Temperature anomaly detection method, device, equipment and storage medium

Abstract

The application discloses a temperature anomaly detection method, device and equipment and a storage medium. The temperature anomaly detection method is applied to an intelligent battery cell unit, the intelligent battery cell unit comprises a battery, and the temperature anomaly detection method comprises the following steps: performing equivalent internal resistance detection according to a pre-input excitation voltage signal to obtain a battery internal resistance set. An initial internal resistance of the battery is screened from the battery internal resistance set to obtain an initial battery internal resistance. A temperature of the battery is screened from a pre-constructed resistance-temperature relationship graph according to a target battery internal resistance to obtain a target battery temperature. A target battery internal resistance of the battery is screened from the battery internal resistance set, internal resistance difference calculation is performed according to the initial battery internal resistance and the target battery internal resistance, and a battery internal resistance difference value is obtained. The target battery temperature, the battery internal resistance difference value and a pre-set anomaly judgment parameter are compared to obtain an anomaly judgment result. The application can screen out abnormal temperature data of the intelligent battery cell, and then accurately detect the temperature data of the intelligent battery cell.

Description

Technical Field

[0001] This invention relates to the field of smart battery technology, and in particular to a method, apparatus, device, and storage medium for detecting temperature anomalies. Background Technology

[0002] Currently, the temperature data of smart battery cells is obtained directly by temperature sensors. However, due to the internal resistance of smart battery cells, the detected temperature data may be abnormal, resulting in inaccurate temperature data. Therefore, how to accurately detect the temperature data of smart battery cells is an urgent problem to be solved. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a temperature anomaly detection method, which can screen out abnormal temperature data of smart battery cells, and thus accurately detect the temperature data of smart battery cells.

[0004] The present invention also proposes a temperature anomaly detection device.

[0005] The present invention also proposes a temperature anomaly detection device.

[0006] The present invention also proposes a computer-readable storage medium.

[0007] In a first aspect, one embodiment of the present invention provides a temperature anomaly detection method applied to a smart battery cell, the smart battery cell including a battery, the temperature anomaly detection method comprising:

[0008] The battery internal resistance set is obtained by detecting the equivalent internal resistance based on the pre-input excitation voltage signal;

[0009] The initial internal resistance of the battery is obtained by screening the battery's internal resistance set.

[0010] The target battery temperature is obtained by screening the target battery internal resistance from the set of battery internal resistances, and then screening the battery temperature from a pre-built resistance-temperature relationship chart based on the target battery internal resistance.

[0011] The internal resistance difference is calculated based on the initial battery internal resistance and the target battery internal resistance to obtain the battery internal resistance difference value;

[0012] The target battery temperature, the battery internal resistance difference, and preset anomaly judgment parameters are compared to obtain the anomaly judgment result.

[0013] The temperature anomaly detection method of this invention has at least the following beneficial effects: The equivalent internal resistance of the battery at the current moment is calculated in real time based on the pre-input excitation voltage signal to obtain a set of battery internal resistances; the initial internal resistance at the start of battery operation is selected from the set of battery internal resistances to obtain the initial battery internal resistance; the internal resistance at the current moment is selected from the set of battery internal resistances to obtain the target battery internal resistance; the battery temperature at the current moment is selected by real-time searching from a pre-constructed resistance-temperature relationship chart based on the target battery internal resistance to obtain the target battery temperature; the difference between the initial battery internal resistance value and the target battery internal resistance value is calculated to obtain the battery internal resistance difference value; the target battery temperature is compared with the corresponding anomaly judgment parameters related to temperature; and the battery internal resistance difference value is compared with the corresponding anomaly judgment parameters related to the internal resistance difference value; the anomaly judgment result is obtained based on the results of the two comparisons. The battery's internal resistance set is calculated by the excitation voltage signal. The initial and target internal resistances of the battery are obtained from the internal resistance set, and the target battery temperature is also obtained. The difference between the initial and target internal resistances is calculated. The target battery temperature is compared with the anomaly judgment parameters, and the difference between the internal resistances is compared with the anomaly judgment parameters to obtain the anomaly judgment result. This can screen out abnormal temperature data of the smart cell and thus accurately detect the temperature data of the smart cell.

[0014] According to other embodiments of the temperature anomaly detection method of the present invention, the smart cell unit further includes a built-in resistor, and the step of performing equivalent internal resistance detection based on a pre-input excitation voltage signal to obtain a battery internal resistance set includes:

[0015] Obtain the voltage signal of the built-in resistor in response to the excitation voltage signal to obtain the built-in resistor voltage signal;

[0016] Obtain the voltage signal of the battery in response to the excitation voltage signal to obtain the battery voltage signal;

[0017] The battery internal resistance set is obtained by performing equivalent internal resistance detection based on the built-in resistor voltage signal and the battery voltage signal.

[0018] According to other embodiments of the present invention, the temperature anomaly detection method further includes, before the steps of screening target battery internal resistances from the set of battery internal resistances and screening battery temperatures from a pre-constructed resistance-temperature relationship chart based on the target battery internal resistances to obtain the target battery temperature:

[0019] The relationship is calculated based on the battery internal resistance set and the preset resistance-temperature relationship model to obtain resistance-temperature relationship data;

[0020] A line chart is drawn based on the resistance-temperature relationship data to obtain the resistance-temperature relationship chart.

[0021] According to other embodiments of the temperature anomaly detection method of the present invention, the anomaly judgment result includes temperature anomaly information and temperature normal information, the anomaly judgment parameters include a first difference threshold and a preset temperature range, and the step of comparing the target battery temperature, the battery internal resistance difference, and the preset anomaly judgment parameters to obtain the anomaly judgment result includes:

[0022] The battery internal resistance difference is compared with the first difference threshold to obtain the first difference comparison result;

[0023] The target battery temperature is compared with a preset temperature range to obtain a temperature comparison result;

[0024] If the first difference comparison result indicates that the internal resistance difference of the battery is greater than the first difference threshold, and the temperature comparison result indicates that the temperature of the target battery is not within the preset temperature range, then the temperature anomaly information is obtained.

