A method and device for tracking and detecting the charging working temperature of a lithium-ion battery

Abstract

The present invention relates to the field of battery temperature detection technology, and discloses a tracking detection method and device for the charging working temperature of a lithium-ion battery. The method includes obtaining the charging and discharging data of a lithium-ion battery, arranging the initial position of the temperature sensor in a temperature control box based on the charging and discharging data of the lithium-ion battery, and generating multiple temperature measurement point data; classifying the multiple temperature measurement point data, and determining the key temperature measurement point based on the classified temperature measurement point data; moving the temperature sensor to the tracking detection position based on the key temperature measurement point, and determining the real-time temperature data corresponding to the key temperature measurement point; fusing the real-time temperature data to generate the charging working temperature of the lithium-ion battery. The present invention realizes the optimized arrangement of the temperature sensor in the cavity of the temperature control box, improves the accuracy of the charging working temperature of the lithium-ion battery, realizes the precise control of the temperature control compensation system, and improves the battery quality.

Description

Technical Field

[0001] The present invention relates to the technical field of battery temperature detection, and in particular to a method and device for tracking and detecting the charging operating temperature of a lithium-ion battery. Background Art

[0002] The temperature control technology for the battery industry's temperature control box cavity is used in the battery industry's formation, capacity division, and OCV testing sections. It mainly provides high or low temperatures for the battery production environment and performs constant temperature environment management and control. If the temperature is not properly controlled, the battery quality will fluctuate.

[0003] However, the heat release of lithium-ion batteries is uneven during charging. The temperature control box is too large and the sensors are improperly arranged, which makes it impossible to sensitively obtain changes in the operating temperature around the lithium-ion battery. The temperature control box cavity cannot properly compensate for the current operating temperature, which directly affects the charging effect and subsequent performance of the battery. Summary of the invention

[0004] In view of this, the present invention provides a method and device for tracking and detecting the charging operating temperature of a lithium-ion battery, so as to solve the problem that the operating temperature detection of the lithium-ion battery is inaccurate, resulting in the inability to accurately perform temperature compensation adjustment on the current operating temperature, and thus the battery quality fluctuates.

[0005] In a first aspect, the present invention provides a method for tracking and detecting the charging working temperature of a lithium-ion battery, which is applied to a temperature control box cavity, wherein a movable guide rail is provided on the inner wall of the temperature control box cavity, and a plurality of temperature sensors are provided on the movable guide rail; the method comprises:

[0006] Obtain the charge and discharge data of the lithium-ion battery, lay out the initial position of the temperature sensor in the temperature control box based on the charge and discharge data of the lithium-ion battery, and generate multiple temperature measurement point data;

[0007] Classify multiple temperature measurement point data, and determine key temperature measurement points based on the classified temperature measurement point data;

[0008] Based on the key temperature measurement points, the temperature sensor is moved to the tracking detection position to determine the real-time temperature data corresponding to the key temperature measurement points;

[0009] The real-time temperature data is fused and processed to generate the lithium-ion battery charging operating temperature.

[0010] The present embodiment provides a tracking and detection method for the charging working temperature of a lithium-ion battery. The initial positions of the temperature sensors are arranged, and then the temperature measurement points collected by the temperature sensors at the initial positions are classified to determine the key temperature measurement points, and then the positions of the temperature sensors are moved, thereby realizing the optimal arrangement of the temperature sensors in the temperature control box cavity. The changes in the peripheral working temperature of the lithium-ion battery can be obtained simply, sensitively and accurately using the least number of temperature measurement points. Moreover, by fusing the real-time temperature data, the generated charging working temperature of the lithium-ion battery has lower volatility and better robustness, thereby further improving the accuracy of the charging working temperature of the lithium-ion battery, realizing the precise control of the temperature control compensation system, and improving the battery quality.

[0011] In an optional implementation, the initial positions of temperature sensors in the temperature control box are arranged based on the charge and discharge data of the lithium-ion battery to generate multiple temperature measurement point data, including:

[0012] Establish the temperature field of the electrochemical reaction of lithium-ion batteries based on the charge and discharge data of lithium-ion batteries;

[0013] Simulate the temperature field of the electrochemical reaction of lithium-ion batteries and generate the operating temperature distribution of lithium-ion batteries;

[0014] The initial position of the temperature sensor in the temperature control box is arranged based on the operating temperature distribution of the lithium-ion battery;

[0015] The lithium-ion battery is subjected to a constant temperature steady-state continuous charging test at different temperatures to generate data at multiple temperature measurement points.

[0016] The present embodiment provides a method for tracking and detecting the charging working temperature of a lithium-ion battery. The temperature field of the electrochemical reaction of the lithium-ion battery is established through the charging and discharging data of the lithium-ion battery, and the temperature field of the electrochemical reaction of the lithium-ion battery is simulated, thereby accurately obtaining the heat release law of the lithium-ion battery, and accurately detecting the temperature distribution of individual devices inside the temperature control box cavity and around the lithium-ion battery under different working conditions by experimental means.

[0017] In an optional embodiment, establishing a temperature field of an electrochemical reaction of a lithium-ion battery based on charge and discharge data of the lithium-ion battery includes:

[0018] Determine the operating current density, open circuit voltage, terminal voltage and lithium-ion battery temperature based on the charge and discharge data of the lithium-ion battery;

[0019] Calculate the heat generation rate based on the operating current density, open circuit voltage, terminal voltage and lithium-ion battery temperature;

[0020] The temperature field of the electrochemical reaction of lithium-ion batteries is established based on the heat generation rate.

[0021] The present embodiment provides a method for tracking and detecting the charging working temperature of a lithium-ion battery, which establishes the temperature field of the electrochemical reaction of the lithium-ion battery through the heat generation rate, thereby achieving accurate acquisition of the heat release law of the lithium-ion battery.

