Charging power distribution method and system for multiple battery resistors in battery changing cabinet

Abstract

The invention provides a charging power distribution method and system for multiple battery resistors in a battery replacement cabinet, and relates to the technical field of electric vehicle battery replacement facilities, and the method comprises the steps: obtaining the charge state and AC internal resistance data of each battery pack in the battery replacement cabinet, carrying out the deviation inspection, comparing with a health threshold value, and marking an abnormal battery pack; a specific frequency test signal is injected into the battery loop, and current and voltage changes are detected to generate a load state distribution characteristic graph; determining the health score of each battery pack based on the charge state deviation value, the AC internal resistance and the load characteristic parameter; after the score of the abnormal battery pack is reduced, the charging priority is distributed according to the adjusted health score sequence, and the total power is dynamically distributed according to the proportion; and when the abnormal battery pack is identified, the power of the abnormal battery pack is limited, and the released resources are redistributed to the healthy and standard battery pack, so that the charging efficiency and the battery health can be considered, and safe and efficient charging management is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle battery swap facilities, and in particular to a method and system for distributing charging power of multiple batteries in a battery swap cabinet. BACKGROUND

[0002] The battery swap cabinet needs to charge multiple groups of batteries with different states, and the real-time power and health state of the batteries are significantly different. The increase in the internal resistance of the aging battery will cause overcharging risk, and the new battery needs efficient power compensation to ensure turnover. Therefore, there is an urgent need for a power distribution method that can adjust resources based on real-time power based on real-time power and avoid safety hazards by monitoring health state to achieve the coordinated optimization of charging safety and efficiency.

[0003] Currently, the mainstream solution is a hierarchical power distribution method based on state of charge. It divides the state of charge of each battery group into levels such as fast charging, regular charging, and trickle charging, and distributes power according to a fixed ratio, prioritizing low state of charge batteries and providing only maintenance power to high state of charge batteries. After the low state of charge batteries are fully charged, the power is transferred.

[0004] However, this solution has obvious shortcomings: it does not consider the health state of the battery, lacks internal resistance detection, cannot identify aging batteries, and is prone to overcharging risk due to abnormal internal resistance; the power distribution is fixed, with poor flexibility, and the resources cannot be accurately redistributed, resulting in low utilization; the load state difference is ignored, which may exacerbate the loss due to uneven current and shorten the battery life. SUMMARY

[0005] The purpose of the present application is to provide a method and system for distributing charging power of multiple batteries in a battery swap cabinet to solve the problem of uneven current exacerbating loss and shortening battery life in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a method for distributing charging power of multiple batteries in a battery swap cabinet, comprising:

[0007] Obtain the state of charge data and alternating current resistance data of each battery group in the battery swap cabinet;

[0008] Perform deviation checking on the state of charge data and alternating current resistance data, and compare the alternating current resistance data with a preset health threshold to identify abnormal battery groups;

[0009] Inject a test signal of a specific frequency into the battery loop, and detect the current and voltage changes generated by the test signal in the loop to generate a distribution feature map representing the load state of each battery group;

[0010] Based on the deviation value of the state of charge data, the alternating current resistance measurement value, and the load characteristic parameters of the distribution feature map, determine the health score of each battery group;

[0011] The health score of the abnormal battery pack is reduced, and the charging priority of the battery packs is sorted according to the health scores of all battery packs after adjustment. Based on the charging priority sorting results, the total available power of the battery swapping cabinet is dynamically allocated to each battery pack according to the proportion of the health scores.

[0012] When a battery pack is identified as an abnormal battery pack, the charging power of the abnormal battery pack is limited, and the power resources released by the load characteristic parameters corresponding to the abnormal battery pack in the distribution feature map are redistributed to battery packs whose health scores meet the preset conditions.

[0013] Optionally, based on the deviation value of the state of charge data, the measured value of AC internal resistance, and the load characteristic parameters of the distribution characteristic map, a health score for each battery pack is determined, including:

[0014] Based on the deviation value of the state of charge data and the AC internal resistance measurement value, combined with the corresponding standard reference value, the state of charge deviation ratio and internal resistance change ratio are calculated respectively to generate a basic score characterizing the basic health status of the battery pack.

[0015] Load characteristic parameters are extracted from the distribution feature map, and the response characteristics of the battery pack under dynamic load are analyzed based on the peak voltage offset and current response delay time of the load characteristic parameters, so as to generate a dynamic score characterizing the performance characteristics of the battery pack.

[0016] Based on the correlation between the basic score and the dynamic score, a fusion score reflecting the overall health status of the battery pack is determined. Based on the historical operating data of the battery pack, the contribution of the fusion score is adaptively adjusted, and the adjusted fusion score is mapped to a preset health score range to obtain the health score of the battery pack.

[0017] Optionally, a test signal of a specific frequency is injected into the battery circuit, and the changes in current and voltage generated by the test signal in the circuit are detected to generate a distribution characteristic map characterizing the load state of each battery pack, including:

[0018] An AC test signal within a preset frequency range is injected into the battery circuit, and the voltage and current responses of each battery pack under the test signal are detected simultaneously.

[0019] Based on the voltage and current responses, characteristic parameters of each battery pack are extracted, and a load state distribution feature map is generated by combining the physical location of the battery packs in the cabinet.

[0020] Optionally, based on the correlation between the basic score and the dynamic score, a fusion score reflecting the overall health status of the battery pack is determined, including:

[0021] Analyze the changing trends of the basic score and the dynamic score over multiple charge-discharge cycles, calculate the probability value that the changing trends are consistent, and normalize the probability value to obtain the correlation strength coefficient.

[0022] Based on the value of the correlation strength coefficient, the weight ratios of the basic score and the dynamic score are determined respectively, wherein the weight ratio of the basic score is positively correlated with the value of the correlation strength coefficient, and the weight ratio of the dynamic score is negatively correlated with the value of the correlation strength coefficient.

[0023] The basic score and dynamic score are weighted and summed according to the determined weight ratio to obtain an initial fusion value. The initial fusion value is then input into a preset smoothing transformation function for nonlinear mapping to generate a fusion score.

[0024] Optionally, based on the historical operating data of the battery pack, the contribution of the fusion score is adaptively adjusted, and the adjusted fusion score is mapped to a preset health score range to obtain the health score of the battery pack, including:

[0025] The historical charge-discharge cycle count, average operating temperature, and historical average charge-discharge rate are extracted from the historical operating data of the battery pack to determine the aging state category of the battery pack.

[0026] Based on the aging state category, a target adjustment strategy corresponding to the current aging state is selected from a plurality of preset adjustment strategies. Based on the parameter mapping relationship defined in the selected target adjustment strategy, the adjustment range of the fusion score is determined, and the adjusted fusion score is generated.

[0027] The adjusted fusion score is converted into a health score within a preset health score range using a piecewise linear mapping function, wherein the piecewise linear mapping function determines the conversion ratio based on multiple preset health status threshold points.

[0028] Optionally, the health score of the abnormal battery pack is reduced, and the battery packs are prioritized for charging based on their adjusted health scores. Then, based on the charging priority ranking, the total available power of the battery swapping cabinet is dynamically allocated to each battery pack according to the proportion of their health scores, including:

[0029] The health score of the identified abnormal battery packs will be reduced, while the health score of the remaining non-abnormal battery packs will remain unchanged.

[0030] The charging priority is sorted according to the adjusted health score of all battery packs, with the battery packs with higher health scores receiving higher charging priority.

[0031] Based on the charging priority ranking, the power value to be allocated to each battery pack is calculated, and the total available power of the battery swapping cabinet is allocated in proportion to the power value.

[0032] Optionally, when a battery pack is identified as an abnormal battery pack, the charging power of the abnormal battery pack is limited, and the power resources released by the load characteristic parameters corresponding to the abnormal battery pack in the distribution characteristic map are reallocated to battery packs whose health scores meet preset conditions, including:

[0033] When a battery pack is identified as an abnormal battery pack, the charging power of the abnormal battery pack is limited to a significantly reduced level, and the amount of power resources that can be reallocated after the charging power of the abnormal battery pack is limited is calculated.

[0034] Battery packs with health scores higher than a preset threshold are selected from the remaining non-abnormal battery packs as beneficiaries, and the reallocatable power resources are allocated to the beneficiaries according to the proportion of their health scores.

[0035] Optionally, based on the voltage and current responses, characteristic parameters of each battery pack are extracted, and a load state distribution characteristic map is generated by combining the physical location of the battery packs within the cabinet, including:

[0036] Based on the voltage and current responses, the dynamic response amplitude and response delay time of each battery pack are calculated, and comprehensive characteristic parameters of each battery pack are generated based on the dynamic response amplitude and response delay time.

