Automatic detection method for rechargeable battery of battery changing cabinet

Abstract

The invention discloses an automatic detection method for a rechargeable battery of a battery changing cabinet, and relates to the technical field of automatic detection of the rechargeable battery of the battery changing cabinet, and the method breaks through the limitation of traditional single parameter monitoring through a health degree prediction model driven by task features, and achieves the early warning of damage in a high-frequency cycle scene. The dynamic charging strategy is combined with impedance spectroscopy and vibration analysis to accurately identify the change of the microstructure in the battery; a thermal coupling compensation mechanism effectively eliminates monitoring distortion of multi-bin concurrent charging, and detection reliability under complex working conditions is improved. The system adopts a lightweight algorithm architecture, the detection dimension is expanded on the premise that hardware is not transformed, and the ability of prejudging sudden faults is remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic detection of charging batteries for battery swapping cabinets, and particularly to an automatic detection method for charging batteries of battery swapping cabinets. Background Art

[0002] The delivery industry has given rise to high-frequency battery cycling scenarios, and the daily peak value of battery swapping at takeaway sites has increased sharply; existing battery swapping cabinets are generally equipped with a third-generation intelligent detection system, integrating multi-sensor fusion technology and a cloud health assessment model, supporting concurrent management of 50 groups of batteries; the leading solutions in the industry have introduced a dynamic threshold adjustment mechanism, such as the SOH health prediction algorithm launched by a certain company in 2023, which corrects the alarm threshold through the number of cycles; such systems have better solved the false alarm problem caused by early fixed thresholds, but still face special challenges in high-frequency scenarios.

[0003] The core defect of existing solutions lies in the lag in detecting microscopic damage; the actual measurement data of a certain takeaway platform in 2023 shows that 67% of battery bulging failures occur between two system health warnings; although the latest national standard DB4403-2024 compulsorily requires non-contact temperature monitoring, it cannot capture the internal structural changes caused by the growth of lithium dendrites; industry-leading solutions such as the BaaS3.0 system of a certain automobile manufacturer predict internal resistance mutations through differential analysis of the charging curve, but have a high false positive rate in fast charging scenarios; more seriously, the thermal coupling effect during concurrent charging of multiple batteries causes single-point temperature measurement to be distorted, resulting in a high false negative rate.

[0004] The current relatively new solutions mainly break through along two paths: capturing internal abnormal sounds of the battery through a microphone array, but its algorithm fails in a 50dB ambient noise environment; and the multi-frequency impedance spectroscopy technology proposed in the patent application, which requires adding a dedicated detection circuit, resulting in increased costs; some manufacturers have tried to introduce a transfer learning framework to transfer the battery attenuation mode of electric vehicles to the two-wheeler scenario, but the prediction deviation is amplified due to the difference in charge and discharge rates; although these solutions are effective in specific scenarios, they have not fundamentally solved the contradiction between the accumulation of microscopic damage caused by high-frequency cycling and the prediction of sudden failures, revealing the adaptation gap between the detection dimension and the business scenario. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides an automatic detection method for charging batteries of a battery swapping cabinet to solve the problems that existing detection solutions rely on fixed thresholds and single-dimensional sensing data and are difficult to cope with the accumulation of microscopic damage caused by high-frequency fast charging and the interference of complex thermal environments.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: An embodiment of the present invention provides an automatic detection method for charging batteries of a battery swapping cabinet, which includes, Step S1, construct a task coverage model, which establishes a battery health prediction relationship based on historical charge and discharge task characteristics; The task characteristics include a triple parameter of task interval duration standard deviation, charging start SOC dispersion, and environmental temperature change rate. Among them, the model establishes a non-linear mapping relationship between the triple parameter and the capacity attenuation rate through a gated recurrent unit; Step S2, obtain multi-dimensional operation data of the target battery in real time. The multi-dimensional data at least includes temperature field distribution, vibration frequency, and impedance spectrum characteristics; Step S3, generate a dynamic charging strategy according to the task coverage model. The strategy includes a charging priority queue and a cut-off voltage adjustment rule; Step S4, dynamically adjust charging control parameters based on multi-dimensional data. The parameters include a charging current gradient and a charging cut-off voltage threshold; Step S5, output a detection result including a hidden damage risk level, and the result is associated with a battery replacement cabinet bin allocation instruction.