[0022] In an optional implementation, classifying a plurality of temperature measurement point data, and determining a key temperature measurement point based on the classified temperature measurement point data, includes:

[0023] The fuzzy cluster analysis method is used to classify the data of multiple temperature measurement points to generate temperature measurement points of different categories;

[0024] Based on different categories of temperature measurement points, the key temperature measurement points are determined using the correlation analysis method.

[0025] The present embodiment provides a tracking and detection method for the charging working temperature of a lithium-ion battery. The fuzzy cluster analysis method is used to classify the temperature measurement points collected by the sensor according to the temperature change law, and then the correlation analysis method is used to select temperature characteristic points among the temperature measurement points. This can better optimize the distribution of temperature sensors, accurately measure the temperature field of the temperature control box cavity, and establish an accurate working temperature error compensation model. This can achieve the purpose of obtaining the working temperature changes around the lithium-ion battery in a simple, sensitive and accurate manner using the least number of temperature measurement points while ensuring the accuracy of the compensation temperature.

[0026] In an optional implementation, a plurality of temperature measurement point data are classified using a fuzzy cluster analysis method to generate temperature measurement points of different categories, including:

[0027] An initial data matrix is ​​constructed based on data from multiple temperature measurement points, and the initial data matrix is ​​standardized to generate a normalized matrix;

[0028] Determine the similarity coefficients of matrix elements in the normalized matrix, and construct a fuzzy similarity matrix based on the similarity coefficients of the matrix elements;

[0029] Based on the fuzzy similarity matrix, the equivalent matrix is ​​determined, and fuzzy clustering analysis is performed on the equivalent matrix to generate temperature measurement points of different categories.

[0030] The present embodiment provides a method for tracking and detecting the charging working temperature of a lithium-ion battery. By constructing a matrix and performing fuzzy clustering analysis, the method achieves accurate classification of temperature measurement points, thus laying a foundation for the subsequent selection of temperature feature points.

[0031] In an optional implementation, based on different types of temperature measurement points, a correlation analysis method is used to determine key temperature measurement points, including:

[0032] Select temperature characteristic points from different categories of temperature measurement points, input the temperature characteristic points into the temperature error prediction model, and generate a complex determination coefficient;

[0033] The complex determination coefficients are sorted, the maximum complex determination coefficient is selected, and the temperature measuring point corresponding to the maximum complex determination coefficient is used as the key temperature measuring point.

[0034] This embodiment provides a tracking detection method for the charging working temperature of a lithium-ion battery. By selecting temperature characteristic points among the temperature measurement points, the distribution of temperature sensors can be better optimized, the temperature field of the temperature control box cavity can be accurately measured, and an accurate working temperature error compensation model can be established.

[0035] In an optional implementation, the real-time temperature data is fused and processed to generate the lithium-ion battery charging operating temperature, including:

[0036] Performing wavelet preprocessing on the real-time temperature data, and calculating the signal-to-noise ratio data based on the real-time temperature data after wavelet preprocessing;

[0037] The signal-to-noise ratio data is processed by wavelet reconstruction to generate the charging operating temperature of the lithium-ion battery.

[0038] The present embodiment provides a tracking and detection method for the charging working temperature of a lithium-ion battery, which performs wavelet preprocessing and wavelet reconstruction processing on real-time temperature data. The method is simple, fast, and has a good information retention effect, so that the charging working temperature of the lithium-ion battery has low volatility and good robustness.

[0039] In a second aspect, the present invention provides a device for tracking and detecting the charging operating temperature of a lithium-ion battery, the device comprising:

[0040] A layout module is used to obtain the charge and discharge data of the lithium-ion battery, layout the initial positions of the temperature sensors in the temperature control box based on the charge and discharge data of the lithium-ion battery, and generate multiple temperature measurement point data;

[0041] A classification module, used for classifying a plurality of temperature measurement point data and determining key temperature measurement points based on the classified temperature measurement point data;

[0042] A tracking and detection module is used to move the temperature sensor to a tracking and detection position based on a key temperature measurement point to determine the real-time temperature data corresponding to the key temperature measurement point;

[0043] The fusion processing module is used to perform fusion processing on the real-time temperature data to generate the charging operating temperature of the lithium-ion battery.

[0044] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for tracking and detecting the charging operating temperature of a lithium-ion battery of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for tracking and detecting the charging operating temperature of a lithium-ion battery according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 is a schematic structural diagram of a temperature sensor and a movable guide rail according to an embodiment of the present invention;

[0048] Figure 2 is a top view of a temperature control box according to an embodiment of the present invention;

[0049] Figure 3 It is a flow chart of a method for tracking and detecting the charging operating temperature of a lithium-ion battery according to an embodiment of the present invention;

[0050] Figure 4 is a schematic diagram of key temperature measurement points in an upper cavity according to an embodiment of the present invention;

[0051] Figure 5 is a schematic diagram of key temperature measurement points in a middle cavity according to an embodiment of the present invention;

[0052] Figure 6 is a schematic diagram of key temperature measurement points in a lower cavity according to an embodiment of the present invention;

[0053] Figure 7 is a flow chart of another method for tracking and detecting the charging operating temperature of a lithium-ion battery according to an embodiment of the present invention;

[0054] Figure 8 is a schematic diagram of the layout of the initial position of the temperature sensor in the upper cavity according to an embodiment of the present invention;

[0055] Fig. 9is a schematic diagram of the layout of the initial position of the temperature sensor in the middle cavity according to an embodiment of the present invention;

[0056] Fig.10 is a schematic diagram of the layout of the initial position of the temperature sensor in the lower cavity according to an embodiment of the present invention;

[0057] Fig.11 is a flow chart of another method for tracking and detecting the charging operating temperature of a lithium-ion battery according to an embodiment of the present invention;

[0058] Fig.12 is a flow chart of another method for tracking and detecting the charging operating temperature of a lithium-ion battery according to an embodiment of the present invention;

[0059] Fig.13 is a flow chart of a wavelet Kalman filter data fusion algorithm based on signal-to-noise ratio implementation according to an embodiment of the present invention;

[0060] Fig.14 is a structural block diagram of a device for tracking and detecting the charging operating temperature of a lithium-ion battery according to an embodiment of the present invention;

[0061] Fig.15 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0063] The installation process of related temperature sensors is as follows: first, a large number of sensors are installed at different locations according to engineering judgment, and then a small number of temperature sensors are selected through statistical analysis and other methods to model the temperature error components. This will result in: (1) Due to inaccurate engineering judgment, the prediction accuracy and robustness of temperature error compensation control are seriously affected; (2) Establishing the corresponding relationship between temperature error and temperature field requires a lot of time and resources; (3) Excessive arrangement of temperature measurement points will result in a huge amount of data that cannot be effectively processed, and the coupling between data will also affect subsequent processing.

