Multi-gun charging dynamic power distribution method and system and medium

Abstract

The invention relates to a multi-gun charging dynamic power distribution method and system and a medium, and the method comprises the steps: constructing a multi-dimensional priority scoring matrix, and distributing a dynamic priority for each charging gun; a three-stage power distribution algorithm is adopted, basic power is guaranteed firstly, then residual power is distributed according to priorities, and finally the power which is not fully utilized is optimized and adjusted; intelligent adaptive feedback is carried out, the actual charging state of each charging gun is monitored in real time, and a power distribution strategy is adjusted every 30 seconds according to a monitoring result; context awareness and emergency processing are carried out, and special power distribution strategies are designed for different scenes; quick response starting is carried out, and the maximum power requirement is preferentially met within the preset time period when vehicle charging starts; the peak shifting power distribution based on data prediction prevents the storage and charging all-in-one machine from being overloaded in a certain time period, the intelligent power distribution under limited power grid capacity and energy storage electric quantity is realized, the resource utilization efficiency and the user satisfaction are remarkably improved, and the method has wide application prospects.

Description

Technical Field

[0001] This application relates to the field of charging gun power distribution technology, and in particular to a method, system and medium for dynamic power distribution of multi-gun charging. Background Technology

[0002] With the booming development of the new energy vehicle industry, the number of electric vehicles on the road has increased dramatically, and multi-gun simultaneous charging scenarios at charging stations are becoming increasingly common. However, existing multi-gun charging power allocation technologies have many problems. Traditional power allocation methods often adopt a fixed power allocation strategy, such as allocating power equally to each charging gun. This method does not take into account actual conditions such as vehicle battery status and user demand. When grid capacity or energy storage capacity is limited, it is impossible to achieve efficient resource utilization, which can easily lead to situations where some vehicles charge too slowly while other vehicles do not fully utilize their allocated power, resulting in low overall charging efficiency.

[0003] Some power allocation schemes based on simple rules allocate power solely according to the order in which vehicles connect, lacking comprehensive consideration of factors such as emergency charging needs and user importance. This makes it difficult to meet diverse user needs, resulting in low user satisfaction. Furthermore, existing technologies lack effective emergency response mechanisms to handle unexpected situations such as grid capacity fluctuations and charging equipment failures, easily leading to charging system instability and even equipment damage or safety hazards. In addition, during peak charging periods, existing technologies cannot achieve reasonable peak-shifting, often causing the integrated charging and energy storage units to be overloaded at certain times, further exacerbating resource shortages and low charging efficiency. Summary of the Invention

[0004] The purpose of this application is to provide a method, system and medium for dynamic power allocation of multi-gun charging, which realizes intelligent power allocation under limited grid capacity and energy storage capacity, significantly improves resource utilization efficiency and user satisfaction, and has broad application prospects.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] In a first aspect, embodiments of this application provide a method for dynamic power allocation in multi-gun charging, comprising the following steps:

[0007] Construct a multi-dimensional priority scoring matrix and assign dynamic priority to each charging gun;

[0008] A three-stage power allocation algorithm is adopted: first, the basic power is guaranteed; then, the remaining power is allocated according to priority; and finally, the underutilized power is optimized and adjusted.

[0009] Intelligent adaptive feedback monitors the actual charging status of each charging gun in real time and adjusts the power distribution strategy every 30 seconds based on the monitoring results;

[0010] Context awareness and emergency response, with specialized power allocation strategies designed for different scenarios;

[0011] Quick-response startup prioritizes meeting the highest power demand during the preset period when vehicle charging begins;

[0012] Peak-shifting power allocation based on data prediction: By analyzing battery information and historical charging data, the peak power demand period for each vehicle is predicted, and peak-shifting adjustments are made in combination with the time window allowed by the user to avoid overloading of the integrated storage and charging unit during a certain period.

[0013] The construction of the multidimensional priority scoring matrix is ​​specifically as follows:

[0014] Priority score = Priority score = ×(Vehicle SOC Score) + ×(User Level Score) + ×(Charging urgency) + ×(historical charging behavior) + ×(Expected departure time) + ×(battery state coefficient), where These are weighting coefficients, which are dynamically adjusted based on the scenario.