[0037] Obtain the installation slot of each battery pack in the battery swapping cabinet, convert the installation slot into a coordinate position, and map the comprehensive feature parameters of each battery pack to the corresponding coordinate position to form a feature distribution point set;

[0038] Spatial interpolation is performed on the feature distribution point set to generate a continuous feature value distribution surface. Based on the feature value distribution surface, contour lines are drawn and filled with color gradients to form a visualized load state distribution feature map.

[0039] Optionally, a deviation check is performed on the state-of-charge data and the AC internal resistance data, and the AC internal resistance data is compared with a preset health threshold to identify abnormal battery packs, including:

[0040] Calculate the deviations of the state of charge data and AC internal resistance data of each battery pack relative to the average value in the cabinet, and obtain the state of charge deviation value and AC internal resistance deviation value respectively.

[0041] If the state of charge deviation value or AC internal resistance deviation value exceeds the preset deviation threshold, or the AC internal resistance data exceeds the preset health threshold, it is identified as an abnormal battery pack.

[0042] Secondly, this application provides a charging power distribution system for multiple battery resistors within a battery swapping cabinet, comprising:

[0043] The acquisition module is used to acquire the state of charge data and AC internal resistance data of each battery pack in the battery swapping cabinet;

[0044] The identification module is used to check the deviation between the state of charge data and the AC internal resistance data, and compare the AC internal resistance data with a preset health threshold to identify abnormal battery packs.

[0045] The generation module is used to inject a test signal of a specific frequency into the battery circuit and detect the changes in current and voltage generated by the test signal in the circuit, and generate a distribution feature map characterizing the load state of each battery pack.

[0046] The calculation module is used to determine the health score of each battery pack based on the deviation value of the state of charge data, the AC internal resistance measurement value, and the load characteristic parameters of the distribution characteristic map.

[0047] The sorting module is used to reduce the health score of the abnormal battery pack, sort the battery packs according to the adjusted health scores of all battery packs, and dynamically allocate the total available power of the battery swapping cabinet to each battery pack according to the proportion of the health scores based on the charging priority sorting results.

[0048] The allocation module is used to limit the charging power of the abnormal battery pack when the battery pack is identified as the abnormal battery pack, and to redistribute the power resources released by the load characteristic parameters corresponding to the abnormal battery pack in the distribution characteristic map to the battery packs whose health scores meet the preset conditions.

[0049] The charging power allocation method for multiple battery banks in a battery swapping cabinet provided in this application acquires the state of charge (SOC) data and AC internal resistance data of each battery bank within the cabinet. This provides core foundational data for subsequent battery status assessment, identification of abnormal battery banks, and allocation of charging power, ensuring the criticality and comprehensiveness of the data source. By performing deviation checks on the SOC and AC internal resistance data and comparing the AC internal resistance data with a preset health threshold to identify abnormal battery banks, it is possible to promptly detect data anomalies and accurately locate battery banks with poor health conditions, providing a basis for subsequent differentiated power allocation. By injecting a test signal of a specific frequency into the battery circuit, it detects current and voltage changes to generate a distribution characteristic map representing the load state of each battery bank, allowing for a direct understanding of the load state of each battery bank. Real-time load status of battery packs overcomes the limitations of relying solely on basic data for evaluation; by determining the health score of each battery pack based on state-of-charge deviation, AC internal resistance measurement, and load characteristic parameters, the health level of batteries can be comprehensively quantified from multiple dimensions, providing a scientific standard for charging priority ranking; by reducing the health score of abnormal battery packs, ranking them according to the adjusted health score, and dynamically allocating the available total power proportionally, charging resources can be tilted towards healthy battery packs, improving overall charging efficiency; by identifying abnormal battery packs and limiting their charging power, and redistributing the released power resources to battery packs with qualified health scores, the risk of overcharging abnormal battery packs can be avoided, while maximizing the use of idle power resources, balancing charging safety and resource utilization.

[0050] Furthermore, an AC test signal within a preset frequency range is injected into the battery circuit, and the voltage and current responses of each battery pack under this test signal are simultaneously detected. Characteristic parameters of each battery pack are then extracted based on the voltage and current responses, and a load state distribution feature map is generated by combining this with the physical location of the battery packs within the cabinet. By injecting an AC test signal within a preset frequency range and simultaneously detecting the response, data related to the load characteristics of the battery packs can be captured more accurately, and the extracted characteristic parameters are more targeted. The load state distribution feature map generated by combining the physical location not only clearly presents the load differences of each battery pack but also correlates it with their actual installation location within the battery swapping cabinet, providing a more concrete reference for subsequent power allocation and cabinet circuit optimization, further improving the accuracy and practicality of load assessment. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1A schematic flowchart illustrating a charging power distribution method for multiple battery resistors in a battery swapping cabinet, provided in an embodiment of this application;

[0053] Figure 2 A flowchart illustrating a specific implementation of a charging power distribution method for multiple battery resistors in a battery swapping cabinet, as provided in an embodiment of this application.

[0054] Figure 3 A schematic diagram illustrating a specific implementation of a charging power distribution method for multiple battery resistors in a battery swapping cabinet, as provided in this application embodiment;

[0055] Figure 4 This is a schematic diagram of a charging power distribution system for multiple battery resistors in a battery swapping cabinet, provided as an embodiment of this application. Detailed Implementation

[0056] In the power allocation scenario of multi-battery pack charging in battery swapping cabinets, the existing graded scheme based on state of charge has obvious shortcomings: it allocates power only based on the battery's charge level, without paying attention to the battery's health status, and cannot identify aging batteries with increased internal resistance, which can easily lead to safety risks due to overcharging; it also uses a fixed power allocation ratio, so when some batteries need to limit power, the released resources cannot be accurately allocated to other batteries, resulting in low overall power utilization; at the same time, it ignores the load differences of each battery pack, which may accelerate battery wear and shorten its lifespan due to uneven current distribution, making it difficult to balance charging safety and efficiency.

[0057] To address these issues, this application proposes a method for allocating charging power to multiple battery packs within a battery swapping cabinet. This method first acquires the charge and internal resistance data of each battery pack to identify abnormal battery packs. Then, it detects the load status through test signals and calculates a battery health score based on charge deviation, internal resistance, and load parameters. Finally, it sorts the batteries according to their health scores and dynamically allocates power, while simultaneously limiting the power of abnormal battery packs and transferring freed-up resources to healthy batteries. This solution addresses battery health through internal resistance detection, mitigating the risk of overcharging; it replaces a fixed ratio with dynamic power allocation, improving resource utilization; and it considers differences in load status, reducing battery wear. It fundamentally solves the shortcomings of existing solutions and achieves synergistic optimization of charging safety and efficiency.

[0058] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] The core of this application is to provide a method for distributing charging power among multiple battery resistors in a battery swapping cabinet. A flowchart of one specific implementation is shown below.Figure 1 As shown, the method includes:

[0060] S101. Obtain the state of charge data and AC internal resistance data of each battery pack in the battery swapping cabinet.

[0061] Among them, the state of charge data is used to reflect the proportion of the battery's current charge to its total capacity, while the AC internal resistance data is used to reflect key indicators of the battery's health status.

[0062] Specifically, the battery management system collects basic information such as battery voltage, current, and temperature in real time, and then calculates the state of charge data by combining algorithms such as Coulomb counting, open-circuit voltage method, or Kalman filtering. By applying a small-amplitude AC signal of a specific frequency to the battery pack and detecting the resulting voltage response, the AC internal resistance data can be obtained based on Ohm's law: internal resistance = voltage change / current change. The battery swapping cabinet can integrate a dedicated detection module to automatically complete the detection during battery connection or charging intervals without affecting the normal charging process.

[0063] S102. Perform a deviation check on the state of charge data and AC internal resistance data, and compare the AC internal resistance data with a preset health threshold to identify abnormal battery packs.

[0064] Optionally, step S102 may specifically include the following steps:

[0065] S1021. Calculate the deviation of the state of charge data and AC internal resistance data of each battery pack relative to the average value in the cabinet, and obtain the state of charge deviation value and AC internal resistance deviation value respectively.

[0066] S1022. If the state of charge deviation value or AC internal resistance deviation value exceeds the preset deviation threshold, or the AC internal resistance data exceeds the preset health threshold, then it is identified as an abnormal battery pack.

[0067] In the above steps, the state of charge (SCC) deviation value refers to the difference between the SCC data of a single battery pack and the average SCC data of all battery packs in the battery swapping cabinet, reflecting the degree of deviation of the battery pack's capacity from the overall average capacity; the AC internal resistance deviation value refers to the difference between the AC internal resistance data of a single battery pack and the average AC internal resistance data of all battery packs in the battery swapping cabinet, reflecting the degree of deviation of the battery pack's conductivity from the overall average conductivity; the preset deviation threshold is a reasonable upper limit of fluctuation range set in advance based on the normal operating state of the battery packs in the battery swapping cabinet, exceeding this range indicates that the battery pack's state may be abnormal; the preset health threshold is a safe upper limit of AC internal resistance set in advance based on the battery pack's design standards and long-term usage experience, exceeding this upper limit indicates that the battery pack's health may be poor; abnormal battery packs refer to battery packs whose SCC deviation value, AC internal resistance deviation value exceeds the preset deviation threshold, or whose AC internal resistance data exceeds the preset health threshold. These battery packs may have safety risks or efficiency problems during charging.

