Early warning method for internal temperature rise of energy storage battery, and temperature field construction method and system

Abstract

The invention relates to the technical field of energy storage battery temperature monitoring, in particular to an early warning method for internal temperature rise of an energy storage battery and a temperature field construction method and system. Calibration experiments are carried out on sample batteries of the same model at various environment temperatures, and a sound velocity-temperature linear relation function is obtained through fitting by adopting a least square method; calculating the similarity between the transmitted and received signals by using a cross-correlation function so as to extract the flight time of the ultrasonic wave; on the basis of the flight time variation and the sound velocity-temperature relation, the path temperature rise is rapidly calculated, and early warning is performed when the threshold value is exceeded; meanwhile, the interior of the battery is discretized into a plurality of three-dimensional monitoring units, the infinitesimal length of each ultrasonic path in the three-dimensional monitoring units is calculated, a discrete summation equation is established, and a three-dimensional temperature field is reconstructed through linear iteration solution. Therefore, the problems that the internal temperature of the battery is difficult to accurately obtain and early warning is delayed are effectively solved, comprehensive safety guarantee is provided for the energy storage battery, and the operation reliability is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage battery temperature monitoring technology, specifically to an early warning method, a temperature field construction method, and a system for energy storage battery internal temperature rise. Background Technology

[0002] As core equipment in new energy power generation and grid energy storage, the operational safety of energy storage batteries directly determines the reliability of the entire energy storage system. Temperature is a critical parameter affecting the performance and safety of energy storage batteries. If localized overheating or abnormal temperature rise inside the battery is not monitored and warned in time, it can easily lead to thermal runaway, resulting in safety accidents such as battery fires. Therefore, developing accurate, real-time, and non-invasive internal temperature rise monitoring and early warning technology for energy storage batteries is of great practical significance for ensuring the stable operation of energy storage systems.

[0003] Currently, temperature monitoring of energy storage batteries mainly employs two types of technologies: traditional contact temperature measurement and non-contact temperature measurement. Contact temperature measurement, represented by thermocouples and thermistors, acquires temperature by attaching sensors to the battery surface or embedding them near the tabs. Non-contact temperature measurement includes infrared temperature measurement and fiber optic temperature measurement, which rely on infrared radiation characteristics or fiber optic sensing principles to obtain the battery surface temperature. A more advanced approach attempts to infer the internal temperature from the surface temperature distribution, but this essentially still relies on indirect deduction from external temperature measurement data and does not directly obtain temperature information of the core area inside the battery.

[0004] Based on the above analysis, it can be seen that contact temperature measurement can only obtain the surface or near-surface temperature of the battery and cannot detect abnormal temperature rise in key areas such as internal tabs and cell connections; non-contact temperature measurement such as infrared and fiber optics is easily affected by ambient light and dust, and the measurement accuracy depends on the surface condition of the battery, and it is also difficult to penetrate the battery shell to capture internal temperature changes; the scheme of estimating the internal temperature based on the surface temperature has a large error in the calculation result due to the complex internal structure of the battery and the nonlinear thermal conduction characteristics, and cannot meet the high accuracy requirements of thermal runaway early warning.

[0005] It is evident that existing commonly used temperature measurement methods are insufficient to obtain the internal temperature of batteries, thus failing to detect potential temperature rise risks inside batteries in advance. This results in insufficient timeliness and accuracy of early warnings, and an inability to provide comprehensive protection for the safe operation of energy storage batteries. Summary of the Invention

[0006] To address the technical problem of failing to detect potential temperature rise risks within batteries due to the difficulty in obtaining their internal temperatures, this invention provides an early warning method for internal temperature rise in energy storage batteries. Based on this early warning method, and to further address the challenge of accurately obtaining the internal temperature of batteries, this invention also provides a method for constructing an internal temperature field within an energy storage battery. Building upon the early warning and construction methods, this invention also provides hardware for the application methods, namely, an internal temperature monitoring system for an energy storage battery and an energy storage battery.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for early warning of internal temperature rise in an energy storage battery includes the following early warning steps: Based on the real-time flight time t of the ultrasonic wave propagating along its current propagation path inside the battery meas and standard flight time t at standard temperature ref Calculate the change in flight time Δt=t meas -t ref ; Based on Δt, calculate the temperature rise ΔT along the current propagation path: ; In the formula, L represents the length of the current propagation path; v ref denoted as the standard speed of sound when ultrasound propagates in a battery at standard temperature; 'a' represents the coefficient of the first-order term in the linear function of the speed of sound and temperature when ultrasound propagates in a battery. When ΔT exceeds the preset temperature rise threshold, an early warning signal is generated and output.

[0008] As a further improvement to the above scheme: t meas The acquisition process is as follows: Based on the transmitted and received ultrasonic waves propagating along the current propagation path inside the battery, a cross-correlation function is established between them. The time offset corresponding to the peak value of the cross-correlation function is extracted, and this time offset is t. meas The cross-correlation function is: ; In the formula, R(τ) represents the cross-correlation function; s ref (t) represents the transmitted signal at time t; τ represents the time offset; s ref (t+τ) represents the received signal at time t+τ; dt represents the time derivative.

[0009] As a further improvement to the above scheme, the process of obtaining the linear relationship function between sound speed and temperature is as follows: Sample batteries of the same model were placed in a temperature-controlled environment, and the real-time flight time t of the ultrasonic waves along each propagation path was measured sequentially at multiple ambient temperatures T.meas And calculate the corresponding speed of sound v=L / t meas Data points (T, v) are generated; then, the least squares method is used to fit the linear relationship function of sound speed-temperature based on all data points (T, v), which is v = a·T + b, where b is a constant term.

