Charging method based on intelligent dynamic power allocation, and multi-gun charger apparatus

Abstract

A charging method based on intelligent dynamic power allocation. The method comprises: acquiring the current SOC, a real-time power demand, a rated power and infrared position detection information of an electric vehicle; on the basis of the current SOC, the real-time power demand, the rated power and the infrared position detection information of the electric vehicle, and in combination with a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold value incentive strategy and an overall-power utilization maximization strategy, performing power allocation and generating a corresponding control instruction; and controlling the output of a charger topology switch on the basis of the control instruction. Further disclosed is a multi-gun charger apparatus using the charging method. The charging method includes a moderate power allocation strategy and takes into account the demands of both a charger control system and users, and can effectively improve the overall charging efficiency, reduce the charging time, and improve the utilization rate of charging piles.

Description

A charging method with intelligent dynamic power distribution and a multi-gun charger device Technical Field

[0001] The present invention relates to the field of charging technology, and in particular to a charging method with intelligent dynamic power distribution and a multi-gun charger device. Background Art

[0002] With the shift to electrification, the growth of traditional gasoline and gas-powered vehicles has slowed or even shrunk. Electric vehicles, however, with their inherent new energy properties and intelligent driving controls, offer significant advantages in terms of policy support and user experience, leading to explosive growth. However, compared to the refueling and recharging speeds of traditional gasoline vehicles, slow charging remains a key pain point for electric vehicles. To improve the charging experience, the government and businesses have invested in the construction of numerous charging facilities, charging stations, and battery swap stations. While battery swapping is fast, the availability of these facilities is limited, and the number of battery swap stations is limited, leading to high maintenance costs. Public acceptance is limited, with the exception of the rental and transportation sectors. Building vehicle-mounted charging stations is cost-effective and easy to deploy on a large scale, so currently, the mainstream method is to charge vehicles with batteries built into the vehicle itself. However, despite years of development, the number of charging stations and charging stations remains insufficient, leading to long wait times on highways, especially during holidays. Recently, high-voltage, high-power charging technology has emerged that can effectively reduce charging times, but it has not yet been widely deployed and is limited to fixed charging stations of some automakers. Multi-charger charging piles can effectively utilize parking spaces and also improve charging efficiency. However, most charging piles currently lack automatic power distribution. Even though some experts have designed multi-charger charging methods with on-demand distribution, they remain unimplemented and lack intelligence. They do not fully consider charging waiting factors and do not maximize charging efficiency under current conditions. Therefore, reducing charging time, increasing charging pile utilization, and improving charging efficiency are several key areas for solving the problem of electric vehicle charging.

[0003] Summary of the Invention

[0004] In order to solve the problems of the prior art in reducing charging time, improving charging pile utilization, and improving charging efficiency, the present invention proposes a charging method with intelligent dynamic power distribution, comprising:

[0005] Obtain the current SOC, real-time power demand, rated power and infrared position detection information of the electric vehicle;

[0006] Based on the current SOC, real-time power demand, rated power and infrared position detection information of the electric vehicle, combined with a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold incentive strategy and a whole-machine power maximization utilization strategy, power allocation is performed and corresponding control instructions are generated;

[0007] A control output of the charger topology switch is performed based on the control instruction.

[0008] Preferably, the power allocation and generation of corresponding control instructions based on the current SOC, real-time power demand, rated power and infrared position detection information of the electric vehicle in combination with a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold incentive strategy and a whole-machine power maximization utilization strategy include:

[0009] When the number of charging vehicles is full and the number of vehicles waiting to be charged does not exceed the set value, the dynamic average power allocation strategy is used to allocate power;

[0010] When the number of vehicles waiting to charge is 0, the power is allocated using the first-come-first-served allocation strategy;

[0011] When the number of charging vehicles is full and the number of vehicles waiting to be charged exceeds the set value, the reverse SOC threshold incentive strategy is used for power allocation.

