Intelligent charging and battery swapping cabinet control method and system

Abstract

The application discloses a kind of intelligent charging and battery replacement cabinet control method and system, wherein method includes: real-time acquisition and charging and battery replacement cabinet related multi-source data, the multi-source data include the state data of battery in battery compartment, user demand data, power grid data and environmental data;The multi-source data is sent to the preset computing model, and a dynamic comprehensive charging priority index is calculated for the battery in each battery compartment;According to the high-low order result of the comprehensive charging priority index of all batteries, in combination with the total input power constraint of charging cabinet, dynamically allocate the charging power in the current scheduling period;Based on the charging power in the current scheduling period, the battery is charged, and the feedback data of the battery is obtained in real time, and the charging parameters are adjusted according to the feedback data of the battery.The application collects the multi-dimensional data of charging and battery replacement cabinet, and carries out comprehensive analysis, adjusts charging priority, battery replacement recommendation index and the like in real time, and guarantees user satisfaction.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and more specifically, to an intelligent charging and swapping cabinet control method and system. Background Technology

[0002] Existing charging and swapping cabinet control methods are mostly based on first-come, first-served or simple battery power thresholds for charging scheduling, lacking a comprehensive consideration of battery health, user urgency, grid load, and operating costs. This leads to problems such as uneven battery lifespan degradation, excessively long waiting times for users during peak periods, significant grid impact, and low energy efficiency for operators. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, the present invention aims to provide an intelligent charging and swapping cabinet control method and system that enables precise and adaptive scheduling of the charging and swapping processes of multiple battery compartments within the cabinet, thereby ensuring user satisfaction.

[0004] The first aspect of this invention provides a smart charging and swapping cabinet control method, comprising:

[0005] Real-time collection of multi-source data related to the charging and swapping cabinet, including battery status data in the battery compartment, user demand data, power grid data, and environmental data;

[0006] The multi-source data is sent to a preset calculation model to calculate a dynamic comprehensive charging priority index for each battery in the battery compartment.

[0007] Based on the ranking of all batteries according to their comprehensive charging priority index, and combined with the total input power constraint of the charging cabinet, the charging power in the current scheduling cycle is dynamically allocated.

[0008] The battery is charged based on the charging power within the current scheduling cycle, and the battery feedback data is acquired in real time. The charging parameters are then adjusted based on the battery feedback data.

[0009] This plan also includes:

[0010] Based on the pre-set battery swapping system, the system obtains the user's identity and the compliance of the battery specifications and models they use; at the same time, it obtains the user's historical battery swapping behavior data, including the battery swapping period, the battery capacity range of the swapped battery, and the health level of the batteries selected in the past.

[0011] When the battery swapping system receives a battery swapping request from a user, the system queries information on all swappable batteries in the battery swapping cabinet, including the battery's current charge level, health factor, current temperature, charging completion time, and battery specifications and model.

[0012] The replaceable batteries in the battery swapping system are filtered according to the battery specifications and models of the user terminals to obtain a list of replaceable batteries.

[0013] Based on users’ historical battery swapping behavior data, calculate a user-side battery swapping recommendation index for the batteries in the swappable battery list;

[0014] The battery compartment with the highest user-side battery swapping recommendation index in the replaceable battery list is set as the optimal recommended compartment, and feedback is sent to the user through a preset screen.

[0015] This plan also includes:

[0016] After the user places the battery to be charged into the empty compartment and closes the compartment door, the system will obtain the battery's preliminary diagnostic data after a set time.

[0017] Obtain the previous preliminary diagnostic data of the battery;

[0018] The preliminary diagnostic data of the battery is compared and analyzed with the standard values ​​of the battery model to determine the abnormal index of the corresponding battery.

[0019] The abnormality index of the corresponding battery is divided into categories to obtain the abnormality level of the battery.

[0020] If the abnormality level of the battery is greater than the preset classification threshold, the battery will be isolated and a prompt message will be triggered.

[0021] This plan also includes:

[0022] For each charging and swapping cabinet node in the preset battery swapping system, extract the historical battery swapping data, historical battery swapping request data, and corresponding historical weather data and time nodes of the corresponding charging and swapping node;

[0023] Obtain current weather data and time points;

[0024] By comparing and analyzing current weather data and time points with historical weather data and time points, similarity values ​​of weather data and time points are obtained.

[0025] If the similarity value of weather data and time points is greater than the preset similarity threshold, the corresponding historical battery swapping data and historical battery swapping request data will be saved based on the same time point.

[0026] The average number of battery swaps in the historical battery swap data is calculated to obtain the historical average number of battery swaps at the corresponding time point.

[0027] The average number of historical battery swapping requests in the historical battery swapping request data is calculated to obtain the historical average number of battery swapping requests at the corresponding time point.