[0010] A method for constructing the internal temperature field of an energy storage battery includes the following construction steps: The battery interior is divided into M×N×P uniform three-dimensional monitoring units according to the length, width, and thickness directions; based on the coordinates of the M×N transmitting and receiving probe arrays symmetrically arranged on the upper and lower surfaces of the battery, the micro-element length of each propagation path in each three-dimensional monitoring unit is calculated. The real-time flight time of each transmission path is calculated using early warning methods; Based on the infinitesimal length and real-time flight time, a discrete summation equation is established: ; In the formula, t meas,k ds represents the real-time flight time corresponding to the k-th propagation path; mnp,k T represents the infinitesimal length of the k-th propagation path within the three-dimensional monitoring unit located at (m,n,p), where m∈[1,M], n∈[1,N], and p∈[1,P]; mnp This represents the temperature of the three-dimensional monitoring unit located at (m,n,p); The discrete summation equation is solved iteratively using a tomographic imaging algorithm to obtain the temperature of all three-dimensional monitoring units after iterative convergence. The temperatures are then integrated to form a three-dimensional temperature field inside the battery.

[0011] As a further improvement to the above scheme: T mnp The acquisition process is as follows: Obtain the flight distance L of the ultrasonic wave from the transmitting probe to (m,n,p). mnp and real-time flight time t meas,mnp Based on L mnp and t meas,mnp Calculate the change in flight time Δt mnp =t meas,mnp -t ref ; According to Δt mnp Calculate the temperature rise from the transmitting probe to (m,n,p). The sum of the battery surface temperature at the transmitting probe and the temperature rise is T. mnp .

[0012] As a further improvement to the above scheme, the linearized iterative solution process is as follows: Step 1: Set the initial temperature for all 3D monitoring units. All are standard temperatures T ref The initial sound velocity of each three-dimensional monitoring unit was calculated based on the linear relationship function between sound velocity and temperature. ; Step 2: Measure the temperature of the three-dimensional monitoring unit in the i-th iteration. The nonlinear terms in the discrete summation equation exist The Taylor expansion is expressed as a first-order approximation: ; in, , This represents the temperature correction amount for the i-th iteration; Step 3: Substitute the first-order approximate expression into the discrete summation equation to obtain... A system of linear equations with unknowns: ; In this system of linear equations, the left-hand side represents the time-of-flight error of the k-th propagation path during the i-th iteration. The right side of the equal sign represents a linear combination of temperature corrections; Step 4: Solve the linear equations using algebraic reconstruction techniques to obtain the temperature correction for each three-dimensional monitoring unit. ; According to the formula Update the temperature of each three-dimensional monitoring unit; Step 5: Calculate the absolute value of temperature change for all three-dimensional monitoring units. If the maximum value is less than the preset accuracy threshold, the iteration stops and the current temperature of all three-dimensional monitoring units is output; otherwise, return to step two to continue the iteration.

[0013] As a further improvement to the above scheme, the specific process of solving the linear equation system using algebraic reconstruction technology is as follows: For the k-th propagation path, calculate its theoretical flight time for the i-th iteration. ; Calculate the error between real-time flight time and theoretical flight time. ; Update the temperature correction using the following formula: ; In the formula, λ represents the relaxation factor.

[0014] As a further improvement to the above scheme: solve for the coordinates of the two intersection points of the propagation path and the current three-dimensional monitoring unit, and calculate the distance between the two intersection points using the spatial distance formula. This distance is the infinitesimal length of the propagation path in the current three-dimensional monitoring unit.

[0015] An internal temperature monitoring system for an energy storage battery, comprising: The probe array module includes a symmetrical array of transmitting and receiving probes arranged on the upper and lower surfaces of the battery, used to transmit and receive ultrasonic signals. The signal processing and acquisition module is electrically connected to the probe array module. It is used to generate excitation signals to drive the probe to emit and to acquire the echo signals after they have been propagated by the battery. The data processing and control module communicates with the signal processing and acquisition module and performs the following operations: The control probe array module operates according to a predetermined sequence; The echo signal is processed to extract the real-time flight time of the ultrasonic wave along each propagation path; Based on real-time flight time, a warning method is executed to provide real-time warnings, and / or a method for constructing the internal temperature field of the energy storage battery is executed to construct the internal temperature field of the battery. Based on the calculation results, status data and early warning signals are generated; The communication interface module is used to output status data and warning signals to the battery management system or external monitoring equipment.

[0016] An energy storage battery, which integrates an internal temperature monitoring system.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention compares the real-time flight time of ultrasonic waves propagating along the battery's internal path with the standard flight time at a standard temperature to obtain the change in flight time reflecting temperature changes. Then, combining this with the previously calibrated linear relationship function between sound velocity and temperature, and substituting it into a precisely derived temperature rise calculation formula, the internal temperature rise corresponding to that path is directly calculated. The entire process does not require embedding sensors inside the battery or damaging the battery structure, achieving non-invasive internal temperature correlation acquisition and overcoming the technical bottleneck of "inability to directly detect internal temperature." Simultaneously, the calculation logic is simple and efficient, requiring no complex iterative calculations, and can quickly output temperature rise results. It can promptly capture minute temperature rise anomalies in key areas such as internal tabs and cell connections. Compared to traditional monitoring methods that rely on surface temperature, this significantly improves the timeliness and accuracy of temperature rise risk detection, completely solving the problem of missed detection and delayed warnings due to the difficulty in accessing internal temperatures. This provides direct and reliable technical support for early warning of thermal runaway in energy storage batteries.