[0064] Designing an efficient data fusion algorithm is a challenging task because multi-sensor data fusion algorithms involve multiple aspects such as energy efficiency, latency, data accuracy, network topology, data compression, and distributed data processing. The algorithm fuses information from different sensors, removes redundant information, reduces the amount of transmitted data, and achieves the goals of saving energy, improving data collection efficiency and accuracy, while minimizing the impact on temperature error compensation control latency and robustness.

[0065] Therefore, the optimal layout of temperature sensors and signal fusion are key issues that need to be improved and developed in the temperature detection technology for lithium-ion battery charging. If the least number of temperature measurement points can be used while still ensuring the accuracy of compensated temperature, it will bring economic benefits and convenience to the practical application of temperature compensation technology.

[0066] According to an embodiment of the present invention, there is provided an embodiment of a method for tracking and detecting the working temperature of a lithium-ion battery during charging. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0067] In this embodiment, a method for tracking and detecting the working temperature of a lithium-ion battery during charging is provided. Wherein, a movable guide rail 2 is provided on the inner wall of the temperature control box cavity 1, and a plurality of temperature sensors 3 are provided on the movable guide rail 2. As Figure 1-2 shown, the movable guide rail 2 is arranged in a "king" shape, with a total of three groups of movable guide rails 2, which are respectively attached to the upper, middle, and lower layers inside the temperature control box cavity 1. The size of the movable guide rail 2 is the same as the internal size of the temperature control box cavity 1; each temperature sensor 3 is equipped with a slider at the bottom, which can move to the corresponding position according to the controller's instruction. The movable guide rail 2 is equipped with an electric linear push rod device, which is equipped with a slider and a connecting rod to drive the temperature sensor 3 slider to move in the four degrees of freedom directions of front, back, left, and right along the movable guide rail 2; the controller is a mobile terminal, such as a mobile phone, a tablet computer, etc. Figure 1 is a flowchart of a method for tracking and detecting the working temperature of a lithium-ion battery according to an embodiment of the present invention. As Figure 3 shown, the process includes the following steps:

[0068] Step S301, obtain the charge and discharge data of the lithium-ion battery, and based on the charge and discharge data of the lithium-ion battery, layout the initial positions of the temperature sensors in the temperature control box to generate multiple temperature measurement point data.

[0069] Step S302, classify the multiple temperature measurement point data, and determine the key temperature measurement points based on the classified temperature measurement point data.

[0070] Step S303: moving the temperature sensor 3 to a tracking detection position based on the key temperature measurement point, and determining the real-time temperature data corresponding to the key temperature measurement point.

[0071] Specifically, the controller sends instructions to the electric linear actuator device, thereby controlling the slider of the temperature sensor 3 to move along the guide rail to the tracking detection position.

[0072] For example, 17 temperature sensors 3 are assembled in total. After the temperature sensors 3 in the temperature control box cavity 1 are moved to the tracking detection position, 7 temperature sensors 3 are placed in the upper cavity. The layout of the key temperature measurement points is as follows: Figure 4 As shown, five sensors are placed in the middle cavity in a "cross" shape. The layout of the key temperature measurement points is as follows Figure 5 As shown, five sensors are placed in the lower cavity in a "cross" shape. The layout of the key temperature measurement points is as follows Figure 6 shown.

[0073] Step S304, fusing the real-time temperature data to generate the charging operating temperature of the lithium-ion battery.

[0074] Specifically, the real-time temperature data is fused using a wavelet Kalman filter data fusion algorithm based on signal-to-noise ratio.

[0075] The present embodiment provides a tracking and detection method for the charging working temperature of a lithium-ion battery. The initial positions of the temperature sensors are arranged, and then the temperature measurement points collected by the temperature sensors at the initial positions are classified to determine the key temperature measurement points, and then the positions of the temperature sensors are moved, thereby realizing the optimal arrangement of the temperature sensors in the temperature control box cavity. The changes in the peripheral working temperature of the lithium-ion battery can be obtained simply, sensitively and accurately using the least number of temperature measurement points. Moreover, by fusing the real-time temperature data, the generated charging working temperature of the lithium-ion battery has lower volatility and better robustness, thereby further improving the accuracy of the charging working temperature of the lithium-ion battery, realizing the precise control of the temperature control compensation system, and improving the battery quality.

[0076] In this embodiment, a method for tracking and detecting the charging working temperature of a lithium-ion battery is provided, which can be used in the above-mentioned mobile terminals, such as mobile phones, tablet computers, etc. Figure 7 FIG. 1 is a flow chart of a method for tracking and detecting the charging operating temperature of a lithium-ion battery according to an embodiment of the present invention. Figure 7 As shown, the process includes the following steps:

[0077] Step S701, acquiring charging and discharging data of a lithium-ion battery, laying out initial positions of temperature sensors in a temperature control box based on the charging and discharging data of the lithium-ion battery, and generating data of a plurality of temperature measurement points.

[0078] Specifically, the above step S701 includes:

[0079] Step S7021, establishing a temperature field of the electrochemical reaction of the lithium-ion battery based on the charge and discharge data of the lithium-ion battery.

[0080] In some optional implementations, the above step S7021 includes:

[0081] Step a1, determining the operating current density, open circuit voltage, terminal voltage and temperature of the lithium-ion battery based on the charge and discharge data of the lithium-ion battery.

