A temperature compensation method, device and readable storage medium

Abstract

The embodiment of the present application relates to a temperature compensation method, device and readable storage medium, and the present application comprises: primary compensation, including establishing a baseline model by polynomial fitting based on a plurality of groups of original output values of MEMS pressure sensors and surface temperature values of lithium ion batteries, for obtaining primary compensation output values of the MEMS pressure sensors; dynamic compensation, including pre-establishing a database for obtaining the relationship between internal temperature values of the lithium ion batteries and temperature dynamic compensation coefficients of the MEMS pressure sensors, and calculating dynamic compensation output values of the MEMS pressure sensors based on the primary compensation output values and the temperature dynamic compensation coefficients; feedback calibration, including automatically correcting the baseline model in reverse by periodically obtaining the deviation between measured values and predicted values of voltage polarization amplitude of the lithium ion batteries; the present application dynamically compensates temperature errors of the MEMS pressure sensors by constructing a multi-stage compensation system and voltage polarization amplitude characteristics.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, specifically to a temperature compensation method, device, and readable storage medium. Background Technology

[0002] In the design and operation of a Battery Management System (BMS), temperature compensation is a crucial element in ensuring stable battery performance, extending battery life, and guaranteeing safety. Among traditional temperature compensation schemes, the fixed-coefficient method was widely used in early lead-acid batteries and some low-speed electric vehicle lithium batteries due to its simplicity, ease of implementation, and low cost. However, with the development of power batteries towards higher energy density and power density, and the increasing demands on battery performance accuracy in various applications, the core deficiency of this method—failing to consider the nonlinear temperature rise caused by internal heat generation during battery charging and discharging—has become increasingly apparent, becoming a significant factor restricting the accuracy of battery management.

[0003] Specifically, the fixed-coefficient method cannot address the specific impacts of nonlinear thermal degradation. For example, it leads to significant deviations in SOC calculation accuracy, affecting range and charging safety; unreasonable charging and discharging power limits restrict performance and lifespan; and inaccurate thermal management system control exacerbates energy consumption and safety risks. In other words, the traditional temperature compensation fixed-coefficient method is essentially a simplified scheme based on "static, linear, and surface temperature," and its core flaw lies in its disregard for the actual temperature characteristics of the battery during charging and discharging—which are "dynamic, nonlinear, and dominated by internal heat generation." With the increasing complexity of power battery applications and the rising performance requirements, this method is no longer sufficient to meet the demands for high-precision and high-safety management. Summary of the Invention

[0004] In view of the above problems, this invention provides a temperature compensation method, device and readable storage medium, which dynamically compensates for the temperature error of MEMS pressure sensors by constructing a multi-level compensation system and voltage polarization amplitude characteristics, and is applicable to high-voltage operating conditions such as power batteries and energy storage batteries.

[0005] According to one aspect of the present invention, a temperature compensation method is provided, comprising: Primary compensation includes establishing a baseline model based on the original output values ​​of multiple MEMS pressure sensors and the surface temperature value of the lithium-ion battery using polynomial fitting, which is used to obtain the primary compensation output value of the MEMS pressure sensors. Dynamic compensation includes pre-establishing a database to obtain the relationship between the internal temperature value of the lithium-ion battery and the temperature dynamic compensation coefficient of the MEMS pressure sensor, and calculating the dynamic compensation output value of the MEMS pressure sensor based on the primary compensation output value and the temperature dynamic compensation coefficient. Feedback calibration includes periodically obtaining the deviation between the measured and predicted values ​​of the voltage polarization amplitude of the lithium-ion battery and automatically correcting the baseline model in reverse.

[0006] In some implementations, the primary compensation output value is obtained as follows: ; Wherein, P_comp1 is the primary compensation output value, P_raw is the raw output value of the MEMS pressure sensor, T_surface is the surface temperature value of the lithium-ion battery, and a, b, and c are polynomial fitting coefficients obtained by least squares fitting calibration.