[0008] As a preferred solution of the automatic detection method for charging batteries of a battery replacement cabinet according to the present invention, wherein: the construction of the task coverage model includes: Extract battery characteristic parameters under different charge and discharge tasks within a preset period. The parameters include task interval duration, environmental temperature change rate, and charging current peak distribution; Establish a mapping relationship between task density and capacity attenuation rate. The task density is defined by the ratio of the number of charge and discharge times per unit time to the task interval duration; Generate a dynamic health prediction threshold, and the threshold is adaptively adjusted according to the current task density.

[0009] As a preferred solution of the automatic detection method for charging batteries of a battery replacement cabinet according to the present invention, wherein: in the process of establishing the mapping relationship between the triple parameter and the capacity attenuation rate in step S1, input the historical task triple sequence into the gated recurrent unit GRU, and the process includes: Define the input vector: , wherein, represents the task characteristic triple at time , represents the task interval duration standard deviation, represents the charging start SOC dispersion, represents the environmental temperature change rate; Update gate calculation: , wherein, represents the update gate vector, is the S-shaped activation function, is the update gate input weight matrix, is the set of real numbers, is the input vector at time is the recurrent weight matrix, is the hidden state at time is the update gate bias, represents the hidden state dimension; Reset gate calculation: , where, represents the reset gate vector, is the reset gate input weight, is the reset gate recurrent weight, is the reset gate bias; Candidate hidden state calculation: , where, is the candidate hidden state, is the hyperbolic tangent activation, is the candidate state input weight, is the candidate state recurrent weight, represents element-wise multiplication, is the candidate bias, Hidden state update: , where, represents the hidden state at time ; Output mapping and coefficient expansion: , where, represents the predicted capacity decay rate, is the output weight vector, is the hidden state at the end of the sequence, represents the length of the input sequence, is the output bias; , where, is the global scaling coefficient, is the base weight vector.

[0010] As a preferred solution of the automatic detection method for the charging battery of the battery swapping cabinet described in the present invention, wherein: the acquisition of the multi-dimensional operation data includes: Applying multi-band micro-current excitation in the charging preparation stage and collecting the battery impedance spectrum response signal; Extracting the impedance amplitude and phase information of the characteristic frequency band through Fourier transform; Match the information with the cloud aging feature library and output the lithium dendrite growth risk index; The multi-band micro-current excitation covers the frequency range of 1 Hz - 10 kHz, and the adjacent frequency points are distributed at logarithmic intervals.

[0011] As a preferred solution of the automatic detection method for charging batteries of a battery swapping cabinet according to the present invention, wherein: in step S2, in the charging preparation stage, a multi-band micro-current excitation in the range of 1 Hz–10 kHz is applied to the target battery, and the frequency points are distributed on a logarithmic scale, including: Frequency point selection, the formula is: , wherein, represents the th excitation frequency point, represents the minimum frequency, represents the maximum frequency, represents the total number of frequency points, is the frequency point index; Excitation amplitude, the formula is: , wherein, represents the micro-current applied at the frequency , represents 0.05 times of the rated capacity of the battery, Allocate the action duration: , wherein, is the excitation duration of the th frequency point, is the total duration of the scanning period.

[0012] As a preferred solution of the automatic detection method for charging batteries of a battery swapping cabinet according to the present invention, wherein: the generation of the dynamic charging strategy includes: Generate a charging priority queue according to the battery health and the urgency of the task requirements. The batteries with a health lower than the first preset value and a task urgency higher than the second preset value in the queue are allocated to the fast charging positions; Enable the pulse repair mode for the batteries with a health higher than the third preset value. The mode includes alternately applying the rated current and the preset ratio of current reduction charging stage.