[0018] 2. This invention divides the battery interior into M×N×P uniform three-dimensional monitoring units, transforming the continuous internal temperature field into precisely solvable discrete unit temperatures. This achieves comprehensive monitoring of the entire battery interior, avoiding omissions of temperatures in critical areas. Secondly, relying on an M×N array of transmitting and receiving probes symmetrically arranged on the upper and lower surfaces of the battery, multi-directional intersecting ultrasonic propagation paths are formed. The length of each path within each unit is accurately calculated through three-dimensional spatial computation. Combined with the linear relationship between sound speed and temperature, a direct correlation is established between "real-time flight time - micro-element length - unit temperature," ensuring the reliability of the physical basis for temperature calculation. Finally, the total flight time of each propagation path is decomposed into the sum of the propagation times of each three-dimensional monitoring unit through a discrete summation equation. Then, a tomographic imaging algorithm is used for linearized iterative solution. Through multiple iterations and corrections, the calculated unit temperature value approaches the true value infinitely, effectively offsetting errors caused by factors such as non-uniformity of the internal battery medium and ultrasonic propagation interference. The entire process does not require damaging the battery structure, achieving non-invasive internal temperature detection. This not only breaks through the bottleneck of traditional technology that "cannot penetrate the outer shell to obtain the internal temperature," but also significantly improves the accuracy and comprehensiveness of internal temperature measurement through the collaborative design of global discretization, multi-path constraints, and high-precision iterative solution, ultimately achieving accurate reconstruction of the three-dimensional temperature field inside the battery. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the method flow in this invention.

[0020] Figure 2 This is a diagram showing the distribution of the ultrasonic probes on the top of the battery in this invention.

[0021] Figure 3 This is a diagram showing the division of the three-dimensional monitoring unit in this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention discloses a complete solution including temperature rise early warning, a temperature field construction method, and a supporting monitoring system and dedicated energy storage battery. For example... Figure 1As shown, by utilizing the correlation between ultrasound and temperature, a linear relationship between sound speed and temperature is obtained through sample calibration, and a high-precision time of flight is extracted through a cross-correlation function. Based on this, the path temperature rise is quickly calculated and an over-threshold warning is achieved. Simultaneously, the battery is discretized into three-dimensional monitoring units, and through the calculation of infinitesimal element lengths, the establishment of discrete summation equations, and linearization iteration, a three-dimensional temperature field is reconstructed. This effectively solves the problems of difficulty in accurately obtaining internal temperature and delayed warnings, providing key technical support for the safe and efficient operation of energy storage systems.

[0024] I. Early warning methods for internal temperature rise in energy storage batteries

[0025] This section first calibrates the sample battery of the same model at multiple temperature points under a controlled temperature environment, and then uses the least squares method to fit the linear relationship function of sound speed-temperature. Next, by extracting the peak value of the cross-correlation function of the transmitted and received ultrasonic signals, the real-time flight time of the ultrasonic wave along the propagation path inside the battery is obtained. Combined with the standard flight time at a standard temperature, the change in flight time is calculated, and the path temperature rise is calculated by substituting it into the temperature rise calculation formula. When the temperature rise exceeds the preset threshold, an early warning signal is generated and output in real time, realizing a rapid and non-invasive early warning of the internal temperature rise of the battery.

[0026] (a) Sound speed-temperature linear relationship function

[0027] Select a sample battery of the same model as the energy storage battery to be monitored, place it in a temperature-controlled constant temperature chamber, and set multiple continuous ambient temperatures. The temperature range covers the normal operating temperature and critical temperature range of the energy storage battery (0℃-80℃, with a step of 5℃).

[0028] For each ambient temperature, after maintaining a stable temperature in the incubator for 30 minutes, the internal temperature of the sample battery becomes uniform and the same as the ambient temperature. Ultrasonic waves are emitted into the sample battery via a transmitting probe mounted on its upper surface, and the echo signals propagating along a preset propagation path are collected by a receiving probe on the lower surface of the sample battery. Based on the principle of cross-correlation, a cross-correlation function between the transmitted and received signals is established: (1-1) In the formula, R(τ) represents the cross-correlation function; s ref (t) represents the transmitted signal at time t; τ represents the time offset; s ref (t+τ) represents the received signal at time t+τ; dt represents the time derivative.

[0029] Numerical calculations are performed on the cross-correlation function R(τ) to find the time offset corresponding to its peak value. This time offset is the real-time flight time t of the ultrasonic wave along the current propagation path. meas .

[0030] The cross-correlation function can effectively suppress the effects of noise and waveform distortion by calculating the similarity between the transmitted and received signals. Even if the received signal is attenuated or slightly distorted, as long as the core characteristics of the transmitted signal are retained, the cross-correlation function will still form a significant peak at the time-shift matching position of the signal. The time shift corresponding to this peak is the accurate flight time of the ultrasound.

[0031] Given the length L of the preset propagation path (obtained through three-dimensional spatial measurement based on the positions of the ultrasonic transmitter and receiver), the velocity of sound is calculated using the formula v = L / t. meas Calculate the speed of sound v corresponding to each ambient temperature to form the corresponding data point (T, v).

[0032] Collect all data points (T, v) corresponding to all ambient temperatures and all propagation paths (multiple propagation paths are distributed throughout the battery). Use the least squares method to perform linear fitting on the data points, with the objective function being v = a. T+b, where a is the coefficient of the first-order term and b is the constant term; by solving the minimization problem, the specific values ​​of the fitting coefficients a and b are obtained, thereby obtaining the linear relationship function between sound speed and temperature.

[0033] (II) Calculation and early warning of temperature rise along the transmission path

[0034] Predetermine the standard temperature T ref (Selecting a typical normal operating temperature of 25℃), calculate the standard speed of sound v based on the linear relationship function of speed of sound and temperature. ref .

