A battery cell life evaluation method, a battery cell protection method, and an evaluation device

Abstract

The present application belongs to the technical field of energy storage safety related, especially relates to a kind of battery life evaluation method, battery protection method and evaluation device.The present application obtains the operation data inside battery monomer periodically, and the operation data is input into preset risk assessment model;Risk assessment model calculates risk parameters according to operation data, and risk assessment model calculates safety parameters according to risk parameters;Wherein, operation data includes temperature and pressure operation data.Through the operation data of the pressure and temperature of the battery when evaluating the battery coefficient is collected, and the risk parameters of the battery are calculated, thereby avoiding the existing evaluation method to exist indirectness and hysteresis of safety monitoring, single dimension of safety risk assessment, inaccurate economic value evaluation and other problems.

Description

Technical Field

[0001] This invention belongs to the technical field of energy storage safety, and in particular relates to a cell life assessment method, a cell protection method, and an assessment device. Background Technology

[0002] Currently, battery management systems or energy management systems (hereinafter referred to as systems) in applications such as energy storage power stations and electric vehicles generally use battery health status as the core indicator for assessing the remaining value of the battery and determining whether it needs to be retired.

[0003] In existing technologies, the lifespan health of battery cells is typically assessed by methods such as measuring the actual usable capacity or energy released during a full charge-discharge cycle and comparing it to the factory-rated capacity / energy; monitoring the increase in internal resistance during use; and data-driven methods, which utilize big data and machine learning algorithms to analyze historical battery operating data (such as voltage, current, and external temperature profiles) to predict trends in state of health (SOH). To compensate for the shortcomings of simple lifespan assessment in terms of safety, existing technologies also incorporate external sensors to evaluate battery lifespan, further assisting in safety monitoring. This includes placing temperature sensors on the surface of the battery module or battery pack casing to monitor abnormal temperature rises; or installing flammable gases (such as...) inside the battery compartment. (CO) or electrolyte vapor sensors are used to detect chemicals after a battery leaks.

[0004] While existing technologies can assess battery life and implement safety measures based on the assessment results, they suffer from problems such as indirect and delayed safety monitoring, limited dimensions of safety risk assessment, and inaccurate economic value assessment. Summary of the Invention

[0005] To address the problems of indirect and delayed safety monitoring, limited dimensions of safety risk assessment, and inaccurate economic value assessment in existing technologies and evaluation methods, this invention proposes a cell life assessment method, a cell protection method, and an assessment device.

[0006] The present invention solves the above problems through the following technical solutions: In a first aspect, the present invention proposes a method for evaluating battery cell lifespan, comprising: Periodically acquire the internal operating data of a single battery cell and input the operating data into a preset risk assessment model; The risk assessment model calculates risk parameters based on operational data, and then calculates safety parameters based on the risk parameters. Execute the preset security policy based on the security parameters; The operational data includes the temperature and air pressure data of individual battery cells.

[0007] This invention acquires operational data from within a single battery cell periodically, inputs this data into a preset risk assessment model, calculates risk parameters based on the operational data, and then calculates safety parameters based on these risk parameters. A preset safety strategy is then executed based on the safety parameters. The operational data includes temperature and air pressure data. By collecting air pressure and temperature data of the battery cell during the evaluation of its performance, and calculating the cell's risk parameters to execute safety strategies, this invention avoids the problems of indirect and delayed safety monitoring, limited dimensions of safety risk assessment, and inaccurate economic value assessment found in existing evaluation methods.

[0008] In some implementations, the operating data for the temperature and pressure of a single cell include: absolute pressure, instantaneous pressure change rate, cell temperature change rate, and current decay parameters.

[0009] In some implementations, the risk assessment model calculates risk parameters based on operational data, including: Obtain the preset air pressure warning threshold and air pressure limit threshold, and calculate the first risk parameter based on the air pressure warning threshold, air pressure limit threshold and absolute air pressure; Obtain a preset reference pressure change rate, and calculate the second risk parameter based on the reference pressure change rate and the instantaneous pressure change rate; Obtain the preset temperature change rate limit and the expected temperature change rate, and calculate the third risk parameter based on the cell temperature change rate, the temperature change rate limit, and the expected temperature change rate. Obtain the preset initial decay parameters and performance lifespan end threshold, and calculate the fourth risk parameter based on the initial decay parameters, performance lifespan end threshold, and current decay parameters.