[0013] As a preferred solution of the automatic detection method for charging batteries of a battery swapping cabinet according to the present invention, wherein: The task modeling module is used to construct and update the task coverage model; The data acquisition module includes a distributed temperature sensor array, a piezoelectric vibration sensor, and an impedance spectrum analysis unit; The strategy generation module generates a charging control instruction according to the real-time task requirements and the battery status; The parameter adjustment module dynamically adjusts the output current and cut-off voltage of the charging device; The result output module maps the detection result to the status indicator of the battery swapping cabinet compartment.

[0014] As a preferred solution of the automatic detection method for charging batteries of a battery swapping cabinet according to the present invention, wherein: the data acquisition module further includes: The impedance spectrum analysis unit is integrated into the charging interface of the battery swapping compartment and includes a multi-band signal generator and a synchronous sampling circuit; The vibration sensor array is arranged on the inner wall of the battery swapping compartment and uses MEMS piezoelectric ceramic materials to capture vibration signals in the frequency band of 20 Hz - 10 kHz; The vibration sensor array performs wavelet packet decomposition on the collected vibration signals, and extracts the sub-frequency band with an energy ratio exceeding a preset threshold in the third-layer decomposition coefficients as the characteristic frequency band.

[0015] As a preferred solution of the automatic detection method for charging batteries of a battery swapping cabinet according to the present invention, wherein: the strategy generation module further includes: The task density calculation unit statistically analyzes the time distribution characteristics of the delivery tasks associated with the target battery in real time; The thermal coupling compensation unit corrects the temperature monitoring value of the current compartment according to the temperature gradient distribution of adjacent compartments; The correction includes calculating the temperature gradient vector of three adjacent compartments. When the vector norm exceeds the preset threshold, the actual temperature field distribution inside the compartment is inversed by using the heat conduction partial differential equation.

[0016] As a preferred solution of the automatic detection method for charging batteries of a battery swapping cabinet according to the present invention, wherein: in the thermal coupling compensation subunit of the strategy generation module, the temperature gradient vector of the current compartment is calculated through the spatial positions and temperature measurement values of three adjacent compartments and compared with the threshold to determine whether to start the partial differential equation inversion. The process includes: Let: , where, represents the position vector of the th compartment, represents the position vector of the th compartment, represents the position vector of the th compartment, The corresponding temperature measurement values are: , Among them, represents the temperature measured at the th bin, represents the temperature measured at the th bin, represents the temperature measured at the th bin; Calculate the gradient vector between bin and : , where represents the temperature gradient vector between the th and the th bins, represents the Euclidean norm of the vector; Calculate the gradient vector between bin and : , where represents the temperature gradient vector between the th and the th bins; Fuse the two gradient vectors to obtain the current bin temperature gradient: , where represents the temperature gradient vector of the th bin; Calculate the gradient magnitude and compare it with the threshold: , where represents the magnitude of the temperature gradient vector, represents the preset gradient threshold.

[0017] The beneficial effects of the present invention are as follows: Through the health prediction model driven by task characteristics, the present invention breaks through the limitations of traditional single-parameter monitoring and realizes early warning of damage in high-frequency cyclic scenarios; the dynamic charging strategy combines impedance spectroscopy and vibration analysis to accurately identify changes in the internal microstructure of the battery; the thermal coupling compensation mechanism effectively eliminates monitoring distortion in multi-bin concurrent charging and improves the detection reliability under complex working conditions. The system adopts a lightweight algorithm architecture to expand the detection dimension without modifying the hardware, significantly enhancing the ability to predict sudden failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0019] Figure 1 It is a schematic flowchart of the automatic detection method for the charging battery of the battery swapping cabinet in Embodiment 1. Specific embodiments

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings in the specification.

