Battery safety state evaluation method and system for battery swap cabinet based on multi-sensor fusion

Abstract

This invention discloses a method and system for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion. The method includes the following steps: S1: Collecting real-time temperature data, current data, and voltage data; S2: Determining the heat source mode attribute and outputting the corresponding heat source discrimination factor; S3: Calculating the standard impedance modulus and dynamic impedance modulus at the current moment; S4: Calculating the comprehensive safety risk index by analyzing the heat source discrimination factor, standard impedance modulus, and dynamic impedance modulus; S5: Comparing the comprehensive safety risk index with a preset warning threshold in real time. This invention distinguishes between the internal and external heat source modes of the battery, ensuring that subsequent assessments are only triggered when a genuine internal anomaly occurs, thereby reducing the false alarm rate caused by environmental factors.

Description

Technical Field

[0001] This invention relates to the field of battery technology for battery swapping cabinets, and specifically to a method and system for assessing the safety status of batteries in battery swapping cabinets based on multi-sensor fusion. Background Technology

[0002] In the high-frequency operation scenario of shared battery swapping cabinets, batteries are in a cycle of fast charging, fast discharging, and resting for a long time, which can easily lead to early hidden faults such as lithium plating and micro-internal short circuits. Therefore, it is necessary to use a battery safety status assessment method for battery swapping cabinets.

[0003] However, most current battery safety status assessment methods for battery swapping cabinets only place a single-point temperature sensor on the battery surface, which cannot distinguish whether the heat originates from internal chemical reactions within the battery or from external environmental heat conduction (such as the cabinet being exposed to the sun in summer or heat radiation from adjacent batteries). At the same time, most methods measure the DC internal resistance (DCR) during the charging interval, but early micro-internal short circuits or lithium plating in batteries often manifest as nonlinear dynamic impedance changes, and the impedance value may still be within the normal range under static conditions, resulting in poor safety status assessment results. Summary of the Invention

[0004] To address this, the present invention provides a method and system for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion, in order to solve the problems in the prior art that cannot distinguish between environmental heat conduction and internal chemical heat generation, and that the static impedance value leads to poor safety status assessment results.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The first aspect is a method for assessing the battery safety status of battery swapping cabinets based on multi-sensor fusion, which includes the following steps:

[0007] S1: Real-time temperature data at various measuring points are collected based on temperature sensors before battery charging and during the resting period between charging intervals. Simultaneously, battery current and voltage data are also collected.

[0008] S2: Based on real-time temperature data, determine whether the heat source is in internal or external heat source mode, and output the corresponding heat source discrimination factor. ;

[0009] S3: Calculate and extract voltage amplitude based on the current and voltage data collected by S1. and current amplitude Then calculate the standard impedance magnitude at the current moment. and dynamic impedance magnitude ;

[0010] S4: Heat source discrimination factor Standard impedance modulus and dynamic impedance magnitude A comprehensive security risk index is obtained through fusion calculation. ;

[0011] The calculation formula is as follows:

[0012]

[0013] in, This is a preset amplification factor, representing an exponent that non-linearly amplifies the relative impedance deviation. The preset temperature rise rate weighting coefficient, For real-time temperature rise rate, The preset upper limit for the safe temperature rise rate;

[0014] S5: Integrating the security risk index Compared with the preset Level 1 warning and Level II warning It performs real-time comparisons and outputs tiered control commands ranging from normal charging to emergency shutdown.

[0015] Furthermore, when S1 collects current data, it sends a modulation command to the digital data processor of the charging module to control the power conversion circuit to superimpose a sinusoidal AC current component on the basis of a constant DC current, and then collects the transient voltage at the battery terminal through a high-precision analog-to-digital converter.

[0016] Furthermore, the specific steps of S2 are as follows:

[0017] S2.1: Divide the real-time temperature data obtained in S1 into a central region group and an edge region group according to the physical position of the temperature sensor on the tray. Calculate the arithmetic mean of the two groups of real-time temperature data and the total temperature to obtain the central average temperature, the edge average temperature and the total average temperature.