[0012] Preferably, the calculation formula for the power distribution is as follows:

[0013] Where, P y,i is the charging power pre-allocated to the i-th electric vehicle; P e,i The required charging power for the i-th electric vehicle; i is the incentive adjustment coefficient, i=1~N; P e,k is the required charging power of the kth electric vehicle; N is the total number of electric vehicles to be charged; k is an intermediate variable; and E is the total charging power of the charging pile.

[0014] Preferably, when the number of vehicles waiting for charging is 0, power allocation is performed using a first-come, first-served allocation strategy, including:

[0015] If the charging pile can simultaneously meet the maximum charging power requirements of a set number of electric vehicles, then normal charging will be carried out; otherwise, the charging power requirements of the electric vehicles that arrive first will be met to the maximum extent possible, and then the maximum power will be concentrated to charge other electric vehicles.

[0016] Preferably, when the number of vehicles being charged is full and the number of vehicles waiting to be charged exceeds a set value, a reverse SOC threshold incentive strategy is used to distribute power, including:

[0017] Set standard SOC thresholds based on actual vehicle usage scenarios and periods;

[0018] Setting an incentive adjustment coefficient according to the standard SOC threshold value and a user-set electric vehicle charging target SOC value;

[0019] Power allocation is performed based on the excitation adjustment coefficient and the power allocation calculation formula.

[0020] Preferably, the step of setting the incentive adjustment coefficient according to the standard SOC threshold value in combination with the electric vehicle charging target SOC value set by the user includes:

[0021] If the electric vehicle charging target SOC value set by the user is greater than the standard SOC threshold, the incentive adjustment coefficient is set to be less than 1; otherwise, the incentive adjustment coefficient is set to be greater than 1.

[0022] Preferably, the method further includes allocating power using a strategy for maximizing overall machine power utilization, including:

[0023] The maximum actual power output value corresponding to the actual number of modules allocated to the electric vehicle by the charger is used as the output target;

[0024] The power allocation calculation formula, the incentive adjustment coefficient calculation formula, the fact that the charging power of all electric vehicles is positive and the sum of the power supplied by all electric vehicles is less than the charger power are used as constraints;

[0025] Calculation is performed based on the output target and the constraint conditions to obtain the charging power of the electric vehicle.

[0026] Based on the same inventive concept, the present invention also proposes a multi-gun charger device, comprising:

[0027] Information acquisition module, used to obtain the electric vehicle's current SOC, real-time power demand, rated power and infrared position detection information;

[0028] A power allocation strategy control module is configured to formulate a power allocation strategy and generate corresponding control instructions based on the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle in combination with a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold incentive strategy, and a whole-machine power maximization utilization strategy;

[0029] The charger topology switch control output module is used to perform control output of the charger topology switch based on the control instruction.

[0030] Preferably, the power allocation strategy control module includes:

[0031] The dynamic average power allocation strategy submodule is used to allocate power using the dynamic average power allocation strategy when the number of charging vehicles is full and the number of vehicles waiting to be charged does not exceed the set value;

[0032] The user first-come-first-served allocation strategy submodule is used to allocate power using the user first-come-first-served allocation strategy when the number of vehicles waiting to charge is 0;

[0033] The reverse SOC threshold incentive strategy submodule is used to adopt the reverse SOC threshold incentive strategy for power distribution when the number of charging vehicles is full and the number of vehicles waiting to be charged exceeds a set value.

[0034] Preferably, the calculation formula for power allocation in the dynamic average power allocation strategy submodule is as follows:

[0035] Where, P y,i is the charging power pre-allocated to the i-th electric vehicle; P e,i The required charging power for the i-th electric vehicle; i is the incentive adjustment coefficient, i=1~N; P e,k is the required charging power of the kth electric vehicle; N is the total number of electric vehicles to be charged; k is an intermediate variable; E is the total charging power of the charging pile.

[0036] Preferably, the user first-come, first-served allocation strategy submodule is specifically used to:

[0037] If the charging pile can simultaneously meet the maximum charging power requirements of a set number of electric vehicles, then normal charging will be carried out; otherwise, the charging power requirements of the electric vehicles that arrive first will be met to the maximum extent possible, and then the maximum power will be concentrated to charge other electric vehicles.