[0068] In this embodiment, firstly, step S1021 calculates the deviations of the state-of-charge (SOC) data and AC internal resistance data of each battery pack relative to the average value in the cabinet, obtaining the SOC deviation value and AC internal resistance deviation value. Specifically, the SOC data of all battery packs in the battery swapping cabinet are first summarized, and then the sum of these data is divided by the total number of battery packs to obtain the average SOC value in the cabinet. Then, the average SOC value of a single battery pack is subtracted from the average SOC value to obtain the SOC deviation value for that battery pack. The calculation logic for the AC internal resistance deviation value is the same: first, the AC internal resistance data of all battery packs is summarized and the average value is calculated; then, the average SOC value of a single battery pack is subtracted from the average SOC value to obtain the AC internal resistance deviation value. For example, if a battery swapping cabinet contains 5 battery packs with SOC values ​​of 30%, 35%, 40%, 45%, and 50%, the average value is first calculated as (30% + 35% + 40% + 45% + 50%) ÷ 5 = 40%, and then the SOC deviation value for each battery pack is calculated separately.

[0069] The first group is -10%, the second group is -5%, the third group is 0%, the fourth group is 5%, and the fifth group is 10%. If the AC internal resistance data of these 5 groups of batteries are 50mΩ, 55mΩ, 60mΩ, 65mΩ, and 70mΩ respectively, the average value is (50mΩ+55mΩ+60mΩ+65mΩ+70mΩ)÷5=60mΩ, and the corresponding AC internal resistance deviation values ​​are -10mΩ, -5mΩ, 0mΩ, 5mΩ, and 10mΩ respectively.

[0070] Next, step S1022 determines whether to identify abnormal battery packs. First, pre-set preset deviation thresholds and preset health thresholds are retrieved. Then, the state-of-charge deviation value and AC internal resistance deviation value of each battery pack obtained in step S1021 are compared with the preset deviation thresholds, and simultaneously, the AC internal resistance data of each battery pack is compared with the preset health threshold. If the state-of-charge deviation value, AC internal resistance deviation value, or AC internal resistance data of a battery pack exceeds the preset deviation threshold, or exceeds the preset health threshold, the battery pack can be identified as an abnormal battery pack. For example, continuing with the above battery swapping cabinet scenario, the preset deviation thresholds are set to ±8% (state-of-charge deviation) and ±8mΩ (AC internal resistance deviation), and the preset health threshold is set to 68mΩ (AC internal resistance). After comparison, it was found that the state of charge deviation of the first group of batteries was -10%, which exceeded the lower limit of -8%, and the state of charge deviation of the fifth group of batteries was 10%, which exceeded the upper limit of 8%. In addition, the AC internal resistance of the fifth group of batteries was 70mΩ, which exceeded the health threshold of 68mΩ. Therefore, the first and fifth groups of batteries were identified as abnormal battery groups. The state of charge deviation and AC internal resistance deviation of the other three groups of batteries were all within the preset deviation threshold range, and the AC internal resistance data did not exceed the health threshold. Therefore, they were judged as normal battery groups.

[0071] In practical applications, a battery swapping station of a certain battery swapping company, model A, is equipped with 10 sets of lithium battery packs of brand B. Before charging during the morning peak hours each day, the station automatically executes step S102. First, the battery management system collects the state of charge (SOC) data and AC internal resistance data of the 10 battery packs. Assume that the collected SOC data are 25%, 28%, 32%, 30%, 35%, 42%, 38%, 26%, 45%, and 33%, and the AC internal resistance data are 52mΩ, 54mΩ, 56mΩ, 58mΩ, 62mΩ, 64mΩ, 61mΩ, 53mΩ, 66mΩ, and 57mΩ.

[0072] Next, S1021 is executed, and the average state of charge is calculated to be 33.4%. The deviation values ​​of the state of charge for each battery pack are -8.4%, -5.4%, -1.4%, -3.4%, 1.6%, 4.6%, -7.4%, 11.6%, and -0.4%, respectively. The average AC internal resistance is 58.3mΩ, and the deviation values ​​of the AC internal resistance for each battery pack are -6.3mΩ, -4.3mΩ, -2.3mΩ, -0.3mΩ, 3.7mΩ, 5.7mΩ, 2.7mΩ, -5.3mΩ, 7.7mΩ, and -1.3mΩ, respectively. Then, the battery swapping cabinet has preset thresholds for state of charge deviation of ±8%, AC internal resistance deviation of ±7mΩ, and AC internal resistance health threshold of 65mΩ. After comparison, it was found that the state of charge deviation of the first group was -8.4%, which exceeded the lower limit; the state of charge deviation of the sixth group was 8.6%, which exceeded the upper limit; and the AC internal resistance deviation of the ninth group was 7.7mΩ, which exceeded the upper limit, and the AC internal resistance data of 66mΩ exceeded the health threshold. Therefore, the first, sixth, and ninth groups of batteries were marked as abnormal battery groups, and the remaining 7 groups were normal battery groups. Differential control will be adopted for abnormal battery groups during subsequent charging.

[0073] In the overall scheme of step S102 above, by calculating the deviation between the battery pack status data and the average value, battery packs that deviate significantly from the overall status can be quickly identified, preventing individual battery packs from being overlooked due to abnormal status. By combining preset deviation thresholds and health thresholds for dual judgment, both the fluctuation of the battery pack relative to the overall situation and the health standards of the battery itself are taken into account, making the identification of abnormal battery packs more comprehensive and accurate. At the same time, the entire process can be completed automatically by the battery swapping cabinet without manual intervention, which not only ensures the efficiency of judgment but also reduces human error, laying the foundation for the reasonable allocation of subsequent charging power and the guarantee of charging safety, and effectively avoiding charging risks or efficiency problems that may be caused by the failure to detect abnormal battery packs in time.

[0074] S103. Inject a test signal of a specific frequency into the battery circuit and detect the changes in current and voltage generated by the test signal in the circuit to generate a distribution feature map characterizing the load state of each battery pack.

[0075] Optionally, step S103 may specifically include the following steps:

[0076] S1031. Inject an AC test signal within a preset frequency range into the battery circuit and simultaneously detect the voltage and current responses of each battery pack under the test signal.

[0077] S1032. Based on the voltage and current responses, extract the characteristic parameters of each battery pack, and generate a load state distribution characteristic map by combining the physical location of the battery pack in the cabinet.

[0078] Specifically, step S1032 may include the following processes: calculating the dynamic response amplitude and response delay time of each battery pack based on the voltage response and current response, and generating comprehensive characteristic parameters of each battery pack based on the dynamic response amplitude and response delay time; obtaining the installation slot of each battery pack in the battery swapping cabinet, converting the installation slot into a coordinate position, mapping the comprehensive characteristic parameters of each battery pack to the corresponding coordinate position to form a feature distribution point set; performing spatial interpolation calculation on the feature distribution point set to generate a continuous feature value distribution surface, and drawing contour lines and filling them with color gradients based on the feature value distribution surface to form a visualized load state distribution feature map.

[0079] In the above steps, the specific frequency test signal refers to an AC signal within a preset frequency range used to detect the load characteristics of the battery circuit. This signal has a small amplitude, will not affect the normal charging of the battery, and can accurately reflect the load status. The preset frequency range is an AC signal frequency interval set in advance according to the battery type and the characteristics of the battery swapping cabinet circuit, ensuring that the signal can effectively excite the load response of the battery circuit. The voltage response refers to the voltage change generated at the two ends of each battery pack after the test signal is injected into the battery circuit, reflecting the voltage feedback characteristics of the battery pack to the test signal. The current response refers to the current change flowing through each battery pack after the test signal is injected into the battery circuit, reflecting the current feedback characteristics of the battery pack to the test signal. The characteristic parameters are key indicators extracted from the voltage response and current response that can characterize the load status of the battery pack, including dynamic response amplitude and response delay time. The comprehensive characteristic parameter is a single parameter calculated based on the fusion of dynamic response amplitude and response delay time, used to comprehensively evaluate the load status of the battery pack.

[0080] The installation slot is a fixed location number within the battery swapping cabinet used to place battery packs; the coordinate position is a specific point in two-dimensional or three-dimensional space mapped to the installation slot, used to locate the physical distribution of battery packs within the cabinet; the feature distribution point set is a dataset formed by associating the comprehensive feature parameters of each battery pack with its corresponding coordinate position; spatial interpolation calculation is an algorithm that calculates the feature values ​​of adjacent positions using a known feature distribution point set, used to generate a continuous feature value distribution surface; the feature value distribution surface is a continuous surface obtained through spatial interpolation that reflects the load feature values ​​at each position within the cabinet; the contour lines are curves connecting the same feature values ​​on the feature value distribution surface; the color gradient is a color gradient range set according to the magnitude of the feature values; the combination of these two forms a visualized load state distribution feature map, which can intuitively show the differences in load state between each battery pack and the entire cabinet.