[0018] S2.2: Calculate the average temperature difference between the center average temperature and the edge average temperature. ;

[0019] S2.3: Divide the difference between the average temperature difference at the current moment and the average temperature difference at the previous moment by the time interval to obtain the overall temperature rise rate at the bottom of the battery. ;

[0020] S2.4: Average temperature difference and rate of temperature rise Compare with the pre-stored experimentally calibrated radial temperature difference thresholds. and minimum temperature rise rate threshold Compare the results and adjust the heat source discrimination factor accordingly. Assign a value.

[0021] Furthermore, the heat source discrimination factor in S2.4 When assigning values, if the average temperature difference Greater than the radial temperature difference threshold and Greater than the minimum temperature rise rate threshold This indicates that the circuit outputs a high level, determining that the current thermal effect is in the internal heat source mode. (Heat source discrimination factor) Conversely, in the external heat source mode, the heat source discrimination factor... 0.

[0022] Furthermore, the specific steps of S3 are as follows:

[0023] S3.1: Perform single-frequency discrete Fourier transform on the current data and voltage data respectively, and obtain the real part projection and imaginary part projection of the current data and voltage data respectively;

[0024] S3.2: Calculate the voltage amplitude using the Euclidean norm and current amplitude ;

[0025] S3.3: Based on voltage amplitude and current amplitude Calculate the current battery injection frequency Dynamic impedance magnitude under The calculation formula is as follows:

[0026]

[0027] in, Voltage amplitude, This refers to the current amplitude.

[0028] S3.4: Input a pre-built benchmark database and determine the standard impedance modulus by combining the current battery state of charge and the current total average battery temperature.

[0029] Furthermore, in step S3.4, the standard impedance modulus is determined by first reading the current battery state of charge and obtaining the current total average battery temperature from step S2.

[0030] Using the battery state of charge and total average temperature as indexes, a lookup table is performed in the benchmark database. If the battery state of charge and total average temperature fall on a preset grid point, the corresponding impedance modulus value is directly read as the standard impedance modulus value. ;

[0031] If the index falls between grid lines, the standard impedance modulus is calculated using a bilinear interpolation algorithm. The calculation formula is as follows:

[0032]

[0033] in, , , as well as These are the standard impedance values ​​of the four grid points closest to the current point. and This is the preset difference coefficient.

[0034] Furthermore, the hierarchical strategy in the hierarchical control instruction is as follows:

[0035] like Determine if the battery is healthy or only experiencing minor fluctuations;

[0036] like , An early internal defect was identified;

[0037] like If the condition is abnormal, it is determined that a serious thermal runaway precursor has occurred or an internal short circuit has already occurred.

[0038] Secondly, a battery safety status assessment system for battery swapping cabinets based on multi-sensor fusion includes a data acquisition module, a heat source identification module, a dynamic calculation module, and a fusion decision module.

[0039] The data acquisition module can collect real-time temperature data at various measuring points before battery charging and during the resting period between charging intervals, as well as battery current and voltage data.

[0040] The heat source discrimination module is connected to the temperature sensing subunit, and is used to receive real-time temperature data, perform calculation and discrimination functions, and output heat source discrimination factors;

[0041] The dynamic calculation module is connected to the electrical signal acquisition unit to receive current data and voltage data, perform calculation functions, and output dynamic impedance modulus and standard impedance modulus.

[0042] The fusion decision module is connected to the heat source discrimination module and the dynamic calculation module. It is used to receive heat source discrimination factors, dynamic impedance modulus, standard impedance modulus and temperature rise rate, and perform fusion calculation and hierarchical decision-making functions to output control commands.