[0028] Subtract the historical average number of battery swap requests for the corresponding time point from the historical average number of battery swaps for the corresponding time point to obtain the first value.

[0029] If the first value is greater than the preset number of times threshold, the corresponding charging and swapping cabinet node will trigger an inventory imbalance prompt at the current time node and generate battery scheduling information.

[0030] The battery scheduling information is sent to a preset management terminal for display.

[0031] In this solution, the preset calculation model includes a method for calculating the comprehensive charging priority index, specifically:

[0032] ;in, Indicating the urgency factor of user needs, This indicates the real-time health factor of the battery. Indicates the grid-friendliness factor. This represents the cost-effectiveness factor of charging. Indicates the warehouse state factor. , , , and These represent the dynamic weight coefficients of the corresponding factors.

[0033] In this solution, the steps for obtaining the user demand urgency factor are as follows:

[0034] When a user accesses the preset battery swapping system, the user level coefficient of the corresponding user is obtained;

[0035] Based on user demand data, determine the remaining time for the current user's scheduled power withdrawal; based on power grid data, determine the real-time demand density coefficient for the current area.

[0036] Based on the user level coefficient of the corresponding user terminal, the remaining time of the current user terminal's scheduled power withdrawal, and the real-time demand density coefficient of the current area, the corresponding user demand urgency factor is determined, and the formula is as follows: ;in , and These are the corresponding weight coefficients. This represents the user level coefficient on the user's end. This indicates the remaining time for the user to reserve power. This indicates the maximum waiting time for appointments. This represents the real-time demand density coefficient for the region.

[0037] In this solution, the calculation model for the real-time battery health factor is specifically as follows:

[0038] ,in For the initial battery health, Where k is the number of complete battery cycles, and k is the cycle decay constant. Expressed as the temperature rise stress coefficient, This is the charging current. This is due to battery temperature rise.

[0039] A second aspect of the present invention provides an intelligent charging and swapping cabinet control system, including a memory and a processor. The memory stores a program for an intelligent charging and swapping cabinet control method. When the processor executes the program for the intelligent charging and swapping cabinet control method, it performs the following steps:

[0040] Real-time collection of multi-source data related to the charging and swapping cabinet, including battery status data in the battery compartment, user demand data, power grid data, and environmental data;

[0041] The multi-source data is sent to a preset calculation model to calculate a dynamic comprehensive charging priority index for each battery in the battery compartment.

[0042] Based on the ranking of all batteries according to their comprehensive charging priority index, and combined with the total input power constraint of the charging cabinet, the charging power in the current scheduling cycle is dynamically allocated.

[0043] The battery is charged based on the charging power within the current scheduling cycle, and the battery feedback data is acquired in real time. The charging parameters are then adjusted based on the battery feedback data.

[0044] This plan also includes:

[0045] Based on the pre-set battery swapping system, the system obtains the user's identity and the compliance of the battery specifications and models they use; at the same time, it obtains the user's historical battery swapping behavior data, including the battery swapping period, the battery capacity range of the swapped battery, and the health level of the batteries selected in the past.

[0046] When the battery swapping system receives a battery swapping request from a user, the system queries information on all swappable batteries in the battery swapping cabinet, including the battery's current charge level, health factor, current temperature, charging completion time, and battery specifications and model.

[0047] The replaceable batteries in the battery swapping system are filtered according to the battery specifications and models of the user terminals to obtain a list of replaceable batteries.

[0048] Based on users’ historical battery swapping behavior data, calculate a user-side battery swapping recommendation index for the batteries in the swappable battery list;

[0049] The battery compartment with the highest user-side battery swapping recommendation index in the replaceable battery list is set as the optimal recommended compartment, and feedback is sent to the user through a preset screen.

[0050] This plan also includes:

[0051] After the user places the battery to be charged into the empty compartment and closes the compartment door, the system will obtain the battery's preliminary diagnostic data after a set time.

[0052] Obtain the previous preliminary diagnostic data of the battery;

[0053] The preliminary diagnostic data of the battery is compared and analyzed with the standard values ​​of the battery model to determine the abnormal index of the corresponding battery.

[0054] The abnormality index of the corresponding battery is divided into categories to obtain the abnormality level of the battery.

[0055] If the abnormality level of the battery is greater than the preset classification threshold, the battery will be isolated and a prompt message will be triggered.

[0056] One or more technical solutions proposed in this application have at least the following technical effects:

[0057] 1. This invention achieves multi-objective collaborative optimization, overcoming the limitations of single-dimensional scheduling. Existing technologies are mostly based on simple rules such as "first-come, first-served" or "lowest power priority," which have a single objective and often result in neglecting other aspects. This invention constructs a comprehensive charging priority index, considering multiple dimensions such as user demand urgency, battery health, grid load, operational cost-effectiveness, and equipment status. This achieves the optimal balance among multiple mutually constraining objectives such as ensuring user experience, extending asset lifespan, supporting grid stability, and reducing operating costs, thus solving the fundamental problems of conflicting objectives and low efficiency in traditional methods.