[0010] In some implementations, the risk assessment model calculates safety parameters based on risk parameters; including: the risk assessment model calculates safety parameters based on a first risk parameter, a second risk parameter, a third risk parameter, a fourth risk parameter, and a preset weight parameter.

[0011] In a second aspect, the present invention proposes a battery cell protection method for protecting the battery cell according to the safety parameters obtained as described in any of the first aspects, comprising: The system executes preset security policies based on security parameters, including: obtaining a preset first warning range and executing multi-level warning policies based on the first warning range and security parameters.

[0012] In some implementations, the method further includes: inputting risk parameters into a preset aging model, wherein the aging model calculates first boundary parameters based on the multiple risk parameters; Select the minimum boundary parameter among all first boundary parameters, and execute the preset security policy based on the minimum boundary parameter and the security parameter.

[0013] In some implementations, the operating data of temperature and air pressure of the battery cell include the current attenuation parameter. After obtaining the current attenuation parameter, the method further includes: inputting the current attenuation parameter into a preset aging model, and the aging model calculating the second boundary parameter based on the current attenuation parameter. Select the minimum boundary parameter from the first boundary parameter and the second boundary parameter, and execute the preset security strategy based on the minimum boundary parameter and the security parameter.

[0014] In some implementations, a preset second warning range is obtained, and a multi-level warning strategy is executed based on the second warning range, safety parameters, and minimum boundary parameters.

[0015] Thirdly, the present invention provides an evaluation apparatus, comprising: The data acquisition module is used to acquire the internal operating data of the battery cell during the execution cycle and input the operating data into the preset risk assessment model; the operating data includes operating data of temperature and air pressure.

[0016] The safety parameter calculation module is used to execute the risk assessment model to calculate risk parameters based on the running data, and the risk assessment model to calculate safety parameters based on the risk parameters.

[0017] Fourthly, this invention proposes a cell management system, 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 through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform operations such as the cell life assessment method of the first aspect.

[0018] The beneficial effects of the battery cell life assessment method, battery cell protection method, and assessment device of the present invention are as follows: 1. By introducing indicators such as pressure and temperature rise rate that directly reflect internal chemical stability, this method can capture clear early warning signals before catastrophic events such as thermal runaway occur, realizing a leap from "passive response" to "active prediction" and buying valuable time for intervention measures.

[0019] 2. This method scientifically distinguishes between "performance degradation" and "safety failure," preventing a large number of batteries that only experience performance degradation but remain safe from premature disposal. By accurately assessing their minimum boundary parameters, it provides a reliable basis for the reasonable pricing and application of these batteries in the secondary utilization market, significantly improving the return on investment throughout their entire life cycle.

[0020] 3. The changing trends of each component (risk factor) can help technicians diagnose the specific degradation mechanisms inside the battery.

[0021] 4. Based on The dynamic control strategy transforms the battery / energy management system from a simple protector into an intelligent "health and safety steward." It can finely adjust operating boundaries based on the battery's real-time overall status, maximizing battery lifespan while ensuring safety. Attached Figure Description

[0022] 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 This is a flowchart of the cell life assessment method of the present invention; Figure 2 This is a flowchart of step 200 of the cell life assessment method of the present invention; Figure 3 This is a structural diagram of the evaluation device of the present invention; Figure 4 This is a system structure diagram of the battery cell management system of the present invention. Detailed Implementation

[0023] 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.

[0024] Example 1: like Figures 1-2 As shown in the figure, this embodiment proposes a method for evaluating battery cell life, including the following steps: Step 100: Periodically acquire the internal operating data of a single battery cell and input the operating data into a preset risk assessment model; Specifically, the operating data inside a single battery cell can be periodically acquired through built-in or external sensors, such as voltage and current sensors. In order to acquire air pressure and temperature, high-precision miniature internal temperature and air pressure sensors can also be integrated inside the battery or in the standard module.

