacity, in kWh; Q represents the number of battery-swap heavy trucks; N represents the number of battery-swap heavy trucks per day during the total two-way transportation in a certain period of time; The calculation formula for charging time of a single battery is: Among them, T CH Indicates the charging time of a single battery, unit: min; P CH Indicates the charging power of the charger, unit: kWh / min; k highest Indicates the upper limit depth of battery charge and discharge, unit: %; k lowest Indicates the lower limit depth of battery charge and discharge, unit: %; q B Indicates the vehicle battery capacity, unit: kWh.

8. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 1 is characterized in that: Based on the charging and swapping station scale prediction model, obtaining the dynamic relationship between the charging and swapping equipment parameters and the battery swapping workshop parameters of the charging and swapping station includes the following steps: Calculate the number of charging and swapping workshops based on the operating parameters of the charging and swapping stations; Calculate the number of charging services that a single charging and swapping station can provide per day based on the number of charging and swapping workshops and the number of chargers, and obtain the dynamic relationship between the charging and swapping equipment parameters of the charging and swapping station and the parameters of the swapping workshop; Based on the number of charging services that a single charging and swapping station can provide per day, the final number of battery swapping workshops and the number of chargers owned by the charging and swapping station are determined.

9. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 8 is characterized in that: The calculation formula for the number of charging and swapping workshops is: Among them, N CH Indicates the number of chargers owned by the charging and swapping station; T EX Indicates the single battery replacement time, unit: min; The calculation formula for the number of charging services that a single charging and swapping station can provide per day is: Among them, T CH Indicates the charging time of a single battery, unit: min; T EX Indicates the single battery replacement time, unit: min; N CH Indicates the number of chargers owned by the charging and swapping station; N EX Number of charging and swapping workshops.

10. The method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks according to claim 8 is characterized in that: According to the number of charging services that a single charging and swapping station can provide per day, the final number of battery swapping workshops and the number of chargers owned by the charging and swapping station are determined, including the following steps: When a single charging and swapping station can provide charging services more than or equal to the total number of daily battery swapping of heavy trucks during transportation in a certain period of time, the number of chargers and battery swapping workshops owned by the charging and swapping station shall be output; When the number of charging services that a single charging and swapping station can provide per day is less than the total number of battery swapping heavy trucks that can be used for transportation during a certain period of time, the number of chargers is returned and the above steps are executed repeatedly until the result is output.

11. A vehicle introduction number and charging and swapping station scale prediction system based on battery swapping heavy trucks, characterized in that: include: The acquisition module is used to set the model of the battery-swap heavy-duty truck to be introduced and determine the operating parameters of the battery-swap heavy-duty truck; A quantity calculation module is used to calculate the daily total transportation demand and charging demand of the battery-swap heavy-duty trucks based on the operating parameters of the battery-swap heavy-duty trucks, and to build a vehicle quantity prediction model and a charging demand model for the battery-swap heavy-duty trucks introduced; A scale calculation module, used to construct a charging and swapping station scale prediction model based on the number of battery swapping heavy truck operations and the battery swapping heavy truck vehicle quantity prediction model and charging demand model; The prediction and planning module is used to obtain the dynamic relationship between the charging and swapping equipment parameters and the battery swapping workshop parameters of the charging and swapping station based on the charging and swapping station scale prediction model, so as to provide investors and operators with reasonable investment scale prediction and planning.

12. A device for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks, characterized in that: include: Processor and memory; The memory is used to store computer programs; The processor is connected to the memory and is used to execute the computer program stored in the memory, so that the device for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks executes the method for predicting the number of vehicles introduced and the scale of charging and swapping stations based on battery swapping heavy trucks as described in any one of claims 1 to 10.

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