[0058] 2. By comprehensively analyzing historical battery swapping data and historical battery swapping request data, all charging and swapping cabinets in the region are uniformly scheduled to reduce the problem of inventory imbalance in a single charging and swapping cabinet. Furthermore, user-end batteries are also included in the energy storage end, and batteries that are not needed by users at the moment are scheduled to the cabinets, which improves the peak shaving and valley filling effect, reduces costs, and achieves a win-win situation for social and economic benefits.

[0059] 3. Existing technologies cannot differentiate between users' urgency levels, which may lead to long waiting times for users who urgently need batteries. This invention, by using a user demand urgency factor, comprehensively considers factors such as user level and appointment time urgency, enabling the dispatch system to identify and prioritize the most urgent and efficient battery swapping needs. This significantly reduces the average waiting time for users during peak periods, improves service satisfaction and counter turnover efficiency, and achieves an upgrade from "undifferentiated service" to "differentiated intelligent service". Attached Figure Description

[0060] Figure 1 This diagram illustrates the charging process in the intelligent charging and swapping cabinet control method of the present invention.

[0061] Figure 2 This diagram illustrates the battery swapping process in the intelligent charging and swapping cabinet control method of the present invention.

[0062] Figure 3 A block diagram of an intelligent charging and swapping cabinet control system according to the present invention is shown. Detailed Implementation

[0063] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0064] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0065] Figure 1 A schematic diagram of the charging process in the intelligent charging and swapping cabinet control method of the present invention is shown.

[0066] like Figure 1 As shown, this invention discloses a smart charging and swapping cabinet control method, comprising:

[0067] S101, Real-time collection of multi-source data related to the charging and swapping cabinet, including battery status data in the battery compartment, user demand data, power grid data, and environmental data;

[0068] S102, The multi-source data is sent to a preset calculation model to calculate a dynamic comprehensive charging priority index for each battery in the battery compartment.

[0069] S103: Based on the ranking of the comprehensive charging priority index of all batteries and combined with the total input power constraint of the charging cabinet, dynamically allocate the charging power in the current scheduling cycle.

[0070] S104, charge the battery based on the charging power within the current scheduling cycle, and obtain the battery feedback data in real time, and adjust the charging parameters according to the battery feedback data.

[0071] According to an embodiment of the present invention, the system collects and integrates multi-dimensional data from inside and outside the charging and swapping cabinet in real time. This data includes, but is not limited to: battery status data in each battery compartment, such as real-time voltage, current, temperature, internal resistance, and estimated power; user demand data, such as user reservation information, historical usage frequency, and user information; power grid data, such as time-of-use electricity price signals from the power grid and real-time load rate of the regional power grid; and environmental data, such as the internal ambient temperature and communication status of each compartment of the charging and swapping cabinet. Through a comprehensive charging priority index calculation model, a time-varying, quantified comprehensive charging priority index is calculated for each battery in each battery compartment. This index aims to comprehensively characterize the urgency and value of the battery obtaining charging resources at the current moment. Then, the comprehensive charging priority index values ​​of all batteries in the cabinet are sorted from high to low to form a priority queue. At the same time, the system reads the current total input power limit or available current capacity of the charging and swapping cabinet as a hard constraint condition. According to the order of the priority queue, charging power is allocated sequentially from high-priority batteries until the remaining power capacity cannot meet the charging needs of the next priority battery, thereby generating a charging scheduling instruction set for the current scheduling cycle.

[0072] It should be noted that the overall charging priority index of all batteries requiring charging is summed to obtain the overall charging priority index. Then, the overall charging priority index of each battery is divided by the corresponding overall charging priority index, and the quotient is multiplied by the total input power of the charging cabinet to obtain the charging power of the corresponding battery in the scheduling cycle. If the charging power is greater than the rated power of the corresponding battery, the battery is charged at the rated power. The rated power of the battery is subtracted from the total input power to obtain the remaining power. The remaining power is then used as a constraint to allocate to other batteries, and so on, until all batteries are fully allocated. The battery feedback data includes battery full charge information. For example, if one battery is fully charged, its charging power is allocated to other batteries, satisfying the above allocation steps.

[0073] Figure 2 A schematic diagram of the battery swapping process in the intelligent charging and swapping cabinet control method of the present invention is shown.

[0074] like Figure 2 As shown, according to an embodiment of the present invention, it further includes:

[0075] S201, based on the preset battery swapping system, obtains the user's identity and the compliance of the battery specifications and models; at the same time, it obtains the user's historical battery swapping behavior data, including the battery swapping period, the battery capacity range of the swapped battery, and the health level of the previously selected battery.