[0025] According to other embodiments of the temperature anomaly detection method of the present invention, the step of comparing the battery internal resistance difference with a preset first difference threshold to obtain a first difference comparison result includes:

[0026] Obtain several battery internal resistance difference values ​​within a preset time period;

[0027] The maximum value of the battery internal resistance difference is selected by filtering the maximum value to obtain the target internal resistance difference value;

[0028] The target internal resistance difference is compared with the first difference threshold to obtain the first difference comparison result.

[0029] According to other embodiments of the temperature anomaly detection method of the present invention, the anomaly judgment parameter further includes a second difference threshold, and the step of comparing the target battery temperature, the battery internal resistance difference, and the preset anomaly judgment parameter to obtain an anomaly judgment result further includes:

[0030] If the first difference comparison result indicates that the internal resistance difference of the battery is less than the first difference threshold, and the temperature comparison result indicates that the temperature of the target battery is within the preset temperature range, then a preset number of the internal resistances of the target battery are obtained.

[0031] The internal resistance difference between the batteries is calculated based on the internal resistance of the target batteries of a preset number;

[0032] The difference in internal resistance between the batteries is compared with the second difference threshold to obtain the second difference comparison result;

[0033] If the second difference comparison result indicates that the internal resistance difference between the batteries is greater than the second difference threshold, then the temperature anomaly information is obtained;

[0034] If the second difference comparison result indicates that the internal resistance difference between the batteries is less than the second difference threshold, then the normal temperature information is obtained.

[0035] According to other embodiments of the temperature anomaly detection method of the present invention, the anomaly judgment parameter further includes a cell voltage threshold, and the step of comparing the target battery temperature, the battery internal resistance difference value, and the preset anomaly judgment parameter to obtain an anomaly judgment result further includes:

[0036] If the first difference comparison result indicates that the internal resistance difference of the battery is greater than the first difference threshold, and the temperature comparison result indicates that the temperature of the target battery is higher than the upper limit of the preset temperature range, then the voltage of the smart cell unit is obtained to obtain cell voltage data.

[0037] The cell voltage data is compared with the cell voltage threshold to obtain a voltage comparison result;

[0038] If the voltage comparison result indicates that the cell voltage data is greater than the cell voltage threshold, then the temperature anomaly information is obtained.

[0039] Secondly, one embodiment of the present invention provides a temperature anomaly detection device applied to a smart battery cell, the smart battery cell including a battery, the temperature anomaly detection method comprising:

[0040] The equivalent internal resistance detection module is used to detect the equivalent internal resistance based on the pre-input excitation voltage signal to obtain the battery internal resistance set.

[0041] An initial internal resistance screening module is used to screen the initial internal resistance of the battery from the set of battery internal resistances to obtain the initial battery internal resistance.

[0042] The battery temperature screening module is used to screen the target battery internal resistance from the battery internal resistance set, and to screen the battery temperature from a pre-built resistance-temperature relationship chart according to the target battery internal resistance to obtain the target battery temperature.

[0043] The internal resistance difference calculation module is used to calculate the internal resistance difference based on the initial battery internal resistance and the target battery internal resistance to obtain the battery internal resistance difference value.

[0044] The temperature anomaly detection module is used to compare the target battery temperature, the battery internal resistance difference, and preset anomaly detection parameters to obtain an anomaly detection result.

[0045] The temperature anomaly detection device of this invention has at least the following beneficial effects: the equivalent internal resistance detection module calculates the equivalent internal resistance of the battery at the current moment in real time according to the pre-input excitation voltage signal to obtain the battery internal resistance set; the initial internal resistance screening module screens the initial internal resistance of the battery at the start of operation from the battery internal resistance set to obtain the initial battery internal resistance; the battery temperature screening module screens the internal resistance of the battery at the current moment from the battery internal resistance set to obtain the target battery internal resistance; the target battery internal resistance is searched in real time from a pre-constructed resistance-temperature relationship chart to screen the battery temperature at the current moment to obtain the target battery temperature; the internal resistance difference calculation module calculates the difference between the initial battery internal resistance value and the target battery internal resistance value to obtain the battery internal resistance difference value; the temperature anomaly judgment module compares the target battery temperature with the corresponding anomaly judgment parameters related to temperature, and compares the battery internal resistance difference value with the corresponding anomaly judgment parameters related to the internal resistance difference value, and obtains the anomaly judgment result based on the results of the two comparisons. The battery's internal resistance set is calculated by the excitation voltage signal. The initial and target internal resistances of the battery are obtained from the internal resistance set, and the target battery temperature is also obtained. The difference between the initial and target internal resistances is calculated. The target battery temperature is compared with the anomaly judgment parameters, and the difference between the internal resistances is compared with the anomaly judgment parameters to obtain the anomaly judgment result. This can screen out abnormal temperature data of the smart cell and thus accurately detect the temperature data of the smart cell.

[0046] Thirdly, one embodiment of the present invention provides a temperature anomaly detection device, comprising:

[0047] At least one processor, and,

[0048] A memory communicatively connected to the at least one processor; wherein,

[0049] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the temperature anomaly detection method as described in the first aspect.

[0050] Fourthly, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the temperature anomaly detection method as described in the first aspect.

[0051] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description and the accompanying drawings. Attached Figure Description

[0052] Figure 1 This is a schematic flowchart of a specific embodiment of the temperature anomaly detection method in this invention;

[0053] Figure 2 yes Figure 1 A schematic flowchart of a specific embodiment of step S101;

[0054] Figure 3 This is a schematic flowchart of another specific embodiment of the temperature anomaly detection method in this invention;

[0055] Figure 4 yes Figure 1 A schematic flowchart of a specific embodiment of step S105;

[0056] Figure 5 yes Figure 4 A schematic flowchart of a specific embodiment of step S401;

[0057] Figure 6 yes Figure 1 A schematic diagram of another specific embodiment of step S105;

[0058] Figure 7 yes Figure 1 A schematic diagram of another specific embodiment of step S105;

[0059] Figure 8 This is a schematic flowchart of another specific embodiment of the temperature anomaly detection method in this invention;

[0060] Figure 9 This is a module block diagram of a specific embodiment of the temperature anomaly detection device in this invention;

[0061] Figure 10 This is a circuit diagram of a specific embodiment of the intelligent battery cell unit in this invention.