[0007] In some implementations, the dynamic compensation includes the following sub-steps: A primary database is established based on the voltage polarization amplitude, current range mean square error, and internal temperature values ​​of multiple sets of lithium-ion batteries. A secondary database is established by retrieving multiple sets of internal temperature values ​​of the lithium-ion batteries from the primary database and combining them with the temperature dynamic compensation coefficients of multiple sets of MEMS pressure sensors corresponding to the internal temperature values ​​of the lithium-ion batteries. The current sequence of the lithium-ion battery is collected in real time, the root mean square error of the current interval within the sliding window is calculated, the internal temperature value of the lithium-ion battery is obtained through the first-level database, and the temperature dynamic compensation coefficient of the MEMS pressure sensor is obtained through the second-level database. Based on the primary compensation output value and the temperature dynamic compensation coefficient, the dynamic compensation output value of the MEMS pressure sensor is calculated.

[0008] In some implementations, the primary database is established as follows: The voltage polarization amplitude, current range mean square error, and internal temperature of the lithium-ion battery under different operating conditions were obtained through cyclic charge-discharge experiments, and a three-dimensional relationship matrix of the voltage polarization amplitude, current range mean square error, and internal temperature of the lithium-ion battery was established.

[0009] In some implementations, the primary compensation is initiated when the root mean square error of the current range is detected to be less than or equal to a preset value. When the mean square error of the current range is detected to be greater than a preset value, the dynamic compensation is activated.

[0010] In some implementations, the mean square error of the current range is detected in the following manner: The electrode temperature of the lithium-ion battery is obtained, and the root mean square error of the current range is detected using the electrode temperature. The relationship between the electrode temperature and the root mean square error of the current range is as follows: ; Where σ_I is the mean square error of the current range, and T_core is the electrode temperature.

[0011] In some implementations, the relationship between the tab temperature and the root mean square error of the current range is determined as follows: In a set constant temperature chamber, discharge is performed in steps from 0.5C to 1.5C. The electrode temperature measured by the infrared thermal imager and the root mean square error of the current range are recorded to obtain the relationship between the electrode temperature and the root mean square error of the current range.

[0012] In some implementations, the feedback calibration includes the following sub-steps: The voltage polarization amplitude of the lithium-ion battery at a SOC of 50%, measured by pulse discharge or pulse charge, is used as the predicted value, and the corresponding values ​​a, b, and c are recorded. During program operation, after the lithium-ion battery has completed a set number of charge-discharge cycles, the voltage polarization amplitude of the lithium-ion battery measured by pulse discharge or charge will be inserted when the SOC is 50% as the measured value, and the corresponding a, b, and c will be recorded. The deviations between the measured values ​​and the predicted values ​​are compared, and the values ​​of a, b, and c in the corresponding measured values ​​and the predicted values ​​are compared to see if they have changed. The values ​​of a, b, and c in the measured values ​​that have changed are written into the baseline model.

[0013] According to one aspect of the present invention, a temperature compensation device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of the temperature compensation method as described in any of the above.

[0014] According to one aspect of the present invention, a readable storage medium is provided, the storage medium storing at least one executable instruction, which, when executed on a temperature compensation device, causes the temperature compensation device to perform the operation of the temperature compensation method as described in any of the preceding embodiments.

[0015] The beneficial effects of the temperature compensation method, device, and readable storage medium of this invention are as follows: This invention constructs a multi-level compensation system to perform step-by-step compensation for MEMS pressure sensors, including primary compensation, dynamic compensation, and feedback calibration. By periodically acquiring the deviation between the measured and predicted values ​​of the voltage polarization amplitude of the lithium-ion battery, the baseline model in the primary compensation is automatically corrected in reverse, continuously improving the accuracy of temperature compensation for MEMS pressure sensors. Under operating conditions of -20℃ to 80℃, the pressure measurement error of the MEMS pressure sensor is reduced from ±12% to ±3.6%, and the temperature compensation response time is <200ms, meeting the real-time requirements of the battery management system (BMS). This provides key support for refined and intelligent upgrades and is suitable for high-voltage operating scenarios such as power batteries and energy storage batteries. Attached Figure Description

