Impedance-based battery internal temperature dynamic sensing method, apparatus, and media

Abstract

The application provides an impedance-based battery internal temperature dynamic sensing method, device, equipment and medium, wherein the method comprises the following steps: charging the battery at multiple rates in a constant temperature environment, and measuring the battery impedance and SOC data in real time; obtaining the impedance variation caused by the SOC through a static heat balance experiment, and establishing a third-order polynomial fitting model; inversely solving the battery internal temperature based on the Arrhenius equation, and optimizing the dynamic response through first-order inertia filtering; dynamically correcting the model parameters according to the battery cycle number and working condition parameters, and realizing temperature prediction accuracy calibration in a wide working condition, so as to solve the problems of inaccurate battery surface temperature measurement and damage to the battery caused by the implanted temperature sensor in detecting the internal temperature.

Description

Technical Field

[0001] This invention relates to the field of electrochemical energy storage system monitoring technology, and in particular to a method, device, equipment and medium for dynamic sensing of battery internal temperature based on impedance. Background Technology

[0002] Currently, energy storage battery systems utilize Battery Management Systems (BMS) for health and safety management. A BMS is a real-time monitoring system composed of electronic circuitry that effectively monitors battery voltage, current, temperature, battery cluster insulation status, and battery SOC (State of Charge). It manages the charging and discharging processes of battery clusters safely, provides alarms and emergency protection for potential faults, and performs safe and optimized control of battery modules and clusters to ensure safe, reliable, and stable battery operation. However, current BMS primarily relies on external data such as voltage, current, and temperature for lithium-ion battery status perception and safety warnings. This approach has certain limitations, including slow changes in battery voltage and current signals leading to poor timeliness; misjudgments due to variations in battery type, capacity, and internal conditions; and inconsistent voltage drop patterns caused by different thermal runaway events. Internal battery temperature is a core parameter for assessing battery safety, cycle life, and performance. High temperatures can trigger thermal runaway, leading to serious safety accidents such as fires or explosions; low temperatures can significantly reduce battery capacity, affecting equipment performance.

[0003] Current battery management systems (BMS) typically only detect the surface temperature of the battery and cannot effectively detect the internal temperature state. Traditional thermoelectric / thermistor devices can only measure the surface temperature of the battery, resulting in a gradient error of 3-15°C or even higher compared to the internal temperature. Furthermore, the hysteresis of thermal conduction leads to a dynamic response delay (10-30 seconds).

[0004] Accurate real-time monitoring of battery internal temperature is of great significance for preventing battery thermal runaway, improving the safety monitoring capabilities of BMS, and optimizing battery charging and discharging strategies.

[0005] Therefore, there is an urgent need to provide an impedance-based method for dynamically sensing the internal temperature of a battery to solve the problems of inaccurate surface temperature measurement and damage to the battery caused by implanted temperature sensors detecting internal temperature. Summary of the Invention

[0006] To overcome the problems existing in related technologies, this disclosure provides a method, device, equipment and medium for dynamic sensing of battery internal temperature based on impedance, so as to solve the problems of inaccurate battery surface temperature measurement and damage to the battery caused by implanted temperature sensors detecting internal temperature.

[0007] This specification provides one or more embodiments of an impedance-based method for dynamically sensing the internal temperature of a battery, including the following steps: The battery was charged at multiple rates under constant temperature conditions, and the battery impedance and SOC data were measured in real time. The impedance change caused by SOC was obtained by static thermal equilibrium experiment, and a third-order polynomial fitting model was established. The internal temperature of the battery is solved by inverse solution of the Arrhenius equation, and the dynamic response is optimized by first-order inertial filtering. The model parameters are dynamically adjusted based on the number of battery cycles and operating conditions to achieve accurate temperature prediction under a wide range of operating conditions.

[0008] Preferably, the step of charging the battery at multiple rates under a constant temperature environment and measuring the battery impedance and SOC data in real time specifically includes the following steps: Place the battery in a temperature control chamber and allow it to stand at the target reference temperature until thermal equilibrium is reached. Record the initial impedance and initial SOC value. At the target reference temperature, the system is charged to 100% SOC at different constant current rates. During the charging process, the real-time SOC value and dynamic impedance are recorded at a preset sampling frequency.