[0076] S202, When the battery swapping system receives a battery swapping request from the user, the battery swapping system queries the information of all swappable batteries in the battery swapping cabinet, including the battery's current charge, health factor, current temperature, charging completion time, and battery specifications and model.

[0077] S203, Based on the battery specifications and models of the user terminal, the replaceable batteries in the battery swapping system are screened to obtain a list of replaceable batteries;

[0078] S204, Calculate a user-side battery swapping recommendation index for batteries in the swappable battery list based on the user's historical battery swapping behavior data;

[0079] S205 sets the battery with the highest user-side battery swapping recommendation index in the replaceable battery list as the optimal recommended battery compartment and sends feedback to the user via a preset screen.

[0080] It should be noted that the user-side battery swapping recommendation index for the batteries in the replaceable battery list is set to... Its formula is ;in This represents the user-side battery swapping recommendation index for user u's j-th battery in the swappable battery list at time t. The higher the value, the higher the priority for recommending this battery to the user. , , and This represents the corresponding weighting coefficient; This represents the battery capacity matching factor, with a value ranging from 0 to 1; it reflects the current battery capacity of battery j. Historical preferred power range of user u The degree of matching is expressed by the formula: ,in This indicates the user's preferred median battery level. Indicates the preferred power range; The real-time health factor of the j-th battery is represented, with a value ranging from 0 to 1, where 1 represents the healthiest battery. This represents the time-fit factor, ranging from 0 to 1. For a user u who has made a reservation, this factor reflects the fit between the availability of battery j (e.g., whether it is fully charged) and the user's reserved time. For example, if the battery is fully charged, then... If it is currently charging and the estimated completion time is earlier than the scheduled time, then Increases with increasing lead time; This represents the position availability factor, with a value range of [value range missing]. A fixed coefficient is set based on the physical location (such as height and depth) of the compartment where battery j is located. The value is higher if it is easy to pick up and put down (such as in the middle compartment).

[0081] According to an embodiment of the present invention, it further includes:

[0082] After the user places the battery to be charged into the empty compartment and closes the compartment door, the system will obtain the battery's preliminary diagnostic data after a set time.

[0083] Obtain the previous preliminary diagnostic data of the battery;

[0084] The preliminary diagnostic data of the battery is compared and analyzed with the standard values ​​of the battery model to determine the abnormal index of the corresponding battery.

[0085] The abnormality index of the corresponding battery is divided into categories to obtain the abnormality level of the battery.

[0086] If the abnormality level of the battery is greater than the preset classification threshold, the battery will be isolated and a prompt message will be triggered.

[0087] It should be noted that when the user places the battery to be charged into the empty compartment and closes the compartment door, the system immediately initiates a rapid preliminary diagnostic program. This program applies a small detection current or measurement pulse after the battery is physically connected to the charging interface but before formal charging begins. After a few seconds (less than a set first time), it quickly acquires preliminary diagnostic data for the battery, including initial voltage, internal impedance, interface communication status, and initial temperature. The step of comparing the preliminary diagnostic data with the standard values ​​for the battery model to determine the corresponding battery's anomaly index specifically includes: calculating the difference between the indicator values ​​(initial voltage, internal impedance, and initial temperature) in the preliminary diagnostic data and the standard values ​​for the battery model, taking the absolute value to obtain the indicator difference; dividing the indicator difference by the corresponding standard value to determine the indicator deviation; multiplying the indicator deviation by the corresponding weighting coefficient; and finally summing the multipliers to obtain the corresponding battery's anomaly index. When the interface communication status is normal, the above formula for calculating the battery's anomaly index holds true; when the interface communication status is abnormal, the formula for calculating the corresponding battery's anomaly index does not hold true, and the battery is isolated.

[0088] According to an embodiment of the present invention, it further includes:

[0089] For each charging and swapping cabinet node in the preset battery swapping system, extract the historical battery swapping data, historical battery swapping request data, and corresponding historical weather data and time nodes of the corresponding charging and swapping node;

[0090] Obtain current weather data and time points;

[0091] By comparing and analyzing current weather data and time points with historical weather data and time points, similarity values ​​of weather data and time points are obtained.

[0092] If the similarity value of weather data and time points is greater than the preset similarity threshold, the corresponding historical battery swapping data and historical battery swapping request data will be saved based on the same time point.

[0093] The average number of battery swaps in the historical battery swap data is calculated to obtain the historical average number of battery swaps at the corresponding time point.

[0094] The average number of historical battery swapping requests in the historical battery swapping request data is calculated to obtain the historical average number of battery swapping requests at the corresponding time point.

[0095] Subtract the historical average number of battery swap requests for the corresponding time point from the historical average number of battery swaps for the corresponding time point to obtain the first value.

[0096] If the first value is greater than the preset number of times threshold, the corresponding charging and swapping cabinet node will trigger an inventory imbalance prompt at the current time node and generate battery scheduling information.