[0025] Step 200: The risk assessment model calculates risk parameters based on the operational data, and then calculates safety parameters based on the risk parameters. Specifically, the risk assessment model is used to calculate the overall safety status, which is reflected in safety parameters (hereinafter referred to as...). This parameter is used to quantitatively assess the battery's safety status. Typically, it is a dimensionless scalar between 0 and 1, with a higher value indicating a better battery safety status. Alternatively, a scoring method can be used to determine the cell's safety status. It is a weighted fusion process of multiple safety factors. Safety factors are operational indicators of the cell's safety status, such as pressure risk, pressure change rate risk, temperature rise rate risk, and health status risk. When calculating these factors, normalized data is usually output and then fused according to certain weights to determine the final safety status. Numerical values. Weights are typically dynamically adjusted based on battery type, application scenario, and aging stage. For example, initially, it is preferable to set the health status risk to 0.4, the pressure change rate risk to 0.3, and the pressure risk and pressure change rate risk to 0.15. At the end of the battery's lifespan, when the current degradation parameter (hereinafter referred to as SOH) is <80%, the weights of the pressure risk and pressure change rate risk related to gas production should be appropriately increased, i.e., the temperature rise rate risk and health status risk should be 0.15, the pressure change rate risk 0.3, and the pressure risk 0.4. In other embodiments, safety factors can also be other safety factors.

[0026] This invention acquires operational data from within a single battery cell periodically and inputs this data into a preset risk assessment model. The risk assessment model calculates risk parameters based on the operational data, and then calculates safety parameters based on these risk parameters. The operational data includes temperature and air pressure data. By collecting air pressure and temperature data of the battery cell during the evaluation of the cell's performance and calculating the cell's risk parameters to implement safety strategies, this invention avoids the problems of indirect and delayed safety monitoring, limited dimensions of safety risk assessment, and inaccurate economic value assessment found in existing evaluation methods.

[0027] In some embodiments, the operating data of the cell's temperature and pressure include: absolute pressure, instantaneous pressure change rate, cell temperature change rate, and current decay parameters.

[0028] Specifically, absolute pressure (hereinafter referred to as...) ): The internal absolute air pressure monitored in real time at time t; the instantaneous rate of change of air pressure (hereinafter referred to as The rate of change of cell temperature (hereinafter referred to as...) is obtained by high-frequency sampling and calculation of the air pressure signal. ), which is the real-time monitored cell temperature rise rate; current attenuation parameters (hereinafter referred to as The current SOH is calculated using traditional methods (such as the ampere-hour integration method combined with internal resistance correction). Subsequent risk parameters are then calculated by acquiring these parameters in real time.

[0029] In some embodiments, the risk assessment model calculates risk parameters based on operational data, including the following steps: Step 201: Obtain the preset air pressure warning threshold and air pressure limit threshold, and calculate the first risk parameter based on the air pressure warning threshold, air pressure limit threshold and absolute air pressure; Specifically, the first risk parameter was set as the barometric risk factor. This factor characterizes how close the current internal air pressure is to the safety boundary. Barometric Risk Factor Calculated using the following formula: (1) In formula (1), This is the atmospheric pressure warning threshold, which is the maximum expected normal operating atmospheric pressure of the battery under specific SOH and temperature conditions during normal aging. This value is not a constant, but a dynamic function related to SOH and temperature. This function was obtained by fitting a large amount of experimental data. It can be represented as: (2) In formula (2), The first term, denoted by Celsius, is a function of aging, reflecting the irreversible accumulation of gas production leading to an increase in base pressure as SOx decreases; this is typically a quadratic relationship. The second term is a correction for temperature changes based on the ideal gas law. , , These are empirical constants obtained through experimental testing and data fitting under different aging conditions for specific cell models, with the preferred constant being... , =1300, =830. This is the pressure limit threshold. Once the pressure reaches this value, the battery is considered to have experienced irreversible and dangerous gas production, posing an extremely high risk of thermal runaway. Preferably, [the following is a more accurate translation of the original text:] =2500 (hPa). Set as barometric sensitivity index ( (Preferred to be 2), used to amplify the risk level after the air pressure exceeds the warning threshold, making the growth of risk factors nonlinear and more in line with physical reality.

