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

Abstract

A kind of intelligent dynamic power distribution charging method and multi-gun charger device, comprising: obtaining the current SOC of electric vehicle, real-time power demand, rated power and infrared position detection information;Based on the current SOC of electric vehicle, real-time power demand, rated power and infrared position detection information combination dynamic average power distribution strategy, user first-come-first-served distribution strategy, reverse SOC threshold incentive strategy and whole machine power maximization utilization strategy carry out power distribution and generate corresponding control instruction;Based on the control instruction, the control output of charger topology switch is carried out;The present application proposes to contain moderate power distribution strategy, give consideration to charger control system, user each party demand, can effectively improve overall charging efficiency, reduce charging time, improve charging pile utilization.

Description

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 transition 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. 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 intelligent considerations. They do not fully consider charging waiting factors and maximize charging efficiency under existing 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. Summary of the Invention

[0003] 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:

[0004] Obtain the current SOC, real-time power demand, rated power and infrared position detection information of the electric vehicle;

[0005] 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;

[0006] A control output of the charger topology switch is performed based on the control instruction.

[0007] 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:

[0008] 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;

[0009] When the number of vehicles waiting to charge is 0, the power is allocated using the first-come-first-served allocation strategy;

[0010] 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.

[0011] Preferably, the calculation formula for the power distribution is as follows:

[0012]

[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 target SOC value of the electric vehicle charging set by the user;

[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]

[0036] 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.

[0037] Preferably, the user first-come, first-served allocation strategy submodule is specifically used to:

[0038] 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.

[0039] Preferably, the reverse SOC threshold incentive strategy submodule is specifically used to:

[0040] Set the standard SOC threshold according to the actual vehicle usage scenario and time period;

[0041] Setting an incentive adjustment coefficient according to the standard SOC threshold value and a target SOC value of the electric vehicle charging set by the user;

[0042] Power allocation is performed based on the excitation adjustment coefficient and the power allocation calculation formula.

[0043] Preferably, it also includes a whole machine power maximization utilization strategy submodule, specifically used for:

[0044] 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;

[0045] Calculation is performed based on the output target and the constraint conditions to obtain the charging power of the electric vehicle.

[0046] Compared with the prior art, the application has the beneficial effects that:

[0047] The application provides a charging method for intelligent dynamic power distribution and a multi-gun charger device, which comprises the following steps: acquiring current SOC, real-time power demand, rated power and infrared position detection information of an electric vehicle; performing power distribution based on the current SOC, the real-time power demand, the rated power and the 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 maximum utilization of overall power strategy, and generating corresponding control instructions; and performing control output of charger topology switches based on the control instructions.

[0048] The device has low requirements on hardware and high feasibility. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 A flow chart of the charging method for intelligent dynamic power distribution of the application is shown in the figure.

[0050] Figure 2 A distribution schematic diagram of the user first-come-first-served of the application is shown in the figure.

[0051] Figure 3 A power distribution topology circuit diagram of the integrated double-gun charging equipment of the application is shown in the figure.

[0052] Figure 4 A multi-gun charger device architecture diagram of the application is shown in the figure. DETAILED DESCRIPTION

[0053] The application provides a charging method for intelligent dynamic power distribution and a multi-gun charger device, which comprises the following steps: acquiring current SOC, real-time power demand, rated power and infrared position detection information of an electric vehicle; performing power distribution based on the current SOC, the real-time power demand, the rated power and the 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 maximum utilization of overall power strategy, and generating corresponding control instructions; and performing control output of charger topology switches based on the control instructions.

[0054] Embodiment 1

[0055] A charging method for intelligent dynamic power distribution, the specific process is shown in the figure, which comprises the following steps: Figure 1

[0056] Step 1, acquiring current SOC, real-time power demand, rated power and infrared position detection information of an electric vehicle;

[0057] ​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;

[0058] Step 3: Control output of the charger topology switch based on the control instruction.

[0059] In step 1, the current SOC, real-time power demand, rated power, and infrared position detection information of the electric vehicle are obtained, including:

[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]

[0064] 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.