[0016] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic flowchart of the temperature compensation method of Embodiment 1 provided by the present invention is shown; Figure 2 A flowchart illustrating step 200 of Embodiment 1 provided by the present invention is shown; Figure 3 A flowchart illustrating step 300 of Embodiment 1 provided by the present invention is shown; Figure 4 The recorded data for step 310 of Embodiment 1 provided by the present invention is shown; Figure 5 A schematic diagram of the temperature compensation device according to Embodiment 2 of the present invention is shown. Detailed Implementation

[0017] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0018] Example 1: Figure 1 A temperature compensation method according to the present invention is shown, the method comprising the following steps: 100. Primary compensation, including establishing a baseline model based on the original output values ​​of multiple sets of MEMS pressure sensors and the surface temperature value of the lithium-ion battery using polynomial fitting, to obtain the primary compensation output value of the MEMS pressure sensors.

[0019] The raw output value of a MEMS pressure sensor refers to the electrical signal directly output by the sensor without any compensation processing, or the digital signal after preliminary analog-to-digital conversion. This raw output value includes both the actual pressure signal and the interference signal.

[0020] The surface temperature value of a lithium-ion battery refers to the temperature data collected by a temperature sensor attached to the surface of the lithium-ion battery casing. Typically, a region with a relatively uniform surface temperature distribution is selected as the collection point.

[0021] In lithium-ion battery internal pressure monitoring systems, the raw output value of MEMS pressure sensors is affected by a variety of interference factors. Among them, the surface temperature of the lithium-ion battery is the most significant source of systematic interference. Temperature changes can alter the physical properties of the sensor's sensitive element, the thermal expansion coefficient of the encapsulation material, and the vapor pressure of the electrolyte inside the lithium-ion battery, causing the raw output value to deviate from the true pressure value.

[0022] In this embodiment, under a controlled laboratory environment, the typical temperature and pressure ranges throughout the entire life cycle of a lithium-ion battery are simulated. Multiple sets of raw output values ​​from MEMS pressure sensors and the corresponding surface temperature values ​​of the lithium-ion battery are collected. A mathematical correlation model, i.e., a baseline model, is constructed using a polynomial fitting algorithm to establish the temperature-raw output value-real pressure relationship. In practical applications, the real-time collected raw output values ​​from the MEMS pressure sensors and the surface temperature values ​​of the lithium-ion battery are input into the model to calculate the initial compensation output value of the MEMS pressure sensors after eliminating temperature interference, laying the foundation for subsequent dynamic compensation.

[0023] A baseline model is established using polynomial fitting. Compared with the traditional fixed coefficient method, polynomial fitting can capture nonlinear temperature disturbances and control the error after primary compensation within a certain threshold. It can meet the basic accuracy requirements of most scenarios, and the model construction can be completed in the laboratory without the need for complex hardware upgrades.

[0024] The primary compensation output value is obtained as follows: ; Where P_comp1 is the primary compensation output value, P_raw is the raw output value of the MEMS pressure sensor, T_surface is the surface temperature value of the lithium-ion battery, and a, b, and c are the polynomial fitting coefficients obtained by least squares fitting calibration.

[0025] 200. Dynamic compensation, including pre-establishing a database to obtain the relationship between the internal temperature value of the lithium-ion battery and the temperature dynamic compensation coefficient of the MEMS pressure sensor, and calculating the dynamic compensation output value of the MEMS pressure sensor based on the primary compensation output value and the temperature dynamic compensation coefficient.

[0026] The temperature dynamic compensation coefficient of a MEMS pressure sensor is obtained by first testing and acquiring temperature-error data, and then modeling and fitting the data using an algorithm. Specifically, in a controllable temperature and pressure environment, the output signal data of the MEMS pressure sensor under different operating conditions is collected. Based on the collected data, a relationship model between temperature and output error is established through mathematical algorithms, and then the temperature dynamic compensation coefficient is calculated.