[0043] This invention has the following advantages: By analyzing the spatial distribution of real-time temperature data from multiple measurement points and combining it with the average temperature difference between the center average temperature and the edge average temperature, a heat source discrimination factor is generated. When the battery is affected by external environmental factors such as summer exposure to direct sunlight or adjacent thermal radiation, the average temperature difference cannot meet the condition of exceeding the radial temperature difference threshold because the heat is conducted from the outside to the inside or distributed evenly. Therefore, the heat source discrimination factor is assigned a value of 0, thus solving the false alarm caused by environmental thermal interference. Furthermore, it distinguishes between the battery's internal and external heat source modes, ensuring that subsequent evaluation is only triggered when a genuine internal anomaly occurs, thereby reducing the false alarm rate caused by environmental factors to near zero.

[0044] Simultaneously, by calculating the dynamic impedance modulus, the microstructural changes at the boundary between the battery charge transfer region and diffusion region are collected. To address the minor impedance deviations caused by early micro-internal short circuits or lithium plating, the scheme introduces a preset amplification factor. Nonlinear amplification of the relative impedance deviation and elimination of the influence of SOC and temperature changes by combining the standard impedance modulus ensure that even if the dynamic impedance modulus has only a small anomaly, it can show a significant increase in the comprehensive safety risk index, thus enabling the very early detection of precursors to thermal runaway.

[0045] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0046] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0047] Figure 1 This is a flowchart illustrating the implementation of the battery safety status assessment method for battery swapping cabinets based on multi-sensor fusion according to the present invention.

[0048] Figure 2 This is a block diagram of the battery safety status assessment system for battery swapping cabinets based on multi-sensor fusion, as described in this invention. Detailed Implementation

[0049] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 A method for assessing the battery safety status of battery swapping cabinets based on multi-sensor fusion includes the following steps:

[0051] S1: Real-time temperature data of various measuring points are collected based on temperature sensors before battery charging and during the resting period between charging intervals. At the same time, the current and voltage data of the battery are also collected. The real-time temperature data reflects the thermal state distribution of different areas at the bottom of the battery. This facilitates the simultaneous acquisition of real-time temperature data reflecting the thermal distribution state of the battery and voltage data reflecting the electrochemical dynamic characteristics of the battery, and removes DC interference, providing standardized input data for subsequent heat source identification and impedance calculation.

[0052] Multiple temperature sensors are installed at the bottom of the battery compartment tray of the battery swapping cabinet. The multiple sensors synchronously read the real-time temperature data of each measuring point at a preset sampling frequency (value range 5Hz to 20Hz).

[0053] When acquiring current data, a modulation command is sent to the digital data processor (DSP) of the charging module to control the power conversion circuit to superimpose a sinusoidal alternating current component onto a constant direct current. Then, the transient voltage at the battery terminal is acquired through a high-precision analog-to-digital converter (ADC); and the current value of the battery at the current moment is acquired simultaneously. This allows for the acquisition of dynamic voltage data of the battery during charge transfer by applying a weak AC excitation in a specific frequency band, providing basic data for subsequent extraction of low-frequency impedance modulus values ​​that are sensitive to changes in microstructure. The calculation formula is as follows:

[0054]

[0055] in, The injection frequency is a preset fixed value between 10Hz and 50Hz, which is located at the boundary between the charge transfer region and the diffusion region of the battery's electrochemical impedance spectrum. The injection current amplitude is preset to 0.02 to 0.05 times the battery's rated capacity (i.e., 0.02C-0.05C) to ensure that the excitation data is sufficient to stimulate the battery's dynamic response without causing significant polarization effects or heat accumulation.

[0056] S2: Based on real-time temperature data, determine whether the heat source is an internal or external heat source and output the corresponding heat source discrimination factor. This facilitates the differentiation between internal chemical heat generation and external environmental heat transfer, fundamentally eliminating false alarms caused by environmental thermal interference.

[0057] The specific steps of S2 are as follows:

[0058] S2.1: Divide the real-time temperature data obtained in S1 into a central region group and an edge region group according to the physical position of the temperature sensor on the tray. Calculate the arithmetic mean of the two groups of real-time temperature data and the total temperature to obtain the central average temperature, the edge average temperature and the total average temperature.