[0062] Figure 11 This is a schematic diagram of a specific embodiment of electrochemical impedance spectroscopy in this invention;

[0063] Figure 12 This is a schematic diagram of a specific embodiment of the internal resistance-temperature relationship chart in this invention;

[0064] Figure 13 This is a diagram showing the relationship between internal resistance and time in a specific embodiment of the present invention.

[0065] Explanation of reference numerals in the attached figures:

[0066] Equivalent internal resistance detection module 901, initial internal resistance screening module 902, battery temperature screening module 903, internal resistance difference calculation module 904, and temperature anomaly judgment module 905. Detailed Implementation

[0067] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0068] In the description of this invention, if directional descriptions are involved, such as "up," "down," "front," "back," "left," "right," etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, it is only for the convenience of describing the invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. If a feature is referred to as "set," "fixed," "connected," or "installed" on another feature, it can be directly set, fixed, or connected to the other feature, or it can be indirectly set, fixed, connected, or installed on the other feature.

[0069] In the description of the embodiments of the present invention, the term "several" means one or more, and the term "multiple" means two or more. The terms "greater than," "less than," and "exceeding" should be understood as excluding the stated number, while the terms "above," "below," and "within" should be understood as including the stated number. The terms "first" and "second" should be understood as distinguishing technical features, and not as indicating or implying relative importance, the number of indicated technical features, or the order of the indicated technical features.

[0070] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0072] It should be noted that although the system diagram shows functional modules and the flowchart shows the logical order, in some cases, the steps shown or described may be executed in a different order than the module division in the system or the order in the flowchart.

[0073] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0074] In the description of the embodiments of the present invention, the term "several" means one or more, and the term "multiple" means two or more. The terms "greater than," "less than," and "exceeding" should be understood as excluding the stated number, while the terms "above," "below," and "within" should be understood as including the stated number. The terms "first" and "second" should be understood as distinguishing technical features, and not as indicating or implying relative importance, the number of indicated technical features, or the order of the indicated technical features.

[0075] Due to the internal resistance of a battery, the heat generated inside the battery also causes changes in battery temperature. Under the same current conditions, different internal resistances result in different amounts of heat generated inside the battery, and thus different battery temperatures. High internal resistance generates a large amount of Joule heat, causing the battery temperature to rise, leading to a decrease in the battery's discharge operating voltage and a shortened discharge time. This severely impacts battery performance and lifespan, and in severe cases, can even cause an explosion.

[0076] At low temperatures, the electrochemical reaction within the battery is suppressed, leading to an increase in internal resistance. Conversely, at high temperatures, the rate of chemical reactions within the battery accelerates, resulting in a decrease in internal resistance. This demonstrates a correlation between internal resistance and temperature. A sudden and drastic change in internal resistance can be used to infer the presence of excitation fluctuations in the battery's internal temperature.

[0077] Currently, the temperature data of smart battery cells is obtained directly by temperature sensors. However, due to the internal resistance of smart battery cells, the detected temperature data may be abnormal, resulting in inaccurate temperature data. Therefore, how to accurately detect the temperature data of smart battery cells is an urgent problem to be solved.

[0078] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a temperature anomaly detection method, which can screen out abnormal temperature data of smart battery cells, and thus accurately detect the temperature data of smart battery cells.

[0079] Please refer to Figure 1 , Figure 1A schematic flowchart of a temperature anomaly detection method according to an embodiment of the present invention is shown. In some embodiments, it is applied to a smart battery cell, which includes a battery, and the temperature anomaly detection method specifically includes, but is not limited to, steps S101 to S105.

[0080] Step S101: Perform equivalent internal resistance detection based on the pre-input excitation voltage signal to obtain the battery internal resistance set;

[0081] Step S102: Select the initial internal resistance of the battery from the battery internal resistance set to obtain the initial battery internal resistance;

[0082] Step S103: Select the target battery internal resistance from the battery internal resistance set, and select the battery temperature from the pre-built resistance-temperature relationship chart according to the target battery internal resistance to obtain the target battery temperature.

[0083] Step S104: Calculate the internal resistance difference based on the initial battery internal resistance and the target battery internal resistance to obtain the battery internal resistance difference value.

[0084] Step S105: Compare the target battery temperature, the difference in battery internal resistance, and the preset anomaly judgment parameters to obtain the anomaly judgment result.

[0085] In steps S101 to S105 of this embodiment, the equivalent internal resistance of the battery at the current moment is calculated in real time based on the pre-input excitation voltage signal to obtain a set of battery internal resistances. The initial internal resistance at the start of battery operation is selected from the set of battery internal resistances to obtain the initial battery internal resistance. The internal resistance at the current moment of the battery is selected from the set of battery internal resistances to obtain the target battery internal resistance. The battery temperature at the current moment is selected by searching in real time from the pre-constructed resistance-temperature relationship chart based on the target battery internal resistance to obtain the target battery temperature. The difference between the initial battery internal resistance value and the target battery internal resistance value is calculated to obtain the battery internal resistance difference value. The target battery temperature is compared with the corresponding anomaly judgment parameters related to temperature, and the battery internal resistance difference value is compared with the corresponding anomaly judgment parameters related to the internal resistance difference value. The anomaly judgment result is obtained based on the results of the two comparisons. The battery's internal resistance set is calculated by the excitation voltage signal. The initial and target internal resistances of the battery are obtained from the internal resistance set, and the target battery temperature is also obtained. The difference between the initial and target internal resistances is calculated. The target battery temperature is compared with the anomaly judgment parameters, and the difference between the internal resistances is compared with the anomaly judgment parameters to obtain the anomaly judgment result. This can screen out abnormal temperature data of the smart cell and thus accurately detect the temperature data of the smart cell.

[0086] It should be noted that the intelligent battery cell unit includes: a battery, a monitoring chip unit, and an embedded multi-source sensor array. The battery is composed of EIS-based battery cells. The embedded multi-source sensor array includes sensors for detecting cell temperature, cell voltage, and cell current, enabling real-time monitoring of the cell's operating status. The monitoring chip unit includes a pulse excitation device, a pulse current / voltage acquisition unit, and a corresponding impedance calculation unit.