[0035] Measure the length L of the current propagation path and obtain the real-time flight time t through the steps described above. meas and standard flight time t at standard temperature ref ; Calculate the change in flight time Δt=t meas -t ref .

[0036] Based on Δt, calculate the temperature rise ΔT along the current propagation path: (1-2) A preset temperature rise threshold (determined based on battery safety performance testing, with a preset temperature rise threshold of 5℃) is set. The calculated ΔT is compared with the preset threshold. If ΔT > 5℃, a warning signal is immediately generated and output, and transmitted to the battery management system.

[0037] (III) Regional warming

[0038] First, determine the propagation path through the target stereo monitoring unit and calculate the distance from the transmitting probe along each propagation path to the center point of the target stereo monitoring unit. Determine the farthest distance L. maxCalculate the corresponding excitation delay of the transmitting probe: (1-3) In the formula, Δt j Let L be the acoustic time difference of the j-th propagation path; j This represents the length of the j-th propagation path among all propagation paths passing through the target's three-dimensional monitoring unit.

[0039] Next, for the j-th propagation path traversing the target's three-dimensional monitoring unit, its acoustic time difference is calculated based on the flight time: (1-4) Among them, t meas,j t represents the real-time flight time of the j-th propagation path; ref,j The reference flight time is for the j-th propagation path. The acoustic time difference Δt j It is a direct representation of the average temperature rise of the target three-dimensional monitoring unit through which the propagation path passes.

[0040] Then, calculate the amplitude difference: (1-5) Where, ΔA j Let A be the amplitude difference of the j-th propagation path; j Let A be the real-time received acoustic amplitude for the j-th propagation path. 0,j Let α(S) be the reference acoustic amplitude for the j-th propagation path, α(S) be the attenuation coefficient related to the current physical state S, and α0 be the reference attenuation coefficient; the acoustic amplitude difference ΔA j It is a representation of the change in the internal physical state S of the battery through which the propagation path passes.

[0041] For the same target stereo monitoring unit that is covered by J propagation paths, multi-path feature fusion is performed, and the fused acoustic time difference ∑Δt and fused acoustic amplitude difference ∑ΔA of the target stereo monitoring unit are calculated: (1-6) (1-7) Among them, w t,j and w A,j These are the weighting coefficients for the acoustic time difference and acoustic amplitude difference of the j-th propagation path, respectively.

[0042] Finally, an early warning judgment is made, and the judgment logic is as follows: If ∑Δt>Δt 阈值 If so, the temperature of the target 3D monitoring unit is determined to be abnormal.

[0043] If ∑ΔA>ΔA 阈值 If so, the physical state of the target three-dimensional monitoring unit is determined to be abnormal.

[0044] (iv) Derivation of the temperature rise calculation formula

[0045] 1. Relevant physical relationships

[0046] (1) Linear relationship between sound speed and temperature

[0047] As analyzed above, the speed of sound v inside the battery is linearly related to the temperature T: v = a T+b.

[0048] When an ultrasonic wave propagates along a fixed propagation path, the flight time t and the path length L and the speed of sound v satisfy the following condition: t = L / v, where the path length L is a fixed value and is not affected by temperature.

[0049] (2) The physical relationship between flight time and speed of sound

[0050] Standard temperature T ref : Corresponding standard speed of sound v ref =a T ref +b, standard flight time t ref =L / v ref .

[0051] Real-time temperature T=T ref +ΔT: ΔT is the real-time temperature rise (to be determined), corresponding to the real-time speed of sound v=a T+b, real-time flight time t meas =L / v.

[0052] Change in flight time Δt=t meas -t ref This is the difference between real-time flight time and standard flight time.

[0053] 2. Derivation process

[0054] The real-time temperature T=T ref Substituting +ΔT into the linear relationship between the speed of sound and temperature, we get the real-time speed of sound: v=a (T ref +ΔT)+b=(a T ref +b)+a ΔT=v ref +a ΔT, where ΔT represents a small temperature rise, a ΔT v ref Therefore, it satisfies the small perturbation approximation condition.

[0055] Real-time flight time t meas =L / v, so v=v ref +a Substituting ΔT into the equation, we get: (1-8) Based on the small perturbation approximation ( 1) Using Taylor expansion, 1 / (1+x)≈1-x (ignoring higher-order terms), we simplify to get: (1-9) Substituting the simplified result (1-4) into the change in flight time Δt=t meas -t ref From this, we can obtain: (1-10) By transforming formula (1-10), we can obtain the temperature rise calculation formula shown in formula (1-2).

[0056] II. Methods for Constructing the Internal Temperature Field of Energy Storage Batteries

[0057] This section first divides the battery interior into M×N×P uniform three-dimensional monitoring units according to length, width, and thickness. Based on the coordinates of the M×N transmitting and receiving probe arrays symmetrically arranged on the upper and lower surfaces of the battery, the infinitesimal length of each propagation path within each unit is calculated through three-dimensional spatial operations. Then, the real-time flight time of each path is obtained through linear calibration of sound speed-temperature and peak extraction of cross-correlation function, and a discrete summation equation is established. Subsequently, the initial temperature of the unit is determined by combining the standard temperature or the surface temperature at the probe with the path temperature rise. The nonlinear terms in the equation are Taylor expanded into a first-order approximate expression and transformed into a system of linear equations. Algebraic reconstruction technology is used to iteratively solve for the temperature of each unit (stability is adjusted by relaxation factors until the temperature change is less than a preset accuracy threshold). Finally, all unit temperatures are integrated to form a three-dimensional temperature field inside the battery.