[0097] The battery scheduling information is sent to a preset management terminal for display.

[0098] It should be noted that, for example, if counter number 1 experiences peak electricity consumption and inventory imbalance at 8 PM, while counter number 2 experiences off-peak electricity consumption at 8 PM, then fully charged batteries from counter number 2 can be relocated to counter number 1 using battery scheduling information. Furthermore, this battery scheduling can be accomplished by dispatching batteries to users, reducing transportation costs. In addition, batteries with good charge that users do not currently need can be replaced at counters. For example, if user A is near counter number 1 and has a battery with good charge that is not currently needed, user A can replace that battery with one from counter number 1, achieving economic benefits.

[0099] According to an embodiment of the present invention, the preset calculation model includes a method for calculating a comprehensive charging priority index, specifically:

[0100] ;in, This represents the overall charging priority index of the batteries in the i-th battery compartment at time t. Indicating the urgency factor of user needs, This indicates the real-time health factor of the battery. Indicates the grid-friendliness factor. This represents the cost-effectiveness factor of charging. Indicates the warehouse state factor. , , , and These represent the dynamic weight coefficients of the corresponding factors.

[0101] It should be noted that, This represents the overall charging priority index of the batteries in the i-th battery compartment at time t. The higher the value, the higher the priority for obtaining charging resources. The user demand urgency factor for the i-th battery, with a value ranging from 0 to 1; This represents the real-time health factor of the i-th battery, with a value ranging from 0 to 1. A higher value indicates a healthier battery. This represents the power grid friendliness factor, with a value range of [missing information]. ; This represents the warehouse status factor, which is related to warehouse temperature, communication status, etc., and its priority is reduced when there is an anomaly.

[0102] According to an embodiment of the present invention, the step of obtaining the user demand urgency factor specifically includes:

[0103] When a user accesses the preset battery swapping system, the user level coefficient of the corresponding user is obtained;

[0104] Based on user demand data, determine the remaining time for the current user's scheduled power withdrawal; based on power grid data, determine the real-time demand density coefficient for the current area.

[0105] Based on the user level coefficient of the corresponding user terminal, the remaining time of the current user terminal's scheduled power withdrawal, and the real-time demand density coefficient of the current area, the corresponding user demand urgency factor is determined, and the formula is as follows: ;in , and These are the corresponding weight coefficients. This represents the user level coefficient on the user's end. This indicates the remaining time for the user to reserve power. This indicates the maximum waiting time for appointments. This represents the real-time demand density coefficient for the region.

[0106] It should be noted that, This represents the user level coefficient linked to the user account. This coefficient is preset based on the user's long-term usage value or loyalty and is a constant ranging from 0.5 to 1.2. This represents the remaining time until the user's scheduled battery swapping time, calculated based on the user's battery swapping reservation information. The representative area real-time demand density coefficient is obtained by statistically analyzing the frequency or density of battery swapping requests within a preset geographical area surrounding the location of the charging and swapping station over a past time window, and then normalizing the data.

[0107] According to an embodiment of the present invention, the calculation model for the real-time battery health factor is specifically as follows:

[0108] ,in For the initial battery health, Where k is the number of complete battery cycles, and k is the cycle decay constant. Expressed as the temperature rise stress coefficient, This is the charging current. This is due to battery temperature rise.

[0109] It should be noted that, This represents the initial health assessment value of the i-th battery when it is put into operation. This value is derived from the complete diagnostics of the factory test or the first time it is put on the shelf. This represents the number of complete charge-discharge cycles the battery has undergone since it was put into operation. Battery health declines exponentially with the number of cycles. At the same time, during charging, the temperature rise caused by high current or high rate charging will have an additional, cumulative negative impact on health, thus directly linking charging strategy with long-term battery life management.

[0110] According to an embodiment of the present invention, it further includes:

[0111] When the real-time battery health factor is higher than the preset health threshold, the standard fast charging curve is adopted to perform fast charging with the maximum allowable current, giving priority to meeting the user's timeliness needs.

[0112] When the battery's real-time health factor is in the medium range, a gentle charging curve is used, and the battery is charged through the constant current stage of the curve to reduce battery heat generation and internal resistance growth, thereby maintaining the battery.

[0113] When the real-time battery health factor falls below the warning threshold, an alert is triggered and a maintenance prompt message is generated.

[0114] According to an embodiment of the present invention, it further includes: the power grid friendliness factor. The grid friendliness factor is inversely proportional to the time-of-use electricity price signal and the real-time load factor of the regional power grid. The higher the electricity price or the higher the real-time load factor of the regional power grid, the lower the grid friendliness factor.

[0115] According to an embodiment of the present invention, it further includes: the It can be adaptively adjusted according to the power grid condition, and the formula is: ,in This represents the weighting coefficient of the grid friendliness factor at time t. This represents the base weight, and w is the adjustment sensitivity coefficient. The value represents the real-time load rate of the regional power grid at time t; L represents the power grid load warning threshold.