[0082] Step a2, calculating the heat generation rate based on the operating current density, open circuit voltage, terminal voltage and lithium ion battery temperature.

[0083] Specifically, for lithium-ion batteries, when the battery temperature is discharged at a temperature of -30℃ to 70℃, Joule heat accounts for a large proportion of the total heat generated by the battery. Therefore, a mathematical model is established based on the normal working heat generation of lithium-ion batteries, the heat generation power per unit volume of the battery itself, the heat transfer coefficient, the three-dimensional non-steady-state heat conduction equation of the battery, boundary conditions, etc. The calculation formula for the internal heat generation of the conventional charging and discharging process of the relevant lithium-ion battery is as follows:

[0084]

[0085] In the above formula, q represents the heat generation rate (positive value represents heat release, negative value represents heat absorption); I represents the operating current density (positive value represents discharge, negative value represents charge); U t Indicates terminal voltage; U ocv represents open circuit voltage (OCV); T represents the temperature of lithium-ion battery; ΔH i represents the enthalpy change of the ith chemical reaction, r i represents the reaction rate of the i-th reaction; represents the molar enthalpy of the jth battery, which is used to describe the spatial variation of the battery; c j represents the ion concentration of the jth battery cell; v represents the volume, and the value with the superscript avg represents the average concentration in a certain volume.

[0086] The first part on the right side of the equal sign in the above formula (1) is the Joule heat of the resistor (abbreviated as q j ), the second part is the reversible entropy heat or reaction heat (abbreviated as q r ), which is also the entropy change during the charge and discharge process. The third part is the side reaction heat during the aging process (abbreviated as q s ), the fourth part is the mixing process heat (abbreviated as q m ).

[0087] Furthermore, during normal charging and discharging, the battery is in a safe operating range, and the side reaction heat and electrolyte decomposition heat generated by overcharging / overdischarging are very small and can be ignored. s = 0; the heat in the mixing process is caused by the concentration gradient and relaxation inside the battery. During normal charge and discharge, this part of the heat q m The contribution is negligible, so during the charging and discharging process, the calculation formula for the heat generation rate of the battery can be simplified to:

[0088]

[0089] Step a3, establishing a temperature field of the electrochemical reaction of the lithium-ion battery based on the heat generation rate.

[0090] Specifically, the actual battery specifications, physical parameters and other data of the lithium-ion battery are substituted into the above formula (2), and the temperature field of the electrochemical reaction of the lithium-ion battery is established using fluid simulation software (such as COMSOL, ANSYS_FLUENT, etc.).

[0091] Step S7022, simulating the temperature field of the electrochemical reaction of the lithium-ion battery to generate the operating temperature distribution of the lithium-ion battery.

[0092] Specifically, after simulating the temperature field of the electrochemical reaction of the lithium-ion battery, the following conclusions can be drawn: under constant temperature and steady-state continuous charging, the temperature at the center of the battery body is the highest and gradually decreases toward the boundary. Therefore, when laying out the initial position of the temperature sensor, it is necessary to focus on the temperature measurement points at the center and edge of the battery body, and at the same time, arrange as many temperature sensors 3 as possible at locations where the temperature fluctuation is large in the temperature transition range, so as to more comprehensively cover the temperature conditions of each point in the cavity, so that the screened results can better reflect the temperature conditions in the cavity.

[0093] Step S7023, layout the initial positions of temperature sensors in the temperature control box based on the operating temperature distribution of the lithium-ion battery.

[0094] For example, Figure 8-10 As shown, taking the 5V120A-32CH temperature control box negative pressure needle bed as the research object, the temperature measuring points of the temperature control box cavity 1 are arranged, and a total of 33 temperature sensors 3 are installed. The A, B...E, F temperature measuring points are placed at the center of the upper, middle and lower layers of the battery body, and the remaining 27 temperature measuring points are placed at the edge of the battery body.

[0095] Step S7024, performing a constant temperature steady state continuous charging test on the lithium electronic battery at different temperatures to generate a plurality of temperature measurement point data.

[0096] Specifically, in order to obtain the temperature field in the temperature control box cavity 1 and the temperature distribution around the lithium-ion battery under different working conditions, the lithium-ion battery is subjected to a constant temperature steady-state continuous charging test at different temperatures (-30°C ~ 70°C) through the power cabinet. The experiment is carried out at room temperature, and the temperature control box measures a set of temperature test data at each interval of 10°C between -30°C and 70°C, that is, a total of 11 sets of temperature test data; the sampling time of each set of temperature test data is set to 1s, and the duration is 30min. Each time, 33 sets of measuring point temperature data sets collected by 33 temperature sensors 3 can be obtained. During the entire measurement period, 11 sets of temperature test data of 33 measuring point temperatures are recorded and obtained in real time, and then the temperature error between the temperature data of each measuring point is analyzed, focusing on the temperature error of the air inlet, air outlet and the working temperature around the lithium-ion battery of the temperature control box.

[0097] Step S702: classify the multiple temperature measurement point data, and determine the key temperature measurement points based on the classified temperature measurement point data. Figure 3 Step S302 of the illustrated embodiment will not be described in detail here.

[0098] Step S703: Move the temperature sensor 3 to the tracking detection position based on the key temperature measurement point to determine the real-time temperature data corresponding to the key temperature measurement point. Figure 3 Step S303 of the illustrated embodiment will not be described in detail here.

[0099] Step S704: perform fusion processing on the real-time temperature data to generate the charging operating temperature of the lithium-ion battery. Figure 3 Step S304 of the illustrated embodiment will not be described in detail here.

[0100] The present embodiment provides a method for tracking and detecting the charging working temperature of a lithium-ion battery. The temperature field of the electrochemical reaction of the lithium-ion battery is established through the charging and discharging data of the lithium-ion battery, and the temperature field of the electrochemical reaction of the lithium-ion battery is simulated, thereby accurately obtaining the heat release law of the lithium-ion battery, and accurately detecting the temperature distribution of individual devices inside the temperature control box cavity and around the lithium-ion battery under different working conditions by experimental means.