[0081] In this embodiment, firstly, an AC test signal within a preset frequency range is injected into the battery circuit in step S1031, and the voltage and current responses of each battery pack are detected simultaneously. Specifically, a signal generation module and a signal detection module are integrated into the main circuit of the battery swapping cabinet or the branch circuits of each battery pack. The signal generation module generates a small-amplitude AC test signal according to the preset frequency range, and then injects this signal into the battery circuit. Simultaneously, the signal detection module, such as a voltage sensor or a current sensor, is connected to the positive and negative terminals or branch circuits of each battery pack. During the same time period as the injected test signal, it collects real-time data on voltage changes (voltage response) and current changes (current response) across each battery pack, and transmits the collected data to the control unit of the battery swapping cabinet for temporary storage.

[0082] For example, a battery swapping cabinet contains 6 sets of batteries, with a preset frequency range of 50Hz to 500Hz. The signal generation module first generates AC test signals at three frequencies: 50Hz, 200Hz, and 500Hz, and injects them into the battery circuit in sequence. When the 50Hz test signal is injected, the voltage sensor collects the voltage response data of the 6 sets of batteries respectively. For example, the voltage of the first set of batteries fluctuates from 3.2V to 3.22V and then falls back after the signal is injected, and the voltage of the second set fluctuates from 3.21V to 3.23V and then falls back. At the same time, the current sensor collects the current of the first set of batteries fluctuating from 0A to 0.05A and then falling back, and the current of the second set fluctuating from 0A to 0.04A and then falling back. When the other frequency signals are injected, the voltage and current responses are detected synchronously in the same way, and all data are recorded.

[0083] Secondly, the dynamic response amplitude and response delay time of each battery pack are calculated in the first step of step S1032, thereby generating comprehensive characteristic parameters. Specifically, the control unit first preprocesses the collected voltage and current response data, such as removing noise and filtering valid data segments, before calculating the dynamic response amplitude: for voltage response, the difference between the maximum and minimum voltage values ​​within the valid data segment is taken as the voltage response amplitude; for current response, the difference between the maximum and minimum current values ​​within the valid data segment is taken as the current response amplitude. These two can be further integrated into the overall dynamic response amplitude of the load, such as by taking the weighted average of the two, with the weights set according to the battery characteristics. The response delay time is calculated by comparing the injection time of the test signal with the time when the voltage and current responses reach their peak or stable values, taking the time difference as the response delay time. Then, weights are set according to the importance of the dynamic response amplitude and response delay time, and the comprehensive characteristic parameters of each battery pack are calculated using a weighted summation formula.

[0084] For example, continuing with the scenario of the above six battery groups, after processing the response data of the 500Hz test signal, the voltage response amplitude of the first battery group is 0.03V, the current response amplitude is 0.06A, and the normalized dynamic response amplitude is 0.8; the response delay time is 0.015 seconds, and the normalized value is 0.3; substituting into the formula, the comprehensive characteristic parameter is calculated as 0.8×0.6+0.3×0.4=0.48+0.12=0.6. The voltage response amplitude of the second battery group is 0.02V, the current response amplitude is 0.05A, the normalized dynamic response amplitude is 0.7; the response delay time is 0.02 seconds, and the normalized value is 0.4; the comprehensive characteristic parameter is calculated as 0.7×0.6+0.4×0.4=0.42+0.16=0.58. The comprehensive characteristic parameters of the remaining four groups are calculated using the same method.

[0085] Next, the installation slots of each battery pack are acquired and converted into coordinate positions to form a feature distribution point set. In practice, each battery installation slot is numbered before the battery swapping cabinet leaves the factory, and the control unit pre-stores the mapping relationship between the slot number and the coordinate position. The control unit first identifies the current installation slot of each battery pack through the slot sensor or the identification when the battery pack is connected, and then converts the slot number into the corresponding two-dimensional coordinate position according to the pre-stored mapping relationship. After that, the comprehensive feature parameters of each battery pack are associated with the converted coordinate position one by one. For example, the battery in the 1 row and 1 column slot corresponds to the coordinates (0.1, 0.15) and the comprehensive feature parameter 0.6, and the battery in the 1 row and 2 column slot corresponds to (0.3, 0.15) and 0.58. Finally, a feature distribution point set containing the coordinate position and the corresponding comprehensive feature parameter is formed and stored in the database of the control unit.

[0086] For example, six battery packs are installed in row 1, column 1 (slot 1), row 1, column 2 (slot 2), row 1, column 3 (slot 3), row 2, column 1 (slot 4), row 2, column 2 (slot 5), and row 2, column 3 (slot 6), with corresponding coordinate positions of (0.1, 0.15), (0.3, 0.15), (0.5, 0.15), (0.1, 0.45), (0.3, 0.45), and (0.5, 0.45). Combined with the previously calculated comprehensive feature parameters, the feature distribution point set is formed as [(0.1, 0.15, 0.6), (0.3, 0.15, 0.58), (0.5, 0.15, 0.55), (0.1, 0.45, 0.62), (0.3, 0.45, 0.59), (0.5, 0.45, 0.56)].

[0087] Finally, spatial interpolation calculations are performed to generate a feature value distribution surface and draw a load state distribution feature map. Specifically, the control unit calls a spatial interpolation algorithm, using the coordinates of the feature distribution point set as known points. Based on the comprehensive feature parameters of each known point, the feature values ​​of adjacent coordinate positions within the cabinet are calculated. The feature values ​​of all known points and the calculated points are integrated to form a continuous feature value distribution surface covering the entire internal space of the battery swapping cabinet. Then, on the two-dimensional projection of the surface, points with the same feature values ​​are connected to form contour lines. Simultaneously, a color gradient is set according to the feature value magnitude; for example, 0.5-0.55 corresponds to blue, 0.55-0.6 corresponds to green, and 0.6-0.65 corresponds to yellow. The areas between different contour lines are filled with the corresponding colors. Finally, a visualized load state distribution feature map is generated, which can be displayed on the battery swapping cabinet's screen or in the back-end management system, intuitively presenting the differences in load state between each battery pack and different areas within the cabinet.

[0088] For example, by performing inverse distance weighted interpolation on the feature distribution point set of 6 battery groups, the feature values ​​of grid points such as (0.2,0.3) and (0.4,0.3) in the cabinet are calculated, forming a feature value distribution surface. On the projection map, the contour lines with feature value of 0.55 connect points such as (0.5,0.15) and (0.5,0.45), and the contour lines with feature value of 0.6 connect points such as (0.1,0.15) and (0.1,0.45). The blue area covers the area with feature value of 0.5-0.55, such as near slots 3 and 6; the green area covers the area with feature value of 0.55-0.6, such as near slots 2 and 5; and the yellow area covers the area with feature value of 0.6-0.65, such as near slots 1 and 4, forming a clear load state distribution feature map.

[0089] In practical applications, a battery swapping service company deployed the above solution for its C-type battery swapping cabinet, which can accommodate 8 sets of D-brand batteries. The signal generation module of the battery swapping cabinet has a preset frequency range of 100Hz to 800Hz, and the signal detection module uses a high-precision voltage sensor (measurement accuracy 0.001V) and a current sensor (measurement accuracy 0.001A). During off-peak hours at night, the battery swapping cabinet automatically executes the above steps: First, the signal generation module generates AC test signals at three frequencies: 100Hz, 400Hz, and 800Hz, which are sequentially injected into the battery main circuit. Simultaneously, sensors collect voltage and current response data from eight battery groups. For example, when the 400Hz signal is injected, the voltage of the fourth battery group fluctuates from 3.18V to 3.21V, and the current fluctuates from 0A to 0.055A. The data is transmitted to the control unit in real time. Next, the control unit denoises the data and calculates the voltage response amplitude of the fourth battery group as 0.03V, the current response amplitude as 0.055A, the normalized dynamic response amplitude as 0.82, the response delay time as 0.018 seconds, and the voltage reaching its peak value 0.018 seconds after signal injection. After normalization, the voltage is 0.35. The comprehensive characteristic parameter is calculated using weights of 0.6 and 0.4: 0.82 × 0. 6 + 0.35 × 0.4 = 0.492 + 0.14 = 0.632; Then, the control unit identifies the installation slots of the 8 battery groups: slots 1-8, arranged in 2 rows and 4 columns. Slot 1 is converted into coordinates (0.1, 0.2), slot 2 (0.3, 0.2)...slot 8 (0.7, 0.5), and associated with the comprehensive characteristic parameters of each battery to form a point set; finally, a feature value distribution surface is generated through Kriging interpolation, and contour lines with an interval of 0.04 are drawn and filled with color, where 0.55-0.59 is blue, 0.59-0.63 is green, and 0.63-0.67 is yellow, generating a load state distribution feature map. The back-end administrators found through this map that the areas where the 4th and 7th battery groups are located are yellow, indicating better load characteristics, while the 2nd and 5th groups are blue, indicating weaker load characteristics, providing a visual basis for subsequent power allocation.