[0038] Preferably, the reverse SOC threshold incentive strategy submodule is specifically used to:

[0039] Set the standard SOC threshold according to the actual vehicle usage scenario and time period;

[0040] Setting an incentive adjustment coefficient according to the standard SOC threshold value and a user-set electric vehicle charging target SOC value;

[0041] Power allocation is performed based on the excitation adjustment coefficient and the power allocation calculation formula.

[0042] Preferably, it also includes a whole machine power maximization utilization strategy submodule, specifically used for:

[0043] The maximum actual power output value corresponding to the actual number of modules allocated to the electric vehicle by the charger is used as the output target; the calculation formula of the power allocation, the calculation formula of the incentive adjustment coefficient, and the fact that the charging power of all electric vehicles is a positive number and the sum of the power supplied by all electric vehicles is less than the charger power are used as constraints;

[0044] Calculation is performed based on the output target and the constraint conditions to obtain the charging power of the electric vehicle.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] A charging method with intelligent dynamic power distribution includes: obtaining the current SOC, real-time power demand, rated power, and infrared position detection information of an electric vehicle; performing power distribution and generating corresponding control instructions based on the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle in combination with a dynamic average power distribution strategy, a user first-come-first-served distribution strategy, a reverse SOC threshold incentive strategy, and a whole-machine power maximization utilization strategy; and controlling the output of the charger topology switch based on the control instructions. This application proposes a moderate power distribution strategy that takes into account the needs of both the charger control system and the user, which can effectively improve overall charging efficiency, reduce charging time, and increase charging pile utilization.

[0047] The device of the present application has low hardware requirements and is highly feasible to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] FIG1 is a flow chart of a charging method for intelligent dynamic power distribution according to the present invention;

[0049] FIG2 is a schematic diagram of the “first-come, first-served” allocation of users according to the present invention;

[0050] FIG3 is a power distribution topology circuit diagram of an integrated dual-gun charging device according to the present invention;

[0051] FIG4 is a diagram showing the structure of a multi-gun charger device according to the present invention. DETAILED DESCRIPTION

[0052] The present application provides a charging method and a multi-gun charger device with intelligent dynamic power distribution. The charging power distribution strategy includes four distribution strategies: "dynamic average power distribution", "user first-come, first-served" distribution, reverse SOC threshold excitation and "maximum utilization of the entire machine power" distribution strategy. In order to better understand the present invention, the content of the present invention is further explained below in conjunction with the drawings and embodiments of the specification.

[0053] Example 1:

[0054] A charging method with intelligent dynamic power distribution, the specific process of which is shown in Figure 1, includes:

[0055] Step 1: Obtain the current state of charge (SOC), real-time power demand, rated power, and infrared position detection information of the electric vehicle;

[0056] Step 2: Based on the current SOC, real-time power demand, rated power and infrared position detection information of the electric vehicle, a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold incentive strategy and a whole-machine power maximization utilization strategy are combined to allocate power and generate corresponding control instructions;

[0057] Step 3: Control output of the charger topology switch based on the control instruction.

[0058] It can be explained that SOC is generally known to refer to the percentage of the capacity that can be released in the current battery under specified discharge conditions to the available capacity.

[0059] Specifically, in step 1, obtaining the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle specifically includes:

[0060] Collect information such as current SOC, real-time power demand, rated power, infrared position detection, etc. of all electric vehicles.

[0061] In step 2, based on the current SOC, real-time power demand, rated power and infrared position detection information of the electric vehicle, combined with the dynamic average power allocation strategy, the user first-come-first-served allocation strategy, the reverse SOC threshold incentive strategy and the whole-machine power maximization utilization strategy, power allocation and corresponding control instructions are generated, specifically including:

[0062] 1) The general principle is to dynamically average power distribution based on the power of the charging EVs. When the vehicle is full and there are not many waiting vehicles (usually no more than 1 times the number of charging piles), dynamic average power distribution is performed based on the charging demand of each EV. The distribution strategy is described by the formula as follows:

[0063] Where, P y,i is the charging power pre-allocated to the i-th electric vehicle; P e,i The required charging power for the i-th electric vehicle; i is the incentive adjustment coefficient, i=1~N; P e,k is the required charging power of the kth electric vehicle; N is the total number of electric vehicles to be charged; k is an intermediate variable; and E is the total charging power of the charging pile.