[0059] S2.2: Calculate the average temperature difference between the center average temperature and the edge average temperature. The temperature difference indicates the direction of heat conduction in the bottom plane of the battery. A positive value indicates that the heat diffuses from the center to the edge, while a negative or zero value indicates that the heat is conducted from the edge to the center or is evenly distributed.

[0060] S2.3: Divide the difference between the average temperature difference at the current moment and the average temperature difference at the previous moment by the time interval to obtain the overall temperature rise rate at the bottom of the battery. ;

[0061] S2.4: Average temperature difference and rate of temperature rise Compare with the pre-stored experimentally calibrated radial temperature difference thresholds. and minimum temperature rise rate threshold Compare the results and adjust the heat source discrimination factor accordingly. Assignment;

[0062] like Greater than (Indicating that the center temperature is higher than the edge) and Greater than (This indicates the battery is in a state of continuous heating rather than steady state), indicating the circuit outputs a high level, determining that the current thermal effect originates from internal chemical reactions or Joule heating caused by an internal short circuit, i.e., internal heat source mode, heat source discrimination factor. Conversely, if any condition is not met, the heat effect is determined to originate from external environmental conduction (such as exposure to sunlight on the cabinet or adjacent heat radiation) or to be in a steady state, i.e., an external heat source mode, and the heat source discrimination factor is determined. 0.

[0063] S3: Calculate and extract voltage amplitude based on the current and voltage data collected by S1. and current amplitude Then calculate the standard impedance magnitude at the current moment. and dynamic impedance magnitude This provides normalized data for the subsequent calculation of the comprehensive safety risk index, thereby eliminating the influence of changes in the battery's physical characteristics under different operating conditions.

[0064] The specific steps for S3 are as follows:

[0065] S3.1: Perform a single-bin Discrete Fourier Transform (SBDFT) on the current and voltage data respectively, and obtain the real and imaginary projections of the current and voltage data respectively. This step can greatly attenuate power frequency harmonics (50Hz / 60Hz), switching noise, and other incoherent random noise, retaining only the frequencies at which the voltage is attenuated. fundamental component of .

[0066] The aforementioned Single-bin Discrete Fourier Transform (Single-bin DFT) refers to a class of algorithms for spectral analysis of known frequencies. It is mainly used to accurately extract the amplitude and phase information of specific frequency components from noisy time-domain data. This algorithm can also be implemented using lock-in amplifiers, the Goertzel algorithm, or recursive DFT, and is suitable for single-frequency measurement scenarios in embedded systems with high requirements for real-time performance and noise immunity.

[0067] First, calculate the voltage data at the injection frequency. Real part projection at the location And the projection of the imaginary part ,

[0068]

[0069]

[0070] in, The length of the sampling window used in the calculation. For a moment Voltage data values, Injection frequency The frequency domain index value, , The sampling period.

[0071] Similarly, using the same reference data to calculate the current data, the current data at the injection frequency is obtained. Real part projection at the location And the projection of the imaginary part .

[0072] S3.2: Calculate the voltage amplitude using the Euclidean norm and current amplitude This facilitates the conversion of vector information in the frequency domain into physical amplitude in the scalar domain. The calculation formula is as follows:

[0073]

[0074]

[0075] S3.3: Based on voltage amplitude and current amplitude Calculate the current battery injection frequency Dynamic impedance magnitude under This value represents the battery's total impedance under that specific low-frequency excitation. The calculation formula is as follows:

[0076]

[0077] S3.4: Input a pre-built benchmark database and determine the standard impedance modulus by combining the current battery state of charge and the current total average battery temperature.

[0078] The process of constructing the benchmark database is as follows: Before the battery leaves the factory or when it is first connected, the standard impedance modulus of the battery model is determined at different temperature points (such as -20℃, 0℃, 25℃, 45℃) and different SOC points (such as 0%, 25%, 50%, 75%, 100%) through standard test procedures, and a two-dimensional lookup table is formed.