[0015] The vehicle SOC score is determined based on the vehicle's current battery status, with higher scores for lower battery levels; the user level score is based on the user's membership level and consumption history, with higher levels receiving higher scores; the charging urgency is set by the user; the historical charging behavior reflects the user's past charging habits and credit history; the shorter the expected departure time, the higher the score; and the battery state coefficient is determined based on the battery's health condition and charge / discharge characteristics.

[0016] The three-stage power allocation algorithm is as follows:

[0017] Phase 1: Assign a minimum guaranteed power P_min to each connected charging gun and calculate the remaining allocable power;

[0018] The minimum guaranteed power is the minimum power threshold that ensures the hardware safety of the charging gun and the basic charging needs of the battery. Its calculation method is as follows:

[0019] (1) Collect three types of basic parameters:

[0020] The minimum output power of the charging gun, P_gun_min, is obtained from the charging gun hardware specifications.

[0021] The battery base charging power P_battery_base is obtained in real time through the vehicle BMS and dynamically adjusted according to the battery SOC. It is 15kW when SOC≤10% and 8kW when SOC>10%.

[0022] The minimum power supply unit of the power grid, P_power grid_unit, is obtained from the power grid access parameters of the charging station;

[0023] (2) The calculation is based on the principle of “taking the maximum value”, and the formula is: P_min = max (P_gun_min, P_battery_base, P_grid_unit); Second stage: According to the priority scoring matrix, the remaining power is allocated proportionally, and the additional power of charging gun i = the remaining allocable power × (priority score of gun i / the sum of priority scores of all charging guns); Third stage: Check the actual power absorbed by each vehicle, redistribute the underutilized power, and consider the characteristics of the charging curve to predict the changes in power demand in the short term.

[0024] The real-time monitoring of the actual charging status of each charging gun includes the difference between the actual received power and the allocated power, the vehicle battery charging response characteristics, and the determination of charging curve stages.

[0025] Based on the above monitoring results, the rules for adjusting the power allocation strategy every 30 seconds are as follows:

[0026] (1) If the difference between the actual received power and the allocated power of a certain charging gun is ΔP < -5kW (unutilized power), then 80% of the unutilized power of the gun will be recovered and preferentially allocated to the charging guns with ΔP > 5kW and the top 3 priority rankings.

[0027] (2) If ΔP > 5kW (insufficient power) and the total available power ≥ ΔP, then ΔP is directly supplemented; if the total available power < ΔP, then the remaining power is allocated according to the priority score ratio of each charging gun with insufficient power.

[0028] (3) If the battery is detected to enter the constant voltage stage, the power of the gun is adjusted to 1.1 times the current actual received power; if the battery is detected to be overheated (temperature > 45℃), the power is immediately reduced to 0.7 times the current value until the battery temperature < 40℃.

[0029] (4) After adjustment, it is necessary to verify that the total allocated power is less than or equal to the total available power. If it is less than or equal to the total available power, the power of each gun should be reduced in order of priority from low to high until the total power meets the limit.

[0030] The system employs specialized power allocation strategies for different scenarios, including peak-hour mode, emergency mode, night mode, and fault handling mode. Peak-hour mode prioritizes rapid turnaround to improve charging station utilization. Emergency mode provides maximum possible power to users in urgent situations. Night mode evenly distributes and optimizes charging time. Fault handling mode automatically redistributes power when a charging gun malfunctions.

[0031] The rapid response activation prioritizes meeting the highest power demand during a preset period after vehicle charging begins, specifically through the following steps:

[0032] When a new vehicle connects to the charging station: during the initial preset time period after charging begins. Within the initial period, priority is given to meeting the vehicle's maximum power demand P_max, provided that the total grid capacity allows; After the event concludes, the vehicle is automatically incorporated into the dynamic allocation system of the multi-dimensional priority scoring matrix; if the initial time period... If the total grid capacity is insufficient, the power of all vehicles currently charging will be reduced proportionally to ensure that newly connected vehicles receive a certain proportion of P_max.