[0009] Preferably, the method further includes the following steps: The temperature control accuracy of the temperature control box is ±0.5℃; The target reference temperature is 25°C; The time for the plant to reach thermal equilibrium is 12 hours. The different magnification ratios include 1C, 2C, and 3C; The preset sampling frequency is ≥10Hz.

[0010] Preferably, the step of obtaining the impedance change caused by SOC through a static thermal equilibrium experiment and establishing a third-order polynomial fitting model specifically includes the following steps: After charging is complete, allow the battery to rest until its temperature returns to the target reference temperature, and then measure the impedance after resting. Define an impedance change quantity that characterizes the effect of pure SOC change on impedance. Based on the impedance change quantity, construct a third-order polynomial model containing a piecewise correction function. The coefficients of the third-order polynomial model are calibrated by the least squares method.

[0011] Preferably, the piecewise correction function is used to optimize the fitting accuracy of different SOC intervals, and the SOC intervals include at least a low power interval, a medium power interval, and a high power interval.

[0012] Preferably, the step of dynamically correcting model parameters based on battery cycle count and operating condition parameters to achieve temperature prediction accuracy calibration under a wide range of operating conditions specifically includes the following steps: The trigger condition for precision calibration is: the number of battery cycles N ≥ 100; The operating parameters include charging and discharging current and SOC change rate; The coefficients of the third-order polynomial model are dynamically adjusted based on the cycle life correction factor and the operating condition correction factor, and the temperature prediction value is corrected.

[0013] This specification provides one or more embodiments of an impedance-based dynamic temperature sensing device for a battery, including a data acquisition module, a fitting model module, a temperature inversion module, and a self-calibration module. The data acquisition module is used to measure the battery impedance in real time when the battery is at rest and during charging and discharging. The fitting model module is used for real-time SOC calculation during battery rest and charging / discharging. It obtains the impedance change caused by SOC through a resting thermal balance experiment and establishes a third-order polynomial fitting model. The temperature inversion module is used to solve the internal temperature of the battery based on the Arrhenius equation and optimize the dynamic response through first-order inertial filtering. The self-calibration module is used to dynamically correct model parameters based on battery cycle count and operating condition parameters, thereby achieving temperature prediction accuracy calibration under a wide range of operating conditions.

[0014] Preferably, the data acquisition module includes an initial data acquisition unit and a dynamic data acquisition unit; The initial data acquisition unit is used to place the battery in a temperature control chamber, let it stand at the target reference temperature until thermal equilibrium is reached, and record the initial impedance and initial SOC value. The dynamic data acquisition unit is used to charge the system to 100% SOC at different constant current rates at the target reference temperature, and to record the real-time SOC value and dynamic impedance at a preset sampling frequency during the charging process.

[0015] This specification provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the impedance-based dynamic sensing method for internal battery temperature as described above.

[0016] This specification provides one or more embodiments of a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the impedance-based dynamic sensing method for battery internal temperature described above.