[0065] 2) When there is no waiting vehicle, the allocation strategy of "first come, first served" is adopted:

[0066] 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.

[0067] 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:

[0068] If there is only one charging pile in a parking lot with two charging guns on it, and two electric vehicles are charging, the maximum required charging power is also the same. And assuming that the charging pile can meet the maximum charging power of any electric vehicle, but cannot meet the maximum charging power requirements of two electric vehicles at the same time, if average distribution is adopted, each electric vehicle will charge for 1 hour, so the total charging time for each user is 1 hour. If the maximum power is concentrated to charge one of the electric vehicles at this time, its power will be doubled, and its charging time will be shortened to 0.5 hours. After it is fully charged, the maximum power is concentrated to charge the other electric vehicle, and its charging time is also 0.5 hours. Calculated in this way, the charging waiting time for the second user is the same as that of the dynamic power allocation method, which is 1 hour, while the total charging waiting time for the first electric vehicle is reduced to 0.5 hours. The overall charging time of the two users is shortened, which saves the user's charging time in disguise. The schematic diagram is as follows Figure 2 shown.

[0069] 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 λ i , whose formula is as follows:

[0070]

[0071] 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.

[0072] Based on the current SOC, battery capacity, real-time required power, and rated power of the vehicle obtained by each charging terminal, "less charging, faster travel" is encouraged. Different standard SOC thresholds are set according to the 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 λi <1, lower than the standard SOC set value, so that λ i >1, for example, during the peak use period of holidays, the user sets the target SOC value of the electric vehicle to 100%, while the standard SOC threshold set by the system is 75%, then λ i = 0.5, if the user sets the SOC value of the electric vehicle to 50% during the peak use period, then λ i = 2, so as to encourage users to adjust their target SOC value to be lower, so that their vehicles end charging as soon as possible, increase the "turnover rate" of the charging terminal, shorten the waiting time of the queued vehicles, and improve the user experience. This strategy is suitable for social public charging scenarios with large charging order volume and holiday service area charging stations. The premise for effective application of the strategy is that users have developed the habit of "charging less and walking faster", and the habit of adjusting the target SOC value according to their target distance. This use habit can be combined with policies such as "low target charging does not occupy a red packet subsidy" to attract users to consciously develop.

[0073] 4) "Maximum utilization of whole machine power" allocation strategy

[0074] This allocation strategy dynamically adjusts the allocation of the module resources of the whole machine according to the principle of "maximum utilization of whole machine output power". On the basis of the above-mentioned 1)-3) allocation strategies, the maximum power output of the charging machine is not realized because the theoretical allocation involves integer modules and cannot achieve ideal allocation. Usually, the total demand of all charging terminal users is greater than the rated power of the charging machine, and there is no spare charging module.

[0075] For example, for a one-machine dual-gun direct-current charging device with dynamic power using 4 groups of module bridge topology circuit, the power allocation granularity of a 160kW one-machine dual-gun device is a single module of 40kW, as shown in Figure 3 Two electric vehicles are added for charging, with maximum power demand of 90kW and 100kW, respectively, which is greater than the total power of the charging machine 160kW. Because there are only 4 charging modules, the distributable groups and the maximum charging power are as shown in Table 1.

[0076]

[0077] Table 1

[0078] If the first-come-first-served allocation strategy is followed, the allocated group number may be 1 and 3, but if the "maximum utilization of whole machine power" allocation strategy is considered, the allocated group number should be 2, with a maximum output power of 160kW, which fully realizes the maximum power output of the charging machine.

[0079] Therefore, the "maximum utilization of whole machine power" allocation strategy is designed as follows:

[0080] Let the overall power output target be:

[0081]

[0082] Where, P r,i is 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.

[0083] 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:

[0084] P ys,i =α i P y,i

[0085]

[0086] P r,i >0

[0087]

[0088] 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).

[0089] By solving the above integer nonlinear inequality optimization equation, the optimal charging power of the electric vehicle can be obtained.

[0090] 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.

[0091] In step 3, controlling the output of the charger topology switch based on the control instruction specifically includes:

[0092] After the strategy is formulated, a topology switch control instruction for the charger is output to the topology switch of the charger.