[0033] The initial preset time period Within 5-10 minutes, the proportion of newly connected vehicles obtaining P_max is 70%-90%.

[0034] The peak-shifting adjustment includes: extending the charging time or adjusting the high-power period for low-priority vehicles, while maintaining the original power allocation for high-priority vehicles.

[0035] The battery information includes battery SOC, temperature, internal resistance, and charging stage; the historical data includes charging start time, power change curve, and peak power period.

[0036] The user-allowed time window is entered through the APP, including the earliest charging end time and the latest charging end time. Peak-shifting adjustments must be made within this time window.

[0037] Secondly, embodiments of this application provide a multi-gun charging dynamic power distribution system, including,

[0038] The scoring matrix construction module builds a multi-dimensional priority scoring matrix and assigns dynamic priorities to each charging gun.

[0039] The power allocation algorithm module adopts a three-stage power allocation algorithm: first, it ensures the basic power; then, it allocates the remaining power according to priority; and finally, it optimizes and adjusts the underutilized power.

[0040] The intelligent adaptive feedback module monitors the actual charging status of each charging gun in real time and adjusts the power distribution strategy every 30 seconds based on the monitoring results.

[0041] The scenario awareness and emergency response module is designed with specific power allocation strategies for different scenarios;

[0042] The fast-response start-up module prioritizes meeting the highest power demand during a preset period when vehicle charging begins.

[0043] The data-driven peak-shifting power allocation module analyzes battery information and historical charging data to predict the peak power demand period for each vehicle, and then adjusts the peak-shifting power allocation based on the user-allowed time window to avoid overloading the integrated charging and storage unit during a certain period.

[0044] Thirdly, embodiments of this application provide a computer-readable storage medium storing program code, which, when executed by a processor, implements the steps of the multi-gun charging dynamic power allocation method as described above.

[0045] Compared with existing technologies, this invention has significant advantages. In terms of resource utilization, by constructing a multi-dimensional priority scoring matrix, employing a three-stage power allocation algorithm, and using data-predicted peak-shaving power allocation, it can accurately achieve intelligent power allocation under limited grid capacity and energy storage capacity. This not only rationally assigns dynamic priorities to each charging gun, ensuring basic power while efficiently allocating surplus power, but also avoids overloading of the integrated charging and energy storage unit through peak-shaving adjustments, significantly improving the utilization efficiency of charging resources and reducing energy waste.

[0046] In terms of user experience, this invention fully considers the actual needs of users and vehicles. The multi-dimensional priority scoring matrix integrates multiple factors such as vehicle SOC score, user level score, and charging urgency, ensuring that users in urgent situations and vehicles with low battery levels receive priority access to more power, thus improving charging speed. The rapid response activation mechanism prioritizes meeting the highest power demand during the preset period after vehicle charging begins, reducing user waiting time. Furthermore, specialized power allocation strategies designed for different scenarios, such as peak-hour mode and emergency mode, further meet user charging needs in various situations, significantly improving user satisfaction. Simultaneously, intelligent adaptive feedback and fault handling modes ensure the stable operation of the charging system, enhancing the reliability and safety of the entire charging system and demonstrating broad application prospects. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a dynamic power allocation method for multi-gun charging provided in an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating the three-stage power allocation algorithm.

[0050] Figure 3 This is a flowchart illustrating the workflow of the rapid response startup mechanism. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0052] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0053] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0054] like Figures 1 to 3 As shown, this embodiment is achieved through the following technical solution: a dynamic power allocation method for multi-gun charging, comprising the following steps:

[0055] Construct a multi-dimensional priority scoring matrix and assign dynamic priority to each charging gun;

[0056] A three-stage power allocation algorithm is adopted: first, the basic power is guaranteed; then, the remaining power is allocated according to priority; and finally, the underutilized power is optimized and adjusted.

[0057] Intelligent adaptive feedback monitors the actual charging status of each charging gun in real time and adjusts the power distribution strategy every 30 seconds based on the monitoring results.