[0017] This disclosure provides a method, device, equipment, and medium for dynamic sensing of battery internal temperature based on impedance. Its advantages lie in that by charging the battery at multiple rates under constant temperature conditions, it measures battery impedance and SOC data in real time, eliminating ambient temperature interference and covering actual charging scenarios at multiple rates. It records the dynamic changes of impedance and SOC throughout the charging cycle using millisecond or second-level sampling. By obtaining the impedance change caused by SOC through a static thermal equilibrium experiment, it eliminates interference from charging rate and temperature fluctuations, acquiring only the impedance change caused by SOC. This clarifies the correspondence between SOC and impedance, establishing a third-order polynomial fitting model that more accurately fits their nonlinear relationship. This model can be used as an "impedance correction tool" in subsequent temperature inversion to eliminate the influence of SOC on impedance, providing pure input parameters. It can also be used as a "rapid SOC prediction tool" when SOC cannot be directly obtained, inferring SOC from real-time impedance, thus expanding the flexibility of battery state monitoring. Based on the Arrhenius equation, it solves the battery internal temperature and utilizes the previous... The process involves using impedance data after removing the influence of SOC (State of Charge) to inversely determine the internal temperature of the battery, reducing temperature errors and addressing the issues of traditional external sensors failing to reflect the true internal temperature and implanted temperature sensors damaging the battery. This provides crucial data for thermal management and safety protection. Furthermore, first-order inertial filtering optimizes the dynamic response, suppressing temperature fluctuations caused by data acquisition noise and preventing excessive filtering lag. This ensures that temperature data can smoothly and quickly track internal changes even during sudden changes in operating conditions, meeting the dual requirements of real-time performance and stability for the thermal management system. The process also involves dynamically correcting model parameters based on battery cycle count and operating conditions, achieving temperature prediction accuracy calibration across a wide range of operating conditions. This automatically adjusts parameters to adapt to battery aging. Combined with real-time operating parameters, a dynamic mapping between operating conditions and model parameters is established, resolving model deviations caused by different operating conditions and achieving "full lifecycle adaptive optimization." Ultimately, this achieves the goal of "wide operating condition coverage + full lifecycle adaptation" for temperature prediction accuracy calibration, ensuring long-term safe operation and extended battery life. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic flowchart illustrating an impedance-based method for dynamically sensing the internal temperature of a battery, provided for one or more embodiments of this specification. Figure 2Impedance change curves of lithium-ion batteries provided in one or more embodiments of this specification at different charging rates (1C, 2C, 3C) from 0% SOC to 100% SOC and then left to stand at the temperature before charging; Figure 3 The curves showing the effect of SOC on impedance of lithium-ion batteries provided in one or more embodiments of this specification at different charging rates (1C, 2C, 3C) from 0% SOC to 100% SOC. Figure 4 The curves showing the effect of battery temperature on impedance during charging of the lithium-ion battery at different charging rates (1C, 2C, 3C) from 0% SOC to 100% SOC, according to one or more embodiments of this specification. Figure 5 A comparison chart of predicted internal temperature and external measured temperature of lithium-ion batteries at different charging rates (1C, 2C, 3C) provided for one or more embodiments of this specification; Figure 6 A schematic diagram of the structure of an impedance-based dynamic temperature sensing device for a battery internal environment provided for one or more embodiments of this specification; Figure 7 This is a schematic diagram of the structure of a computer device provided for one or more embodiments of this specification. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this invention.

[0021] The present invention will now be described in detail with reference to specific embodiments and accompanying drawings.

[0022] Method Implementation Examples According to embodiments of the present invention, an impedance-based method for dynamically sensing the internal temperature of a battery is provided, such as... Figure 1 The diagram shown is a flowchart illustrating the impedance-based dynamic sensing method for battery internal temperature according to this embodiment. The impedance-based dynamic sensing method for battery internal temperature according to this embodiment includes the following steps: S110. Charge the battery at multiple rates under constant temperature conditions and measure the battery impedance and SOC data in real time. The different rates include 1C, 2C, and 3C.

[0023] S120. Obtain the impedance change caused by SOC through a static thermal equilibrium experiment, and establish a third-order polynomial fitting model.

[0024] S130: The internal temperature of the battery is solved by inverse solution of the Arrhenius equation, and the dynamic response is optimized by first-order inertial filtering.

[0025] S140: Dynamically correct model parameters based on battery cycle count and operating condition parameters to achieve temperature prediction accuracy calibration under a wide range of operating conditions.

[0026] The method provided in this embodiment, by charging the battery at multiple rates under constant temperature conditions and measuring battery impedance and SOC data in real time, can eliminate the interference of ambient temperature, cover the actual charging scenario at multiple rates, and record the dynamic changes of impedance and SOC during the charging cycle completely through millisecond or second-level sampling. The impedance change caused by SOC is obtained through a static thermal equilibrium experiment, which can eliminate interference from charging rate, temperature fluctuations, etc., and only obtain the impedance change caused by SOC, clarifying the correspondence between SOC and impedance. A third-order polynomial fitting model is established, which can more accurately fit the nonlinear relationship between the two. This model can be used as an "impedance correction tool" in subsequent temperature inversion to eliminate the influence of SOC on impedance and provide pure input parameters, and can also be used as a "rapid SOC prediction tool" when SOC cannot be directly obtained, inferring SOC from real-time impedance, thus expanding the flexibility of battery state monitoring. Based on the Arrhenius equation, the internal temperature of the battery is solved by inversely, and the impedance data after eliminating the influence of SOC in the previous steps is used. By reverse-engineering the internal temperature of the battery, temperature errors are reduced, solving the problems of traditional external sensors failing to reflect the true internal temperature and implanted temperature sensors damaging the battery. This provides crucial data for thermal management and safety protection. First-order inertial filtering optimizes the dynamic response, suppressing temperature fluctuations caused by data acquisition noise and avoiding excessive filtering lag. This ensures that temperature data can smoothly and quickly track internal changes even during sudden changes in operating conditions, meeting the dual requirements of real-time performance and stability for the thermal management system. The model parameters are dynamically corrected based on the battery cycle count and operating parameters, achieving temperature prediction accuracy calibration under a wide range of operating conditions. Parameters are automatically adjusted to adapt to battery aging. Combined with real-time operating parameters, a dynamic mapping between operating conditions and model parameters is established, resolving model deviations caused by different operating conditions and achieving "full lifecycle adaptive optimization" of the model. Ultimately, this achieves the temperature prediction accuracy calibration goal of "wide operating condition coverage + full lifecycle adaptation," ensuring long-term safe operation and extended battery life.