[0093] Example 2:

[0094] A multi-gun charger device, such as Figure 4 Shown, including:

[0095] Information acquisition module, used to obtain the electric vehicle's current SOC, real-time power demand, rated power and infrared position detection information;

[0096] 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;

[0097] The charger topology switch control output module is used to perform control output of the charger topology switch based on the control instruction.

[0098] Power allocation strategy control module, including:

[0099] 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;

[0100] 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;

[0101] 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.

[0102] The calculation formula for power allocation in the dynamic average power allocation strategy submodule is as follows:

[0103]

[0104] 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.

[0105] The user first-come-first-served allocation strategy submodule is used to:

[0106] 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.

[0107] The reverse SOC threshold incentive strategy submodule is specifically used to:

[0108] Set the standard SOC threshold according to the actual vehicle usage scenario and time period;

[0109] Setting an incentive adjustment coefficient according to the standard SOC threshold value and a target SOC value of the electric vehicle charging set by the user;

[0110] Power allocation is performed based on the excitation adjustment coefficient and the power allocation calculation formula.

[0111] 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:

[0112] 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.

[0113] The whole machine power maximization strategy submodule is specifically used for:

[0114] 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;

[0115] Calculation is performed based on the output target and the constraint conditions to obtain the charging power of the electric vehicle.

[0116] 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;

[0117] 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.

[0118] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, 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 generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] 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 work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] 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 with intelligent dynamic power distribution, characterized in that: include: 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, 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; Performing control output of the charger topology switch based on the control instruction; The method of allocating power 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 allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold incentive strategy and a whole-machine power maximization utilization strategy includes: 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; When the number of vehicles waiting to charge is 0, the power is allocated using the first-come-first-served allocation strategy; 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; It also includes power allocation using a strategy to maximize overall machine power utilization, including: 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 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; Calculation is performed based on the output target and the constraint conditions to obtain the charging power of the electric vehicle.

2. The method according to claim 1, characterized in that The power distribution calculation formula is as follows: 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.

3. The method according to claim 1, characterized in that When the number of vehicles waiting to be charged is 0, power allocation is performed using a first-come, first-served allocation strategy, including: 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.

4. The method according to claim 1, characterized in that 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: Set standard SOC thresholds based on actual vehicle usage scenarios and periods; Setting an incentive adjustment coefficient according to the standard SOC threshold value and a target SOC value of the electric vehicle charging set by the user; Power allocation is performed based on the excitation adjustment coefficient and the power allocation calculation formula.

5. The method according to claim 4, characterized in that: The step of setting the incentive adjustment coefficient according to the standard SOC threshold value and the electric vehicle charging target SOC value set by the user includes: 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.

6. A multi-gun charger device, characterized in that: include: Information acquisition module, used to obtain the electric vehicle's current SOC, real-time power demand, rated power and infrared position detection information; 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; A charger topology switch control output module, configured to perform control output of the charger topology switch based on the control instruction; The power allocation strategy control module includes: 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; 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; The reverse SOC threshold incentive strategy submodule is used to adopt the reverse SOC threshold incentive strategy for power allocation when the number of charging vehicles is full and the number of vehicles waiting to be charged exceeds a set value; It also includes a submodule for maximizing the utilization strategy of the entire machine power, which is specifically used to: 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; Calculation is performed based on the output target and the constraint conditions to obtain the charging power of the electric vehicle.

7. The device according to claim 6, characterized in that The calculation formula for power allocation in the dynamic average power allocation strategy submodule is as follows: 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.

8. The device according to claim 6, characterized in that The user first-come-first-served allocation strategy submodule is specifically used to: 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.

9. The device according to claim 6, characterized in that The reverse SOC threshold excitation strategy submodule is specifically used to: Set the standard SOC threshold according to the actual vehicle usage scenario and time period; Setting an incentive adjustment coefficient according to the standard SOC threshold value and a target SOC value of the electric vehicle charging set by the user; Power allocation is performed based on the excitation adjustment coefficient and the power allocation calculation formula.

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