[0064] 2) When there is no waiting vehicle, the allocation strategy of "first come, first served" is adopted:

[0065] Infrared sensors can be set up at waiting parking spaces to collect information on the number of charging vehicles. If there are no vehicles waiting, a "first-come, first-served" allocation strategy can be adopted to maximize the charging power for users who arrive first.

[0066] This strategy still follows the principle of dynamic power allocation. First, dynamic power allocation is performed. After allocation, if the charging pile can simultaneously meet the maximum charging power requirements of two electric vehicles, the "first-come, first-served" allocation strategy will not work. If it cannot meet the requirements, even if the maximum charging power requirements of the two electric vehicles are different, the charging power requirement of the electric vehicle that arrives first will still be maximized. Its basic principle is described as follows:

[0067] Suppose a parking lot has only one charging station with two charging guns. Two electric vehicles are charging with the same maximum charging power requirement. Assuming that the charging station can meet the maximum charging power requirements of any one electric vehicle, but not both, if equal power distribution is used, each electric vehicle will charge for one hour, so the total charging time for each user is one hour. If maximum power is concentrated to charge one of the electric vehicles, doubling its power, its charging time is shortened to 0.5 hours. After it is fully charged, maximum power is then concentrated to charge the other electric vehicle, also for 0.5 hours. In this way, the second user's charging wait time is the same as with the dynamic power allocation method, both are one hour, while the first electric vehicle's total charging wait time is reduced to 0.5 hours. This shortens the total charging time for both users, effectively saving charging time. A schematic diagram is shown in Figure 2.

[0068] 3) When there are many waiting vehicles, the number of waiting vehicles exceeds 1 times the number of charging piles, such as during peak charging periods on holidays, a reverse SOC threshold incentive mechanism is implemented to adjust the incentive adjustment coefficient λi. The formula is as follows:

[0069] Where λ i is the incentive adjustment coefficient, and let λ i ∈(0.5~2);S SOC The target SOC value of the electric vehicle set by the user; B SOC Standard SOC threshold set for the system.

[0070] Based on the vehicle's current SOC, battery capacity, real-time required power, and rated power obtained by each charging terminal, "less charging, faster travel" is encouraged. Different standard SOC thresholds are set according to actual vehicle usage scenarios and time periods (for example, the standard threshold for charging stations in highway service areas during peak holiday periods is set to 75%, the standard threshold is set to 80% during peak daytime periods, and it can be set to 90% during non-peak periods). The incentive coefficient λ is adjusted according to the user-set electric vehicle charging target SOC i , which is higher than the standard SOC setting value, making its λ i <1, which is lower than the standard SOC setting value, so that its λ i>1, for example, during the peak period of vehicle use during holidays, the user sets the electric vehicle's target SOC value to 100%, while the system sets the standard SOC threshold to 75%, then λ i =0.5, if the user sets the electric vehicle SOC value to 50% during peak hours, then λ i =2, which encourages users to adjust their target SOC to a lower value, allowing their vehicles to complete charging sooner, increasing the "turnover rate" of charging terminals, shortening waiting times for vehicles in queues, and improving the user experience. This strategy is suitable for public charging scenarios with a high volume of charging orders, as well as charging stations at highway service areas during holidays. Its effective application requires users to develop the habit of "charging less, going faster" and appropriately adjusting their target SOC value based on their target distance. This habit can be encouraged by policies such as "red envelope subsidies for charging at low target without occupying a space" to encourage users to consciously develop this habit.

[0071] 4) “Maximizing the utilization of the entire machine power” allocation strategy

[0072] This allocation strategy dynamically adjusts and allocates module resources for the entire device based on the principle of "maximizing the overall device output power." Based on the allocation strategies 1)-3) above, it does not achieve maximum charger power output. This is because theoretical allocation also involves integer modules, making ideal allocation impossible. Typically, the total demand of all charging end users exceeds the rated power of the charger, leaving no spare charging modules.