[0021] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0022] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0023] Embodiment 1, refer to Figure 1 , this embodiment provides an automatic detection method for the charging battery of a battery swapping cabinet, including the following steps: Step S1, construct a task coverage model, and the model establishes a prediction relationship of battery health based on historical charge and discharge task characteristics; The task characteristics include a triple parameter of the standard deviation of task interval duration, the discreteness of the starting SOC of charging, and the environmental temperature change rate. Among them, the model establishes a non-linear mapping relationship between the triple parameter and the capacity attenuation rate through a gated recurrent unit; The construction of the task coverage model includes: Extract the battery characteristic parameters under different charge and discharge tasks within a preset period, and the parameters include the task interval duration, the environmental temperature change rate, and the distribution of the peak charging current; Establish a mapping relationship between task density and capacity attenuation rate, and the task density is defined by the ratio of the number of charge and discharge times per unit time to the task interval duration; Generate a dynamic health prediction threshold, and the threshold is adaptively adjusted according to the current task density; In the process of establishing the mapping relationship between the triple parameters and the capacity attenuation rate in step S1, the historical task triple sequence is input into the gated recurrent unit (GRU). The process includes: Define the input vector: , where, represents the task feature triple at time , represents the standard deviation of the task interval duration, represents the dispersion of the initial state of charge (SOC) at the start of charging, represents the rate of change of the ambient temperature; Update gate calculation: , where, represents the update gate vector, is the sigmoid activation function, is the input weight matrix of the update gate, is the set of real numbers, is the input vector at the -th time step, is the recurrent weight matrix, is the hidden state at the -th time step, is the bias of the update gate, represents the dimension of the hidden state; Reset gate calculation: , where, represents the reset gate vector, is the input weight of the reset gate, is the recurrent weight of the reset gate, is the bias of the reset gate; Candidate hidden state calculation: , where, is the candidate hidden state, is the hyperbolic tangent activation, is the input weight of the candidate state, is the recurrent weight of the candidate state, represents element-wise multiplication, is the bias of the candidate, Hidden state update: , where, represents the hidden state at the -th time step; Output mapping and coefficient expansion: , Among them, represents the predicted capacity attenuation rate, is the output weight vector, is the final hidden state of the sequence, represents the length of the input sequence, is the output bias; , where is the global scaling coefficient, is the basic weight vector.