[0116] According to an embodiment of the present invention, the charging cost-effectiveness factor is inversely proportional to the estimated time cost and energy cost required to charge the battery from its current capacity to the target capacity.

[0117] Figure 3 A block diagram of an intelligent charging and swapping cabinet control system according to the present invention is shown.

[0118] like Figure 3 As shown, a second aspect of the present invention provides an intelligent charging and swapping cabinet control system 3, including a memory 31 and a processor 32. The memory stores an intelligent charging and swapping cabinet control method program, which, when executed by the processor, performs the following steps:

[0119] Real-time collection of multi-source data related to the charging and swapping cabinet, including battery status data in the battery compartment, user demand data, power grid data, and environmental data;

[0120] The multi-source data is sent to a preset calculation model to calculate a dynamic comprehensive charging priority index for each battery in the battery compartment.

[0121] Based on the ranking of all batteries according to their comprehensive charging priority index, and combined with the total input power constraint of the charging cabinet, the charging power in the current scheduling cycle is dynamically allocated.

[0122] The battery is charged based on the charging power within the current scheduling cycle, and the battery feedback data is acquired in real time. The charging parameters are then adjusted based on the battery feedback data.

[0123] According to an embodiment of the present invention, it further includes:

[0124] Based on the pre-set battery swapping system, the system obtains the user's identity and the compliance of the battery specifications and models they use; at the same time, it obtains the user's historical battery swapping behavior data, including the battery swapping period, the battery capacity range of the swapped battery, and the health level of the batteries selected in the past.

[0125] When the battery swapping system receives a battery swapping request from a user, the system queries information on all swappable batteries in the battery swapping cabinet, including the battery's current charge level, health factor, current temperature, charging completion time, and battery specifications and model.

[0126] The replaceable batteries in the battery swapping system are filtered according to the battery specifications and models of the user terminals to obtain a list of replaceable batteries.

[0127] Based on users’ historical battery swapping behavior data, calculate a user-side battery swapping recommendation index for the batteries in the swappable battery list;

[0128] The battery compartment with the highest user-side battery swapping recommendation index in the replaceable battery list is set as the optimal recommended compartment, and feedback is sent to the user through a preset screen.

[0129] According to an embodiment of the present invention, it further includes:

[0130] After the user places the battery to be charged into the empty compartment and closes the compartment door, the system will obtain the battery's preliminary diagnostic data after a set time.

[0131] Obtain the previous preliminary diagnostic data of the battery;

[0132] The preliminary diagnostic data of the battery is compared and analyzed with the standard values ​​of the battery model to determine the abnormal index of the corresponding battery.

[0133] The abnormality index of the corresponding battery is divided into categories to obtain the abnormality level of the battery.

[0134] If the abnormality level of the battery is greater than the preset classification threshold, the battery will be isolated and a prompt message will be triggered.