[0079] During real-time operation, the system reads the current battery state of charge (SOC) from the BMS interface and obtains the current total average battery temperature from S2. Then, using (SOC, total average temperature) as an index, it looks up the reference database. If (SOC, average temperature) falls on a preset grid point, the corresponding impedance modulus is directly read as the standard impedance modulus. If the value falls between grid lines, the standard impedance modulus is calculated using a bilinear interpolation algorithm. This facilitates subsequent use of standard impedance modulus values. Eliminating the influence of changes in the battery's physical characteristics under varying operating conditions allows subsequent condition assessment testing to focus only on abnormal deviations from a healthy state, thereby improving the robustness and accuracy of the testing. Standard impedance modulus. The calculation formula is as follows:

[0080]

[0081] in, , , as well as The standard impedance values ​​are the four grid points closest to the current point (SOC, total average temperature). and This is the preset difference coefficient.

[0082] S4: Heat source discrimination factor Standard impedance modulus and dynamic impedance magnitude A comprehensive security risk index is obtained through fusion calculation. This is used to assess the battery's safety status and provides the sole decision-making basis for the implementation of subsequent tiered management strategies. (Comprehensive Safety Risk Index) The calculation formula is as follows:

[0083]

[0084] in, This is a preset amplification factor, usually taken as 2 or 3, which represents the exponent for nonlinear amplification of the relative impedance deviation. The preset temperature rise rate weighting coefficient, For real-time temperature rise rate, This is the preset upper limit for the safe temperature rise rate.

[0085] if = 0, meaning the battery is currently in an external heat source or steady-state mode, then regardless of the dynamic impedance magnitude. Regardless of the change, the calculation result of the first item is forced to zero, and subsequent warnings are not executed. This effectively avoids false alarms caused by impedance reference drift due to changes in ambient temperature, and also shields against the influence of sensor measurement noise.

[0086] Only when = 1, meaning the first term will only be activated when the battery is confirmed to be in internal heat source mode. The impedance deviation term and the temperature rise rate term within the activation brackets are superimposed and calculated using an exponential method. Nonlinear amplification is performed. This represents a small impedance anomaly (e.g., a 5% impedance increase due to early lithium deposition), which, after being squared, will be included in the comprehensive safety risk index. The value represents a significant increment, thereby improving the overall sensitivity to early, subtle signs.

[0087] S5: Integrating the security risk index Compared with the preset Level 1 warning and Level II warning Real-time comparison is performed, and tiered control commands ranging from normal charging to emergency shutdown are output to achieve early warning and safety response. The tiered control strategy is as follows:

[0088] like If the battery is healthy or only experiencing minor fluctuations, then the current normal charging / discharging power is maintained without intervention.

[0089] like If an early internal defect is detected (such as a minor internal short circuit or abnormal SEI film decomposition), current-limited charging (reducing current to decrease heat generation) is immediately implemented, the battery is marked as being in a warning state in the background, and a warning log is uploaded, but service is not interrupted to ensure user experience.

[0090] like If the signal is detected, it is determined that a serious thermal runaway precursor has occurred or an internal short circuit has already occurred. The charging circuit relay should be disconnected, the battery compartment electromagnetic lock should be locked (to prevent the fire from spreading), the audible and visual alarms should be activated, and an emergency work order should be sent to the maintenance center to notify professionals to bring fire-fighting equipment to the scene.

[0091] Please see Figure 2 A battery safety status assessment system for battery swapping cabinets based on multi-sensor fusion includes a data acquisition module, a heat source identification module, a dynamic calculation module, and a fusion decision module.

[0092] The data acquisition module can collect real-time temperature data at various measuring points before battery charging and during the resting period between charging intervals, as well as battery current and voltage data.