[0090] In the overall scheme of step S103 above, by injecting an AC test signal of a preset frequency and simultaneously detecting the response, the load characteristics of each battery pack can be accurately captured, avoiding the limitations of relying solely on static data to evaluate the load; it achieves a quantitative evaluation of the load status, ensuring that the load differences between different battery packs are comparable; it transforms the overall load status within the cabinet from abstract data into an intuitive image, facilitating staff to quickly identify battery packs with weak or abnormal loads; the entire process is automated, requiring no manual operation, ensuring both the accuracy of data acquisition and processing and improving the efficiency of load evaluation. This scheme can reflect the load changes of the battery circuit in real time, providing a precise load basis for the subsequent dynamic allocation of charging power, helping to avoid problems such as low charging efficiency or battery damage caused by uneven load, and further ensuring the stable operation of the battery swapping cabinet.

[0091] S104. Based on the deviation value of the state of charge data, the AC internal resistance measurement value, and the load characteristic parameters of the distribution characteristic map, determine the health score of each battery pack.

[0092] Optionally, such as Figure 2 As shown, step S104 may specifically include the following steps:

[0093] S1041. Based on the deviation value of the state of charge data and the AC internal resistance measurement value, combined with the corresponding standard reference value, calculate the state of charge deviation ratio and internal resistance change ratio respectively to generate a basic score characterizing the basic health status of the battery pack.

[0094] S1042. Extract load characteristic parameters from the distribution feature map, and analyze the response characteristics of the battery pack under dynamic load based on the peak voltage offset and current response delay time of the load characteristic parameters, so as to generate a dynamic score characterizing the performance characteristics of the battery pack.

[0095] S1043. Based on the correlation between the basic score and the dynamic score, determine the fusion score that reflects the overall health status of the battery pack;

[0096] Specifically, step S1043 may include the following processes: analyzing the changing trends of the basic score and the dynamic score over multiple charge-discharge cycles, calculating the probability value that the changing trends are consistent, and normalizing the probability value to obtain a correlation strength coefficient; determining the weight ratios of the basic score and the dynamic score based on the value of the correlation strength coefficient, wherein the weight ratio of the basic score is positively correlated with the value of the correlation strength coefficient, and the weight ratio of the dynamic score is negatively correlated with the value of the correlation strength coefficient; weighting and summing the basic score and the dynamic score according to the determined weight ratios to obtain an initial fusion value, and inputting the initial fusion value into a preset smoothing transformation function for nonlinear mapping to generate a fusion score.

[0097] S1044. Based on the historical operating data of the battery pack, the contribution of the fusion score is adaptively adjusted, and the adjusted fusion score is mapped to a preset health score range to obtain the health score of the battery pack.

[0098] Specifically, step S1044 may include the following processes: extracting historical charge-discharge cycle counts, average operating temperature, and historical average charge-discharge rate from the historical operating data of the battery pack to determine the aging state category of the battery pack; selecting a target adjustment strategy corresponding to the current aging state from a plurality of preset adjustment strategies according to the aging state category; determining the adjustment range of the fusion score based on the parameter mapping relationship defined in the selected target adjustment strategy; and generating an adjusted fusion score; converting the adjusted fusion score into a health score within a preset health score range through a piecewise linear mapping function, wherein the piecewise linear mapping function determines the conversion ratio relationship based on a plurality of preset health state threshold points.

[0099] In the above steps, the state of charge deviation ratio is the ratio of the state of charge deviation value to the corresponding standard reference value, quantifying the degree of deviation of the charge from the standard; the internal resistance change ratio is the ratio of the measured AC internal resistance value to the standard internal resistance reference value, reflecting the degree of deviation of conductivity performance; the basic score is calculated based on the above two ratios, characterizing the static basic health level; the load characteristic parameters are extracted from the distribution characteristic map, including peak voltage offset and current response delay time; the peak voltage offset represents the difference between the actual and standard peak voltage, reflecting voltage stability; the current response delay time represents the time difference between signal injection and current peak, reflecting response timeliness; the dynamic score is based on the analysis of load characteristic parameters, characterizing the performance under dynamic load; the correlation strength coefficient is the normalized result of the probability value of the consistent change trend of the basic score and the dynamic score, generally between 0 and 1, used to determine the weight of the two; the fusion score is the comprehensive score after weight allocation and smoothing transformation; the aging state category is divided according to historical operating data including the number of cycles, operating temperature, and charge / discharge rate; the health score is the final result of the adjusted fusion score mapped to a preset range, intuitively reflecting the overall health status.

[0100] In this embodiment of the application, firstly, the ratio is calculated and a basic score is generated through step S1041. Specifically, the standard reference value for the state of charge deviation and the standard reference value for the internal resistance are retrieved first, and the two ratios are calculated respectively: , ,in, This is the state-of-charge deviation ratio. This is the deviation value of the state of charge. This is the standard reference value for the deviation of the state of charge. This represents the rate of change in internal resistance. This is a measured value of AC internal resistance, in milliohms, symbol: , This is the standard reference value for internal resistance, in milliohms, symbol: Next, set the weights for the two ratios, such as... The base score is calculated using a formula; if the result is negative, it is set to 0. ,in, Based on the score, The weighting is the ratio of the state of charge deviation. This is the weighting factor for the rate of change in internal resistance. For example, the battery pack in a battery swapping cabinet... ,but ; , ,but Substituting into the formula, we get The maximum score is set at 1.

[0101] Secondly, parameters are extracted and a dynamic score is generated in step S1042. Specifically, the peak voltage offset and current response delay time are first read from the distribution characteristic map, and the corresponding standard values ​​are determined before calculating the deviation. , ,in, This refers to the peak voltage offset deviation. Peak voltage offset, unit: volt, symbol: V; This is the standard value for peak voltage offset, in volts (V). The deviation of current response delay. The current response delay time, in seconds, symbol: s. This is the standard value for the current response delay time, in seconds (s). Then, weights are assigned, such as... The dynamic score is calculated using a formula, and a negative result is set to 0. in, For dynamic scoring, As the weight for peak voltage offset deviation, This is used as a weight for the deviation in current response delay. For example, continuing with the aforementioned battery pack, , ,but , ,but Substituting into the formula, we get The maximum score is set at 1.

[0102] Next, the weights are determined and a fusion score is generated in step S1043. Specifically, this involves retrieving data from multiple charge / discharge cycles. and The correlation strength coefficient is obtained by normalizing the data after calculating the probability value of the number of periods with consistent statistical trends. ( Between). Setting Calculate the initial fusion value: ,in, This is the initial fusion value. Based on the scoring weights, This is for dynamic scoring weights. Finally, [the following will be implemented]... The input smoothing transformation function generates the fusion score, for example, using the function: ,in, For fusion scoring. For example, the above battery pack showed a consistent trend in 8 out of the past 10 cycles. , Substituting into the function yields (The maximum score is set at 1).

[0103] Finally, the health score is obtained by adjusting the score and mapping it through step S1044. Specifically, historical operating data is first extracted to determine the aging state category, and the corresponding adjustment range is selected. Calculate the adjusted fusion score: ,in, The adjusted fusion score, To adjust the amplitude, results <0 are set to 0, and results >1 are set to 1. Then, a piecewise linear mapping function is used to transform the result to a preset interval, such as... The function determines the proportion based on a threshold point: 0 corresponds to 0 points, 0.5 corresponds to 50 points, and 1 corresponds to 100 points. The calculation formula is as follows: ,in, This is a health score. For example, the historical data of the battery pack mentioned above corresponds to mild aging. ,

[0104] In practical applications, a battery swapping station operated by a certain company can accommodate 10 sets of F-brand lithium batteries, each with a rated capacity of 20Ah, to replenish the power of local shared electric vehicles. During the peak charging period from 10:00 PM to 6:00 AM the following day, step S104 is automatically executed. The battery swapping station collects data from the 10 battery sets through its built-in battery management system. Battery set number 3 has a state of charge of 32%, and the average charge level within the station is 42%. The internal resistance detection module measured its internal resistance to be 58mΩ. The battery swapping cabinet has a preset standard value of 12% for state of charge deviation, a standard value of 55mΩ for internal resistance, a standard value of 0.025V for peak voltage deviation, a standard value of 0.025s for current response delay, and aging status is divided into mild (cycles <600 times, temperature 20-35℃, rate 0.5-1C), moderate, and severe, with corresponding adjustment ranges of +0.06, 0, and -0.04, and a health score range of 0-100. When performing S104 on battery pack No. 3, the state of charge deviation ratio was first calculated to be approximately 0.833 and the internal resistance change ratio to be approximately 1.055, resulting in a basic score of approximately 0.0338. Then, the peak voltage offset of 0.02V and the current response delay time of 0.022s were read from the load state distribution characteristic map, and the deviations were calculated to be 0.8 and 0.88, respectively, resulting in a dynamic score of 0.16. Next, data from the past 12 charge-discharge cycles were retrieved, and the trends of the basic score and dynamic score were consistent over 9 cycles, resulting in a correlation strength coefficient of 0.75. The initial fusion value was calculated to be approximately 0.0654, and after smoothing, the fusion score was approximately 0.0658. Finally, based on its historical data of 550 cycles, an average temperature of 30℃, and a rate of 0.9C, it was determined to be slightly aged. After adjustment, the fusion score was 0.1258, which was mapped to a health score of 13. Based on this, the battery swapping cabinet set its charging priority to medium and allocated charging power reasonably.