[0030] Step 202: Obtain the preset reference pressure change rate, and calculate the second risk parameter based on the reference pressure change rate and the instantaneous pressure change rate; Specifically, the second risk parameter is set as the pressure change rate risk factor, which characterizes the degree of danger of the internal gas production rate and is used to detect sudden, severe reactions such as internal short circuits. Its calculation formula is as follows: (3) In formula (3), The reference pressure change rate represents the upper limit of normal pressure fluctuation under current operating conditions, and is a function of the charge / discharge rate (C-rate) and temperature. This function can be expressed as: (4) In formula (4), C represents the current charge / discharge rate (positive for charging, negative for discharging). The first term describes the nonlinear relationship between the rate of pressure change and the magnitude of the current. The second term is a linear correction for the effect of temperature, where... This is the current temperature in Celsius. , , All are empirical fitting coefficients obtained through calibration using experimental data. Preferably, =0.1hPa / , =0.05hPa / , 0.025 / ℃. Where k is a kurtosis coefficient used to adjust the sensitivity of the risk factor to the rate of change of air pressure, preferably k = 2.5 s / hpa.

[0031] Step 203: Obtain the preset temperature change rate limit and the expected temperature change rate, and calculate the third risk parameter based on the cell temperature change rate, the temperature change rate limit, and the expected temperature change rate; Specifically, the third risk parameter was set as the rate of temperature rise risk factor. This factor is used to identify abnormal self-heating caused by factors such as internal micro-short circuits. Its calculation formula is: (5) In formula (5), The permissible temperature rise rate limit can be set to 5℃ / min. Under non-abuse conditions, the normal temperature rise rate is much lower than this value. Exceeding this rate usually means that a violent side reaction has occurred inside, which may lead to thermal runaway. This represents the expected temperature rise rate based on a battery electro-thermal coupling model. This model is based on the current... Ambient temperature The rate of heat generation and dissipation under normal conditions is predicted by the battery internal resistance R(SOH). The formula is: (6) In formula (6), the internal resistance R(SOH) is the internal resistance of the battery cell that increases with aging (SOH decreases). This relationship is usually calibrated experimentally. As a concrete example: (7) In formula (7), The internal resistance of the new battery. and For a 280Ah LFP cell, the fitting coefficients are preferred. Set to 0.2mΩ. It is 0.8. It is 0.5. Ct is the equivalent heat capacity of the battery cell (J / K), which represents the energy absorbed by the battery cell to increase its temperature by 1K. The equivalent heat dissipation coefficient (W / K) represents the heat dissipation power of the battery cell when the temperature difference between the cell and the environment is 1K. Preferably, for a typical 280Ah square lithium iron phosphate (LFP) energy storage cell, under natural convection conditions, its parameters can be set as: Cth = 4500J / K. =3.5 W / K. This risk factor increases sharply when the actual rate of temperature rise is significantly higher than the model prediction. Ambient temperature This refers to the ambient temperature, which is set according to the operating environment.

[0032] Step 204: Obtain the preset initial decay parameters and performance lifespan end threshold, and calculate the fourth risk parameter based on the initial decay parameters, performance lifespan end threshold, and current decay parameters.

[0033] Specifically, the fourth risk parameter was set as a health status risk factor. It retains the evaluation information of traditional SOH as a characterization of performance degradation. Its calculation formula can be expressed as: (7) In formula (7), The initial SOH (usually 100%). This is the performance lifespan end threshold (e.g., 80%).

[0034] In some embodiments, the risk assessment model calculates safety parameters based on risk parameters; including: the risk assessment model calculates safety parameters based on a first risk parameter, a second risk parameter, a third risk parameter, a fourth risk parameter, and a preset weight parameter.