[0027] In one embodiment, the step of charging the battery at multiple rates under a constant temperature environment and measuring the battery impedance and SOC data in real time specifically includes the following steps: Place the battery in a temperature-controlled chamber (accuracy ±0.5℃) at the target reference temperature. (e.g., at 25℃) Let it stand for 12 hours until thermal equilibrium is reached, and record the initial impedance Z0 and the initial SOC value SOC0 using BMS.

[0028] At the target reference temperature The system is charged sequentially to 100% SOC using constant current at different rates. During the charging process, the real-time SOC value SOC(t) and dynamic impedance Z(t) are recorded at a preset sampling frequency, such as high-speed sampling (sampling frequency ≥ 10Hz).

[0029] The method provided in this embodiment provides high-quality basic data for subsequent operations through standardized steps of "temperature-controlled chamber static thermal equilibrium + multi-rate constant current charging + preset frequency sampling": First, it standardizes the initial state, eliminates temperature difference interference by allowing the system to reach thermal equilibrium, and records the initial impedance and SOC, providing a benchmark for dynamic change analysis; Second, it ensures no charging data omissions, covering mainstream scenarios with multiple rates, and recording the entire cycle with millisecond / second-level sampling, capturing the patterns of impedance and SOC changes; Third, it outputs high-purity data, eliminating temperature interference in a constant temperature environment, and the data only reflects the correlation between "charging rate - impedance - SOC", which not only supports charging performance analysis but also provides accurate input for subsequent experiments and temperature inversion, laying the foundation for the accuracy of the solution. In one embodiment, the impedance change caused by SOC is obtained through a static thermal equilibrium experiment, and a third-order polynomial fitting model is established, specifically including the following steps: After charging is complete, allow the battery to rest until its temperature returns to the target reference temperature T. ref Specifically, by monitoring with a temperature sensor, if the temperature difference is less than 0.3℃ for 1 hour, the impedance Z after settling is measured. rest .

[0030] Define the impedance change as a measure of the effect of pure SOC change on impedance: Among them, Z rest Z represents the impedance after resting. pre The initial impedance before charging is represented, and then the battery impedance Z is measured after allowing it to rest and return to its original temperature (Tref). rest This value characterizes the effect of pure SOC change (0→100%) on impedance, excluding temperature interference. For example... Figure 2 The figure shows the impedance change curves of the lithium-ion battery provided in this embodiment at different charging rates (1C, 2C, 3C) from 0% SOC to 100% SOC and then left to stand at the temperature before charging. A third-order polynomial model with a piecewise correction function is constructed based on the impedance change, wherein the coefficients of the third-order polynomial model are calibrated using the least squares method. The third-order polynomial model is expressed as: ; Among them, Z SOC (SOC) represents the impedance component caused only by changes in battery SOC, αi The coefficients of the cubic polynomial are represented as i = (0, 1, 2, 3), and the coefficients a0 - a3 are determined by the least squares method. SOC i Let i represent the current SOC raised to the power of i, where i = (0, 1, 2, 3), and g(SOC) be the piecewise correction function. .

[0031] The piecewise correction function is used to optimize the fitting accuracy of different SOC intervals, wherein the SOC intervals include at least a low battery interval, a medium battery interval, and a high battery interval. For example... Figure 3 The figure shows the effect curves of the battery SOC on impedance from 0% SOC to 100% SOC for different charging rates (1C, 2C, 3C) of the lithium-ion battery provided in this embodiment.