[0024] Specifically, this design precisely regulates the information flow through the update gate and the reset gate, enabling the model to adaptively balance between long-term and short-term dependencies. The candidate hidden state combines new and old information to generate rich feature representations, and then the predicted value of the capacity attenuation rate is obtained through linear mapping. The parameterized form with the separation of the global scaling coefficient and the basic weight vector reduces the number of training parameters and improves the generalization ability, which is helpful for online fine-tuning and rapid deployment; Step S2: Real-time obtain multi-dimensional operation data of the target battery, and the multi-dimensional data at least includes the temperature field distribution, vibration frequency, and impedance spectrum characteristics; The acquisition of multi-dimensional operation data includes: Apply multi-band micro-current excitation in the charging preparation stage and collect the battery impedance spectrum response signal; Extract the impedance amplitude and phase information of the characteristic frequency band through Fourier transform; Match the information with the cloud aging feature library for similarity and output the lithium dendrite growth risk index; The multi-band micro-current excitation covers the frequency range of 1 Hz - 10 kHz, and the adjacent frequency points are distributed at logarithmic intervals; In step S2, in the charging preparation stage, apply multi-band micro-current excitation in the range of 1 Hz - 10 kHz to the target battery, and the frequency points are distributed on a logarithmic scale, including: Frequency point selection, the formula is: , where represents the th excitation frequency point, represents the minimum frequency, represents the maximum frequency, represents the total number of frequency points, is the frequency point index; Excitation amplitude, the formula is: , where represents the micro-current applied at the frequency , represents 0.05 times the rated capacity of the battery, Allocate the action duration: , Among them, is the duration of the frequency point excitation, and is the total duration of the scanning period; Specifically, the logarithmic scale distribution evenly provides sampling points in the low-frequency and high-frequency regions, taking into account the detection of physical mechanisms such as interfacial polarization and ion migration, improving the comprehensiveness of impedance spectrum analysis. The constant 0.05C microcurrent ensures a high signal-to-noise ratio without causing additional damage to the battery. The uniform duration distribution enables each frequency point to obtain the same integral energy, reducing measurement deviation. Through this excitation rule, the risk signal of dendrite growth can be captured in the low-frequency band, and the interface and electrode processes can be reflected in the high-frequency band; Step S3, generate a dynamic charging strategy according to the task coverage model, and the strategy includes a charging priority queue and a cut-off voltage adjustment rule; The generation of the dynamic charging strategy includes: Generate a charging priority queue according to the battery health and the urgency of the task requirements. Batteries with a health lower than the first preset value and a task urgency higher than the second preset value in the queue are assigned to the fast charging bin; Enable the pulse repair mode for batteries with a health higher than the third preset value. The mode includes alternately applying a rated current and a preset proportion of current reduction charging stages; Step S4, dynamically adjust the charging control parameters based on multi-dimensional data, and the parameters include the charging current gradient and the charging cut-off voltage threshold; Step S5, output the detection result including the hidden damage risk level, and the result is associated with the battery swapping cabinet bin allocation instruction; This embodiment also provides an automatic detection system for charging batteries of a battery swapping cabinet, including: A task modeling module for constructing and updating the task coverage model; A data acquisition module, including a distributed temperature sensor array, a piezoelectric vibration sensor, and an impedance spectrum analysis unit; The data acquisition module further includes: An impedance spectrum analysis unit, integrated into the charging interface of the battery swapping bin, including a multi-band signal generator and a synchronous sampling circuit; A vibration sensor array, arranged on the inner wall of the battery swapping bin, using MEMS piezoelectric ceramic materials to capture vibration signals in the 20Hz - 10kHz frequency band; The vibration sensor array performs wavelet packet decomposition on the collected vibration signals, and extracts the sub-frequency band with an energy proportion exceeding the preset threshold in the third-layer decomposition coefficients as the characteristic frequency band; A strategy generation module for generating charging control instructions according to real-time task requirements and battery status; The strategy generation module further includes: Task density calculation unit, which statistically analyzes the time distribution characteristics of the delivery tasks associated with the target battery in real time; Thermal coupling compensation unit, which corrects the temperature monitoring value of the current bin according to the temperature gradient distribution of adjacent bins; The correction includes calculating the temperature gradient vector of three adjacent bins. When the vector norm exceeds the preset threshold, the actual temperature field distribution inside the bin body is inversed by using the heat conduction partial differential equation; In the thermal coupling compensation subunit of the strategy generation module, the temperature gradient vector of the current bin is calculated through the spatial positions and temperature measurement values of three adjacent bins and compared with the threshold to determine whether to start the inversion of the partial differential equation. The process includes: Let: , where, represents the position vector of the th bin, represents the position vector of the th bin, represents the position vector of the th bin, The corresponding temperature measurement values are: , where, represents the temperature measured at the th bin, represents the temperature measured at the th bin, represents the temperature measured at the th bin; Calculate the gradient vector between bin and : , where, represents the temperature gradient vector between the th and the th bins, represents the Euclidean norm of the vector; Calculate the gradient vector between bin and : , where, represents the temperature gradient vector between the th and the th bins; Fuse the two gradient vectors to obtain the temperature gradient of the current bin: , where, represents the The temperature gradient vector of a position; Calculate the gradient magnitude and compare it with a threshold: , where, represents the magnitude of the temperature gradient vector, represents the preset gradient threshold.

[0025] Specifically, this method constructs the temperature gradient vector in a three-point local difference manner, taking into account the temperature changes and spatial layout of the front and rear positions. The direction of the gradient vector is obtained by normalizing the adjacent position difference vector, and its magnitude is determined by the ratio of the temperature difference to the distance. Through average fusion, it can effectively smooth the sensing noise while retaining the direction information of the thermal coupling effect. Threshold judgment ensures that the high-cost partial differential equation inversion is only triggered when the temperature gradient is abnormal, taking into account both real-time performance and accuracy. The spatial coordinates are provided by the cabinet design parameters, and the temperature is collected by the embedded sensor array. The entire calculation can be executed at high speed on the microcontroller. The preset threshold can be optimized according to the material thermal conductivity and environmental heat dissipation conditions, and has strong adaptability and robustness; Parameter adjustment module, dynamically adjusting the output current and cut-off voltage of the charging device; Result output module, mapping the detection result to the status indicator of the battery swapping cabinet position.