[0035] Specifically, safety parameters The calculation formula can be expressed as: (8) In formula (8), represents any time. , , , These are the weighting coefficients for each safety factor, and + + + =1. These weights can be dynamically adjusted based on battery type, application scenario, and aging stage.

[0036] Example 2: This embodiment also proposes a cell protection method, including: A preset safety strategy is executed according to safety parameters; wherein, the operating data includes the operating data of the temperature and air pressure of the individual battery cells.

[0037] Specifically, the real-time hierarchical security control strategy: the system calculates in real time... The value is used to implement a tiered, dynamic adjustment strategy to achieve the optimal balance between security and performance. Different threshold ranges can be set, and the system will determine the appropriate threshold. The values ​​fall within different threshold ranges, thus determining which level of safety strategy to adopt. By acquiring operational data on temperature and air pressure—which can be obtained by installing sensors on individual battery cells—operational data on air pressure and temperature of the cells can be collected during cell coefficient evaluation. By calculating the cell's risk parameters, safety strategies can be implemented, thereby avoiding problems such as the indirectness and lag of safety monitoring, the single dimension of safety risk assessment, and inaccurate economic value assessment in existing evaluation methods.

[0038] In some embodiments, executing a preset security policy based on security parameters includes: obtaining a preset first warning range, and executing a multi-level warning policy based on the first warning range and security parameters.

[0039] Specifically, the early warning strategy can be divided into three levels of response, including: Level 1 response ( A slight decrease, for example, 0.7 < The measures include issuing a "concern" level alert, slightly limiting the maximum charge / discharge rate of the system (e.g., reducing it by 10%-20%), and appropriately increasing the monitoring frequency of key safety parameters such as internal pressure and temperature rise rate. These preventative measures aim to slow down the battery's degradation trend and prevent further deterioration of its safety status without significantly affecting performance.

[0040] Level II response ( Moderate decrease, 0.2 < ≤ 0.7): Measures include issuing a "warning" level alarm, significantly reducing the maximum charge / discharge rate (e.g., by more than 50%), actively narrowing the voltage operating window, and recommending system maintenance checks. These protective measures buy time to address potential safety hazards and prevent problems from escalating by drastically reducing the battery's operating load.

[0041] Level III response Severe decline ≤ 0.2): Measures include issuing the highest level "Danger" alarm, immediately executing a forced shutdown procedure, disconnecting the battery from the system via a relay, and activating the fire suppression system (if it can be activated proactively). This serves as an emergency stop, ensuring the safety of personnel and equipment with the highest priority, regardless of the SOH value, and preventing catastrophic accidents such as thermal runaway.

[0042] In some embodiments, the operating data of temperature and air pressure of a single cell include the current attenuation parameter. After obtaining the current attenuation parameter, the method further includes step 400: inputting the risk parameters into a preset aging model, and the aging model calculating the first boundary parameter based on the multiple risk parameters. Specifically, step 400 can be set before step 300 to evaluate and predict cell lifespan from multiple dimensions, and to set more specific and diversified safety measures based on boundary parameters and safety parameters. The first boundary parameter can be understood as the safe lifespan boundary (also known as the minimum boundary parameter mentioned above, hereinafter referred to as SLB), which is the battery's... The value fell below the preset safety threshold for the first time. For example, the remaining usage time (e.g., 0.2). Its prediction relies on a prediction model coupled with aging and risk, i.e., a pre-defined aging model. The prediction principle of the aging model can be understood as the model extrapolating... This can be achieved through future trends in air pressure and SOH risk factors, and its formula can be understood as: (8) in, The predicted value can be obtained by establishing its own time series data (or by establishing degradation models for air pressure and SOH risk factors respectively and taking the minimum value (that is, the minimum value of the first boundary parameter).

[0043] The aging model obtains the safety parameters from steps 100-300 and inputs these parameters into a preset aging model. In a preferred embodiment, different aging models are set for different safety parameters to make the final calculated first boundary parameters more targeted and the prediction more accurate. In some optional embodiments, the aging model can also be a uniform, simple model that predicts output based on machine learning or artificial intelligence.