[0101] In this embodiment, a method for tracking and detecting the charging working temperature of a lithium-ion battery is provided, which can be used in the above-mentioned mobile terminals, such as mobile phones, tablet computers, etc. Fig.11 FIG. 1 is a flow chart of a method for tracking and detecting the charging operating temperature of a lithium-ion battery according to an embodiment of the present invention. Fig.11 As shown, the process includes the following steps:

[0102] Step S1101, obtain the charge and discharge data of the lithium-ion battery, and arrange the initial positions of the temperature sensors in the temperature control box based on the charge and discharge data of the lithium-ion battery to generate multiple temperature measurement point data. Figure 7 Step S701 of the illustrated embodiment will not be described in detail here.

[0103] Step S1102: classify the plurality of temperature measurement point data, and determine key temperature measurement points based on the classified temperature measurement point data.

[0104] Specifically, the above step S1102 includes:

[0105] Step S11021, using fuzzy cluster analysis method to classify multiple temperature measurement point data to generate temperature measurement points of different categories.

[0106] In some optional implementations, the above step S11021 includes:

[0107] Step b1, constructing an initial data matrix based on multiple temperature measurement point data, and performing standardization processing on the initial data matrix to generate a normalized matrix.

[0108] Specifically, multiple temperature measurement point data T y As the classified object, multiple groups of temperature data corresponding to each temperature measurement point are obtained, namely T y ={T y1 , T y2 , …, T ym}, y = 1, 2, ..., n, and then get the initial data matrix U:

[0109]

[0110] In the above formula, T ym Represents the mth temperature measurement point data of the yth classification object (i.e., temperature measurement point).

[0111] Furthermore, the initial data matrix is ​​standardized and compressed to the interval (0,1), where the normalized matrix is ​​33×33 and its diagonal elements are 1.

[0112] Step b2, determining the similarity coefficients of the matrix elements in the normalized matrix, and constructing a fuzzy similarity matrix based on the similarity coefficients of the matrix elements.

[0113] Specifically, the matrix element similarity coefficient r ij The calculation formula is as follows:

[0114]

[0115] Among them, T ikrepresents the kth temperature measurement point data of the i-th classification object (i.e., temperature measurement point); T jk Represents the kth temperature measurement point data of the jth classification object (i.e., temperature measurement point).

[0116] Furthermore, based on the similarity coefficients of matrix elements, the fuzzy similarity matrix R is constructed by the maximum and minimum decision method, where:

[0117] r ij =R(T i , T j ) (5)

[0118] Among them, T i represents the temperature measurement point data corresponding to the i-th temperature measurement point, T j Represents the temperature measurement point data corresponding to the j-th temperature measurement point.

[0119] Furthermore, when the fuzzy similarity matrix R is 0, it proves that the matrix elements in the normalized matrix are completely different; when the fuzzy similarity matrix is ​​1, it proves that the two are completely correlated.

[0120] Step b3, determining an equivalent matrix based on the fuzzy similarity matrix, and performing fuzzy clustering analysis on the equivalent matrix to generate temperature measurement points of different categories.

[0121] Specifically, based on the fuzzy similarity matrix R, the transitive closure method is used to determine the minimum fuzzy transitive matrix, that is, the transitive closure of R. The transitive closure of the fuzzy similarity matrix R is an equivalent matrix containing the fuzzy similarity matrix, and then clustering is performed based on the transitive closure of R. Among them, the transitive closure method is to start from the fuzzy similarity matrix R and use the square method to calculate R→R 2 →R 4 →…→R 2L …(L is a non-zero natural number) until the first occurrence of R a =R 2a , get the transitive closure of R, let t(R) = R 2a , t(R) is the equivalent matrix of the fuzzy similarity matrix, a is the exponential coefficient that makes the fuzzy similarity matrix equal; after calculation, it is concluded that when a=4, R 4 =R 8 , therefore, it can be converted into an equivalent matrix t(R).

[0122] Furthermore, fuzzy clustering analysis is performed on the equivalent matrix t(R), and the level number λ∈[0,1] is taken to calculate the λ-truncation matrix P of t(R), and then an equivalent partition of the temperature measurement points is obtained, that is, whether two variables belong to the same category is determined according to the element values ​​in the truncation matrix P.

[0123] For example, by setting λ to 99%, the truncation matrix of 33 temperature measurement point data can be obtained, and the temperature measurement point data can be divided into 4 categories. The data difference between each category should be as large as possible, and the data difference within the category should be as small as possible. The classification of the temperature measurement point change rules in the temperature control box cavity 1 is shown in Table 1 below.

[0124] Table 1:

[0125] Category 1 Category 2 Category 3 Category 4 TA~TF、T5、T14、T23 T1~T4、T6~T9 T10~T13、T15~T18 T19~T22、T23~T27

[0126] Among them, the temperature measuring points of TA~TF, T5, T14, and T23 are arranged at the center of the upper, middle, and lower layers of the temperature control box, which is also the center and peripheral positions of the battery body; the temperature measuring points of T1~T4, T6~T9 are arranged at the edge of the upper layer of the temperature control box, which is also the edge of the battery body; the temperature measuring points of T10~T13, T15~T18 are arranged at the edge of the middle layer of the temperature control box, which is also the edge of the battery body; the temperature measuring points of T19~T22, T23~T27 are arranged at the edge of the lower layer of the temperature control box, which is also the edge of the battery body. The key temperature measuring points of the temperature control box cavity 1 are selected from the above four categories.

[0127] Step S11022: Based on the temperature measurement points of different categories, a correlation analysis method is used to determine the key temperature measurement points.

[0128] In some optional implementations, the above step S11022 includes:

[0129] Step c1, selecting temperature characteristic points from different categories of temperature measurement points, inputting the temperature characteristic points into the temperature error prediction model, and generating a complex determination coefficient.