[0087] Please refer to Figure 10 , Figure 10 A circuit schematic diagram of a smart battery cell unit according to an embodiment of the present invention is shown. In some embodiments, the monitoring chip unit includes... Figure 10 The system includes capacitor C, resistors R and R1, power supply VCC, and controller MCU. The battery consists of cells and internal resistance r. The embedded multi-source sensor group includes an NTC temperature sensor. The internal resistance r is the equivalent internal resistance of the battery.

[0088] Please refer to 11. Figure 11 A schematic diagram of electrochemical impedance spectroscopy (EIS) is shown in an embodiment of the present invention. In some embodiments, the EIS-based battery cell follows the patterns in the electrochemical impedance spectroscopy. Figure 11 The R1 segment is the ultra-high frequency part. If the cell based on EIS is in the R1 segment, the internal resistance in ohms includes the electrolyte, separator, active material, current collector and electrical connection impedance. Figure 11 The R2 segment is the high-frequency part. If the EIS-based cell is in the R2 segment, lithium ions pass through the solid electrolyte impedance Rsei. The EIS-based cell is only affected by temperature and not by SOC. Figure 11 The R3 segment in the diagram represents the intermediate frequency range. If an EIS-based cell is located in the R3 segment, its polarization impedance Rct is affected by both temperature and SOC. Figure 11 In the diagram, segment R4 represents the low-frequency range. If the EIS-based battery cell is located in segment R4, the diffusion resistance of lithium ions in the electrode material reflects the electrode's ability to perform high-rate discharge. In this embodiment of the invention, segment R2 is selected for analyzing the temperature of the smart battery cell.

[0089] Please refer to Figure 2 , Figure 2 A flowchart illustrating the temperature anomaly detection method in an embodiment of the present invention is shown. In some embodiments, the smart cell unit further includes a built-in resistor, and the equivalent internal resistance is detected based on a pre-input excitation voltage signal to obtain the battery internal resistance set, specifically including but not limited to steps S201 to S203.

[0090] Step S201: Obtain the voltage signal of the built-in resistor in response to the excitation voltage signal, and obtain the built-in resistor voltage signal;

[0091] Step S202: Obtain the voltage signal of the battery in response to the excitation voltage signal to obtain the battery voltage signal;

[0092] Step S203: Perform equivalent internal resistance detection based on the built-in resistor voltage signal and the battery voltage signal to obtain the battery internal resistance set.

[0093] In steps S201 to S203 of this embodiment, the monitoring chip unit inputs the excitation voltage signal to the smart cell unit. The smart cell unit collects the voltage signal across the built-in resistor in real time to obtain the voltage signal of the built-in resistor in response to the excitation voltage signal, thus obtaining the built-in resistor voltage signal. The smart cell unit also collects the voltage signal across the battery in real time to obtain the voltage signal of the battery in response to the excitation voltage signal, thus obtaining the battery voltage signal. The peak voltage of the built-in resistor voltage signal is obtained, and the peak voltage of the battery voltage signal is obtained, thus obtaining the battery peak voltage. The ratio of the battery peak voltage to the built-in resistor peak voltage is calculated, and the voltage ratio is multiplied by the resistance value of the built-in resistor to obtain the battery internal resistance set. By inputting the excitation voltage signal to the smart cell unit, collecting the voltage signal across the built-in resistor to obtain the built-in resistor voltage signal, collecting the voltage signal across the battery to obtain the battery voltage signal, and calculating the equivalent resistance of the battery based on the built-in resistor voltage signal and the battery voltage signal to obtain the battery internal resistance set, the internal resistance value of the battery can be obtained and updated in real time according to the excitation voltage signal, improving the accuracy of the internal resistance value.

[0094] It should be noted that the monitoring chip unit applies a sinusoidal excitation voltage signal to both ends of the battery and collects the sinusoidal response signal generated across the built-in resistor, i.e., the built-in resistor voltage signal, and collects the sinusoidal response signal generated across the battery, i.e., the battery voltage signal.

[0095] For example, refer to Figure 10 The built-in resistor is Figure 10 The resistor R in the circuit. The excitation voltage signal applied to the battery by the monitoring chip unit is f(t) = Usin(ωt+θ), and the voltage across resistor R is f2(t) = U2sin(ωt+θ). R The voltage across the battery cell is f1(t) = U1sin(ωt + θ). r Then, the equivalent internal resistance of the battery can be calculated:

[0096]

[0097] The internal resistance of the target battery is obtained as follows:

[0098] In step S102 of some embodiments, when the battery starts working, the monitoring chip unit applies a sinusoidal excitation voltage signal to both ends of the battery and detects the internal resistance of the battery in real time. The target battery internal resistance at each moment after the battery starts working is detected. The first target battery internal resistance detected is selected from the target battery internal resistances and used as the initial resistance of the battery to obtain the initial battery resistance.

[0099] Please refer to Figure 3 , Figure 3 A schematic flowchart of a temperature anomaly detection method according to an embodiment of the present invention is shown. In some embodiments, before selecting the target battery internal resistance from a set of battery internal resistances, and selecting the battery temperature from a pre-constructed resistance-temperature relationship chart based on the target battery internal resistance to obtain the target battery temperature, the temperature anomaly detection method further includes, but is not limited to, steps S301 to S302.

[0100] Step S301: Calculate the relationship based on the battery internal resistance set and the preset resistance-temperature relationship model to obtain resistance-temperature relationship data.

[0101] Step S302: Draw a line graph based on the resistance-temperature relationship data to obtain the resistance-temperature relationship graph.