[0058] The 30-second adjustment process for the intelligent adaptive feedback is as follows:

[0059] (1) Monitoring parameter acquisition (triggered once every 30 seconds):

[0060] Power difference ΔP = Actual power received by the charging gun (P_actual) - Current allocated power (P_share);

[0061] Battery charging response characteristics: The BMS is used to determine whether the battery has entered the constant voltage stage and whether over-temperature protection has been triggered.

[0062] Charging curve stage: Based on the trend of P_actual change over time, determine whether the current stage is constant current, constant voltage, or trickle charge.

[0063] Monitoring scenarios Adjust strategy ΔP < -5kW (Power not utilized) Unused power is recovered and preferentially allocated to charging guns with ΔP > 5kW and priority in the top 3. ΔP > 5kW (insufficient power) If the remaining capacity of the power grid is sufficient, ΔP will be added; otherwise, it will be allocated according to priority ratios. The battery enters the constant voltage stage Reduce the allocated power to P_real × 1.1 to avoid waste. Battery overheating (>45℃) Immediately reduce the allocated power to P_min × 0.7 until the temperature is <40℃.

[0064] (2) Execution adjustments based on different scenarios (refer to the table below):

[0065] (3) Adjustment and verification: Calculate the total allocated power of all charging guns after adjustment. If it is greater than the total available power (grid capacity + energy storage discharge power), reduce the power of each gun by 5% per time according to priority from low to high until the total power is compliant.

[0066] The system is configured with a 30-second timer in the power distribution controller. When the timer is triggered, the following operations are performed:

[0067] (1) Parameter acquisition: P_real is acquired by the charging gun current / voltage sensor, and P_split is read from the power allocation command to calculate ΔP; battery temperature and charging stage parameters are parsed by BMS protocol;

[0068] (2) Adjustment execution: Power adjustment instructions are sent to each charging gun through the PLC (Programmable Logic Controller). For example, if a charging gun has ΔP = -8kW (P_actual = 22kW, P_partial = 30kW), then the recovered power = 8 × 0.8 = 6.4kW. If another charging gun (with a priority score of 50%) has ΔP = 6kW, then the instruction "P_partial + 3.2kW" is sent to that gun.

[0069] (3) Feedback verification: Collect the new P_real data of each gun within 10 seconds after adjustment to confirm whether it meets the requirements of the adjustment command. If the deviation is >1kW, the adjustment logic is re-triggered.

[0070] Context awareness and emergency response, with specialized power allocation strategies designed for different scenarios;

[0071] The relationship between intelligent adaptive feedback and context awareness is as follows:

[0072] (1) Scene trigger judgment: The system detects in real time whether the scene perception trigger conditions are met (peak hours: 9:00-21:00; emergency mode: users mark "emergency charging" through the APP; fault mode: charging gun fault signal trigger; night mode: 23:00-6:00).

[0073] (2) Layered execution:

[0074] Triggering Context Awareness: First, execute the scenario strategy (such as prioritizing power allocation to vehicles with SOC≤30% during peak hours) to determine the "basic allocation power range" of each charging gun (such as gun A's basic allocation of 25-35kW); then, through intelligent adaptive feedback, fine-tune it every 30 seconds within this range (such as gun A actually only uses 25kW, which can be reduced to 28kW, and 2kW can be recycled and allocated to other guns).

[0075] No context awareness triggered: Power is dynamically adjusted in 30-second cycles directly through intelligent adaptive feedback;

[0076] (3) Example: During peak hours (triggered scenario awareness), gun A (SOC=25%) is allocated 25-35kW according to the scenario strategy, with an initial allocation of 30kW; after 30 seconds, the intelligent feedback detects that gun A is only using 26kW (ΔP=-4kW), so it is adjusted to 28kW, and 2kW is recycled and allocated to gun B (SOC=20%, ΔP=3kW).

[0077] The system sets "scene flags" at the software layer: peak period flag = 1, emergency mode flag = 1, fault mode flag = 1, night mode flag = 1, and all flags = 0 when no scene is triggered.

[0078] (1) Flag detection: The flag is detected every 10 seconds. If any flag is 1, the scenario perception is triggered.