[0135] This invention discloses a smart charging and swapping cabinet control method and system. The method includes: real-time acquisition of multi-source data related to the charging and swapping cabinet, including battery status data, user demand data, power grid data, and environmental data; sending the multi-source data to a preset calculation model to calculate a dynamic comprehensive charging priority index for each battery in the battery compartment; dynamically allocating charging power within the current scheduling cycle based on the ranking of the comprehensive charging priority indices of all batteries and the total input power constraint of the charging cabinet; charging the batteries based on the charging power within the current scheduling cycle, and acquiring battery feedback data in real time, adjusting charging parameters according to the battery feedback data. This invention, by collecting multi-dimensional data from the charging and swapping cabinet and performing comprehensive analysis, adjusts charging priority and battery swapping recommendation index in real time, ensuring user satisfaction.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0137] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0138] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0139] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A control method for an intelligent charging and swapping cabinet, characterized in that, include: Real-time collection of multi-source data related to the charging and swapping cabinet, including battery status data in the battery compartment, user demand data, power grid data, and environmental data; The multi-source data is sent to a preset calculation model to calculate a dynamic comprehensive charging priority index for each battery in the battery compartment. Based on the ranking of all batteries according to their comprehensive charging priority index, and combined with the total input power constraint of the charging cabinet, the charging power in the current scheduling cycle is dynamically allocated. Specifically, the comprehensive charging priority index of all batteries requiring charging is accumulated to obtain a total comprehensive charging priority index. Then, the comprehensive charging priority index of each battery is divided by the corresponding total comprehensive charging priority index, and the quotient is multiplied by the total input power of the charging cabinet to obtain the charging power of the corresponding battery in the current scheduling cycle. If the charging power is greater than the rated power of the corresponding battery, the battery is charged at its rated power. The rated power of the battery is then subtracted from the total input power to obtain the remaining power. This remaining power is then used as a constraint to allocate to other batteries, and so on, until all batteries are fully allocated. The battery is charged based on the charging power within the current scheduling cycle, and the battery feedback data is obtained in real time. The charging parameters are then adjusted based on the battery feedback data. Also includes: After the user places the battery to be charged into the empty compartment and closes the compartment door, the system will obtain the battery's preliminary diagnostic data after a set time. The preliminary diagnostic data of the battery is compared and analyzed with the standard values ​​of the battery model to determine the abnormality index of the corresponding battery. Specifically, this includes calculating the difference between the indicator value in the preliminary diagnostic data and the standard value in the battery model, taking the absolute value to obtain the indicator difference, dividing the indicator difference by the corresponding standard value to determine the indicator deviation, multiplying the indicator deviation by the corresponding weighting coefficient, and then summing the multiplications to obtain the abnormality index of the corresponding battery. The indicator values ​​in the preliminary diagnostic data include initial voltage, internal impedance, and initial temperature. The abnormality index of the corresponding battery is divided into categories to obtain the abnormality level of the battery. If the abnormality level of the battery is greater than the preset classification threshold, the battery will be isolated and a prompt message will be triggered. Also includes: For each charging and swapping cabinet node in the preset battery swapping system, extract the historical battery swapping data, historical battery swapping request data, and corresponding historical weather data and time nodes of the corresponding charging and swapping node; Obtain current weather data and time points; By comparing and analyzing current weather data and time points with historical weather data and time points, similarity values ​​of weather data and time points are obtained. If the similarity value of weather data and time points is greater than the preset similarity threshold, the corresponding historical battery swapping data and historical battery swapping request data will be saved based on the same time point. The average number of battery swaps in the historical battery swap data is calculated to obtain the historical average number of battery swaps at the corresponding time point. The average number of historical battery swapping requests in the historical battery swapping request data is calculated to obtain the historical average number of battery swapping requests at the corresponding time point. Subtract the historical average number of battery swap requests for the corresponding time point from the historical average number of battery swaps for the corresponding time point to obtain the first value. If the first value is greater than the preset number of times threshold, the corresponding charging and swapping cabinet node will trigger an inventory imbalance prompt at the current time node and generate battery scheduling information. The battery scheduling information is sent to a preset management terminal for display; the battery scheduling is completed by dispatching to the user terminal, and also includes: replacing the user terminal's unused, healthy battery with a charge to the counter to complete the battery scheduling.

2. The intelligent charging and swapping cabinet control method according to claim 1, characterized in that, Also includes: Based on the pre-set battery swapping system, the system obtains the user's identity and the compliance of the battery specifications and models they use; at the same time, it obtains the user's historical battery swapping behavior data, including the battery swapping period, the battery capacity range of the swapped battery, and the health level of the batteries selected in the past. When the battery swapping system receives a battery swapping request from a user, the system queries information on all swappable batteries in the battery swapping cabinet, including the battery's current charge level, health factor, current temperature, charging completion time, and battery specifications and model. The replaceable batteries in the battery swapping system are filtered according to the battery specifications and models of the user terminals to obtain a list of replaceable batteries. Based on users’ historical battery swapping behavior data, calculate a user-side battery swapping recommendation index for the batteries in the swappable battery list; The battery compartment with the highest user-side battery swapping recommendation index in the replaceable battery list is set as the optimal recommended compartment, and feedback is sent to the user through a preset screen.

3. The intelligent charging and swapping cabinet control method according to claim 1, characterized in that, The preset calculation model includes a method for calculating the comprehensive charging priority index, specifically: ;in, This represents the overall charging priority index of the batteries in the i-th battery compartment at time t. Indicating the urgency factor of user needs, This indicates the real-time battery health factor. Indicates the grid-friendliness factor. This represents the cost-effectiveness factor of charging. Indicates the warehouse state factor. , , , and These represent the dynamic weight coefficients of the corresponding factors.

4. The intelligent charging and swapping cabinet control method according to claim 3, characterized in that, The steps for obtaining the user demand urgency factor are as follows: When a user accesses the preset battery swapping system, the user level coefficient of the corresponding user is obtained; Based on user demand data, determine the remaining time for the current user's scheduled power withdrawal; based on power grid data, determine the real-time demand density coefficient for the current area. Based on the user level coefficient of the corresponding user terminal, the remaining time of the current user terminal's scheduled power withdrawal, and the real-time demand density coefficient of the current area, the corresponding user demand urgency factor is determined, and the formula is as follows: ;in , and These are the corresponding weight coefficients. This represents the user level coefficient on the user's end. This indicates the remaining time for the user to reserve power. This indicates the maximum waiting time threshold for appointments. This represents the real-time demand density coefficient for the region.