[0130] Specifically, by analyzing the correlation between a single temperature measurement point and the temperature error obtained in step S7024, the selected temperature measurement points are combined, and the complex determination coefficient of each combination is calculated. The temperature characteristic value of the temperature measurement point with the largest complex determination coefficient and the modified complex determination coefficient is selected to establish a temperature error prediction model, wherein the temperature error prediction model is expressed as follows: The calculation formula of the complex determination coefficient is as follows:

[0131]

[0132]

[0133] In the above formula, represents the complex determination coefficient, represents the modified multiple determination coefficient, SSR represents the sum of squares of deviation, SST represents the sum of squares of regression, and SSE represents the sum of squares of residuals. represents the arithmetic mean of the temperature measurement values, n represents the number of temperature measurement points, and p represents the number of independent variables introduced into the model.

[0134] Step c2, sorting the complex determination coefficients, selecting the maximum complex determination coefficient, and taking the temperature measurement point corresponding to the maximum complex determination coefficient as the key temperature measurement point.

[0135] For example, as the number of temperature characteristic points of the temperature measuring point increases, the values ​​of the complex determination coefficient and the modified complex determination coefficient also increase accordingly. Analyzing from the three directions of X, Y, and Z, in the measuring point combination TA, TB, T1, T3, T7, T9, T11, T13, T14, T15, T17, T19, T21, T23, T25, and T27, the values ​​of the complex determination coefficient and the modified complex determination coefficient are the largest; therefore, the key temperature characteristic point variables in the temperature control box cavity 1 are finally determined to be TA, TB, T1, T3, T7, T9, T11, T13, T14, T15, T17, T19, T21, T23, T25, and T27, and the corresponding temperature measuring points are as follows: Figure 2 As shown, these 17 temperature measurement points will be used for temperature error modeling.

[0136] Step S1103: Move the temperature sensor 3 to the tracking detection position based on the key temperature measurement point to determine the real-time temperature data corresponding to the key temperature measurement point. Figure 7 Step S703 of the illustrated embodiment will not be described in detail here.

[0137] Step S1104: perform fusion processing on the real-time temperature data to generate the charging operating temperature of the lithium-ion battery. Figure 7 Step S704 of the illustrated embodiment will not be described in detail here.

[0138] The present embodiment provides a tracking and detection method for the charging working temperature of a lithium-ion battery. The fuzzy cluster analysis method is used to classify the temperature measurement points collected by the sensor according to the temperature change law, and then the correlation analysis method is used to select temperature characteristic points among the temperature measurement points. This can better optimize the distribution of temperature sensors, accurately measure the temperature field of the temperature control box cavity, and establish an accurate working temperature error compensation model. This can achieve the purpose of obtaining the working temperature changes around the lithium-ion battery in a simple, sensitive and accurate manner using the least number of temperature measurement points while ensuring the accuracy of the compensation temperature.

[0139] In this embodiment, a method for tracking and detecting the charging working temperature of a lithium-ion battery is provided, which can be used in the above-mentioned mobile terminals, such as mobile phones, tablet computers, etc. Fig.12 FIG. 1 is a flow chart of a method for tracking and detecting the charging operating temperature of a lithium-ion battery according to an embodiment of the present invention. Fig.12 As shown, the process includes the following steps:

[0140] Step S1201, obtain the charge and discharge data of the lithium-ion battery, and arrange the initial positions of the temperature sensors in the temperature control box based on the charge and discharge data of the lithium-ion battery to generate multiple temperature measurement point data. Fig.11 Step S1101 of the illustrated embodiment will not be described in detail here.

[0141] Step S1202: classify the multiple temperature measurement point data, and determine the key temperature measurement points based on the classified temperature measurement point data. Fig.11 Step S1102 of the illustrated embodiment will not be described in detail here.

[0142] Step S1203: Move the temperature sensor 3 to the tracking detection position based on the key temperature measurement point to determine the real-time temperature data corresponding to the key temperature measurement point. Fig.11 Step S1103 of the illustrated embodiment will not be described in detail here.

[0143] Step S1204, fusing the real-time temperature data to generate the lithium-ion battery charging operating temperature.

[0144] Specifically, the above step S1204 includes:

[0145] Step S12041, perform wavelet preprocessing on the real-time temperature data, and calculate signal-to-noise ratio data based on the real-time temperature data after wavelet preprocessing.

[0146] Specifically, Fig.13 As shown, the wavelet technology is used to remove the Gaussian white noise in the data collected by each sensor (i.e., real-time temperature data), including data cleaning, data transformation and numerical specification; the wavelet technology is used to compress the real-time temperature data; the wavelet technology is used to stratify the real-time temperature data, and the Kalman filtering is performed on the similar high-frequency coefficients and detailed low-frequency coefficients after stratification; the maximum and minimum closeness method is used to calculate the signal-to-noise ratio of the real-time temperature data (i.e., signal-to-noise ratio data).

[0147] Step S12042, performing wavelet reconstruction processing on the signal-to-noise ratio data to generate the charging operating temperature of the lithium-ion battery.

[0148] Specifically, the approximate coefficients of the signal-to-noise ratio data are fused, and the wavelet technique is used to reconstruct the data to obtain the fusion result, i.e., the charging operating temperature of the lithium-ion battery.

[0149] Furthermore, the charging operating temperature of the lithium-ion battery is input into the temperature error compensation control system to establish a high-precision constant temperature environment of the cavity.

[0150] The present embodiment provides a tracking and detection method for the charging working temperature of a lithium-ion battery, which performs wavelet preprocessing and wavelet reconstruction processing on real-time temperature data. The method is simple, fast, and has a good information retention effect, so that the charging working temperature of the lithium-ion battery has low volatility and good robustness.

[0151] The following is a specific example to illustrate the specific steps of tracking and detecting the charging operating temperature of a lithium-ion battery.