[0032] Temperature inversion steps: based on the Arrhenius equation: ; The internal temperature can be determined by inverse calculation, and the temperature inversion formula is as follows: ; in, The dynamic response is optimized by first-order inertial filtering. ,in The filter coefficient is 0.7-0.9, which can be adjusted according to the operating conditions. T last This represents the temperature value at the previous moment. T final Z represents the final temperature after the last temperature inversion and filtering calculation. T Z represents the impedance that is only affected by temperature. T0 Represented as temperature T ref Battery temperature-sensitive impedance, Z meas This indicates the current data collection value. Z SOC (SOC) cur This represents the impedance of the battery under the influence of its state of charge (SOC). E a For activation energy, R Expressed as the gas constant, T ref Indicates the reference temperature value. T cur This indicates the temperature currently derived. Figure 4 The curves showing the effect of battery temperature on impedance during the charging process of lithium-ion batteries at different charging rates (1C, 2C, 3C) from 0% SOC to 100% SOC, provided in the embodiments of the present invention, are shown.

[0033] The method provided in this embodiment eliminates the interference of residual heat during charging and accurately obtains the impedance change caused by pure SOC by allowing the battery to rest until the target temperature is reached after charging and measuring the impedance. Then, the coefficients are calibrated by the least squares method to construct a third-order polynomial model with piecewise correction functions. This model can accurately fit the nonlinear relationship between SOC and impedance and provide reliable model support for subsequent elimination of the influence of SOC on impedance, inversion of battery temperature, and analysis of battery status. In one embodiment, the model parameters are dynamically adjusted based on the battery cycle count and operating condition parameters to achieve temperature prediction accuracy calibration under a wide range of operating conditions. This includes the following steps: The trigger condition for precision calibration is: the number of battery cycles N ≥ 100, and the operating parameters include charge / discharge current and SOC change rate.

[0034] The coefficients of the third-order polynomial model are dynamically adjusted based on the cycle life correction factor and the operating condition correction factor, and the temperature prediction value is corrected. Specifically, this is achieved through... Dynamically modified SOC-impedance model, modified SOC-impedance model: .

[0035] Where, ∆Z aging Z represents the impedance increase caused by battery aging. N Z' represents the impedance value after N battery cycles, Z0 represents the reference impedance value of the battery in its initial state, and Z' represents the impedance value after N battery cycles. SOC This indicates the impedance value after introducing aging correction. This represents the rate of change of SOC.

[0036] Will replace Substituting into the formula, we get:

[0037] Correction factor calculated based on charge / discharge current I and SOC change rate. , where I nom Rated current, I The charging and discharging current is given, and k1 and k2 are calibration coefficients (determined through bench testing, typical values ​​are k1=0.3 and k2=5). The temperature prediction value is corrected as follows: ; in, This is a sign function for the rate of temperature change, enabling temperature prediction accuracy calibration under a wide range of operating conditions.

[0038] like Figure 5 The figure shown is a comparison chart of the predicted internal temperature and the external measured temperature of the lithium-ion battery at different charging rates (1C, 2C, 3C) provided in this embodiment.

[0039] Device Examples According to embodiments of the present invention, an impedance-based dynamic temperature sensing device for a battery is provided, such as... Figure 6 The diagram shown is a structural schematic of the impedance-based dynamic sensing device for internal battery temperature provided in this embodiment. The impedance-based dynamic sensing device for internal battery temperature according to this embodiment includes a data acquisition module 61, a fitting model module 62, a temperature inversion module 63, and a self-calibration module 64.

[0040] The data acquisition module 61 is used to charge the battery at multiple rates in a constant temperature environment and measure the battery impedance and SOC data in real time.

[0041] The fitting model module 62 is used for real-time SOC calculation during battery rest and charging / discharging. It obtains the impedance change caused by SOC through a resting thermal balance experiment and establishes a third-order polynomial fitting model.

[0042] Temperature inversion module 63 is used to solve the internal temperature of the battery based on the Arrhenius equation and optimize the dynamic response through first-order inertial filtering.

[0043] The self-calibration module 64 is used to dynamically correct model parameters based on battery cycle count and operating condition parameters, thereby achieving temperature prediction accuracy calibration under a wide range of operating conditions.