5. The intelligent charging and swapping cabinet control method according to claim 3, characterized in that, The calculation model for the real-time battery health factor is specifically as follows: ,in For the initial battery health, Let k be the number of complete battery cycles, and k be the cycle decay constant. Expressed as the temperature rise stress coefficient, This is the charging current. This is due to battery temperature rise.

6. A smart charging and swapping cabinet control system, characterized in that, The system includes a memory and a processor. The memory stores a program for a smart charging and swapping cabinet control method. When the processor executes the program, the smart charging and swapping cabinet control method performs the following steps: Real-time collection of multi-source data related to the charging and swapping cabinet, including battery status data in the battery compartment, user demand data, power grid data, and environmental data; The multi-source data is sent to a preset calculation model to calculate a dynamic comprehensive charging priority index for each battery in the battery compartment. Based on the ranking of all batteries according to their comprehensive charging priority index, and combined with the total input power constraint of the charging cabinet, the charging power in the current scheduling cycle is dynamically allocated. Specifically, the comprehensive charging priority index of all batteries requiring charging is accumulated to obtain a total comprehensive charging priority index. Then, the comprehensive charging priority index of each battery is divided by the corresponding total comprehensive charging priority index, and the quotient is multiplied by the total input power of the charging cabinet to obtain the charging power of the corresponding battery in the current scheduling cycle. If the charging power is greater than the rated power of the corresponding battery, the battery is charged at its rated power. The rated power of the battery is then subtracted from the total input power to obtain the remaining power. This remaining power is then used as a constraint to allocate to other batteries, and so on, until all batteries are fully allocated. The battery is charged based on the charging power within the current scheduling cycle, and the battery feedback data is obtained in real time. The charging parameters are then adjusted based on the battery feedback data. Also includes: After the user places the battery to be charged into the empty compartment and closes the compartment door, the system will obtain the battery's preliminary diagnostic data after a set time. The preliminary diagnostic data of the battery is compared and analyzed with the standard values ​​of the battery model to determine the abnormality index of the corresponding battery. Specifically, this includes calculating the difference between the indicator value in the preliminary diagnostic data and the standard value in the battery model, taking the absolute value to obtain the indicator difference, dividing the indicator difference by the corresponding standard value to determine the indicator deviation, multiplying the indicator deviation by the corresponding weighting coefficient, and then summing the multiplications to obtain the abnormality index of the corresponding battery. The indicator values ​​in the preliminary diagnostic data include initial voltage, internal impedance, and initial temperature. The abnormality index of the corresponding battery is divided into categories to obtain the abnormality level of the battery. If the abnormality level of the battery is greater than the preset classification threshold, the battery will be isolated and a prompt message will be triggered. Also includes: For each charging and swapping cabinet node in the preset battery swapping system, extract the historical battery swapping data, historical battery swapping request data, and corresponding historical weather data and time nodes of the corresponding charging and swapping node; Obtain current weather data and time points; By comparing and analyzing current weather data and time points with historical weather data and time points, similarity values ​​of weather data and time points are obtained. If the similarity value of weather data and time points is greater than the preset similarity threshold, the corresponding historical battery swapping data and historical battery swapping request data will be saved based on the same time point. The average number of battery swaps in the historical battery swap data is calculated to obtain the historical average number of battery swaps at the corresponding time point. The average number of historical battery swapping requests in the historical battery swapping request data is calculated to obtain the historical average number of battery swapping requests at the corresponding time point. Subtract the historical average number of battery swap requests for the corresponding time point from the historical average number of battery swaps for the corresponding time point to obtain the first value. If the first value is greater than the preset number of times threshold, the corresponding charging and swapping cabinet node will trigger an inventory imbalance prompt at the current time node and generate battery scheduling information. The battery scheduling information is sent to a preset management terminal for display; the battery scheduling is completed by dispatching to the user terminal, and also includes: replacing the user terminal's unused, healthy battery with a charge to the counter to complete the battery scheduling.

7. The intelligent charging and swapping cabinet control system according to claim 6, characterized in that, Also includes: Based on the pre-set battery swapping system, the system obtains the user's identity and the compliance of the battery specifications and models they use; at the same time, it obtains the user's historical battery swapping behavior data, including the battery swapping period, the battery capacity range of the swapped battery, and the health level of the batteries selected in the past. When the battery swapping system receives a battery swapping request from a user, the system queries information on all swappable batteries in the battery swapping cabinet, including the battery's current charge level, health factor, current temperature, charging completion time, and battery specifications and model. The replaceable batteries in the battery swapping system are filtered according to the battery specifications and models of the user terminals to obtain a list of replaceable batteries. Based on users’ historical battery swapping behavior data, calculate a user-side battery swapping recommendation index for the batteries in the swappable battery list; The battery compartment with the highest user-side battery swapping recommendation index in the replaceable battery list is set as the optimal recommended compartment, and feedback is sent to the user through a preset screen.

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