[0027] Specifically, changes in the internal temperature of a lithium-ion battery cause a change in the temperature coefficient of resistance of the MEMS pressure sensor, which in turn alters the resistance of its sensing element, thus affecting the sensor's output signal. The temperature dynamic compensation coefficient of the MEMS pressure sensor is a parameter used to correct for the impact of the lithium-ion battery's internal temperature on the sensor's measurement accuracy. When the internal temperature of the lithium-ion battery changes, the MEMS pressure sensor adjusts its output signal according to the preset temperature dynamic compensation coefficient.

[0028] The change in the internal temperature of the lithium-ion battery is a key factor triggering temperature compensation in MEMS pressure sensors. The dynamic temperature compensation coefficient is determined based on the characteristic tests of the MEMS pressure sensor at different temperatures, reflecting the pattern of sensor output signal change with temperature. When the internal temperature of the lithium-ion battery deviates from the calibration temperature, the MEMS pressure sensor corrects the measurement results according to the dynamic temperature compensation coefficient to ensure measurement accuracy.

[0029] Based on the primary compensation output value, and combined with the temperature dynamic compensation coefficient, the dynamic compensation output value of the MEMS pressure sensor is calculated.

[0030] 300. Feedback calibration, including periodically obtaining the deviation between the measured and predicted values ​​of the voltage polarization amplitude of the lithium-ion battery, and automatically correcting the baseline model in reverse.

[0031] Feedback calibration can directly improve the practicality and accuracy of the fitting results. In the polynomial fitting of primary compensation, feedback calibration is used to correct the deviation between the baseline model and the actual working conditions, so that the final compensation effect can continuously meet the accuracy requirements.

[0032] See Figure 3 In one example, step 300, feedback calibration, includes the following sub-steps: 310. The voltage polarization amplitude of the lithium-ion battery when the SOC is 50% is measured by pulse discharge or pulse charge as the predicted value, and the corresponding a, b, and c are recorded.

[0033] Specifically, every 500 iterations, a set of predicted values ​​and corresponding records a, b, and c are tested and recorded. (Reference) Figure 4, which represents the correspondence between the predicted value obtained from every 500 cycles and a, b, c.

[0034] 320. During program operation, after the lithium-ion battery has completed a set number of charge-discharge cycles, the voltage polarization amplitude of the lithium-ion battery measured by pulse discharge or charge will be inserted as the measured value when the SOC is 50% at the next time. At the same time, the corresponding a, b, and c will be recorded.

[0035] Specifically, every 10 cycles, a set of measured values ​​and corresponding records a, b, and c are measured and recorded.

[0036] 330. Compare the deviation between the measured value and the predicted value, and compare whether the values ​​of a, b, and c of the corresponding measured value and the values ​​of a, b, and c of the predicted value have changed. Write the values ​​of a, b, and c of the measured value that have changed into the baseline model.

[0037] Specifically, the predicted value refers to the value measured in advance, that is, the value recorded in the laboratory. By comparing the deviation between the measured value and the predicted value, it can be proved that the lithium-ion battery has degraded. After degradation, the internal resistance changes. After the internal resistance changes, the internal temperature value of the lithium-ion battery will also change, and the surface temperature value of the lithium-ion battery will also be affected. Therefore, the coefficients a, b, and c in the primary compensation need to be corrected in reverse.

[0038] See Figure 2 In some implementations, step 200, dynamic compensation, includes the following sub-steps: 210. Based on the voltage polarization amplitude, current range mean square error, and internal temperature values ​​of multiple sets of lithium-ion batteries, establish a primary database.

[0039] Specifically, the primary database is established by obtaining the voltage polarization amplitude, current range mean square error, and internal temperature value of the lithium-ion battery under different operating conditions through cyclic charge-discharge experiments, and then establishing a three-dimensional relationship matrix of the voltage polarization amplitude, current range mean square error, and internal temperature value of the lithium-ion battery.

[0040] To obtain the voltage polarization amplitude, the current control and voltage sampling capabilities of the battery management system (BMS) can be utilized to capture the voltage response of the lithium-ion battery when the charging and discharging current changes, thereby calculating the magnitude of the polarization voltage and collecting the voltage polarization amplitude of multiple lithium-ion batteries. The voltage polarization amplitude is collected in the range of 30%-70% SOC.