[0044] The device provided in this embodiment includes a data acquisition module 61 that measures battery impedance in real time by charging the battery at multiple rates under constant temperature conditions. This eliminates interference from ambient temperature and covers actual charging scenarios at multiple rates. It records the dynamic changes in impedance throughout the charging cycle using millisecond or second-level sampling. The fitting model module 62 obtains the impedance change caused by SOC through a static thermal equilibrium experiment. This eliminates interference from charging rate and temperature fluctuations, acquiring only the impedance change caused by SOC. It clarifies the correspondence between SOC and impedance, establishing a third-order polynomial fitting model that more accurately fits the nonlinear relationship between the two. This model can be used as an "impedance correction tool" in subsequent temperature inversion to eliminate the influence of SOC on impedance and provide pure input parameters. It can also be used as a "rapid SOC prediction tool" when SOC cannot be directly obtained, using real-time impedance to infer SOC and expand the flexibility of battery state monitoring. The temperature inversion module 63 solves the internal temperature of the battery based on the Arrhenius equation, utilizing the previous... The impedance data, after removing the influence of SOC, is used to inversely determine the internal temperature of the battery, reducing temperature error and solving the problem that traditional external sensors cannot reflect the true internal temperature. This provides crucial information for thermal management and safety protection. First-order inertial filtering optimizes the dynamic response, suppressing temperature fluctuations caused by data acquisition noise and avoiding excessive filtering lag. This ensures that temperature data can smoothly and quickly track internal changes even during sudden changes in operating conditions, meeting the dual requirements of real-time performance and stability for the thermal management system. The self-calibration module 64 dynamically corrects model parameters based on battery cycle count and operating parameters, achieving temperature prediction accuracy calibration under a wide range of operating conditions. It automatically adjusts parameters to adapt to battery aging states and, combined with real-time operating parameters, establishes a dynamic mapping between operating conditions and model parameters, resolving model deviations caused by different operating conditions. This achieves "full lifecycle adaptive optimization" of the model, ultimately reaching the goal of "wide operating condition coverage + full lifecycle adaptation" for temperature prediction accuracy calibration, ensuring long-term safe operation and extended battery life.

[0045] In one embodiment, the data acquisition module 61 includes an initial data acquisition unit and a dynamic data acquisition unit.

[0046] The initial data acquisition unit is used to place the battery in a temperature control chamber, let it stand at the target reference temperature until thermal equilibrium is reached, and record the initial impedance and initial SOC value.

[0047] The dynamic data acquisition unit is used to charge the system to 100% SOC at different constant current rates at the target reference temperature, and to record the real-time SOC value and dynamic impedance at a preset sampling frequency during the charging process.

[0048] The device provided in this embodiment provides high-quality basic data for subsequent operations through standardized steps of "temperature-controlled chamber static thermal equilibrium + multi-rate constant current charging + preset frequency sampling": Firstly, it standardizes the initial state, eliminates temperature difference interference by allowing the device to stand until thermal equilibrium, and records the initial impedance and SOC, providing a benchmark for dynamic change analysis; secondly, it ensures no charging data omissions, covers mainstream scenarios with multiple rates, and records the entire cycle with millisecond / second-level sampling, capturing the patterns of impedance and SOC changes; thirdly, it outputs high-purity data, eliminates temperature interference in a constant temperature environment, and the data only reflects the correlation between "charging rate - impedance - SOC", which not only supports charging performance analysis but also provides accurate input for subsequent experiments and temperature inversion, laying the foundation for the accuracy of the solution. The embodiments of the present invention are device embodiments corresponding to the above method embodiments. The specific operations of each module processing step can be understood with reference to the description of the method embodiments, and will not be repeated here.

[0049] like Figure 7 As shown, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the impedance-based dynamic sensing method for internal battery temperature in the above embodiments, or when the computer program is executed by a processor, it implements the impedance-based dynamic sensing method for internal battery temperature in the above embodiments.

[0050] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0051] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and the contents not described in detail in the specification of the present invention are known to those skilled in the art.