[0105] In the overall scheme of step S104 above, by combining static and dynamic data to evaluate battery health, the one-sidedness of a single dimension is avoided, making the evaluation more comprehensive. The correlation strength coefficient is dynamically weighted to adapt to the state correlation characteristics of batteries at different cycles, improving the accuracy of the scoring. The scoring is adjusted based on historical operating data, fully considering the impact of long-term use on the battery, making the results more realistic. Finally, the scores are mapped to a unified score range, enabling a direct comparison of the health status of different battery packs. The entire process is automated, requiring no manual intervention, ensuring efficiency and reducing human error. This provides a reliable basis for subsequent charging management and maintenance plan formulation, contributing to the differentiated management and safe, efficient operation of battery packs.

[0106] S105. Reduce the health score of the abnormal battery pack, sort the battery packs by charging priority according to the adjusted health scores of all battery packs, and dynamically allocate the total available power of the battery swapping cabinet to each battery pack according to the proportion of the health scores based on the charging priority sorting results.

[0107] Optionally, step S105 may specifically include the following steps:

[0108] S1051. Reduce the health score of the identified abnormal battery packs, while keeping the original health score of the remaining non-abnormal battery packs unchanged.

[0109] S1052. The charging priority is sorted according to the adjusted health scores of all battery packs, wherein the battery pack with the higher health score receives the higher charging priority.

[0110] S1053. Based on the charging priority ranking, calculate the power value to be allocated to each battery pack, and allocate the total available power of the battery swapping cabinet according to the proportion of the power value.

[0111] In the above steps, abnormal battery packs refer to those identified as having performance abnormalities or safety risks through preliminary testing. Their characteristics may include sudden changes in internal resistance and abnormal temperature increases. The health score is a quantitative indicator reflecting the overall performance and health status of the battery pack; a higher value indicates a better battery condition. Charging priority is the ranking result used to determine the charging sequence and resource allocation priority of battery packs, directly affecting the power allocation ratio. Available total power refers to the total actual charging power that the battery swapping cabinet can allocate to each battery pack after deducting its own operating losses. The power allocation ratio is the proportion of power that each battery pack should receive relative to the total available power, calculated based on the health score.

[0112] In this embodiment, firstly, step S1051 adjusts the health score of abnormal battery groups. This step retrieves the previously identified list of abnormal battery groups and their original health scores through the battery management system built into the battery swapping cabinet. Simultaneously, it reads preset score reduction rules, which set different reduction amounts based on the degree of abnormality. For example, a slight abnormality reduces the score by a fixed value, a severe abnormality reduces it proportionally, while non-abnormal battery groups retain their original health scores. For instance, in a battery swapping cabinet with 5 battery groups, battery group 2 was previously identified as slightly abnormal with an original health score of 60. The preset reduction for slight abnormality is 15 points. The health scores of the other 4 non-abnormal battery groups are 90, 85, 75, and 80 points respectively. After executing S1051, the health score of battery group 2 is adjusted to 45 points, while the scores of the other battery groups remain unchanged.

[0113] Secondly, charging priority is sorted in step S1052. This step uses a numerical sorting algorithm to sort all the adjusted battery pack health scores in descending order, placing the battery pack with the highest score first to obtain the highest charging priority. The priority decreases sequentially as the scores decrease, thus establishing a complete charging priority sequence. For example, continuing the above example, the adjusted health scores are 90 points for battery pack 1, 45 points for battery pack 2, 85 points for battery pack 3, 75 points for battery pack 4, and 80 points for battery pack 5. By sorting in descending order, the priority order is 1, 3, 5, 4, 2, meaning that battery pack 1 has the highest charging priority and battery pack 2 has the lowest priority.

[0114] Next, step S1053 calculates the allocated power value for each battery pack and distributes the total available power proportionally. Specifically, the power detection module of the battery swapping cabinet first measures the input power of the entire cabinet, subtracts a preset operating loss power (e.g., 200W), and obtains the total available power. Then, the power allocation is calculated using a proportional allocation algorithm, combined with the adjusted health scores of each battery pack.

[0115] The formula is: ;

[0116] in, For the first The power distribution value of each battery pack For the first The adjusted health score of each battery pack. The sum of the adjusted health scores for all battery packs. This represents the total number of battery packs in the battery swapping cabinet. This refers to the total available power of the battery swapping cabinet. For example, in the above example, the total available power is detected to be 10kW. After adjustment, the total health score is 90+45+85+75+80=375 points. The power allocation for each battery group is calculated as follows: Battery 1 is (90 / 375)×10kW=2.4kW, Battery 3 is (85 / 375)×10kW≈2.267kW, Battery 5 is (80 / 375)×10kW≈2.133kW, Battery 4 is (75 / 375)×10kW=2kW, and Battery 2 is (45 / 375)×10kW=1.2kW. The battery swapping cabinet allocates the 10kW total available power to the 5 battery groups according to this result.

[0117] In practical application, the B-type battery swapping cabinet of Company A can accommodate 10 sets of Type C lithium batteries. During the peak charging period in the early morning, the cabinet automatically executes step S105. The cabinet first retrieves information on previously identified abnormal battery groups through its built-in system. It finds that battery groups 7 and 9 are moderately and slightly abnormal, respectively, with original health scores of 50 and 70. The preset reduction is 20 points for moderate abnormality and 15 points for slight abnormality. The remaining 8 non-abnormal battery groups have health scores between 65 and 95. After executing S1051, the score of battery group 7 is adjusted to 30 points, and that of battery group 9 to 55 points, while the others remain unchanged. Then, S1052 is executed, and a priority sequence is obtained by sorting in descending order. Battery group 1, with the highest score, is placed first, and battery group 7, with the lowest score, is placed last. Next, the battery swapping cabinet detected that the total input power of the cabinet was 20kW. After deducting 1kW of operating loss, the total usable power was 19kW. The total health score of all battery packs after adjustment was calculated to be 720 points. The power allocation of each battery pack was calculated proportionally. Battery pack No. 1 received the highest allocated power, and No. 7 received the lowest allocated power. The battery swapping cabinet completed the power allocation according to this result and started charging.

[0118] In the overall scheme of step S105 above, by specifically reducing the health score of abnormal battery packs, accurate differentiation of battery status is achieved, providing a reasonable basis for subsequent resource allocation. The priority ranking method based on health scores ensures that battery packs in better condition receive charging resources first, meeting the needs of efficient energy replenishment. The use of a proportional allocation algorithm for dynamic power allocation achieves a reasonable division of the total available power, avoiding resource waste caused by average allocation. The entire process is implemented through automation technology, requiring no manual intervention, which ensures operational efficiency, improves the scientific nature and safety of charging management, and extends the overall lifespan of the batteries.

[0119] S106. When a battery pack is identified as an abnormal battery pack, the charging power of the abnormal battery pack is limited, and the power resources released by the load characteristic parameters corresponding to the abnormal battery pack in the distribution characteristic map are redistributed to battery packs whose health scores meet the preset conditions.

[0120] Optionally, step S106 may specifically include the following steps:

[0121] S1061. When the battery pack is identified as the abnormal battery pack, the charging power of the abnormal battery pack is limited to a significantly reduced level, and the amount of power resources that can be reallocated after the charging power of the abnormal battery pack is limited is calculated.

[0122] S1062. Select battery packs with health scores higher than a preset threshold from the remaining non-abnormal battery packs as beneficiaries, and allocate the reallocatable power resources to the beneficiaries according to the health score ratio of the beneficiaries.

[0123] In the above steps, "abnormal battery pack" refers to a battery pack that has been identified as having an abnormal health status through preliminary detection; "significantly reduced power level" is the upper limit of safe charging power set for abnormal battery packs, which is far lower than the allocated power of normal battery packs; "redistributable power resources" is the excess power released from the original allocated power of abnormal battery packs after their power is restricted; "health score preset threshold" is the health standard for determining whether non-abnormal battery packs are eligible to receive additional power; "beneficiary" refers to non-abnormal battery packs with health scores higher than the preset threshold, which can obtain redistributed power resources; "health score ratio" is the proportion of each beneficiary's health score to the sum of all beneficiaries' health scores, used to determine the share of additional power allocation.