[0026] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An automatic detection method for the charging battery of a battery swapping cabinet, characterized in that, including Step S1: Construct a task coverage model, which establishes a battery health prediction relationship based on historical charge and discharge task characteristics; The task characteristics include a triple parameter of task interval duration standard deviation, charging start SOC dispersion, and environmental temperature change rate. Among them, the model establishes a non-linear mapping relationship between the triple parameter and the capacity attenuation rate through a gated recurrent unit; Step S2: Real-time obtain multi-dimensional operation data of the target battery. The multi-dimensional data at least includes temperature field distribution, vibration frequency, and impedance spectrum characteristics; Step S3: Generate a dynamic charging strategy according to the task coverage model. The strategy includes a charging priority queue and a cut-off voltage adjustment rule; Step S4: Dynamically adjust charging control parameters based on multi-dimensional data. The parameters include a charging current gradient and a charging cut-off voltage threshold; Step S5: Output a detection result including a hidden damage risk level, and the result is associated with a battery swapping cabinet position allocation instruction.

2. The automatic detection method for the charging battery of a battery swapping cabinet according to claim 1, wherein The construction of the task coverage model includes: Extract battery characteristic parameters under different charge and discharge tasks within a preset period. The parameters include task interval duration, environmental temperature change rate, and charging current peak distribution; Establish a mapping relationship between task density and capacity attenuation rate. The task density is defined by the ratio of the number of charge and discharge times per unit time to the task interval duration; Generate a dynamic health prediction threshold, and the threshold is adaptively adjusted according to the current task density.

3. The automatic detection method for the charging battery of a battery swapping cabinet according to claim 2, wherein, In the process of establishing the mapping relationship between the triple parameter and the capacity attenuation rate in Step S1, input the historical task triple sequence into the gated recurrent unit GRU. The process includes: Define the input vector: ， Among them, represents the task feature triple at a certain moment, represents the standard deviation of the task interval duration, represents the charging start SOC dispersion, represents the environmental temperature change rate; Update gate calculation: ， Among them, represents the update gate vector, is the sigmoid activation function, is the input weight matrix of the update gate, is the set of real numbers, is the input vector at time is the recurrent weight matrix, is the hidden state at time is the bias of the update gate, represents the hidden state dimension; Reset gate calculation: ， Among them, represents the reset gate vector, is the reset gate input weight, is the reset gate recurrent weight, is the reset gate bias; Candidate hidden state calculation: ， Among them, is the candidate hidden state, is the hyperbolic tangent activation, is the candidate state input weight, is the candidate state recurrent weight, represents element-wise multiplication, is the candidate bias, Hidden state update: ， Among them, represents the hidden state at the Output mapping and coefficient expansion: ， Among them, represents the predicted capacity attenuation rate, is the output weight vector, is the final hidden state of the sequence, represents the length of the input sequence, is the output bias; , among which, is the global scaling coefficient, is the basic weight vector.

4. The automatic detection method for the charging battery of a battery swapping cabinet according to claim 1, wherein, The acquisition of the multi-dimensional operation data includes: Apply a multi-band micro-current excitation during the charging preparation stage and collect the battery impedance spectrum response signal; Extract the impedance amplitude and phase information of the characteristic frequency band through Fourier transform; Match the information with the cloud aging feature library and output the lithium dendrite growth risk index; The multi-band micro-current excitation covers a frequency range of 1 Hz - 10 kHz, and the adjacent frequency points are distributed at logarithmic intervals.