[0058] (I) Division of Three-Dimensional Monitoring Units

[0059] like Figure 2 As shown, the interior of the battery is divided into M×N×P uniform three-dimensional monitoring units according to its length, width, and thickness. Simultaneously, M×N transmitting and receiving probe arrays are symmetrically arranged on the upper and lower surfaces of the battery (the upper surface is the transmitting probe array, and the lower surface is the receiving probe array). A three-dimensional Cartesian coordinate system is established with the bottom left corner of the battery as the preset origin, and the three-dimensional coordinates of each probe are obtained.

[0060] (ii) Calculating the length of the infinitesimal element

[0061] Based on the three-dimensional coordinates of the ultrasonic transmitting and receiving probes, or the linear equation of the propagation path between them, this linear equation passes through the corresponding three-dimensional monitoring unit and intersects the unit at two points. The coordinates of these two points are obtained in a three-dimensional Cartesian coordinate system, and the distance between them is the infinitesimal length of the current propagation path within the current three-dimensional monitoring unit.

[0062] By repeating the above steps, the micro-element lengths of the current propagation path within all the three-dimensional monitoring units it passes through, as well as the micro-element lengths of all other propagation paths within their respective units, can be obtained.

[0063] (iii) Obtaining the temperature of the three-dimensional monitoring unit

[0064] Determine the flight distance L of the ultrasonic wave from the transmitting probe to the center of the current stereo monitoring unit (the stereo monitoring unit is relatively small, and the coordinates of its center represent the position of the entire stereo monitoring unit). mnp .

[0065] Following the steps described above for obtaining real-time flight time, measure the real-time flight time t of the ultrasonic wave from the transmitting probe to the center of the current stereo monitoring unit. meas,mnp Based on L mnp and t meas,mnp Calculate the change in flight time Δt mnp =t meas,mnp -t ref .

[0066] Substituting these values ​​into formula (1-2), the temperature rise along the propagation path from the transmitting probe to the center of the current 3D monitoring unit is calculated. The surface temperature of the battery at the transmitting probe is measured and added to this temperature rise; the sum is the temperature T of the current 3D monitoring unit. mnp .

[0067] (iv) Establishing the discrete summation equation

[0068] 1. Corresponding physical relationship

[0069] (1) Path integral principle of flight time

[0070] The total flight time t of the ultrasonic wave along the k-th propagation path from the transmitting probe to the receiving probe meas,k , which is the sum of the propagation times of the ultrasonic wave in all three-dimensional monitoring units along its propagation path. For continuous space, the total flight time satisfies the path integral relationship: (2-1) In the formula, ds mnp,k The length of the infinitesimal element within the three-dimensional monitoring unit s located at (m,n,p) represents the length of the k-th propagation path, where m∈[1,M], n∈[1,N], and p∈[1,P].mnp,k Let represent the ultrasonic velocity of the k-th propagation path within the three-dimensional monitoring unit s located at (m,n,p).

[0071] (2) Sound speed-temperature linear correlation

[0072] As can be seen from the previous calibration, the ultrasonic velocity and the temperature at the location satisfy a linear relationship: v(s)=a·T(s)+b; where T(s) is the temperature at point s of the three-dimensional monitoring unit.

[0073] (3) Spatial discretization assumption

[0074] To transform the continuous integral into a computable discrete form, the battery interior is divided into M×N×P uniform three-dimensional monitoring units along the length, width, and thickness directions (the size of each three-dimensional monitoring unit is much smaller than the overall battery size). It is assumed that the temperature within each three-dimensional monitoring unit is uniform, i.e., the temperature within the same three-dimensional monitoring unit is temperature T. mnp This transforms integral equations that cannot be directly solved into a system of algebraic equations that can be solved by numerical algorithms, thus providing a foundation for subsequent linearization iterations.

[0075] 2. Derivation process

[0076] (1) Discretization of path elements

[0077] like Figure 3 As shown, the k-th propagation path passes through several three-dimensional monitoring units. The total length of the propagation path is divided into multiple discrete unit segments according to the number of three-dimensional monitoring units it passes through. The length of the infinitesimal element within the three-dimensional monitoring unit located at (m,n,p) is denoted as d. smnp,k The sum of the lengths of all infinitesimal elements equals the total path length, that is: (2-2) (2) Discretized representation of the propagation time of infinitesimal elements Because the temperature is uniform within each three-dimensional monitoring unit (T) mnp Based on the linear relationship between sound speed and temperature, the current sound speed within the stereo monitoring unit is v. mnp =a·T mnp +b. Therefore, the propagation time of ultrasound within this three-dimensional monitoring unit is: dt mnp,k =ds mnp,k / v mnp =ds mnp,k / (a·T mnp +b).

[0078] (3) Discrete summation transformation of total flight time

[0079] The total flight time is the sum of the propagation times of all three-dimensional monitoring units. This is achieved by replacing the continuous path integral with a summation form for discrete units, i.e.: (2-3) (4) Substitute into the infinitesimal propagation time expression: dt mnp,k =ds mnp,k / (a·T mnp Substituting +b) into formula (2-3), we finally obtain the discrete summation equation: (2-4) (v) Linearized iterative solution A tomographic imaging algorithm is used to linearize and iteratively solve the discrete summation equation, obtaining the temperature of all three-dimensional monitoring units after iterative convergence. These temperatures are then integrated to form a three-dimensional temperature field inside the battery. The details are as follows: Step 1: Set the initial temperature for all 3D monitoring units. All are standard temperatures T ref The initial sound velocity of each three-dimensional monitoring unit was calculated based on the linear relationship function between sound velocity and temperature. .

[0080] Step 2: Measure the temperature of the three-dimensional monitoring unit in the i-th iteration. The nonlinear terms in the discrete summation equation exist The Taylor expansion is expressed as a first-order approximation: (2-5) in, , This represents the temperature correction amount for the i-th iteration.