[0041] The mean square error of the current range is calculated by collecting all current data within the target range using current sampling devices, such as current sensors and Hall sensors built into the battery management system (BMS), forming a raw dataset. After calculating the average current within the range, the square of the deviation of each current data point from the average is calculated, the sum of these squares is averaged, and then the square root is taken. The mean square error of the current range is typically taken as 10-30 seconds and is related to the thermal time constant τ of the lithium-ion battery.

[0042] Internal temperature values ​​can be estimated through external temperature monitoring combined with algorithms or directly collected by built-in sensors.

[0043] Experiments revealed a strong correlation between the voltage polarization amplitude and the mean square error of the current range during lithium-ion battery discharge (Pearson coefficient > 0.91), a characteristic that can indirectly reflect the actual internal temperature of the lithium-ion battery.

[0044] 220. Retrieve the internal temperature values ​​of multiple sets of lithium-ion batteries from the primary database, and combine them with the temperature dynamic compensation coefficients of multiple sets of MEMS pressure sensors corresponding to the internal temperature values ​​of the multiple sets of lithium-ion batteries to establish a secondary database.

[0045] Changes in the internal temperature of a lithium-ion battery alter the temperature coefficient of resistance in a MEMS pressure sensor, consequently changing the resistance of its sensing element and affecting the sensor's output signal. The temperature dynamic compensation coefficient of a MEMS pressure sensor is a parameter used to correct for the impact of the lithium-ion battery's internal temperature on the sensor's measurement accuracy. When the internal temperature of the lithium-ion battery changes, the MEMS pressure sensor adjusts its output signal according to the preset temperature dynamic compensation coefficient.

[0046] 230. Real-time acquisition of the current sequence of the lithium-ion battery, calculation of the root mean square error of the current interval within the sliding window, obtaining the corresponding internal temperature value of the lithium-ion battery through the primary database, and then obtaining the corresponding temperature dynamic compensation coefficient of the MEMS pressure sensor through the secondary database.

[0047] 240. Based on the primary compensation output value and the temperature dynamic compensation coefficient, the dynamic compensation output value of the MEMS pressure sensor is calculated.

[0048] Specifically, during the dynamic compensation process, by collecting the current sequence and calculating the mean square error of the current interval within the sliding window, the corresponding relationship between the voltage polarization amplitude, the mean square error of the current interval, and the internal temperature value of the lithium-ion battery is found in the primary database using this mean square error. The internal temperature value of the corresponding lithium-ion battery is then obtained. Based on this internal temperature value, the temperature dynamic compensation coefficient corresponding to this internal temperature value is found in the secondary database. Combining the primary compensation output value and this temperature dynamic compensation coefficient, the dynamic compensation output value of the MEMS pressure sensor is obtained.

[0049] The formula for calculating the dynamic compensation output value of a MEMS pressure sensor is as follows:

[0050] Where P_comp2 is the dynamic compensation output value of the MEMS pressure sensor, P_comp1 is the primary compensation output value, and ΔP(T) is the temperature dynamic compensation coefficient.

[0051] In some implementations, the primary compensation is initiated when the root mean square error of the current range is detected to be less than or equal to a preset value; and the dynamic compensation is initiated when the root mean square error of the current range is detected to be greater than the preset value.

[0052] For example, when the mean square error of the current range is detected to be less than or equal to 200A, the primary compensation is initiated; when the mean square error of the current range is detected to be greater than 200A, the dynamic compensation is initiated.

[0053] Experiments revealed a strong correlation between the voltage polarization amplitude and the mean square error of the current range during lithium-ion battery discharge (Pearson coefficient > 0.91). This characteristic indirectly reflects the actual internal temperature of the lithium-ion battery. Specifically, the larger the mean square error of the current range, the larger the actual internal temperature of the lithium-ion battery. Therefore, when the mean square error of the current range is detected to be greater than 200A, it indirectly reflects that the actual internal temperature of the lithium-ion battery is large, which will have a significant impact on the measurement accuracy of the MEMS pressure sensor. Therefore, dynamic compensation needs to be activated.