[0079] (2) Basic framework execution: When the scenario awareness is triggered, the power allocation function of the corresponding scenario is called (such as the peak period function: filter the charging guns with SOC≤30% and allocate the basic power = P_min+10kW).

[0080] (3) Fine-tuning execution: After the basic framework is executed, the 30-second adjustment logic of intelligent adaptive feedback is triggered, and the power after fine-tuning must not exceed the range set by the scenario framework (e.g., the basic power range of a certain gun during peak hours is 20-30kW, and the power after fine-tuning must not be lower than 20kW or higher than 30kW).

[0081] (4) Flag clearing: After the scene conditions disappear (such as the end of the peak period), the corresponding flag is cleared and the system returns to the pure intelligent feedback adjustment mode.

[0082] Rapid response startup prioritizes meeting the highest power demand during a preset period after vehicle charging begins.

[0083] In this method, the execution order of "fast response startup," "construction of a multi-dimensional priority scoring matrix," and "three-stage power allocation algorithm" follows a "basic framework + temporary trigger" pattern, as detailed below:

[0084] (1) Normal operation phase: When no new vehicles are connected, the system is in the "basic framework operation state" - continuously building a multi-dimensional priority scoring matrix (updated every 30 seconds), performing three-stage power allocation based on the matrix, and fine-tuning through intelligent adaptive feedback;

[0085] (2) New gun access triggering stage: When a new vehicle is connected to the charging gun, the system switches to "fast response state" - suspends the matrix update and three-stage allocation of the basic framework, and prioritizes the calculation of P_max of the new gun (obtained through BMS); if the total available power ≥ P_max, then P_max is directly allocated; if it is insufficient, the load reduction ratio K is calculated according to "(total available power - sum of P_min of all original guns) / P_max" (K≥70%), and the power of the original guns is reduced proportionally (ensuring that the power of the original guns ≥ P_min), so that the new gun obtains P_max×K;

[0086] (3) Return to normal phase: After T0 (5-10 minutes) ends, the system will include the new gun in the multi-dimensional priority scoring matrix, recalculate the priority score of all guns, restart the three-stage power allocation (first calculate P_min of all guns, then allocate the remaining power), and restore the "basic framework running state".

[0087] (4) Example: The system originally has gun 1 (P_min=10kW, allocated 15kW) and gun 2 (P_min=12kW, allocated 18kW), with a total available power of 50kW; new gun 3 is connected, P_max=30kW, T0=5 minutes; the total available power - the sum of the original gun P_min = 50-(10+12)=28kW<30kW, the load reduction ratio K=28 / 30≈93%; then the power of gun 1 is reduced to 10+(15-10)×(28 / 30)≈14.7kW, the power of gun 2 is reduced to 12+(18-12)×(28 / 30)≈17.6kW, and gun 3 gets 30×93%≈28kW; after T0 ends, gun 3 is included in the matrix and the three-stage allocation is re-executed.

[0088] The system uses a "state machine" to achieve rapid response and switching of the basic framework:

[0089] (1) State definition: The system includes two core states: "normal state" and "rapid response state";

[0090] (2) State triggering:

[0091] Normal state → Fast response state: The charging gun detects a vehicle connection signal (such as a CC signal) and the BMS completes the handshake, triggering a state switch;

[0092] Fast response state → Normal state: The timer (software timer, configurable from 5 to 10 minutes via system parameters) automatically switches after the countdown ends;

[0093] (3) Status operations:

[0094] Fast response state: Pause the "matrix update thread" and "three-stage allocation thread", and start the "P_max calculation thread" and "power load reduction thread";

[0095] Normal state: Restart the "Matrix Update Thread" and "Three-Phase Allocation Thread", and shut down the "P_max Calculation Thread" and "Power Load Reduction Thread";

[0096] (4) Hardware interaction: When a new charging gun is connected, the system quickly reads the P_max parameter in the BMS via the CAN bus (≤1 second), reads the current total available power through the power grid metering module, calculates the load reduction ratio in real time, and sends adjustment instructions to each charging gun controller.