[0152] Embodiment 1:

[0153] The specific steps of tracking and detecting the charging working temperature of a lithium-ion battery include:

[0154] Step S1, analyzing the heat release law of the lithium-ion battery during the charging process to find the heat release law of the lithium-ion battery;

[0155] Step S2, testing the temperature distribution of each device in the temperature control box cavity and around the lithium-ion battery under different working conditions by means of experiments;

[0156] Step S3, using a fuzzy cluster analysis method to classify the temperature measurement points collected by the sensor according to the temperature change law;

[0157] Step S4, using a correlation analysis method, selecting temperature characteristic points from the temperature measurement points to determine key temperature measurement points;

[0158] Step S5, transmitting the position coordinate information of the key temperature measuring point under the working condition to the controller, and the controller controls the guide rail to move the temperature sensor to the designated temperature measuring point;

[0159] Step S6: obtaining the operating temperature in the temperature control box cavity during the lithium-ion battery charging process by using a wavelet Kalman filter data fusion algorithm based on the signal-to-noise ratio.

[0160] In this embodiment, a tracking and detecting device for the charging operating temperature of a lithium-ion battery is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0161] This embodiment provides a device for tracking and detecting the charging working temperature of a lithium-ion battery. Fig.14 As shown, including:

[0162] The layout module 1401 is used to obtain the charge and discharge data of the lithium-ion battery, and to layout the initial positions of the temperature sensors in the temperature control box based on the charge and discharge data of the lithium-ion battery, and to generate a plurality of temperature measurement point data;

[0163] A classification module 1402 is used to classify the multiple temperature measurement point data and determine the key temperature measurement point based on the classified temperature measurement point data;

[0164] A tracking and detection module 1403 is used to move the temperature sensor to a tracking and detection position based on the key temperature measurement point to determine the real-time temperature data corresponding to the key temperature measurement point;

[0165] The fusion processing module 1404 is used to perform fusion processing on the real-time temperature data to generate the charging operating temperature of the lithium-ion battery.

[0166] In some optional implementations, the layout module 1401 includes:

[0167] An establishing unit for establishing a temperature field of an electrochemical reaction of a lithium-ion battery based on charging and discharging data of the lithium-ion battery;

[0168] A simulation unit is used to simulate the temperature field of the electrochemical reaction of the lithium-ion battery and generate the operating temperature distribution of the lithium-ion battery;

[0169] A layout unit, used for arranging the initial positions of temperature sensors in the temperature control box based on the operating temperature distribution of the lithium-ion battery;

[0170] The test unit is used to perform a constant temperature steady state continuous charging test on a lithium electronic battery at different temperatures to generate data of multiple temperature measurement points.

[0171] In some optional implementations, the establishing unit includes:

[0172] a determination subunit, for determining an operating current density, an open circuit voltage, a terminal voltage, and a temperature of the lithium-ion battery based on charge and discharge data of the lithium-ion battery;

[0173] A calculation subunit, for calculating a heat generation rate based on an operating current density, an open circuit voltage, a terminal voltage, and a temperature of the lithium-ion battery;

[0174] A subunit is created to establish the temperature field of the electrochemical reaction of a lithium-ion battery based on the heat generation rate.

[0175] In some optional implementations, the classification module 1402 includes:

[0176] A classification unit is used to classify the data of multiple temperature measurement points by using a fuzzy cluster analysis method to generate temperature measurement points of different categories;

[0177] The determination unit is used to determine key temperature measuring points based on different categories of temperature measuring points by using a correlation analysis method.

[0178] In some optional embodiments, the classification unit includes:

[0179] A standardization processing subunit is used to construct an initial data matrix based on data of multiple temperature measurement points, perform standardization processing on the initial data matrix, and generate a normalized matrix;

[0180] A construction subunit is used for determining similarity coefficients of matrix elements based on the normalized matrix, and constructing a fuzzy similarity matrix based on the similarity coefficients of the matrix elements;

[0181] The fuzzy cluster analysis subunit is used to determine the equivalent matrix based on the fuzzy similarity matrix, and perform fuzzy cluster analysis on the equivalent matrix to generate temperature measurement points of different categories.

[0182] In some optional implementations, the determining unit includes:

[0183] A selection subunit is used to select temperature characteristic points from different categories of temperature measurement points, input the temperature characteristic points into a temperature error prediction model, and generate a complex determination coefficient;

[0184] The sorting subunit is used to sort the complex determination coefficients, select the maximum complex determination coefficient, and use the temperature measuring point corresponding to the maximum complex determination coefficient as the key temperature measuring point.

[0185] In some optional implementations, the fusion processing module 1404 includes:

[0186] A wavelet preprocessing unit, used for performing wavelet preprocessing on the real-time temperature data, and calculating signal-to-noise ratio data based on the real-time temperature data after wavelet preprocessing;

[0187] The wavelet reconstruction processing unit is used to perform wavelet reconstruction processing on the signal-to-noise ratio data to generate the charging operating temperature of the lithium-ion battery.

[0188] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0189] In this embodiment, a tracking and detection device for the charging operating temperature of a lithium-ion battery is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0190] The embodiment of the present invention also provides a computer device having the above Fig.14A device for tracking and detecting the charging operating temperature of a lithium-ion battery is shown.

[0191] See also Fig.15 , Fig.15 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.15 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.15 A processor 10 is taken as an example.

[0192] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0193] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0194] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0195] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0196] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig.15 The example of connecting through bus is taken in the following.