[0044] More specifically, in some preferred embodiments, the model for air pressure can be set as follows: (9) in, It is a function of gas production rate degradation, and is related to the cyclic operating condition (current). ,temperature (mean state of charge) Related. The system can calculate or predict based on historical operating conditions or preset future task profiles. .

[0045] Step 500: Select the minimum boundary parameter among all the first boundary parameters, and execute the preset security strategy based on the minimum boundary parameter and the security parameter.

[0046] Specifically, minimum boundary parameters can predict the lifespan boundary of a battery cell. Combined with safety parameters, the current state of the battery cell can be used to predict its future state, thereby making further decisions and improving the reliability and effectiveness of those decisions.

[0047] Step 500 can replace step 300 in making a decision. A two-dimensional control strategy can be executed by applying a preset safety strategy using minimum boundary parameters and safety parameters. In some embodiments, the following steps are also included: Step 410: Input the current attenuation parameters into the preset aging model, and the aging model calculates the second boundary parameters based on the current attenuation parameters; Specifically, the aging model for the current decay parameter (state SOH) can be set as follows: (10) In formula (10) With cyclic operating conditions (current) ,temperature (Related to)

[0048] Through the By performing trend forecasting (or taking the minimum value of similar long-term trend forecasts for air pressure and SOH risk factors), the value of any future time can be obtained. of Predicted values, thus solving for .

[0049] Step 600: Select the minimum boundary parameter from the first boundary parameter and the second boundary parameter, and execute the preset security strategy based on the minimum boundary parameter and the security parameter.

[0050] Specifically, minimum boundary parameters can predict the lifespan boundary of a battery cell. Combined with safety parameters, decisions can be made based on the current and predicted states of the battery cell, thereby improving the reliability and effectiveness of the decisions.

[0051] Step 600 can replace step 300 for decision determination. By executing a preset safety strategy using minimum boundary parameters and safety parameters, a two-dimensional control strategy can be implemented.

[0052] In some implementations, a preset second warning range is obtained, and a multi-level warning strategy is executed based on the second warning range, safety parameters, and minimum boundary parameters.

[0053] Specifically, the second early warning range is the range of safety parameter classification and the range of minimum boundary classification, so as to make a two-dimensional early warning strategy decision based on the calculated safety parameters and minimum boundary parameters, which may include the following strategies:

[0054] Example 3: like Figure 3 As shown, this embodiment proposes an evaluation device, including: The data acquisition module 710 is used to perform step 100 to acquire the internal operating data of the battery cell in a cycle and input the operating data into a preset risk assessment model; wherein, the operating data includes operating data of temperature and air pressure.

[0055] The safety parameter calculation module 720 is used to execute step 200 to calculate risk parameters based on operating data using a risk assessment model, and the risk assessment model calculates safety parameters based on the risk parameters; thereby calculating the cell's... Value, thus through The lifespan of the battery cells is assessed.

[0056] Example 4: like Figure 4 As shown in the figure, this embodiment presents a structural schematic diagram of an embodiment of the battery cell management system of the present invention. The specific embodiment of the present invention does not limit the specific implementation of the battery cell management system.

[0057] The cell management system may include: a processor 802, a communications interface 804, a memory 806, and a communication bus 808.

[0058] The processor 802, communication interface 804, and memory 806 communicate with each other via communication bus 808. Communication interface 804 is used to communicate with other network elements, such as clients or other servers. Processor 802 executes program 810, specifically performing the relevant steps in the battery cell life assessment method embodiments as described in Embodiments 1 and / or 2.

[0059] Specifically, program 810 may include program code, which includes computer-executable instructions.

[0060] Processor 802 may 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 simulation system includes one or more processors, which may 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.

[0061] Memory 806 is used to store program 810. Memory 806 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device. The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, embodiments of the present invention are not directed to any particular programming language.

[0062] 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.

[0063] 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.