[0102] In steps S301 to S302 of this embodiment, the historically detected battery internal resistance set is input into a preset resistance-temperature relationship model. The resistance-temperature relationship model calculates the temperature value based on the target battery internal resistance and constructs a relationship between the temperature value and the target battery internal resistance to obtain resistance-temperature relationship data. The target battery internal resistance is used as the Y-axis coordinate data, and the temperature value is used as the X-axis coordinate data. Points are plotted based on the resistance-temperature relationship data, and the points are connected by a line graph to obtain a resistance-temperature relationship chart. By calculating the resistance-temperature relationship data using the battery internal resistance set and the resistance-temperature relationship model, and drawing a line graph based on the resistance-temperature relationship data, a resistance-temperature relationship chart can be obtained. This allows for the direct retrieval of temperature data in subsequent applications by obtaining a chart showing the relationship between the internal resistance value and the battery temperature.

[0103] Please refer to Figure 12 , Figure 12 A schematic diagram of the internal resistance-temperature relationship graph in an embodiment of the present invention is shown. It can be understood that the monitoring chip unit applies a sinusoidal voltage excitation signal to the battery and acquires the sinusoidal response signal generated by the battery. Based on the ratio of excitation to response, the equivalent internal resistance of the battery is calculated. Then, a corresponding relationship curve is plotted between the battery's equivalent internal resistance and the temperature value to obtain the resistance-temperature relationship graph.

[0104] The battery's internal resistance was measured under different ambient temperatures, and the temperature characteristic curve of the battery's internal resistance was plotted based on the experimental data. Related research shows that when the temperature is above zero, the effect of temperature on the battery's internal resistance is minimal, remaining below 1 mΩ. However, when the temperature drops below zero, the battery's resistance increases significantly with decreasing temperature, especially after -20℃, where the internal resistance rises rapidly, even reaching 1.5 mΩ-2.5 mΩ at 25℃. Operating the battery at this resistance value will result in severe electrical losses.

[0105] In step S103 of some embodiments, the corresponding temperature value is found from the resistance-temperature relationship chart according to the target battery internal resistance at the current moment, and the temperature value is used as the current temperature value of the battery to obtain the target battery temperature.

[0106] It should be noted that, referring to Figure 10 The controller MCU inside the monitoring chip unit performs voltage division through resistor R1 to detect the equivalent internal resistance of the battery. It samples the voltage value across the NTC temperature sensor using AD sampling, converts the voltage value into the corresponding target battery internal resistance, and obtains the corresponding target battery temperature by looking up the resistance-temperature relationship chart through the target battery internal resistance.

[0107] Please refer to Figure 4 , Figure 4 A flowchart illustrating the temperature anomaly detection method in an embodiment of the present invention is shown. In some embodiments, the anomaly judgment result includes temperature anomaly information and normal temperature information. The anomaly judgment parameters include a first difference threshold and a preset temperature range. The target battery temperature, the battery internal resistance difference, and the preset anomaly judgment parameters are compared to obtain the anomaly judgment result, specifically including but not limited to steps S401 to S403.

[0108] Step S401: Compare the battery internal resistance difference with the first difference threshold to obtain the first difference comparison result;

[0109] Step S402: Compare the target battery temperature with the preset temperature range to obtain the temperature comparison result;

[0110] Step S403: If the first difference comparison result indicates that the battery internal resistance difference is greater than the first difference threshold, and the temperature comparison result indicates that the target battery temperature is not within the preset temperature range, then temperature anomaly information is obtained.

[0111] In steps S401 to S403 of this embodiment, the battery internal resistance difference is compared with a first difference threshold to obtain a first difference comparison result. The target battery temperature is compared with the upper limit of a preset temperature range and with the lower limit of the preset temperature range to obtain a temperature comparison result. If the first difference comparison result indicates that the battery internal resistance difference is greater than the first difference threshold, and the temperature comparison result indicates that the target battery temperature is greater than the upper limit of the preset temperature range or less than the lower limit of the preset temperature range, then temperature anomaly information is obtained. By comparing the battery internal resistance difference with the first difference threshold to obtain the first difference comparison result, and by comparing the target battery temperature with the preset temperature range to obtain the temperature comparison result, temperature anomaly information is obtained based on the first difference comparison result and the temperature comparison result, which can screen out abnormal temperature data of smart cells.

[0112] It should be noted that if the difference in battery internal resistance exceeds the first difference threshold, it indicates that the battery's equivalent internal resistance has changed abruptly. If the difference in battery internal resistance exceeds the first difference threshold and the target battery temperature is below the lower limit of the preset temperature range, it indicates that the battery has experienced thermal runaway, meaning that the detected temperature is inaccurate, and the BMS management system will issue a corresponding alarm signal in advance.

[0113] If the difference in internal resistance of the battery is greater than the first difference threshold, and the target battery temperature is less than the upper limit of the preset temperature range and greater than the lower limit of the preset temperature range, that is, the target battery temperature is within the preset temperature range, then the temperature is normal.

[0114] Please refer to Figure 5 , Figure 5 A schematic flowchart of a temperature anomaly detection method according to an embodiment of the present invention is shown. In some embodiments, the difference in battery internal resistance is compared with a preset first difference threshold to obtain a first difference comparison result, specifically including but not limited to steps S501 to S503.

[0115] Step S501: Obtain several battery internal resistance differences within a preset time period;

[0116] Step S502: Filter the maximum value of the battery internal resistance difference to obtain the target internal resistance difference;

[0117] Step S503: Compare the target internal resistance difference with the first difference threshold to obtain the first difference comparison result.

[0118] In steps S501 to S503 of this embodiment, the battery internal resistance difference value corresponding to each moment within a preset time period is obtained, resulting in several battery internal resistance difference values. These values ​​are compared to select the maximum value, which is then used to obtain a target internal resistance difference value. This target internal resistance difference value is then compared with a first difference threshold to obtain a first difference comparison result. By comparing the maximum battery internal resistance difference value within the preset time period with the first difference threshold to obtain the first difference comparison result, it can be used to determine whether the internal temperature of the battery has changed abruptly within a short period of time.

[0119] Please refer to Figure 13 , Figure 13 A graph showing the relationship between internal resistance and time is provided. It can be understood that when the equivalent internal resistance difference Δr of the battery exceeds the first difference threshold within a short time Δt, it can be determined that a sudden change in the internal temperature of the battery has occurred.