Claims

1. A method for dynamically sensing the internal temperature of a battery based on impedance, characterized in that, Includes the following steps: The battery was charged at multiple rates under constant temperature conditions, and the battery impedance and SOC data were measured in real time. The impedance change caused by SOC was obtained through a static thermal equilibrium experiment, and a third-order polynomial fitting model was established. The third-order polynomial model is expressed as follows: ； Among them, Z SOC (SOC) represents the impedance component caused only by changes in battery SOC, α i Let the coefficients i = (0, 1, 2, 3) and a0 be the coefficients of the cubic polynomial. a3 is calibrated using the least squares method, SOC i Let i represent the current SOC raised to the power of i, where i = (0, 1, 2, 3), and g(SOC) be the piecewise correction function. Define the impedance change as the measure of the effect of pure SOC variation on impedance: Among them, Z rest Z represents the impedance after resting. pre Indicates the initial impedance before charging; The internal temperature of the battery is solved by inverse kinematics of the Arrhenius equation, and the dynamic response is optimized by first-order inertial filtering. The Arrhenius equation is used as the basis for this optimization. ; The internal temperature can be determined by inverse calculation, and the temperature inversion formula is as follows: ； in, Optimize dynamic response through first-order inertial filtering ,in These are the filter coefficients. T last This represents the temperature value at the previous moment. T final Z represents the final temperature after the last temperature inversion and filtering calculation. T Z represents the impedance that is only affected by temperature. T0 Represented as temperature T ref Battery temperature-sensitive impedance, Z meas This indicates the current data collection value. Z SOC (SOC) cur This represents the impedance of the battery under the influence of its state of charge (SOC). E a For activation energy, R Expressed as the gas constant, T ref Indicates the reference temperature value. T cur This indicates the temperature currently retrieved. The model parameters are dynamically adjusted based on the battery cycle count and operating conditions to achieve temperature prediction accuracy calibration under a wide range of operating conditions. This involves the following steps: The trigger condition for precision calibration is: the number of battery cycles N ≥ 100, and the operating parameters include charge / discharge current and SOC change rate; The coefficients of the third-order polynomial model are dynamically adjusted based on the cycle life correction factor and the operating condition correction factor, and the temperature prediction value is corrected. Specifically, this is achieved through... Dynamically modified SOC-impedance model, modified SOC-impedance model: ； in, Z aging Z represents the impedance increase caused by battery aging. N Z' represents the impedance value after N battery cycles, Z0 represents the reference impedance value of the battery in its initial state, and Z' represents the impedance value after N battery cycles. SOC This indicates the impedance value after introducing aging correction. Indicates the rate of change of SOC; Will replace Substituting into the formula, we get: Correction factor calculated based on charge / discharge current I and SOC change rate. , among which, I nom Rated current, I Here, k1 and k2 are the charging and discharging currents, respectively, and are calibration coefficients determined through bench testing. The predicted temperature value is corrected as follows: ； in, This is a sign function for the rate of temperature change, enabling temperature prediction accuracy calibration under a wide range of operating conditions.

2. The impedance-based dynamic sensing method for battery internal temperature as described in claim 1, characterized in that, The process of charging the battery at multiple rates under constant temperature conditions and measuring battery impedance and SOC data in real time includes the following steps: Place the battery in a temperature control chamber and allow it to stand at the target reference temperature until thermal equilibrium is reached. Record the initial impedance and initial SOC value. At the target reference temperature, the system is charged to 100% SOC at different constant current rates. During the charging process, the real-time SOC value and dynamic impedance are recorded at a preset sampling frequency.

3. The impedance-based dynamic sensing method for battery internal temperature as described in claim 2, characterized in that, It also includes the following steps: The temperature control accuracy of the temperature control box is ±0.5℃; The target reference temperature is 25°C; The time for the plant to reach thermal equilibrium is 12 hours. The different magnification ratios include 1C, 2C, and 3C; The preset sampling frequency is ≥10Hz.

4. The impedance-based dynamic sensing method for battery internal temperature as described in claim 1, characterized in that, The process of obtaining the impedance change caused by SOC through a static thermal equilibrium experiment and establishing a third-order polynomial fitting model includes the following steps: After charging is complete, allow the battery to rest until its temperature returns to the target reference temperature, and then measure the impedance after resting. Define an impedance change quantity that characterizes the effect of pure SOC change on impedance. Based on the impedance change quantity, construct a third-order polynomial model containing a piecewise correction function. The coefficients of the third-order polynomial model are calibrated by the least squares method.

5. The impedance-based dynamic sensing method for battery internal temperature as described in claim 4, characterized in that, The piecewise correction function is used to optimize the fitting accuracy of different SOC intervals, and the SOC intervals include at least the low battery interval, the medium battery interval, and the high battery interval.