[0054] In some implementations, the mean square error of the current range is detected in the following manner: The electrode temperature of the lithium-ion battery is obtained, and the root mean square error of the current range is detected using the electrode temperature. The relationship between the electrode temperature and the root mean square error of the current range is as follows: ; Where σ_I is the mean square error of the current range, and T_core is the electrode temperature.

[0055] Furthermore, the relationship between the tab temperature and the root mean square error of the current range is determined as follows: In a controlled temperature chamber, discharges were performed at stepped rates from 0.5C to 1.5C. The mean square error (MSE) of the current range, measured by an infrared thermal imager, was recorded to obtain the relationship between the electrode temperature and the MSE of the current range. For example, discharges were performed at 0.5C, 0.8C, 1.0C, 1.2C, and 1.5C at 25°C, and the MSE of the current range σ_I and the electrode temperature T_core were recorded. The trend shown by the recorded data revealed... The relational formula.

[0056] In existing technologies, the mean square error of the current range is calculated by collecting all current data within the target range using current sampling devices, such as current sensors and Hall sensors built into the battery management system (BMS), forming a raw dataset. After calculating the average current within the range, the square of the deviation of each current data point from the average value is calculated, the average of the sum of squares is obtained, and then the square root is taken. This calculation process is relatively complex and consumes a lot of computing resources. However, in this embodiment, the mean square error of the current range can be quickly obtained after obtaining the tab temperature using the formula σ_I=0.87·exp(0.032·T_core), enabling rapid compensation response.

[0057] This invention provides a temperature compensation method that predicts the actual internal temperature of a lithium-ion battery by using the mean square error of the current range and establishes a relationship between the internal temperature and a dynamic temperature compensation coefficient. When the internal temperature of the lithium-ion battery changes, the MEMS pressure sensor adjusts its output signal according to the preset dynamic temperature compensation coefficient. Combining the primary compensation output value and the dynamic temperature compensation coefficient, the dynamic compensation output value of the MEMS pressure sensor is obtained, thus dynamically compensating the MEMS pressure sensor and improving the accuracy of temperature compensation. Furthermore, by periodically acquiring the deviation between the measured and predicted values ​​of the voltage polarization amplitude of the lithium-ion battery, the baseline model in the primary compensation is automatically corrected in reverse, continuously improving the accuracy of temperature compensation for the MEMS pressure sensor. This provides key support for the refined and intelligent upgrading of the battery management system (BMS) and is applicable to high-voltage operating scenarios such as power batteries and energy storage batteries.

[0058] Example 2: Figure 5 The diagram shows a structural schematic of an embodiment of a temperature compensation device according to the present invention. The specific embodiments of the present invention do not limit the specific implementation of a temperature compensation device.

[0059] like Figure 5As shown, the temperature compensation device may include: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus.

[0060] The processor 410, communication interface 420, and memory 430 communicate with each other via communication bus 440. The communication interface is used to communicate with other network elements, such as clients or other servers. The processor executes program 450, specifically performing the relevant steps described above in an embodiment of the temperature compensation method of the present invention.

[0061] Specifically, a program may include program code, which includes computer-executable instructions.

[0062] The processor can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The temperature compensation device includes one or more processors, which can be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0063] Memory is used to store executable instructions. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0064] The executable instructions can be executed by a temperature compensation device. Figure 1 Steps 100-300.

[0065] Example 3: According to one aspect of the present invention, a readable storage medium is provided, the storage medium storing at least one executable instruction, which, when executed on a temperature compensation device, causes the temperature compensation device to perform the operation of the temperature compensation method as described in any of the preceding embodiments.

[0066] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments of this invention are not directed to any particular programming language.

[0067] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. Similarly, for the sake of brevity and to aid in understanding one or more aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0068] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0069] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A temperature compensation method, characterized in that, include: Primary compensation includes establishing a baseline model based on the original output values ​​of multiple MEMS pressure sensors and the surface temperature value of the lithium-ion battery using polynomial fitting, which is used to obtain the primary compensation output value of the MEMS pressure sensors. Dynamic compensation includes pre-establishing a database to obtain the relationship between the internal temperature value of the lithium-ion battery and the temperature dynamic compensation coefficient of the MEMS pressure sensor, and calculating the dynamic compensation output value of the MEMS pressure sensor based on the primary compensation output value and the temperature dynamic compensation coefficient. Feedback calibration includes periodically obtaining the deviation between the measured and predicted values ​​of the voltage polarization amplitude of the lithium-ion battery and automatically correcting the baseline model in reverse.