[0064] 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 method for evaluating battery cell life, characterized in that, include: Periodically acquire the internal operating data of a single battery cell and input the operating data into a preset risk assessment model; The risk assessment model calculates risk parameters based on the operational data, and the risk assessment model calculates safety parameters based on the risk parameters; The operating data includes the temperature and air pressure data of individual battery cells; The operating data of the battery cell's temperature and pressure include: absolute pressure, instantaneous pressure change rate, battery cell temperature change rate, and current attenuation parameters; The risk assessment model calculates risk parameters based on the operational data, including: Obtain the preset air pressure warning threshold and air pressure limit threshold, and calculate the first risk parameter based on the air pressure warning threshold, air pressure limit threshold and absolute air pressure; Obtain a preset reference air pressure change rate, and calculate a second risk parameter based on the reference air pressure change rate and the instantaneous air pressure change rate; Obtain the preset temperature change rate limit and the expected temperature change rate, and calculate the third risk parameter based on the cell temperature change rate, the temperature change rate limit, and the expected temperature change rate. Obtain the preset initial decay parameter and performance lifespan end threshold, and calculate the fourth risk parameter based on the initial decay parameter, performance lifespan end threshold and current decay parameter.

2. The cell life assessment method according to claim 1, characterized in that, The risk assessment model calculates safety parameters based on the risk parameters, including: The risk assessment model calculates the safety parameters based on the first risk parameter, the second risk parameter, the third risk parameter, the fourth risk parameter, and the preset weight parameters.

3. A method for protecting battery cells, characterized in that, The method for protecting the battery cell according to the safety parameters as described in any one of claims 1-2 after obtaining the safety parameters, includes: Obtain a preset first warning range, and execute a multi-level warning strategy based on the first warning range and the security parameters.

4. The cell protection method according to claim 3, characterized in that, Also includes: The risk parameters are respectively input into a preset aging model, and the aging model calculates the first boundary parameters based on the multiple risk parameters. Select the minimum boundary parameter among all the first boundary parameters, and execute the preset security policy based on the minimum boundary parameter and the security parameter.

5. The cell protection method according to claim 4, characterized in that, The operating data for the temperature and pressure of a single battery cell includes current attenuation parameters. After obtaining the current attenuation parameters, the data also includes: The current decay parameter is input into a preset aging model, and the aging model calculates the second boundary parameter based on the current decay parameter; Select the minimum boundary parameter between the first boundary parameter and the second boundary parameter, and execute a preset security strategy based on the minimum boundary parameter and the security parameter.

6. The cell protection method according to claim 5, characterized in that, Obtain a preset second warning range, and execute a multi-level warning strategy based on the second warning range, the safety parameters, and the minimum boundary parameters.

7. An evaluation device, characterized in that, include: The data acquisition module is used to acquire the internal operating data of a single battery cell during the execution cycle and input the operating data into a preset risk assessment model. A safety parameter calculation module is used to execute the risk assessment model to calculate risk parameters based on the operating data, and the risk assessment model to calculate safety parameters based on the risk parameters; wherein, the operating data includes the operating data of the temperature and air pressure of the individual battery cells; The operating data of the battery cell's temperature and pressure include: absolute pressure, instantaneous pressure change rate, battery cell temperature change rate, and current attenuation parameters; The risk assessment model calculates risk parameters based on the operational data, including: Obtain the preset air pressure warning threshold and air pressure limit threshold, and calculate the first risk parameter based on the air pressure warning threshold, air pressure limit threshold and absolute air pressure; Obtain a preset reference air pressure change rate, and calculate a second risk parameter based on the reference air pressure change rate and the instantaneous air pressure change rate; Obtain the preset temperature change rate limit and the expected temperature change rate, and calculate the third risk parameter based on the cell temperature change rate, the temperature change rate limit, and the expected temperature change rate. Obtain the preset initial decay parameter and performance lifespan end threshold, and calculate the fourth risk parameter based on the initial decay parameter, performance lifespan end threshold and current decay parameter.

8. A cell management system, characterized in that, The term includes: 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 cell life assessment method as described in any one of claims 1-2.

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