[0197] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0198] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0199] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for tracking and detecting the charging working temperature of a lithium-ion battery, characterized in that: Applied to a temperature control box cavity, a movable guide rail is provided on the inner wall of the temperature control box cavity, and a plurality of temperature sensors are provided on the movable guide rail; the method comprises: Acquire charge and discharge data of a lithium-ion battery, arrange initial positions of temperature sensors in a temperature control box based on the charge and discharge data of the lithium-ion battery, and generate data of a plurality of temperature measurement points; Classifying the plurality of temperature measurement point data, and determining key temperature measurement points based on the classified temperature measurement point data; Based on the key temperature measurement point, the temperature sensor is moved to a tracking detection position to determine the real-time temperature data corresponding to the key temperature measurement point; The real-time temperature data is fused and processed to generate a charging operating temperature of the lithium-ion battery; The step of classifying the plurality of temperature measurement point data and determining key temperature measurement points based on the classified temperature measurement point data includes: Using fuzzy cluster analysis method to classify the plurality of temperature measurement point data to generate temperature measurement points of different categories; Based on the temperature measuring points of different categories, determining the key temperature measuring points by using a correlation analysis method; The determining of the key temperature measurement points by using a correlation analysis method based on the temperature measurement points of different categories includes: Select temperature characteristic points from the temperature measuring points of different categories, input the temperature characteristic points into the temperature error prediction model, and generate a complex determination coefficient; wherein, by analyzing the correlation between the temperature error between a single temperature measuring point and the temperature data of each measuring point, the selected temperature measuring points are combined, and the complex determination coefficient of each combination is calculated, and the temperature characteristic value of the temperature measuring point with the largest complex determination coefficient and the corrected complex determination coefficient is selected to establish a temperature error prediction model; wherein, the temperature error prediction model is expressed as follows: In the above formula, represents the complex determination coefficient, represents the modified multiple determination coefficient, SSR represents the sum of squares of deviation, SST represents the sum of squares of regression, and SSE represents the sum of squares of residuals. represents the arithmetic mean of the temperature measurement values, n represents the number of temperature measurement points, and p represents the number of independent variables introduced into the model; The complex determination coefficients are sorted, a maximum complex determination coefficient is selected, and a temperature measurement point corresponding to the maximum complex determination coefficient is used as the key temperature measurement point.

2. The method according to claim 1, characterized in that The initial positions of the temperature sensors in the temperature control box are arranged based on the charge and discharge data of the lithium-ion battery to generate a plurality of temperature measurement point data, including: Establishing a temperature field of an electrochemical reaction of a lithium-ion battery based on the charge and discharge data of the lithium-ion battery; Simulating the temperature field of the electrochemical reaction of the lithium-ion battery to generate the operating temperature distribution of the lithium-ion battery; Arranging the initial positions of the temperature sensors in the temperature control box based on the operating temperature distribution of the lithium-ion battery; A constant temperature steady state continuous charging test is performed on the lithium electronic battery at different temperatures to generate a plurality of temperature measurement point data.

3. The method according to claim 2, characterized in that The step of establishing a temperature field for an electrochemical reaction of a lithium-ion battery based on the charge and discharge data of the lithium-ion battery comprises: Determining an operating current density, an open circuit voltage, a terminal voltage, and a temperature of the lithium-ion battery based on the charge and discharge data of the lithium-ion battery; Calculating a heat generation rate based on the operating current density, the open circuit voltage, the terminal voltage, and the temperature of the lithium-ion battery; A temperature field of the electrochemical reaction of the lithium ion battery is established based on the heat generation rate.

4. The method according to claim 1, characterized in that: The fuzzy cluster analysis method is used to classify the plurality of temperature measurement point data to generate temperature measurement points of different categories, including: constructing an initial data matrix based on the plurality of temperature measurement point data, and performing standardization processing on the initial data matrix to generate a normalized matrix; Determining matrix element similarity coefficients based on the normalized matrix, and constructing a fuzzy similarity matrix based on the matrix element similarity coefficients; An equivalence matrix is ​​determined based on the fuzzy similarity matrix, and fuzzy clustering analysis is performed on the equivalence matrix to generate temperature measurement points of different categories.

5. The method according to claim 1, characterized in that The step of fusing the real-time temperature data to generate the charging operating temperature of the lithium-ion battery includes: Performing wavelet preprocessing on the real-time temperature data, and calculating signal-to-noise ratio data based on the real-time temperature data after the wavelet preprocessing; The signal-to-noise ratio data is subjected to wavelet reconstruction processing to generate the charging operating temperature of the lithium-ion battery.

6. A tracking and detecting device for charging working temperature of a lithium-ion battery, characterized in that: The device comprises: A layout module, used to obtain the charge and discharge data of the lithium-ion battery, layout the initial positions of the temperature sensors in the temperature control box based on the charge and discharge data of the lithium-ion battery, and generate a plurality of temperature measurement point data; A classification module, used to classify the plurality of temperature measurement point data, and determine key temperature measurement points based on the classified temperature measurement point data; A tracking and detection module, used to move the temperature sensor to a tracking and detection position based on the key temperature measurement point, and determine the real-time temperature data corresponding to the key temperature measurement point; A fusion processing module, used for fusing the real-time temperature data to generate a charging operating temperature of the lithium-ion battery; The classification modules include: A classification unit is used to classify the data of multiple temperature measurement points by using a fuzzy cluster analysis method to generate temperature measurement points of different categories; A determination unit, used for determining key temperature measurement points based on different categories of temperature measurement points by using a correlation analysis method; The identified units include: The selection subunit is used to select temperature characteristic points from different categories of temperature measuring points, input the temperature characteristic points into the temperature error prediction model, and generate a complex determination coefficient; wherein, by analyzing the correlation between the temperature error between a single temperature measuring point and the temperature data of each measuring point, the selected temperature measuring points are combined, and the complex determination coefficient of each combination is calculated, and the temperature characteristic value of the temperature measuring point with the largest complex determination coefficient and the corrected complex determination coefficient is selected to establish a temperature error prediction model; wherein, the temperature error prediction model is expressed as follows: In the above formula, represents the complex determination coefficient, represents the modified multiple determination coefficient, SSR represents the sum of squares of deviation, SST represents the sum of squares of regression, and SSE represents the sum of squares of residuals. represents the arithmetic mean of the temperature measurement values, n represents the number of temperature measurement points, and p represents the number of independent variables introduced into the model; The sorting subunit is used to sort the complex determination coefficients, select the maximum complex determination coefficient, and use the temperature measuring point corresponding to the maximum complex determination coefficient as the key temperature measuring point.

7. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the tracking and detection method for charging operating temperature of a lithium-ion battery according to any one of claims 1 to 5 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the tracking and detection method for the charging operating temperature of a lithium-ion battery according to any one of claims 1 to 5.

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