[0081] Step 3: Substitute the first-order approximate expression into the discrete summation equation to obtain... A system of linear equations with unknowns: (2-6) In this system of linear equations, the left-hand side represents the time-of-flight error of the k-th propagation path during the i-th iteration. The right side of the equal sign represents a linear combination of temperature corrections.

[0082] Step 4: Solve the linear equations using algebraic reconstruction techniques to obtain the temperature correction for each three-dimensional monitoring unit. (The specific process of solving a system of linear equations using algebraic reconstruction techniques is as follows:) For the k-th propagation path, calculate its theoretical flight time for the i-th iteration. ; Calculate the error between real-time flight time and theoretical flight time. ; Update the temperature correction using the following formula: (2-7) In the formula, λ represents the relaxation factor, which is usually between 0.1 and 1.0.

[0083] According to the formula Update the temperature of each three-dimensional monitoring unit.

[0084] Step 5: Calculate the absolute value of temperature change for all three-dimensional monitoring units. If the maximum value is less than the preset accuracy threshold, the iteration stops and the current temperature of all three-dimensional monitoring units is output; otherwise, return to step two to continue the iteration.

[0085] Step 6: Integrate all the obtained temperatures to form a three-dimensional temperature field inside the battery.

[0086] III. Examples of Internal Temperature Rise Early Warning and Temperature Field Construction for Energy Storage Batteries

[0087] (I) Setting of basic experimental parameters

[0088] 1. Setting basic parameters for energy storage batteries

[0089] It uses lithium iron phosphate (LFP) cells, with a rated voltage of 51.2V, a rated capacity of 200Ah, and a nominal energy of 10.24kWh. The battery has a rectangular shape and a length of L. bat =65cm, width W bat =52cm, thickness H bat =22cm. Normal operating temperature range is 0℃~55℃ for charging and -20℃~60℃ for discharging. Nominal cycle life ≥6000 cycles (at 25℃ and 80% depth of discharge).

[0090] 2. Division of three-dimensional monitoring units

[0091] The battery is divided into 5×3×2 (M=5, N=3, P=2) uniform three-dimensional monitoring units according to its length, width, and thickness.

[0092] 3. Probe array configuration

[0093] A 5×3 (M=5, N=3) transmitter / receiver array is symmetrically arranged on the upper and lower surfaces of the battery. Each probe is located at the center of the surface grid, and the probe operates at a frequency of 500kHz.

[0094] 4. Standard parameter settings

[0095] Standard temperature T ref=25℃, after calibration, the linear relationship function between sound speed and temperature is obtained as v = -2.82T + 971.4 (unit: m / s), that is, the coefficient of the first term is a = -2.82m / (s·℃), and the constant term is b = 971.4m / s; the standard speed of sound v ref =(-2.82)×25+971.4=901m / s; preset temperature rise threshold 5℃, iteration accuracy threshold 0.1℃, relaxation factor λ=0.8.

[0096] (II) Sound speed-temperature calibration experimental data

[0097] Selected sample batteries of the same model were calibrated at multiple temperature points in a temperature-controlled constant temperature chamber. Some experimental data are shown in Table 1.

[0098] Table 1. Experimental data for sound velocity-temperature calibration

[0099] Based on the data in Table 1, the data points were fitted using the least squares method, and the linear relationship function between sound speed and temperature was obtained as v = -2.82T + 971.4, with a goodness of fit R² = 0.99832, which meets the accuracy requirements.

[0100] (III) Implementation process and data of temperature rise warning

[0101] 1. Measuring flight time

[0102] A vertical propagation path (the k=3rd propagation path) is selected in the central region of the battery, from the upper surface transmitting probe T3,2 to the lower surface receiving probe R3,2; the selected path length L=0.10m, that is, from the center of the three-dimensional monitoring unit (3,2,1) to the center of the three-dimensional monitoring unit (3,2,2). The real-time flight time t is extracted by the cross-correlation function. meas =112.75μs, the standard flight time t of this propagation path at standard temperature. ref =110.99μs.

[0103] 2. Calculate the temperature rise

[0104] Change in flight time Δt=t meas -t ref =112.75-110.99=1.76μs.

[0105] Temperature rise calculation: .

[0106] 3. Early Warning Results

[0107] Since ΔT=5.07℃>the preset threshold of 5℃, the system immediately generates and outputs a first-level warning signal, which is then transmitted to the battery management system.

[0108] (iv) Implementation process and data of three-dimensional temperature field construction

[0109] 1. Calculation of infinitesimal element length

[0110] Taking the k=3 propagation path (from the upper surface transmitting probe T3,2 to the lower surface receiving probe R3,2) as an example, its straight line equation is calculated in a three-dimensional coordinate system, passing through the following three-dimensional monitoring unit. The result of the micro-element length calculation is shown in Table 2.

[0111] Table 2. Length of Infinite Elements

[0112] 2. Linearized iterative solution (SART algorithm)

[0113] (1) Initialization

[0114] Set the initial temperature of all three-dimensional monitoring units. The temperature was measured by a surface temperature sensor.

[0115] Initial speed of sound: .

[0116] (2) First iteration calculation

[0117] Theoretical time calculation: Theoretical time based on initial temperature field calculation path 3: ; Error calculation: Actual measurement ,error .

[0118] Temperature Correction: The error is positive (indicating it's actually hotter), requiring an increase in unit temperature; after correction, the central area unit temperature is updated to... .

[0119] (3) Final convergence result

[0120] After 5 iterations, the time error satisfies <0.1μs, and the final temperature field core data is output as shown in Table 3.