[0097] Peak-shifting power allocation based on data prediction: By analyzing battery information and historical charging data, the peak power demand period for each vehicle is predicted, and peak-shifting adjustments are made in combination with the time window allowed by the user to avoid overloading of the integrated storage and charging unit during a certain period.

[0098] The construction of the multidimensional priority scoring matrix is ​​specifically as follows:

[0099] Priority score = Priority score = ×(Vehicle SOC Score) + ×(User Level Score) + ×(Charging urgency) + ×(historical charging behavior) + ×(Expected departure time) + ×(battery state coefficient), where These are weighting coefficients, which are dynamically adjusted based on the scenario.

[0100] The vehicle SOC score is determined based on the vehicle's current battery status, with higher scores for lower battery levels; the user level score is based on the user's membership level and consumption history, with higher levels receiving higher scores; the charging urgency is set by the user; the historical charging behavior reflects the user's past charging habits and credit history; the shorter the expected departure time, the higher the score; and the battery state coefficient is determined based on the battery's health condition and charge / discharge characteristics.

[0101] The three-stage power allocation algorithm is as follows:

[0102] Phase 1: Allocate the minimum guaranteed power to each connected charging gun and calculate the remaining allocable power.

[0103] The calculation of the minimum guaranteed power (P_min) needs to consider both hardware and battery characteristics, as detailed below:

[0104] Parameter sources: P_gun_min is obtained from the charging gun specifications (e.g., 10kW), P_battery_base is obtained in real time through the BMS (e.g., 8kW when SOC=15%), and P_grid_unit is obtained from grid access parameters (e.g., 5kW).

[0105] Calculation logic: P_min = max (P_gun_min, P_battery_base, P_grid_unit), Example: If P_gun_min = 10kW, P_battery_base = 8kW, P_grid_unit = 5kW, then P_min = 10kW;

[0106] Remaining allocable power = Total available power (grid capacity + energy storage discharge power) - Sum of all charging gun P_min The system communicates with the charging gun controller via RS485 interface to read P_gun_min, interacts with the vehicle BMS via CAN bus to obtain P_battery_base (including SOC and battery type parameters), and reads P_grid_unit via the grid metering module. Parameters are synchronized and P_min is recalculated every 30 seconds to ensure real-time matching with battery and grid status. For example, if a charging station has a grid capacity of 200kW and connects to 5 charging guns with P_min values ​​of 10kW, 12kW, 10kW, 15kW, and 13kW respectively, then the remaining allocable power = 200 - (10+12+10+15+13) = 140kW. The second stage involves allocating the remaining power proportionally according to the priority scoring matrix: Charging gun i's additional power = remaining allocable power × (gun i's priority score / the sum of all charging guns' priority scores). The third stage involves checking the actual power absorbed by each vehicle, redistributing underutilized power, and considering charging curve characteristics to predict short-term power demand changes.

[0107] The real-time monitoring of the actual charging status of each charging gun includes the difference between the actual received power and the allocated power, the vehicle battery charging response characteristics, and the determination of charging curve stages.

[0108] The system employs specialized power allocation strategies for different scenarios, including peak hour mode, emergency mode, night mode, and fault handling mode. Peak hour mode prioritizes rapid turnaround to improve charging station utilization. Emergency mode provides maximum possible power to users in urgent situations. Night mode evenly distributes and optimizes charging time. Fault handling mode automatically redistributes power when a charging gun malfunctions.

[0109] Based on the existing charging control system, the following modules will be added: a user information collection interface, which obtains the user's expected charging time, urgency level, etc. through an APP or charging card, and connects to the user's historical database; and a vehicle status real-time monitoring system, which obtains detailed battery status through BMS communication, analyzes charging curve characteristics, and predicts subsequent charging power demand.

[0110] The rapid response activation prioritizes meeting the highest power demand during a preset period after vehicle charging begins, specifically through the following steps:

[0111] When a new vehicle connects to the charging station: during the initial preset time period after charging begins. Within the initial period, priority is given to meeting the vehicle's maximum power demand P_max, provided that the total grid capacity allows; After the event concludes, the vehicle is automatically incorporated into the dynamic allocation system of the multi-dimensional priority scoring matrix; if the initial time period... If the total grid capacity is insufficient, the power of all vehicles currently charging will be reduced proportionally to ensure that newly connected vehicles receive a certain proportion of P_max.