[0120] In step S402 of some embodiments, the preset temperature range is set according to the target battery temperature. The upper limit of the preset temperature range can be set to a temperature value 1°C higher than the target battery temperature, and the lower limit of the preset temperature range can be set to a temperature value 1°C lower than the target battery temperature, etc. This application does not specifically limit this.

[0121] Please refer to Figure 6 , Figure 6 A flowchart illustrating the temperature anomaly detection method in an embodiment of the present invention is shown. In some embodiments, the anomaly judgment parameter further includes a second difference threshold. Comparing the target battery temperature, the battery internal resistance difference, and the preset anomaly judgment parameter to obtain the anomaly judgment result may include, but is not limited to, steps S601 to S605.

[0122] Step S601: If the first difference comparison result indicates that the battery internal resistance difference is less than the first difference threshold and the temperature comparison result indicates that the target battery temperature is within the preset temperature range, obtain a preset number of target battery internal resistances.

[0123] Step S602: Calculate the internal resistance difference based on the internal resistance of the target number of batteries to obtain the internal resistance difference between batteries.

[0124] Step S603: Compare the internal resistance difference between the batteries with the second difference threshold to obtain the second difference comparison result;

[0125] Step S604: If the second difference comparison result indicates that the internal resistance difference between batteries is greater than the second difference threshold, then temperature anomaly information is obtained.

[0126] Step S605: If the second difference comparison result indicates that the internal resistance difference between batteries is less than the second difference threshold, then normal temperature information is obtained.

[0127] In steps S601 to S605 of this embodiment, if the first difference comparison result indicates that the battery internal resistance difference is less than the first difference threshold and the temperature comparison result indicates that the target battery temperature is within a preset temperature range, a preset number of target battery internal resistances are randomly selected from several target battery internal resistances obtained from historical detection. Based on this preset number, the corresponding internal resistance difference is calculated for each target battery internal resistance to obtain the inter-battery internal resistance difference. This inter-battery internal resistance difference is then compared with a second difference threshold to obtain a second difference comparison result. If the second difference comparison result indicates that the inter-battery internal resistance difference is greater than the second difference threshold, then temperature anomaly information is obtained. If the second difference comparison result indicates that the inter-battery internal resistance difference is less than the second difference threshold, then normal temperature information is obtained. Calculating the inter-battery internal resistance difference using a preset number of target battery internal resistances and obtaining temperature anomaly information based on the second difference comparison result between the inter-battery internal resistance difference and the second difference threshold can improve the accuracy of screening abnormal temperature data in smart battery cells.

[0128] In step S602 of some embodiments, the preset number can be two, and the difference between the internal resistances of the two target batteries is calculated to obtain the internal resistance difference between the batteries; the preset number can be three, and the pairwise difference between the internal resistances of the three target batteries is calculated to obtain the internal resistance difference between the two batteries, and the average value of the internal resistance difference between the two batteries is calculated to obtain the corresponding internal resistance difference between the batteries; wherein, when the preset number is more than three, the corresponding internal resistance difference between the batteries is obtained by the same method as three, and this application does not specifically limit this.

[0129] Please refer to Figure 7 , Figure 7 A flowchart illustrating the temperature anomaly detection method in an embodiment of the present invention is shown. In some embodiments, the anomaly judgment parameters further include a cell voltage threshold. Comparing the target battery temperature, the battery internal resistance difference, and the preset anomaly judgment parameters to obtain the anomaly judgment result may include, but is not limited to, steps S701 to S703.

[0130] Step S701: If the first difference comparison result indicates that the battery internal resistance difference is greater than the first difference threshold, and the temperature comparison result indicates that the target battery temperature is within the preset temperature range, then the voltage of the smart cell unit is obtained to obtain the cell voltage data.

[0131] Step S702: Compare the cell voltage data with the cell voltage threshold to obtain the voltage comparison result;

[0132] Step S703: If the voltage comparison result indicates that the cell voltage data is greater than the cell voltage threshold, then the temperature anomaly information is obtained.

[0133] In steps S701 to S703 of this embodiment, if the first difference comparison result indicates that the battery internal resistance difference is greater than the first difference threshold and the temperature comparison result indicates that the target battery temperature is within a preset temperature range, then the voltage across the smart cell unit is acquired through an embedded multi-source sensor group to obtain cell voltage data. The value of the cell voltage data is compared with the cell voltage threshold to obtain a voltage comparison result. If the voltage comparison result indicates that the value of the cell voltage data is greater than the cell voltage threshold, then temperature anomaly information is obtained. By acquiring the cell voltage data of the smart cell unit, comparing the value of the cell voltage data with the cell voltage threshold to obtain a voltage comparison result, and obtaining temperature anomaly information based on the voltage comparison result, the accuracy of screening for temperature data anomalies in smart cells can be improved.

[0134] It should be noted that if the first difference comparison result indicates that the battery internal resistance difference is greater than the first difference threshold, the temperature comparison result indicates that the target battery temperature is within the preset temperature range, and the voltage comparison result indicates that the cell voltage data is less than the cell voltage threshold, then the temperature information is normal.

[0135] Please refer to Figure 8 , Figure 8 A flowchart illustrating the temperature anomaly detection method in an embodiment of the present invention is shown. In some embodiments, the initial equivalent internal resistance of the battery within a preset time period Δt is calculated based on a pre-input excitation voltage signal to obtain the initial battery internal resistance Ri. The equivalent internal resistance of the battery at the current moment is calculated based on the excitation voltage signal to obtain the target battery internal resistance ri, where i represents the i-th battery pack. The current battery temperature is obtained through an NTC temperature sensor to obtain the target battery temperature Tj, where j represents the j-th battery pack. The target battery temperature Tj, the initial battery internal resistance Ri, and the target battery internal resistance ri are transmitted to the BMS management system. The BMS management system calculates the difference between the initial battery internal resistance Ri and the target battery internal resistance ri to obtain the battery internal resistance difference Δr. The BMS management system saves the target battery temperature Tj and the battery internal resistance difference Δr, and compares the battery internal resistance difference Δr with a first difference threshold ra to obtain a first difference comparison result. The target battery temperature Tj is compared with a preset temperature range to obtain a temperature comparison result. The difference between the internal resistance *ri* of any two target batteries is calculated to obtain the internal resistance difference between the batteries. This internal resistance difference is then compared with a second difference threshold to obtain a second difference comparison result. Based on the first difference comparison result, the temperature comparison result, and the second difference comparison result, temperature anomaly information or normal temperature information is obtained and reported to the main control module in real time. If the temperature anomaly information is detected, the main control module controls the BMS system to collect the voltage of the smart cell unit and determine whether the voltage is normal to obtain the temperature anomaly information or normal temperature information.