[0124] In this embodiment, firstly, step S1061 limits the charging power of the abnormal battery pack and calculates the amount of resources that can be reallocated. Specifically, the battery swapping cabinet control unit first retrieves the list of abnormal battery packs and their allocated power values ​​in S105, then reads the preset power limiting rules, such as limiting a minor abnormality to 30% of the original allocated power and a severe abnormality to 20%, and calculates the upper limit of the actual charging power of the abnormal battery pack according to the rules; then, the actual limited power is subtracted from the original allocated power to obtain the amount of power resources that can be reallocated. For example, if battery pack #2 in a battery swapping cabinet has a minor malfunction and is allocated 1500W of power in S105, with a preset limit of 30% of the original power for minor malfunctions, then its actual charging power limit is 1500W × 30% = 450W, and the amount of power resources that can be reallocated is 1500W - 450W = 1050W. Battery pack #4 has a severe malfunction and was originally allocated 1200W of power, with a limit of 20%, resulting in an actual power of 240W. The released resources are 1200W - 240W = 960W. The total released resources are 1050W + 960W = 2010W.

[0125] Secondly, step S1062 filters beneficiaries and allocates released power resources. Specifically, the control unit first selects battery packs with health scores higher than a preset threshold (e.g., 70 points) from the non-abnormal battery packs as beneficiaries, then calculates the sum of the health scores of all beneficiaries, and finally allocates the power resources according to the "beneficiary health score". The proportion of the total, which determines the reallocatable power resources, is used to allocate them to the beneficiaries, according to the formula: ,in, For the first The extra power gained by each beneficiary For the first The health score of each beneficiary The sum of the health scores of all beneficiaries. For the number of beneficiaries, This represents the total amount of power resources that can be reallocated. For example, continuing the above scenario, the health scores of non-abnormal battery packs 1 (90 points), 3 (85 points), and 5 (80 points) are all above 70 points, making them beneficiaries, with a total of [missing information]. Total released resources: 20.1 million, allocated proportionally: Player 1 receives... Number 3 won Number 5 won Ultimately, the total power of Unit 1 was... Number 3 is Number 5 is .

[0126] In practical application, a battery swapping cabinet of a certain battery swapping company, model F, is equipped with 6 groups of G brand batteries. During daily charging management, the cabinet initially identifies battery group 2 as slightly abnormal and battery group 5 as severely abnormal. Previously, these two groups were allocated charging powers of 1800W and 1600W respectively. Based on preset power limitation rules, the cabinet limits the charging power of the slightly abnormal battery group 2 to 30% of its original allocated power (540W), and the charging power of the severely abnormal battery group 5 to 20% of its original allocated power (320W). This releases 1260W and 1280W of power resources from battery groups 2 and 5 respectively, totaling 2540W available for redistribution. Subsequently, the cabinet reads the health scores of all non-abnormal battery groups. The preset health score threshold is 65 points. After screening, battery groups 1 (92 points), 3 (88 points), 4 (75 points), and 6 (68 points) all meet the criteria and become beneficiaries of the power redistribution. The battery swapping station calculates the total health score of the four beneficiaries to be 323 points. Then, according to the proportion of their respective health scores, the 2540W of released resources are allocated to the four beneficiaries. In the end, the charging power of each beneficiary is improved on the original basis, while the abnormal battery pack is charged at the limited safe power, which ensures both safety and makes full use of charging resources.

[0127] In the overall solution of step S106 above, by limiting the charging power of abnormal battery packs to a low level, the safety risks of overcharging and overheating are reduced from the source, ensuring the overall operational safety of the battery swapping cabinet. The released power resources are redistributed to healthy battery packs, avoiding resource idleness and waste, and improving the overall charging efficiency of the cabinet. Based on health scores, beneficiaries are selected and allocated proportionally, ensuring that additional resources flow to battery packs in better condition, meeting the dual requirements of efficient energy replenishment and battery protection. The entire process is automated, requiring no manual intervention, ensuring timely processing, and balancing safety and efficiency through precise power regulation, thus extending the overall lifespan of the battery packs.

[0128] The following is a complete embodiment for steps S101 to S106:

[0129] like Figure 3 As shown, a battery swapping cabinet of a certain battery swapping operation company is equipped with 8 groups of I-brand lithium batteries and uses the power distribution method described above for charging management. First, the battery swapping cabinet obtains the state of charge (SOC) data and AC internal resistance data of each battery group in real time through built-in sensors. The SOC data of battery groups 1 to 8 are 35%, 42%, 38%, 40%, 32%, 45%, 36%, and 39%, respectively, and the AC internal resistance data are between 50mΩ and 85mΩ.

[0130] Next, the system performed a deviation check on the data and found that the state of charge deviation of battery pack No. 6 reached 12%, and its AC internal resistance of 85mΩ exceeded the preset health threshold of 70mΩ. Battery pack No. 7's internal resistance of 78mΩ was close to the threshold, and its data fluctuations were abnormal. Both were identified as abnormal battery packs. Subsequently, the system injected a 1kHz specific frequency test signal into each battery circuit and detected that the current and voltage changes of battery packs No. 6 and No. 7 were significantly higher than those of other groups, generating a distribution characteristic map containing parameters such as load stability and response speed.

[0131] Based on the state-of-charge deviation (SOC) value, internal resistance measurement, and load characteristic parameters, the system calculates the initial health score for each battery pack. Battery packs 6 and 7 score 58 and 62 respectively, while the rest score above 75. The system then reduces the scores of the abnormal battery packs: battery pack 6 to 40 and battery pack 7 to 45. The adjusted scores are prioritized as follows: 1 > 3 > 8 > 4 > 2 > 5 > 7 > 6. The 12kW available total power is then allocated proportionally, with battery packs 6 and 7 initially receiving lower power allocations. Finally, the system limits the power of the abnormal battery packs: battery pack 6 is charged at 30% of its original allocation, and battery pack 7 at 40%, releasing 2.1kW of resources. Five battery packs with health scores above 70, including packs 1 and 3, are selected as beneficiaries, and resources are redistributed proportionally based on their health scores.

[0132] The charging power allocation method for multiple battery resistors within the battery swapping cabinet provided in this application achieves accurate identification of abnormal battery packs through multi-dimensional data collection and analysis, avoiding safety hazards caused by misjudgment based on a single parameter. A health scoring system based on state of charge, internal resistance, and load characteristics comprehensively reflects the true state of the batteries, providing a scientific basis for power allocation. By ranking and proportionally allocating health scores, healthy battery packs are prioritized for charging. Combined with power restrictions and resource reallocation for abnormal battery packs, this reduces the risk of overcharging and overheating of abnormal batteries, avoids power waste, and improves the overall charging efficiency of the cabinet. The entire process operates automatically without manual intervention, ensuring the safe operation of the battery swapping cabinet while optimizing the charging and discharging rhythm of the battery packs and extending their overall service life.

[0133] Figure 4 This is a schematic diagram illustrating a specific implementation of a charging power distribution system for multiple battery resistors within a battery swapping cabinet, as provided in this application embodiment. (Refer to...) Figure 4 The system may include:

[0134] The acquisition module 41 is used to acquire the state of charge data and AC internal resistance data of each battery pack in the battery swapping cabinet;

[0135] The identification module 42 is used to check the deviation between the state of charge data and the AC internal resistance data, and compare the AC internal resistance data with a preset health threshold to identify abnormal battery packs.

[0136] The generation module 43 is used to inject a test signal of a specific frequency into the battery circuit and detect the changes in current and voltage generated by the test signal in the circuit to generate a distribution feature map characterizing the load state of each battery pack.

[0137] Calculation module 44 is used to determine the health score of each battery pack based on the deviation value of the state of charge data, the AC internal resistance measurement value, and the load characteristic parameters of the distribution characteristic map.

[0138] The sorting module 45 is used to reduce the health score of the abnormal battery pack, sort the battery packs according to the adjusted health scores of all battery packs, and dynamically allocate the total available power of the battery swapping cabinet to each battery pack according to the proportion of the health scores based on the charging priority sorting results.

[0139] The allocation module 46 is used to limit the charging power of the abnormal battery pack when the battery pack is identified as the abnormal battery pack, and to redistribute the power resources released by the load characteristic parameters corresponding to the abnormal battery pack in the distribution characteristic map to the battery packs whose health scores meet the preset conditions.

[0140] The charging power distribution system for multiple battery resistors in the battery swapping cabinet of this application embodiment is used to implement the aforementioned charging power distribution method for multiple battery resistors in the battery swapping cabinet. Therefore, the specific implementation of the charging power distribution system for multiple battery resistors in the battery swapping cabinet can be found in the embodiment section of the charging power distribution method for multiple battery resistors in the battery swapping cabinet mentioned above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0141] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the charging power distribution method for multiple battery resistors in the battery swapping cabinet described above.