[0073] For example, for an integrated dual-charger DC charger, the dynamic power distribution uses a four-module bridge topology. The power allocation granularity for a 160kW dual-charger DC charger is 40kW per module, as shown in Figure 3. Consider two EVs charging, with maximum power demands of 90kW and 100kW, respectively, exceeding the charger's total power of 160kW. Since there are only four charging modules, the available groups and maximum charging powers are shown in Table 1.

[0074] Table 1

[0075] If the allocation strategy is based on first-come, first-served, the assigned group numbers may be 1 and 3. However, if the allocation strategy is based on maximizing the power utilization of the entire machine, the assigned group number should be 2, with a maximum output power of 160kW, fully realizing the maximum power output of the charger.

[0076] Therefore, the allocation strategy for “maximizing the utilization of the entire machine power” is designed as follows:

[0077] Let the overall power output target be:

[0078] Where, P r,iis the actual power output corresponding to the actual number of modules allocated to the i-th electric vehicle by the charger; i is the order of the electric vehicles; N is the total number of electric vehicles charged; and J is the power output target of the whole machine.

[0079] In addition to formulas (1) and (2), the constraints also include that the charging power of all electric vehicles is positive and the sum should be less than the charger power, as shown below:

[0080] Where, P ys,i is the ideal charging power of the i-th electric vehicle considering the maximum overall power output adjustment coefficient; α i To maximize the overall power output adjustment coefficient; P y,i is the charging power pre-allocated to the i-th electric vehicle; P r,i is the actual power output corresponding to the actual number of modules allocated by the charger to the i-th electric vehicle; E0 is the power of a single module; E is the total charging power of the charging pile; i is the order of the electric vehicles; N is the total number of electric vehicles charged; n is the number of modules allocated by the charger to the i-th electric vehicle, n = (1 ~ N-1).

[0081] By solving the above integer nonlinear inequality optimization equation, the optimal charging power of the electric vehicle can be obtained.

[0082] Although this strategy may not be able to meet the power requirements of each charging terminal, from the perspective of the entire device, it can ensure that the entire device operates at maximum power output. Through dynamic adjustment, the module power transferred from a certain charging terminal can meet the greater power requirements of other charging terminals.

[0083] In step 3, controlling the output of the charger topology switch based on the control instruction specifically includes:

[0084] After the strategy is formulated, a topology switch control instruction for the charger is output to the topology switch of the charger.

[0085] Example 2:

[0086] A multi-gun charger device, as shown in FIG4 , comprises:

[0087] Information acquisition module, used to obtain the electric vehicle's current SOC, real-time power demand, rated power and infrared position detection information;

[0088] A power allocation strategy control module is configured to formulate a power allocation strategy and generate corresponding control instructions based on the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle in combination with a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold incentive strategy, and a whole-machine power maximization utilization strategy;

[0089] The charger topology switch control output module is used to perform control output of the charger topology switch based on the control instruction.

[0090] Power allocation strategy control module, including:

[0091] The dynamic average power allocation strategy submodule is used to allocate power using the dynamic average power allocation strategy when the number of charging vehicles is full and the number of vehicles waiting to be charged does not exceed the set value;

[0092] The user first-come-first-served allocation strategy submodule is used to allocate power using the user first-come-first-served allocation strategy when the number of vehicles waiting to charge is 0;

[0093] The reverse SOC threshold incentive strategy submodule is used to adopt the reverse SOC threshold incentive strategy for power distribution when the number of charging vehicles is full and the number of vehicles waiting to be charged exceeds a set value.

[0094] The calculation formula for power allocation in the dynamic average power allocation strategy submodule is as follows:

[0095] Where, P y,i is the charging power pre-allocated to the i-th electric vehicle; P e,i The required charging power for the i-th electric vehicle; i is the incentive adjustment coefficient, i=1~N; P e,k is the required charging power of the kth electric vehicle; N is the total number of electric vehicles to be charged; k is an intermediate variable; and E is the total charging power of the charging pile.