[0136] In addition, this application also discloses a temperature anomaly detection device, please refer to... Figure 9 , Figure 9 This invention discloses a module block diagram of a temperature anomaly detection device according to an embodiment of the present invention. The temperature anomaly detection device is applied to a smart battery cell unit, which includes a battery, and can implement the aforementioned temperature anomaly detection method. The temperature anomaly detection device includes: an equivalent internal resistance detection module 903, an initial internal resistance screening module 902, a battery temperature screening module 903, an internal resistance difference calculation module 904, and a temperature anomaly judgment module 905. The equivalent internal resistance detection module 901, the initial internal resistance screening module 902, the battery temperature screening module 903, the internal resistance difference calculation module 904, and the temperature anomaly judgment module 905 are all communicatively connected.

[0137] The equivalent internal resistance detection module 901 performs equivalent internal resistance detection based on the pre-input excitation voltage signal to obtain a set of battery internal resistances. The initial internal resistance screening module 902 filters the battery's initial internal resistance from the set of battery internal resistances to obtain the initial battery internal resistance. The battery temperature screening module 903 filters the target battery internal resistance from the set of battery internal resistances, and then filters the battery's temperature from a pre-built resistance-temperature relationship chart based on the target battery internal resistance to obtain the target battery temperature. The internal resistance difference calculation module 904 calculates the internal resistance difference based on the initial battery internal resistance and the target battery internal resistance to obtain the battery internal resistance difference value. The temperature anomaly judgment module 905 compares the target battery temperature, the battery internal resistance difference value, and preset anomaly judgment parameters to obtain the anomaly judgment result.

[0138] The equivalent internal resistance detection module 901 calculates the battery's equivalent internal resistance in real time based on the pre-input excitation voltage signal, obtaining a set of battery internal resistances. The initial internal resistance screening module 902 filters the initial internal resistance at the start of battery operation from the set of battery internal resistances, obtaining the initial battery internal resistance. The battery temperature screening module 903 filters the battery's internal resistance at the current moment from the set of battery internal resistances, obtaining the target battery internal resistance. Based on the target battery internal resistance, it searches in real time from a pre-built resistance-temperature relationship chart to filter the battery's temperature at the current moment, obtaining the target battery temperature. The internal resistance difference calculation module 904 calculates the difference between the initial battery internal resistance value and the target battery internal resistance value, obtaining the battery internal resistance difference value. The temperature anomaly judgment module 905 compares the target battery temperature with the corresponding anomaly judgment parameters related to temperature, and compares the battery internal resistance difference value with the corresponding anomaly judgment parameters related to internal resistance difference, obtaining the anomaly judgment result based on the results of the two comparisons. The battery's internal resistance set is calculated by the excitation voltage signal. The initial and target internal resistances of the battery are obtained from the internal resistance set, and the target battery temperature is also obtained. The difference between the initial and target internal resistances is calculated. The target battery temperature is compared with the anomaly judgment parameters, and the difference between the internal resistances is compared with the anomaly judgment parameters to obtain the anomaly judgment result. This can screen out abnormal temperature data of the smart cell and thus accurately detect the temperature data of the smart cell.

[0139] The operation process of the temperature anomaly detection device in this embodiment is specifically described above. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 The steps S101 to S105, S201 to S203, S301 and S302, S401 to S403, S501 to S503, S601 to S605 and S701 to S703 of the temperature anomaly detection method are not described in detail here.

[0140] Another embodiment of the present invention discloses a temperature anomaly detection device, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform, for example... Figure 1 Control method steps S101 to S105 Figure 2 Control method steps S201 to S203 Figure 3 Control method steps S301 and S302 Figure 4Control method steps S401 to S403 Figure 5 Control method steps S501 to S503 Figure 6 The control method steps S601 to S605 and Figure 7 The temperature anomaly detection method in steps S701 to S703 of the control method.

[0141] Another embodiment of the present invention discloses a computer-readable storage medium, the storage medium comprising: storing computer-executable instructions for causing a computer to perform... Figure 1 Control method steps S101 to S105 Figure 2 Control method steps S201 to S203 Figure 3 Control method steps S301 and S302 Figure 4 Control method steps S401 to S403 Figure 5 Control method steps S501 to S503 Figure 6 The control method steps S601 to S605 and Figure 7 The temperature anomaly detection method in steps S701 to S703 of the control method.

[0142] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0144] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. Furthermore, the embodiments of the present invention and the features thereof can be combined with each other unless otherwise specified.

Claims

1. A temperature abnormality detection method characterized by comprising: The temperature anomaly detection method is applied to a smart battery cell unit, and the smart battery cell unit comprises a battery. An equivalent internal resistance is detected according to a pre-input excitation voltage signal to obtain a battery internal resistance set; An initial internal resistance of the battery is screened from the battery internal resistance set to obtain an initial battery internal resistance; A target battery internal resistance of the battery is screened from the battery internal resistance set, and a temperature of the battery is screened from a pre-constructed resistance-temperature relationship chart according to the target battery internal resistance to obtain a target battery temperature; An internal resistance difference is calculated according to the initial battery internal resistance and the target battery internal resistance to obtain a battery internal resistance difference value; The target battery temperature, the battery internal resistance difference value and a pre-set anomaly judgment parameter are compared to obtain an anomaly judgment result; The anomaly judgment result comprises temperature anomaly information and temperature normal information, the anomaly judgment parameter comprises a first difference threshold, a pre-set temperature range and a second difference threshold, and the comparison of the target battery temperature, the battery internal resistance difference value and the pre-set anomaly judgment parameter to obtain the anomaly judgment result comprises: The battery internal resistance difference value is compared with the first difference threshold to obtain a first difference comparison result; The target battery temperature is compared with the pre-set temperature range to obtain a temperature comparison result; If the first difference comparison result indicates that the battery internal resistance difference value is greater than the first difference threshold, and the temperature comparison result indicates that the target battery temperature is not within the pre-set temperature range, the temperature anomaly information is obtained; If the first difference comparison result indicates that the battery internal resistance difference value is less than the first difference threshold, and the temperature comparison result indicates that the target battery temperature is within the pre-set temperature range, a pre-set number of target battery internal resistances are obtained; For the pre-set number of target battery internal resistances, internal resistance difference values of any two different target battery internal resistances are calculated to obtain difference values corresponding to any two different target battery internal resistances, and a battery-to-battery internal resistance difference value is determined according to the difference values corresponding to each different target battery internal resistance; The battery-to-battery internal resistance difference value is compared with the second difference threshold to obtain a second difference comparison result; The temperature anomaly information or the temperature normal information is determined according to the second difference comparison result.