[0142] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the charging power distribution method for multiple battery resistors in the battery swapping cabinet described above.

[0143] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0144] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the charging power distribution method for multiple battery resistors in a battery swapping cabinet.

[0145] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0146] The foregoing has provided a detailed description of the charging power distribution method, system, electronic equipment, and storage medium for multiple battery resistors within a battery swapping cabinet provided by this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for distributing charging power among multiple battery resistors in a battery swapping cabinet, characterized in that, include: Acquire the state of charge data and AC internal resistance data of each battery pack in the battery swapping cabinet; The state of charge data and AC internal resistance data are checked for deviation, and the AC internal resistance data is compared with a preset health threshold to identify abnormal battery packs. A test signal of a specific frequency is injected into the battery circuit, and the changes in current and voltage generated by the test signal in the circuit are detected to generate a distribution feature map characterizing the load state of each battery pack. Based on the deviation value of the state of charge data, the AC internal resistance measurement value, and the load characteristic parameters of the distribution characteristic map, the health score of each battery pack is determined. The health score of the abnormal battery pack is reduced, and the charging priority of the battery packs is sorted according to the health scores of all battery packs after adjustment. Based on the charging priority sorting results, the total available power of the battery swapping cabinet is dynamically allocated to each battery pack according to the proportion of the health scores. When a battery pack is identified as an abnormal battery pack, the charging power of the abnormal battery pack is limited, and the power resources released by the load characteristic parameters corresponding to the abnormal battery pack in the distribution feature map are redistributed to battery packs whose health scores meet the preset conditions.

2. The method according to claim 1, characterized in that, Based on the deviation values ​​of the state of charge data, the measured AC internal resistance values, and the load characteristic parameters of the distribution characteristic map, a health score for each battery pack is determined, including: Based on the deviation value of the state of charge data and the AC internal resistance measurement value, combined with the corresponding standard reference value, the state of charge deviation ratio and internal resistance change ratio are calculated respectively to generate a basic score characterizing the basic health status of the battery pack. Load characteristic parameters are extracted from the distribution feature map, and the response characteristics of the battery pack under dynamic load are analyzed based on the peak voltage offset and current response delay time of the load characteristic parameters, so as to generate a dynamic score characterizing the performance characteristics of the battery pack. Based on the correlation between the basic score and the dynamic score, a fusion score reflecting the overall health status of the battery pack is determined. Based on the historical operating data of the battery pack, the contribution of the fusion score is adaptively adjusted, and the adjusted fusion score is mapped to a preset health score range to obtain the health score of the battery pack.

3. The method according to claim 1, characterized in that, A test signal of a specific frequency is injected into the battery circuit, and the changes in current and voltage generated by the test signal in the circuit are detected to generate a distribution characteristic map characterizing the load state of each battery pack, including: An AC test signal within a preset frequency range is injected into the battery circuit, and the voltage and current responses of each battery pack under the test signal are detected simultaneously. Based on the voltage and current responses, characteristic parameters of each battery pack are extracted, and a load state distribution feature map is generated by combining the physical location of the battery packs in the cabinet.

4. The method according to claim 2, characterized in that, Based on the correlation between the basic score and the dynamic score, a fusion score reflecting the overall health status of the battery pack is determined, including: Analyze the changing trends of the basic score and the dynamic score over multiple charge-discharge cycles, calculate the probability value that the changing trends are consistent, and normalize the probability value to obtain the correlation strength coefficient. Based on the value of the correlation strength coefficient, the weight ratios of the basic score and the dynamic score are determined respectively, wherein the weight ratio of the basic score is positively correlated with the value of the correlation strength coefficient, and the weight ratio of the dynamic score is negatively correlated with the value of the correlation strength coefficient. The basic score and dynamic score are weighted and summed according to the determined weight ratio to obtain an initial fusion value. The initial fusion value is then input into a preset smoothing transformation function for nonlinear mapping to generate a fusion score.

5. The method according to claim 2, characterized in that, Based on the battery pack's historical operating data, the contribution of the fusion score is adaptively adjusted, and the adjusted fusion score is mapped to a preset health score range to obtain the battery pack's health score, including: The historical charge-discharge cycle count, average operating temperature, and historical average charge-discharge rate are extracted from the historical operating data of the battery pack to determine the aging state category of the battery pack. Based on the aging state category, a target adjustment strategy corresponding to the current aging state is selected from a plurality of preset adjustment strategies. Based on the parameter mapping relationship defined in the selected target adjustment strategy, the adjustment range of the fusion score is determined, and the adjusted fusion score is generated. The adjusted fusion score is converted into a health score within a preset health score range using a piecewise linear mapping function, wherein the piecewise linear mapping function determines the conversion ratio based on multiple preset health status threshold points.

6. The method according to claim 1, characterized in that, The health score of the abnormal battery pack is reduced, and the charging priority of the battery packs is ranked according to the adjusted health scores. Based on the charging priority ranking results, the total available power of the battery swapping cabinet is dynamically allocated to each battery pack according to the proportion of the health scores, including: The health score of the identified abnormal battery packs will be reduced, while the health score of the remaining non-abnormal battery packs will remain unchanged. The charging priority is sorted according to the adjusted health scores of all battery packs, with the battery packs with higher health scores receiving higher charging priority. Based on the charging priority ranking, the power value to be allocated to each battery pack is calculated, and the total available power of the battery swapping cabinet is allocated according to the proportion of the power value.

7. The method according to claim 1, characterized in that, When a battery pack is identified as an abnormal battery pack, the charging power of the abnormal battery pack is limited, and the power resources released by the load characteristic parameters corresponding to the abnormal battery pack in the distribution characteristic map are reallocated to battery packs whose health scores meet preset conditions, including: When a battery pack is identified as an abnormal battery pack, the charging power of the abnormal battery pack is limited to a significantly reduced level, and the amount of power resources that can be reallocated after the charging power of the abnormal battery pack is limited is calculated. Battery packs with health scores higher than a preset threshold are selected from the remaining non-abnormal battery packs as beneficiaries, and the reallocatable power resources are allocated to the beneficiaries according to the proportion of their health scores.

8. The method according to claim 3, characterized in that, Based on the voltage and current responses, characteristic parameters of each battery pack are extracted, and a load state distribution feature map is generated by combining the physical location of the battery packs within the cabinet, including: Based on the voltage and current responses, the dynamic response amplitude and response delay time of each battery pack are calculated, and comprehensive characteristic parameters of each battery pack are generated based on the dynamic response amplitude and response delay time. Obtain the installation slot of each battery pack in the battery swapping cabinet, convert the installation slot into a coordinate position, and map the comprehensive feature parameters of each battery pack to the corresponding coordinate position to form a feature distribution point set; Spatial interpolation is performed on the feature distribution point set to generate a continuous feature value distribution surface. Based on the feature value distribution surface, contour lines are drawn and filled with color gradients to form a visualized load state distribution feature map.

9. The method according to claim 1, characterized in that, The state-of-charge data and AC internal resistance data are checked for discrepancies, and the AC internal resistance data is compared with a preset health threshold to identify abnormal battery packs, including: Calculate the deviations of the state of charge data and AC internal resistance data of each battery pack relative to the average value in the cabinet, and obtain the state of charge deviation value and AC internal resistance deviation value respectively. If the state of charge deviation value or AC internal resistance deviation value exceeds the preset deviation threshold, or the AC internal resistance data exceeds the preset health threshold, it is identified as an abnormal battery pack.

10. A charging power distribution system for multiple battery resistors in a battery swapping cabinet, characterized in that, include: The acquisition module is used to acquire the state of charge data and AC internal resistance data of each battery pack in the battery swapping cabinet; The identification module is used to check the deviation between the state of charge data and the AC internal resistance data, and compare the AC internal resistance data with a preset health threshold to identify abnormal battery packs. The generation module is used to inject a test signal of a specific frequency into the battery circuit and detect the changes in current and voltage generated by the test signal in the circuit, and generate a distribution feature map characterizing the load state of each battery pack. The calculation module is used to determine the health score of each battery pack based on the deviation value of the state of charge data, the AC internal resistance measurement value, and the load characteristic parameters of the distribution characteristic map. The sorting module is used to reduce the health score of the abnormal battery pack, sort the battery packs according to the adjusted health scores of all battery packs, and dynamically allocate the total available power of the battery swapping cabinet to each battery pack according to the proportion of the health scores based on the charging priority sorting results. The allocation module is used to limit the charging power of the abnormal battery pack when the battery pack is identified as the abnormal battery pack, and to redistribute the power resources released by the load characteristic parameters corresponding to the abnormal battery pack in the distribution characteristic map to the battery packs whose health scores meet the preset conditions.

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