[0096] The user first-come-first-served allocation strategy submodule is used to:

[0097] If the charging pile can simultaneously meet the maximum charging power requirements of a set number of electric vehicles, then normal charging will be carried out; otherwise, the charging power requirements of the electric vehicles that arrive first will be met to the maximum extent possible, and then the maximum power will be concentrated to charge other electric vehicles.

[0098] The reverse SOC threshold excitation strategy submodule is specifically used to:

[0099] Set the standard SOC threshold according to the actual vehicle usage scenario and time period;

[0100] Setting an incentive adjustment coefficient according to the standard SOC threshold value and a user-set electric vehicle charging target SOC value;

[0101] Power allocation is performed based on the excitation adjustment coefficient and the power allocation calculation formula.

[0102] The incentive adjustment coefficient is set according to the standard SOC threshold value and the electric vehicle charging target SOC value set by the user, including:

[0103] If the electric vehicle charging target SOC value set by the user is greater than the standard SOC threshold, the incentive adjustment coefficient is set to be less than 1; otherwise, the incentive adjustment coefficient is set to be greater than 1.

[0104] The whole machine power maximization strategy submodule is specifically used for:

[0105] The maximum actual power output value corresponding to the actual number of modules allocated to electric vehicles by the charger is used as the output target; the calculation formula for power allocation, the calculation formula for the incentive adjustment coefficient, and the fact that the charging power of all electric vehicles is a positive number and the sum of the power supplied by all electric vehicles is less than the charger power are used as constraints;

[0106] Calculation is performed based on the output target and the constraint conditions to obtain the charging power of the electric vehicle.

[0107] The information collection collects the current SOC, real-time power demand, rated power, infrared position detection and other information of all electric vehicles, and inputs it into the power allocation strategy control module. The power allocation strategy control module includes four power allocation strategies, including dynamic average power allocation strategy, user first-come-first-served allocation strategy, reverse SOC threshold incentive strategy, and whole-machine power maximization utilization strategy;

[0108] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0110] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0112] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A charging method for intelligent dynamic power distribution, characterized in that Including: Obtain the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle; Based on the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle, combine dynamic average power distribution strategy, first-come-first-served user distribution strategy, reverse SOC threshold incentive strategy, and overall machine power maximization utilization strategy to perform power distribution and generate corresponding control instructions; Based on the control instructions, perform control output of the charger topology switch.

2. The method according to claim 1, wherein The step of performing power distribution and generating corresponding control instructions based on the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle, combining dynamic average power distribution strategy, first-come-first-served user distribution strategy, reverse SOC threshold incentive strategy, and overall machine power maximization utilization strategy includes: When the number of charging vehicles is full and the number of waiting charging vehicles does not exceed the set value, use the dynamic average power distribution strategy for power distribution; When the number of waiting charging vehicles is 0, use the first-come-first-served user distribution strategy for power distribution; When the number of charging vehicles is full and the number of waiting charging vehicles exceeds the set value, adopt the reverse SOC threshold incentive strategy for power distribution.

3. The method according to claim 2, wherein The calculation formula for the power distribution is as follows: Wherein, P y,i is the charging power pre-allocated to the i-th electric vehicle; P e,i is the required charging power of the i-th electric vehicle; λ i is the incentive adjustment coefficient, i = 1 to N; P e,k is the required charging power of the k-th electric vehicle; N is the total number of electric vehicle chargings; k is an intermediate variable; E is the total charging power of the charging piles.

4. The method according to claim 2, characterized in that, The step of using the first-come-first-served user distribution strategy for power distribution when the number of waiting charging vehicles is 0 includes: If the charging pile can meet the maximum charging power requirements of a set number of electric vehicles at the same time, charge normally; otherwise, maximize the charging power requirements of the first-arriving electric vehicles and then concentrate the maximum power to charge other electric vehicles.