2. The temperature compensation method according to claim 1, characterized in that, The primary compensation output value is obtained as follows: ； Wherein, P_comp1 is the primary compensation output value, P_raw is the raw output value of the MEMS pressure sensor, T_surface is the surface temperature value of the lithium-ion battery, and a, b, and c are polynomial fitting coefficients obtained by least squares fitting calibration.

3. The temperature compensation method according to claim 1, characterized in that, The dynamic compensation includes the following sub-steps: A primary database is established based on the voltage polarization amplitude, current range mean square error, and internal temperature values ​​of multiple sets of lithium-ion batteries. A secondary database is established by retrieving multiple sets of internal temperature values ​​of the lithium-ion batteries from the primary database and combining them with the temperature dynamic compensation coefficients of multiple sets of MEMS pressure sensors corresponding to the multiple sets of internal temperature values ​​of the lithium-ion batteries. The current sequence of the lithium-ion battery is collected in real time, the root mean square error of the current interval within the sliding window is calculated, the internal temperature value of the lithium-ion battery is obtained through the first-level database, and the temperature dynamic compensation coefficient of the MEMS pressure sensor is obtained through the second-level database. Based on the primary compensation output value and the temperature dynamic compensation coefficient, the dynamic compensation output value of the MEMS pressure sensor is calculated.

4. The temperature compensation method according to claim 3, characterized in that, The primary database is established as follows: The voltage polarization amplitude, current range mean square error, and internal temperature of the lithium-ion battery under different operating conditions were obtained through cyclic charge-discharge experiments, and a three-dimensional relationship matrix of the voltage polarization amplitude, current range mean square error, and internal temperature of the lithium-ion battery was established.

5. The temperature compensation method according to claim 3, characterized in that, When the root mean square error of the current range is detected to be less than or equal to a preset value, the primary compensation is activated. When the mean square error of the current range is detected to be greater than a preset value, the dynamic compensation is activated.

6. The temperature compensation method according to claim 5, characterized in that, The mean square error of the current range is detected in the following manner: The electrode temperature of the lithium-ion battery is obtained, and the root mean square error of the current range is detected using the electrode temperature. The relationship between the electrode temperature and the root mean square error of the current range is as follows: ； Where σ_I is the mean square error of the current range, and T_core is the electrode temperature.

7. The temperature compensation method according to claim 6, characterized in that, The relationship between the tab temperature and the root mean square error of the current range is determined as follows: In a set constant temperature chamber, discharge is performed in steps from 0.5C to 1.5C. The electrode temperature measured by the infrared thermal imager and the root mean square error of the current range are recorded to obtain the relationship between the electrode temperature and the root mean square error of the current range.

8. The temperature compensation method according to claim 2, characterized in that, The feedback calibration includes the following sub-steps: The voltage polarization amplitude of the lithium-ion battery at a SOC of 50%, measured by pulse discharge or pulse charge, is used as the predicted value, and the corresponding values ​​a, b, and c are recorded. During program operation, after the lithium-ion battery has completed a set number of charge-discharge cycles, the voltage polarization amplitude of the lithium-ion battery measured by pulse discharge or charge will be inserted when the SOC is 50% as the measured value, and the corresponding a, b, and c will be recorded. The deviations between the measured values ​​and the predicted values ​​are compared, and the values ​​of a, b, and c in the corresponding measured values ​​and the predicted values ​​are compared to see if they have changed. The values ​​of a, b, and c in the measured values ​​that have changed are written into the baseline model.

9. A temperature compensation device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the temperature compensation method as described in any one of claims 1-8.

10. A readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the temperature compensation device, causes the temperature compensation device to perform the operation of the temperature compensation method as described in any one of claims 1-8.

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