2. The temperature abnormality detection method according to claim 1, characterized by, The smart battery cell unit further comprises a built-in resistor, and the equivalent internal resistance detection according to the pre-input excitation voltage signal to obtain the battery internal resistance set comprises: A voltage signal of the built-in resistor responding to the excitation voltage signal is obtained to obtain a built-in resistor voltage signal; A voltage signal of the battery responding to the excitation voltage signal is obtained to obtain a battery voltage signal; The equivalent internal resistance is detected according to the built-in resistor voltage signal and the battery voltage signal to obtain the battery internal resistance set.

3. The temperature abnormality detection method according to claim 1, characterized by, Before the target battery internal resistance of the battery is screened from the battery internal resistance set, the temperature of the battery is screened from the pre-constructed resistance-temperature relationship chart according to the target battery internal resistance to obtain the target battery temperature, the temperature anomaly detection method further comprises: According to the battery internal resistance set and a preset resistance temperature relationship model, relationship calculation is performed to obtain resistance temperature relationship data; According to the resistance temperature relationship data, a broken line chart is drawn to obtain the resistance temperature relationship chart.

4. The temperature abnormality detection method according to claim 3, characterized by, The comparison of the battery internal resistance difference value and the preset first difference threshold value to obtain a first difference comparison result includes: A plurality of battery internal resistance difference values in a preset time period are obtained; The maximum value of the battery internal resistance difference value is screened to obtain a target internal resistance difference value; The target internal resistance difference value is compared with the first difference threshold value to obtain the first difference comparison result.

5. The temperature abnormality detection method according to claim 3, characterized by, The determination of the temperature abnormal information or the temperature normal information according to the second difference comparison result includes: If the second difference comparison result indicates that the battery internal resistance difference value is greater than the second difference threshold value, the temperature abnormal information is obtained; If the second difference comparison result indicates that the battery internal resistance difference value is less than the second difference threshold value, the temperature normal information is obtained.

6. The temperature abnormality detection method according to claim 3, characterized by, The abnormal judgment parameter further includes a battery cell voltage threshold value, and the comparison of the target battery temperature, the battery internal resistance difference value, and the preset abnormal judgment parameter to obtain an abnormal judgment result further includes: If the first difference comparison result indicates that the battery internal resistance difference value is greater than the first difference threshold value, and the temperature comparison result indicates that the target battery temperature is higher than the upper limit value of the preset temperature range, the voltage of the intelligent battery cell unit is obtained to obtain battery cell voltage data; The battery cell voltage data is compared with the battery cell voltage threshold value to obtain a voltage comparison result; If the voltage comparison result indicates that the battery cell voltage data is greater than the battery cell voltage threshold value, the temperature abnormal information is obtained.

7. A temperature abnormality detection device characterized by comprising: The temperature abnormality detection method is applied to an intelligent battery cell unit, and the intelligent battery cell unit includes a battery. An equivalent internal resistance detection module is configured to perform equivalent internal resistance detection according to a pre-input excitation voltage signal to obtain a battery internal resistance set. An initial internal resistance screening module is configured to screen an initial internal resistance of the battery from the battery internal resistance set to obtain an initial battery internal resistance. A battery temperature screening module is configured to screen a target battery internal resistance of the battery from the battery internal resistance set, and screen a temperature of the battery from a pre-constructed resistance temperature relationship chart according to the target battery internal resistance to obtain a target battery temperature. An internal resistance difference calculation module is configured to perform internal resistance difference calculation according to the initial battery internal resistance and the target battery internal resistance to obtain a battery internal resistance difference value. A temperature abnormality judgment module is configured to compare the target battery temperature, the battery internal resistance difference value, and a preset abnormal judgment parameter to obtain an abnormal judgment result. The abnormal judgment result includes temperature abnormal information and temperature normal information, and the abnormal judgment parameter includes a first difference threshold value, a preset temperature range, and a second difference threshold value. The comparison of the battery internal resistance difference value and the first difference threshold value to obtain a first difference comparison result includes: comparing the target battery temperature with a preset temperature range to obtain a temperature comparison result; if the first difference comparison result indicates that the battery resistance difference is greater than the first difference threshold value, and the temperature comparison result indicates that the target battery temperature is not within the preset temperature range, obtaining the temperature abnormality information; if the first difference comparison result indicates that the battery resistance difference is less than the first difference threshold value, and the temperature comparison result indicates that the target battery temperature is within the preset temperature range, obtaining a preset number of target battery resistances; for the preset number of target battery resistances, for any two different target battery resistances, calculating a resistance difference value to obtain a difference value corresponding to any two different target battery resistances, and determining an inter-battery resistance difference value according to the difference value corresponding to each different target battery resistance; comparing the inter-battery resistance difference value with a second difference threshold value to obtain a second difference comparison result; determining temperature abnormality information or temperature normal information according to the second difference comparison result.

8. A temperature abnormality detection apparatus characterized by comprising: comprise: at least one processor, and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the temperature abnormality detection method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions for causing a computer to execute the temperature abnormality detection method of any one of claims 1 to 6.

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