5. The method according to claim 2, wherein The step of adopting the reverse SOC threshold incentive strategy for power distribution when the number of charging vehicles is full and the number of waiting charging vehicles exceeds the set value includes: Set the standard SOC threshold according to the actual vehicle usage scenario and usage time period; Set the incentive adjustment coefficient according to the standard SOC threshold in combination with the target SOC value for electric vehicle charging set by the user; Based on the incentive adjustment coefficient, combine the calculation formula of power distribution to perform power distribution.

6. The method according to claim 5, wherein The step of setting the incentive adjustment coefficient according to the standard SOC threshold in combination with the target SOC value for electric vehicle charging set by the user includes: If the target SOC value for electric vehicle charging set by the user is greater than the standard SOC threshold, set the incentive adjustment coefficient to be less than 1; otherwise, set the incentive adjustment coefficient to be greater than 1.

7. The method according to claim 2, wherein It also includes using the overall machine power maximization utilization strategy for power distribution, including: Taking the maximum actual power output value corresponding to the actual number of modules allocated to the electric vehicle by the charger as the output target; Taking the calculation formula of power distribution, the calculation formula of the incentive adjustment coefficient, all electric vehicle charging powers are positive and the sum of all electric vehicle power supply powers is less than the charger power as constraints; Based on the output target and constraints, perform calculations to obtain the charging power of the electric vehicle.

8. A multi-gun charger device, characterized in that, Including: An information acquisition module for obtaining the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle; A power distribution strategy control module, which is used to formulate a power distribution strategy and generate corresponding control instructions based on the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle, in combination with the dynamic average power distribution strategy, the first-come-first-served user distribution strategy, the reverse SOC threshold incentive strategy, and the overall machine power maximization utilization strategy; A charger topology switch control output module, which is used to perform the control output of the charger topology switch based on the control instruction.

9. The device according to claim 8, characterized in that, The power distribution strategy control module includes: A dynamic average power distribution strategy sub-module, which is used to perform power distribution using the dynamic average power distribution strategy when the number of charging vehicles is full and the number of waiting charging vehicles does not exceed the set value; A first-come-first-served user distribution strategy sub-module, which is used to use the user's first-come-first-served distribution strategy for power distribution when the number of waiting charging vehicles is 0; A reverse SOC threshold incentive strategy sub-module, which is used to perform power distribution using the reverse SOC threshold incentive strategy when the number of charging vehicles is full and the number of waiting charging vehicles exceeds the set value.

10. The device according to claim 9, characterized in that, The calculation formula for power allocation in the dynamic average power allocation strategy sub-module is as follows: Wherein, P y,i is the charging power pre-allocated to the i-th electric vehicle; P e,i is the required charging power of the i-th electric vehicle; λ i is the incentive adjustment coefficient, i = 1 to N; P e,k is the required charging power of the k-th electric vehicle; N is the total number of electric vehicle chargings; k is an intermediate variable; E is the total charging power of the charging piles.

11. The device according to claim 9, characterized in that, The first-come-first-served user distribution strategy sub-module is specifically used for: If the charging pile can simultaneously meet the maximum charging power requirements of the set number of electric vehicles, normal charging is carried out; otherwise, after maximizing the charging power requirements of the first-arriving electric vehicles, the maximum power is concentrated to charge other electric vehicles.

12. The device according to claim 9, wherein, The reverse SOC threshold incentive strategy sub-module is specifically used for: Setting a standard SOC threshold according to the actual vehicle usage scenario and usage period; Setting an incentive adjustment coefficient according to the standard SOC threshold in combination with the target SOC value for electric vehicle charging set by the user; Performing power distribution based on the incentive adjustment coefficient in combination with the calculation formula of the power distribution.

13. The device according to claim 9, characterized in that, It also includes an overall machine power maximization utilization strategy sub-module, which is specifically used for: Taking the maximum actual power output value corresponding to the actual number of modules allocated to the electric vehicle by the charger as the output target; taking the calculation formula of the power distribution, the calculation formula of the incentive adjustment coefficient, all electric vehicle charging powers are positive and the sum of the power supplies of all electric vehicles is less than the charger power as constraints; Performing calculations based on the output target and constraints to obtain the charging power of the electric vehicle.

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