6. The impedance-based dynamic sensing method for battery internal temperature as described in claim 1, characterized in that, The process of dynamically correcting model parameters based on battery cycle count and operating condition parameters to achieve temperature prediction accuracy calibration under a wide range of operating conditions includes the following steps: The trigger condition for precision calibration is: the number of battery cycles N ≥ 100; The operating parameters include charging and discharging current and SOC change rate; The coefficients of the third-order polynomial model are dynamically adjusted based on the cycle life correction factor and the operating condition correction factor, and the temperature prediction value is corrected.

7. A battery internal temperature dynamic sensing device based on impedance, characterized in that, It includes a data acquisition module, a fitting model module, a temperature inversion module, and a self-calibration module; The data acquisition module is used to charge the battery at multiple rates under constant temperature conditions and measure the battery impedance and SOC data in real time. The fitting model module is used to obtain the impedance change caused by SOC through a static thermal equilibrium experiment, and to establish a third-order polynomial fitting model, which is expressed as: ； Where ZSOC(SOC) represents the impedance component caused only by the change in battery SOC, αi represents the coefficients of the cubic polynomial i = (0, 1, 2, 3), and the coefficients a0 a3 is calibrated using the least squares method, SOCi represents the current SOC raised to the power of i, i = (0, 1, 2, 3), g(SOC) is a piecewise correction function, and the impedance change is defined as the amount of impedance change that characterizes the effect of pure SOC change on impedance: Among them, Z rest Z represents the impedance after resting. pre Indicates the initial impedance before charging; The temperature inversion module is used to solve for the internal temperature of the battery based on the Arrhenius equation and optimize the dynamic response through a first-order inertial filter. The Arrhenius equation is as follows: ; The internal temperature can be determined by inverse calculation, and the temperature inversion formula is as follows: ； in, Optimize dynamic response through first-order inertial filtering ,in These are the filter coefficients. T last This represents the temperature value at the previous moment. T final Z represents the final temperature after the last temperature inversion and filtering calculation. T Z represents the impedance that is only affected by temperature. T0 Represented as temperature T ref Battery temperature-sensitive impedance, Z meas This indicates the current data collection value. Z SOC (SOC) cur This represents the impedance of the battery under the influence of its state of charge (SOC). E a For activation energy, R Expressed as the gas constant, T ref Indicates the reference temperature value. T cur This indicates the temperature currently retrieved. The self-calibration module is used to dynamically correct model parameters based on battery cycle count and operating condition parameters to achieve temperature prediction accuracy calibration under a wide range of operating conditions. Specifically, it includes the following steps: The trigger condition for precision calibration is: the number of battery cycles N ≥ 100, and the operating parameters include charge / discharge current and SOC change rate; The coefficients of the third-order polynomial model are dynamically adjusted based on the cycle life correction factor and the operating condition correction factor, and the temperature prediction value is corrected. Specifically, this is achieved through... Dynamically modified SOC-impedance model, modified SOC-impedance model: ; in, Z aging Z represents the impedance increase caused by battery aging. N Z' represents the impedance value after N battery cycles, Z0 represents the reference impedance value of the battery in its initial state, and Z' represents the impedance value after N battery cycles. SOC This indicates the impedance value after introducing aging correction. Indicates the rate of change of SOC; Will replace Substituting into the formula, we get: Correction factor calculated based on charge / discharge current I and SOC change rate. , where I nom Rated current, I Here, k1 and k2 are the charging and discharging currents, respectively, and are calibration coefficients determined through bench testing. The predicted temperature value is corrected as follows: ； in, This is a sign function for the rate of temperature change, enabling temperature prediction accuracy calibration under a wide range of operating conditions.

8. The impedance-based dynamic temperature sensing device for a battery as described in claim 7, characterized in that, The data acquisition module includes an initial data acquisition unit and a dynamic data acquisition unit; The initial data acquisition unit is used to place the battery in a temperature control chamber, let it stand at the target reference temperature until thermal equilibrium is reached, and record the initial impedance and initial SOC value. The dynamic data acquisition unit is used to charge the system to 100% SOC at different constant current rates at the target reference temperature, and to record the real-time SOC value and dynamic impedance at a preset sampling frequency during the charging process.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the impedance-based dynamic sensing method for battery internal temperature as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the impedance-based dynamic sensing method for battery internal temperature as described in any one of claims 1 to 6.

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