[0121] Table 3 Core data of the temperature field

[0122] According to the data in Table 3, there are obvious hot spots in the center of the battery ((3,2,1) and (3,2,2)), which is consistent with the warning conclusion.

[0123] 3. Implementation effect verification

[0124] (1) Accuracy verification

[0125] Measured value: The reading of the fiber optic temperature sensor pre-embedded at the center position is 31.0℃.

[0126] Calculated value: The mean value at the center of the temperature field constructed in this embodiment is... .

[0127] Conclusion: The error is only ±0.1℃, which proves the extremely high accuracy of the parametric model based on the value of a = -2.82.

[0128] (2) Response speed

[0129] Early warning calculation (single path) time: <10ms.

[0130] 3D reconstruction (full field) time: 120ms.

[0131] Compared to traditional thermocouples (response hysteresis > 2s), this solution achieves true real-time internal monitoring.

[0132] The time from signal acquisition to early warning output is 8ms, and the time to complete temperature field reconstruction is 120ms, which is much faster than traditional monitoring technology (≥500ms), thus achieving real-time monitoring.

[0133] (3) Reliability verification

[0134] After running continuously for 72 hours, the system did not produce any false alarms (such as mistaking normal temperature rise for a fault) or missed alarms, verifying the robustness of the algorithm in the linear range of vT.

[0135] IV. Internal Temperature Monitoring System for Energy Storage Batteries

[0136] This energy storage battery internal temperature monitoring system is an integrated system adapted for non-invasive temperature monitoring and early warning of energy storage batteries. At its core, it uses ultrasonic technology to realize early warning of internal temperature rise and accurate construction of three-dimensional temperature field. The system is composed of four core modules working together, and its functions cover the entire process of "signal transmission - data acquisition - algorithm calculation - result output".

[0137] The system's probe array module comprises an M×N array of transmitting and receiving probes symmetrically arranged on the upper and lower surfaces of the battery. These probes are responsible for directional transmission of ultrasonic signals and receiving echo signals propagating within the battery, providing a foundation for subsequent data acquisition. The signal processing and acquisition module is electrically connected to the probe array module. It generates high-voltage pulse excitation signals to drive the probes to transmit ultrasonic waves and performs preprocessing such as filtering and amplification on the echo signals to ensure signal quality. The data processing and control module, as the core of the system, not only controls the probe array to operate in a predetermined sequence but also extracts the real-time flight time of ultrasonic waves along each propagation path using a cross-correlation function. It simultaneously executes a temperature rise warning algorithm (calculating temperature rise based on the change in flight time and the linear relationship between sound speed and temperature, and issuing a warning when the threshold is exceeded) and a temperature field construction algorithm (dividing into three-dimensional monitoring units, calculating the length of micro-elements, establishing discrete summation equations, and solving them through linearized iterative solutions) to complete data computation and analysis. The communication interface module handles data output, transmitting temperature rise data, three-dimensional temperature field data, and warning signals in real time to the battery management system or external monitoring equipment, enabling visualization of monitoring results and system linkage.

[0138] The entire system does not require damage to the battery structure. Relying on the collaborative mode of "rapid early warning + precise reconstruction", it is adapted to complex outdoor environments and unattended scenarios. It can promptly detect overheating and temperature anomalies inside the battery, providing comprehensive technical support for the safe operation of energy storage batteries.

[0139] V. Energy Storage Batteries

[0140] This invention relates to a dedicated energy storage battery adapted for non-invasive ultrasonic temperature monitoring. It combines core energy storage functions with monitoring capabilities, and its overall structural design revolves around ensuring stable ultrasonic wave propagation and accurate internal temperature capture. The battery body adopts a regular rectangular structure, with the internal cell assembly and electrolyte forming a continuous and uniform propagation medium space free of significant air bubbles or gaps, ensuring stable ultrasonic wave penetration along a preset path. The upper and lower surfaces have flat probe mounting areas with low surface roughness and perpendicular to the internal medium layer, which, along with positioning grooves, achieve tight fit and precise positioning of the transmitting and receiving probes. Key structures such as the tabs and cell assembly avoid the core monitoring area of ​​the probe array, reducing obstruction and interference to ultrasonic wave propagation. Simultaneously, it integrates a complete internal temperature monitoring system for the energy storage battery. This system can acquire real-time internal temperature rise data and three-dimensional temperature field information, enabling rapid early warning of internal temperature anomalies without affecting energy storage performance. It is suitable for complex application scenarios such as unattended outdoor operations, providing dual protection for the safe operation of the energy storage system.

[0141] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for early warning of internal temperature rise in an energy storage battery, characterized in that, The following warning steps are included: Based on the real-time flight time t of the ultrasonic wave propagating along its current propagation path inside the battery meas and standard flight time t at standard temperature ref Calculate the change in flight time Δt=t meas -t ref ; Based on Δt, calculate the temperature rise ΔT along the current propagation path: ； In the formula, L represents the length of the current propagation path; v ref denoted as the standard speed of sound when ultrasound propagates in a battery at standard temperature; 'a' represents the coefficient of the first-order term in the linear function of the speed of sound and temperature when ultrasound propagates in a battery. When ΔT exceeds the preset temperature rise threshold, an early warning signal is generated and output.

2. The early warning method for internal temperature rise of an energy storage battery according to claim 1, characterized in that, t meas The acquisition process is as follows: Based on the transmitted and received ultrasonic waves propagating along the current propagation path inside the battery, a cross-correlation function is established between them. The time offset corresponding to the peak value of the cross-correlation function is extracted, and this time offset is t. meas The cross-correlation function is: ； In the formula, R(τ) represents the cross-correlation function; s ref (t) represents the transmitted signal at time t; τ represents the time offset; s ref (t+τ) represents the received signal at time t+τ; dt represents the time derivative.