[0112] The initial preset time period Within 5-10 minutes, the proportion of newly connected vehicles obtaining P_max is 70%-90%.

[0113] The peak-shifting adjustment includes: extending the charging time or adjusting the high-power period for low-priority vehicles, while maintaining the original power allocation for high-priority vehicles.

[0114] The battery information includes battery SOC, temperature, internal resistance, and charging stage; the historical data includes charging start time, power change curve, and peak power period.

[0115] The user-allowed time window is entered through the APP, including the earliest charging end time and the latest charging end time. Peak-shifting adjustments must be made within this time window.

[0116] This application provides a multi-gun charging dynamic power distribution system, including,

[0117] The scoring matrix construction module builds a multi-dimensional priority scoring matrix and assigns dynamic priorities to each charging gun.

[0118] The power allocation algorithm module adopts a three-stage power allocation algorithm: first, it ensures the basic power; then, it allocates the remaining power according to priority; and finally, it optimizes and adjusts the underutilized power.

[0119] The intelligent adaptive feedback module monitors the actual charging status of each charging gun in real time and adjusts the power distribution strategy every 30 seconds based on the monitoring results.

[0120] The scenario awareness and emergency response module is designed with specific power allocation strategies for different scenarios;

[0121] The fast-response start-up module prioritizes meeting the highest power demand during a preset period when vehicle charging begins.

[0122] The data-driven peak-shifting power allocation module analyzes battery information and historical charging data to predict the peak power demand period for each vehicle, and then adjusts the peak-shifting power allocation based on the user-allowed time window to avoid overloading the integrated charging and storage unit during a certain period.

[0123] This application provides a computer-readable storage medium storing program code. When the program code is executed by a processor, it implements the steps of the multi-gun charging dynamic power allocation method as described above.

[0124] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0129] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0130] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0131] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for dynamic power allocation in multi-gun charging, characterized in that, Includes the following steps: Construct a multi-dimensional priority scoring matrix and assign dynamic priority to each charging gun; A three-stage power allocation algorithm is adopted: first, the basic power is guaranteed; then, the remaining power is allocated according to priority; and finally, the underutilized power is optimized and adjusted. Intelligent adaptive feedback monitors the actual charging status of each charging gun in real time and adjusts the power distribution strategy every 30 seconds based on the monitoring results; Context awareness and emergency response, with specialized power allocation strategies designed for different scenarios; Quick-response startup prioritizes meeting the highest power demand during the preset period when vehicle charging begins; Peak-shifting power allocation based on data prediction: By analyzing battery information and historical charging data, the peak power demand period for each vehicle is predicted, and peak-shifting adjustments are made in combination with the time window allowed by the user to avoid overloading of the integrated storage and charging unit during a certain period.

2. The multi-gun charging dynamic power allocation method according to claim 1, characterized in that, The construction of the multidimensional priority scoring matrix is ​​specifically as follows: Priority score = ×(Vehicle SOC Score) + ×(User Level Score) + ×(Charging urgency) + ×(historical charging behavior) + ×(Expected departure time) + ×(battery state coefficient), where These are weighting coefficients, which are dynamically adjusted based on the scenario.

3. The multi-gun charging dynamic power distribution method according to claim 2, characterized in that, The vehicle SOC score is determined based on the vehicle's current battery status, with higher scores for lower battery levels; the user level score is based on the user's membership level and consumption history, with higher levels receiving higher scores; the charging urgency is set by the user; the historical charging behavior reflects the user's past charging habits and credit history; the shorter the expected departure time, the higher the score; and the battery state coefficient is determined based on the battery's health condition and charge / discharge characteristics.