5. The automatic detection method for the charging battery of a battery swapping cabinet according to claim 4, wherein, In Step S2, during the charging preparation stage, apply a multi-band micro-current excitation in the range of 1 Hz - 10 kHz to the target battery, and the frequency points are distributed on a logarithmic scale, including: Frequency point selection, the formula is: ， Among them, represents the th excitation frequency point, represents the minimum frequency, represents the maximum frequency, represents the total number of frequency points, is the frequency point index; Excitation amplitude, the formula is: ， Among them, represents the microcurrent applied at the frequency where, represents 0.05 times the rated capacity of the battery of. Allocate the action duration: ， Among them, is the frequency point excitation duration, and is the total duration of the scanning period.

6. The automatic detection method for the charging battery of a battery swapping cabinet according to claim 1, wherein, The generation of the dynamic charging strategy includes: Generate a charging priority queue according to the battery health and the urgency of the task requirements. The batteries with a health lower than the first preset value and a task urgency higher than the second preset value in the queue are assigned to the fast charging positions; Enable a pulse repair mode for the batteries with a health higher than the third preset value. The mode includes alternately applying a rated current and a preset ratio of current reduction charging stage.

7. An automatic detection system for charging batteries of a battery swapping cabinet, based on the automatic detection method for charging batteries of a battery swapping cabinet according to any one of claims 1 to 6, characterized in that, including: A task modeling module for constructing and updating the task coverage model; A data acquisition module including a distributed temperature sensor array, a piezoelectric vibration sensor, and an impedance spectrum analysis unit; A strategy generation module for generating a charging control instruction according to real-time task requirements and battery status; Parameter adjustment module, dynamically adjusting the output current and cut-off voltage of the charging device; Result output module, mapping the detection results to the status indicator of the battery swapping cabinet compartment.

8. The automatic detection method for the charging battery of a battery swapping cabinet according to claim 7, wherein, The data acquisition module further includes: Impedance spectrum analysis unit, integrated in the charging interface of the battery swapping compartment, including a multi-band signal generator and a synchronous sampling circuit; Vibration sensor array, arranged on the inner wall of the battery swapping compartment, using MEMS piezoelectric ceramic material to capture vibration signals in the 20Hz - 10kHz frequency band; The vibration sensor array performs wavelet packet decomposition on the collected vibration signals, and extracts the sub-frequency band with an energy ratio exceeding the preset threshold in the third layer decomposition coefficients as the characteristic frequency band.

9. The automatic detection method for the charging battery of a battery swapping cabinet according to claim 8, wherein The strategy generation module further includes: Task density calculation unit, real-time statistically analyzing the time distribution characteristics of the distribution tasks associated with the target battery; Thermal coupling compensation unit, correcting the temperature monitoring value of the current compartment according to the temperature gradient distribution of adjacent compartments; The correction includes calculating the temperature gradient vector of three adjacent compartments, and when the vector norm exceeds the preset threshold, using the heat conduction partial differential equation to invert the actual temperature field distribution inside the compartment body.

10. The automatic detection method for the charging battery of a battery swapping cabinet according to claim 9, characterized in that, In the thermal coupling compensation subunit of the strategy generation module, through the spatial positions and temperature measurement values of three adjacent compartments, calculate the temperature gradient vector of the current compartment and compare it with the threshold to determine whether to start the partial differential equation inversion. The process includes: Let: , Among them, represents the position vector of the th bin, represents the position vector of the th bin, The corresponding temperature measurement values are: ， Among them, indicates the temperature measured at the th bin, indicates the temperature measured at the th bin, indicates the temperature measured at the th bin; Calculate the position and the gradient vector between: ， Among them, represents the temperature gradient vector between the ith and jth positions, and represents the Euclidean norm of the vector; Calculated position and Gradient vector between: ， Among them, represents the temperature gradient vector between the warehouse positions; Fusing two gradient vectors to obtain the temperature gradient of the current compartment: ， Among them, represents the temperature gradient vector of the Calculating the gradient norm and comparing it with the threshold: ， Among them, represents the magnitude of the temperature gradient vector, represents a preset gradient threshold value.

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