[0093] The data acquisition module includes a temperature sensing unit and an electrical signal acquisition unit. The temperature sensing unit is composed of multiple temperature sensors, which are installed at the bottom of the battery compartment tray of the battery swapping cabinet. The temperature sensors synchronously collect real-time temperature data of each measuring point before battery charging and during the resting period between charging intervals at a preset sampling frequency of 5Hz to 20Hz.

[0094] The electrical signal acquisition unit consists of a charging control module and a high-precision analog-to-digital converter. The charging control module includes a digital signal processor, which is used to send modulation commands to the power conversion circuit to control the superposition of sinusoidal AC current components on the basis of constant DC current.

[0095] A high-precision analog-to-digital converter is used to simultaneously acquire transient voltage data at the battery terminal and current battery data at the current moment.

[0096] The heat source discrimination module is connected to the temperature sensing subunit to receive real-time temperature data, perform the calculation and discrimination function in step S2, and output the heat source discrimination factor;

[0097] The dynamic calculation module is connected to the electrical signal acquisition unit to receive current and voltage data, and to perform the calculation function in step S3, outputting the dynamic impedance modulus and the standard impedance modulus.

[0098] The fusion decision module is connected to the heat source discrimination module and the dynamic calculation module. It is used to receive heat source discrimination factors, dynamic impedance modulus, standard impedance modulus and temperature rise rate, and execute the fusion calculation and hierarchical decision function in steps S4 and S5, and output control commands.

[0099] This invention analyzes the spatial distribution of real-time temperature data from multiple measurement points and calculates the average temperature difference between the center and edge average temperatures to generate a heat source discrimination factor. When the battery is exposed to direct sunlight in summer or subjected to adjacent thermal radiation, the average temperature difference cannot meet the condition of exceeding the radial temperature difference threshold because heat is conducted from the outside to the inside or distributed evenly. Therefore, the heat source discrimination factor is assigned a value of 0, thus solving the problem of false alarms caused by environmental thermal interference. Utilizing the laws of physical heat conduction, the invention distinguishes between internal and external heat source modes of the battery, ensuring that subsequent evaluation is only triggered when a genuine internal anomaly occurs. This reduces the false alarm rate caused by environmental factors to near zero.

[0100] Simultaneously, by calculating the dynamic impedance modulus, the microstructural changes at the boundary between the battery charge transfer region and diffusion region are collected. To address the minor impedance deviations caused by early micro-internal short circuits or lithium plating, the scheme introduces a preset amplification factor. Nonlinear amplification of the relative impedance deviation and elimination of the influence of SOC and temperature changes by combining the standard impedance modulus ensure that even if the dynamic impedance modulus has only a small anomaly, it can show a significant increase in the comprehensive safety risk index, thus enabling the very early detection of precursors to thermal runaway.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion, characterized in that, Includes the following steps: S1: Real-time temperature data at various measuring points are collected based on temperature sensors before battery charging and during the resting period between charging intervals. Simultaneously, battery current and voltage data are also collected. S2: Based on real-time temperature data, determine whether the heat source is an internal or external heat source and output the corresponding heat source discrimination factor. ; S3: Calculate and extract voltage amplitude based on the current and voltage data collected by S1. and current amplitude Then calculate the standard impedance magnitude at the current moment. and dynamic impedance magnitude ; S4: Heat source discrimination factor Standard impedance modulus and dynamic impedance magnitude A comprehensive security risk index is obtained through fusion calculation. ; The calculation formula is as follows: in, This is a preset amplification factor, representing an exponent that non-linearly amplifies the relative impedance deviation. The preset temperature rise rate weighting coefficient, For real-time temperature rise rate, The preset upper limit for the safe temperature rise rate; S5: Integrating the security risk index Compared with the preset Level 1 warning and Level II warning It performs real-time comparisons and outputs tiered control commands ranging from normal charging to emergency shutdown.

2. The method for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion according to claim 1, characterized in that, When S1 collects current data, it sends a modulation command to the digital data processor of the charging module to control the power conversion circuit to superimpose a sinusoidal AC current component on the constant DC current, and then collects the transient voltage at the battery terminal through a high-precision analog-to-digital converter.