3. The early warning method for internal temperature rise of an energy storage battery according to claim 1, characterized in that, The process of obtaining the linear relationship function between sound speed and temperature is as follows: Sample batteries of the same model were placed in a temperature-controlled environment, and the real-time flight time t of the ultrasonic waves along each propagation path was measured sequentially at multiple ambient temperatures T. meas And calculate the corresponding speed of sound v=L / t meas Data points (T, v) are generated; then, the least squares method is used to fit the linear relationship function of sound speed-temperature based on all data points (T, v), which is v = a·T + b, where b is a constant term.

4. A method for constructing the internal temperature field of an energy storage battery, characterized in that, The following are the construction steps: The battery interior is divided into M×N×P uniform three-dimensional monitoring units according to the length, width, and thickness directions; based on the coordinates of the M×N transmitting and receiving probe arrays symmetrically arranged on the upper and lower surfaces of the battery, the micro-element length of each propagation path in each three-dimensional monitoring unit is calculated. The real-time flight time of each propagation path is calculated using the early warning method for internal temperature rise of an energy storage battery as described in any one of claims 1-3. Based on the infinitesimal length and real-time flight time, a discrete summation equation is established: ； In the formula, t meas,k ds represents the real-time flight time corresponding to the k-th propagation path; mnp,k Let represent the length of the infinitesimal element within the three-dimensional monitoring unit located at (m,n,p) of the kth propagation path, where m∈[1,M], n∈[1,N], and p∈[1,P]. T mnp This represents the temperature of the three-dimensional monitoring unit located at (m,n,p); The discrete summation equation is solved iteratively using a tomographic imaging algorithm to obtain the temperature of all three-dimensional monitoring units after iterative convergence. The temperatures are then integrated to form a three-dimensional temperature field inside the battery.

5. The method for constructing the internal temperature field of an energy storage battery according to claim 4, characterized in that, T mnp The acquisition process is as follows: Obtain the flight distance L of the ultrasonic wave from the transmitting probe to (m,n,p). mnp and real-time flight time t meas,mnp Based on L mnp and t meas,mnp Calculate the change in flight time Δt mnp =t meas,mnp -t ref ; According to Δt mnp Calculate the temperature rise from the transmitting probe to (m,n,p). The sum of the battery surface temperature at the transmitting probe and the temperature rise is T. mnp .

6. The method for constructing the internal temperature field of an energy storage battery according to claim 5, characterized in that, The linearized iterative solution process is as follows: Step 1: Set the initial temperature for all 3D monitoring units. All are standard temperatures T ref The initial sound velocity of each three-dimensional monitoring unit was calculated based on the linear relationship function between sound velocity and temperature. ; Step 2: Measure the temperature of the three-dimensional monitoring unit in the i-th iteration. The nonlinear terms in the discrete summation equation exist The Taylor expansion is expressed as a first-order approximation: ； in, , This represents the temperature correction amount for the i-th iteration; Step 3: Substitute the first-order approximate expression into the discrete summation equation to obtain... A system of linear equations with unknowns: ； In this system of linear equations, the left-hand side represents the time-of-flight error of the k-th propagation path during the i-th iteration. The right side of the equal sign represents a linear combination of temperature corrections; Step 4: Solve the linear equations using algebraic reconstruction techniques to obtain the temperature correction for each three-dimensional monitoring unit. ; According to the formula Update the temperature of each three-dimensional monitoring unit; Step 5: Calculate the absolute value of temperature change for all three-dimensional monitoring units. If the maximum value is less than the preset accuracy threshold, the iteration stops and the current temperature of all three-dimensional monitoring units is output; otherwise, return to step two to continue the iteration.

7. The method for constructing the internal temperature field of an energy storage battery according to claim 6, characterized in that, The specific process of solving a system of linear equations using algebraic reconstruction techniques is as follows: For the k-th propagation path, calculate its theoretical flight time for the i-th iteration. ; Calculate the error between real-time flight time and theoretical flight time. ; Update the temperature correction using the following formula: ； In the formula, λ represents the relaxation factor.

8. The method for constructing the internal temperature field of an energy storage battery according to claim 4, characterized in that, Find the coordinates of the two intersection points between the propagation path and the current three-dimensional monitoring unit, and calculate the distance between the two intersection points using the spatial distance formula. This distance is the infinitesimal length of the propagation path in the current three-dimensional monitoring unit.

9. A temperature monitoring system for the internal temperature of an energy storage battery, characterized in that, include: The probe array module includes a symmetrical array of transmitting and receiving probes arranged on the upper and lower surfaces of the battery, used to transmit and receive ultrasonic signals. The signal processing and acquisition module is electrically connected to the probe array module. It is used to generate excitation signals to drive the probe to emit and to acquire the echo signals after they have been propagated by the battery. The data processing and control module communicates with the signal processing and acquisition module and performs the following operations: The control probe array module operates according to a predetermined sequence; The echo signal is processed to extract the real-time flight time of the ultrasonic wave along each propagation path; Based on real-time flight time, a method for early warning of internal temperature rise of an energy storage battery as described in any one of claims 1-3 is executed to provide real-time early warning, and / or a method for constructing an internal temperature field of an energy storage battery as described in any one of claims 4-8 is executed to construct the internal temperature field of the battery. Based on the calculation results, status data and early warning signals are generated; The communication interface module is used to output status data and warning signals to the battery management system or external monitoring equipment.

10. An energy storage battery, characterized in that, The battery integrates an internal temperature monitoring system for an energy storage battery as described in claim 9.

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