4. The multi-gun charging dynamic power allocation method according to claim 1, characterized in that, The three-stage power allocation algorithm is as follows: Phase 1: Assign a minimum guaranteed power P_min to each connected charging gun and calculate the remaining allocable power; The minimum guaranteed power is the minimum power threshold that ensures the hardware safety of the charging gun and the basic charging needs of the battery. Its calculation method is as follows: (1) Collect three types of basic parameters: The minimum output power of the charging gun, P_gun_min, is obtained from the charging gun hardware specifications. The battery base charging power P_battery_base is obtained in real time through the vehicle BMS and is dynamically adjusted according to the battery SOC. It is 15kW when SOC≤10% and 8kW when SOC>10%. The minimum power supply unit of the power grid, P_power grid_unit, is obtained from the power grid access parameters of the charging station; (2) The calculation is based on the principle of "taking the maximum value". The formula is: P_min = max (P_gun_min, P_battery_base, P_grid_unit); Second stage: According to the priority scoring matrix, the remaining power is allocated proportionally. The additional power of charging gun i = the remaining allocable power × (priority score of gun i / the sum of priority scores of all charging guns); Phase 3: Check the actual power absorbed by each vehicle, redistribute the underutilized power, and consider the characteristics of the charging curve to predict changes in power demand in the short term.

5. The multi-gun charging dynamic power distribution method according to claim 1, characterized in that, The real-time monitoring of the actual charging status of each charging gun includes the difference between the actual received power and the allocated power, the vehicle battery charging response characteristics, and the determination of charging curve stages.

6. The multi-gun charging dynamic power allocation method according to claim 1, characterized in that, The power allocation strategies designed for different scenarios include peak hour mode, emergency mode, night mode and fault handling mode. The peak hour mode prioritizes rapid turnaround and improves the utilization rate of charging stations. The emergency mode provides maximum possible power for emergency users; the night mode evenly distributes and optimizes charging time. The fault handling mode automatically redistributes power when a gun malfunctions.

7. The multi-gun charging dynamic power allocation method according to claim 1, characterized in that, The rapid response activation prioritizes meeting the highest power demand during a preset period after vehicle charging begins, specifically through the following steps: When a new vehicle connects to the charging station: during the initial preset time period after charging begins. Within the initial period, priority is given to meeting the vehicle's maximum power demand P_max, provided that the total grid capacity allows; After the event concludes, the vehicle is automatically incorporated into the dynamic allocation system of the multi-dimensional priority scoring matrix; if the initial time period... If the total grid capacity is insufficient, the power of all vehicles currently charging will be reduced proportionally to ensure that newly connected vehicles receive a certain proportion of P_max. The initial preset time period Within 5-10 minutes, the proportion of newly connected vehicles obtaining P_max is 70%-90%.

8. The multi-gun charging dynamic power distribution method according to claim 1, characterized in that, The peak-shifting adjustment includes: extending the charging time or adjusting the high-power period for low-priority vehicles, while maintaining the original power allocation for high-priority vehicles. The battery information includes battery SOC, temperature, internal resistance, and charging stage; the historical data includes charging start time, power change curve, and peak power period. The user-allowed time window is entered through the APP, including the earliest charging end time and the latest charging end time. Peak-shifting adjustments must be made within this time window.

9. A multi-gun charging dynamic power distribution system, characterized in that, include The scoring matrix construction module builds a multi-dimensional priority scoring matrix and assigns dynamic priorities to each charging gun. The power allocation algorithm module adopts a three-stage power allocation algorithm: first, it ensures the basic power; then, it allocates the remaining power according to priority; and finally, it optimizes and adjusts the underutilized power. The intelligent adaptive feedback module monitors the actual charging status of each charging gun in real time and adjusts the power distribution strategy every 30 seconds based on the monitoring results. The scenario awareness and emergency response module is designed with specific power allocation strategies for different scenarios; The fast-response start-up module prioritizes meeting the highest power demand during a preset period when vehicle charging begins. The data-driven peak-shifting power allocation module analyzes battery information and historical charging data to predict the peak power demand period for each vehicle, and then adjusts the peak-shifting power allocation based on the user-allowed time window to avoid overloading the integrated charging and storage unit during a certain period.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code, which, when executed by a processor, implements the steps of the multi-gun charging dynamic power allocation method as described in any one of claims 1 to 8.

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