3. The method for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion according to claim 1, characterized in that, The specific steps of S2 are as follows: S2.1: Divide the real-time temperature data obtained in S1 into a central region group and an edge region group according to the physical position of the temperature sensor on the tray. Calculate the arithmetic mean of the two groups of real-time temperature data and the total temperature to obtain the central average temperature, the edge average temperature and the total average temperature. S2.2: Calculate the average temperature difference between the center average temperature and the edge average temperature. ; S2.3: Divide the difference between the average temperature difference at the current moment and the average temperature difference at the previous moment by the time interval to obtain the overall temperature rise rate at the bottom of the battery. ; S2.4: Average temperature difference and rate of temperature rise Compare with the pre-stored experimentally calibrated radial temperature difference thresholds. and minimum temperature rise rate threshold Compare the results and adjust the heat source discrimination factor accordingly. Assign a value.

4. The method for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion according to claim 3, characterized in that, The heat source discrimination factor in S2.4 When assigning values, if the average temperature difference Greater than the radial temperature difference threshold and Greater than the minimum temperature rise rate threshold This indicates that the circuit outputs a high level, determining that the current thermal effect is in the internal heat source mode. (Heat source discrimination factor) Conversely, in the external heat source mode, the heat source discrimination factor...

0.

5. The method for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion according to claim 1, characterized in that, The specific steps of S3 are as follows: S3.1: Perform single-frequency discrete Fourier transform on the current data and voltage data respectively, and obtain the real part projection and imaginary part projection of the current data and voltage data respectively; S3.2: Calculate the voltage amplitude using the Euclidean norm and current amplitude ; S3.3: Based on voltage amplitude and current amplitude Calculate the current battery injection frequency Dynamic impedance magnitude under The calculation formula is as follows: in, Voltage amplitude, This refers to the current amplitude. S3.4: Input a pre-built benchmark database and determine the standard impedance modulus by combining the current battery state of charge and the current total average battery temperature.

6. The method for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion according to claim 5, characterized in that, S3.4 determines the standard impedance modulus value by first reading the current battery state of charge and obtaining the current total average battery temperature from S2; Using the battery state of charge and total average temperature as indexes, a lookup table is performed in the benchmark database. If the battery state of charge and total average temperature fall on a preset grid point, the corresponding impedance modulus value is directly read as the standard impedance modulus value. ; If the index falls between grid lines, the standard impedance modulus is calculated using a bilinear interpolation algorithm. The calculation formula is as follows: in, , , as well as These are the standard impedance values ​​of the four grid points closest to the current point. and This is the preset difference coefficient.

7. The method for assessing the battery safety status of a battery swapping cabinet based on multi-sensor fusion according to claim 1, characterized in that, The hierarchical strategy in the hierarchical control command is as follows: like If so, the battery is determined to be healthy or has only minor fluctuations; like If so, it is determined that an early internal defect exists; like If the condition is abnormal, it is determined that a serious thermal runaway precursor has occurred or an internal short circuit has already occurred.

8. A battery safety status assessment system for battery swapping cabinets based on multi-sensor fusion, characterized in that, It includes a data acquisition module, a heat source identification module, a dynamic calculation module, and a fusion decision module; The data acquisition module can collect real-time temperature data at various measuring points before battery charging and during the resting period between charging intervals, as well as battery current and voltage data. The heat source discrimination module is connected to the temperature sensing subunit, and is used to receive real-time temperature data, perform calculation and discrimination functions, and output heat source discrimination factors; The dynamic calculation module is connected to the electrical signal acquisition unit to receive current data and voltage data, perform calculation functions, and output dynamic impedance modulus and standard impedance modulus. The fusion decision module is connected to the heat source discrimination module and the dynamic calculation module. It is used to receive heat source discrimination factors, dynamic impedance modulus, standard impedance modulus and temperature rise rate, and perform fusion calculation and hierarchical decision-making functions to output control commands.

Images