Methods, devices, systems, and electric vehicles for assessing battery health status.

Abstract

This application provides a method, apparatus, system, and electric vehicle for assessing battery health status, relating to the field of battery technology. The method includes: acquiring operating parameters of the battery to be assessed; inputting the operating parameters into a quantitative correlation model to obtain a capacity decay prediction value output by the quantitative correlation model, wherein the quantitative correlation model characterizes the correlation between the operating parameters and gas characteristic indicators of the battery to be assessed, and the correlation between the gas characteristic indicators and the capacity decay prediction value; and determining the battery health status based on the capacity decay prediction value. The technical solution of this application enables early assessment of battery health status.

Description

Technical Field

[0001] This application relates to the field of battery technology, specifically to a method, apparatus, system, and electric vehicle for assessing battery health status. Background Technology

[0002] The State of Health (SOH) of a battery (such as a lithium-ion battery) is a key indicator for measuring the degree of performance degradation, typically defined as the percentage of the battery's current maximum capacity to its initial capacity. Accurate SOH assessment is crucial for the safe management, lifespan prediction, and secondary use of battery systems. To achieve this, related technologies often rely on the monitoring and analysis of measurable external electrical parameters of the battery.

[0003] For example, the mainstream SOH assessment methods currently include direct calibration based on capacity, indirect measurement based on internal resistance or impedance, and analysis based on voltage curves. These methods share the common feature that they all indirectly infer the internal health status of a battery by measuring its external electrical characteristics after aging; however, this inference inherently has a lag. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method, apparatus, system, and electric vehicle for assessing battery health status, which can enable early assessment of battery health status.

[0005] In a first aspect, embodiments of this application provide a method for assessing the health status of a battery, comprising: obtaining operating parameters of the battery to be assessed; inputting the operating parameters into a quantitative correlation model to obtain a capacity decay prediction value output by the quantitative correlation model, wherein the quantitative correlation model is used to characterize the correlation between the operating parameters and the gas characteristic indicators of the battery to be assessed, and the correlation between the gas characteristic indicators and the capacity decay prediction value; and determining the health status of the battery based on the capacity decay prediction value.

[0006] Secondly, embodiments of this application provide a battery health status assessment device, comprising: an acquisition module for acquiring operating condition parameters of the battery to be assessed; a prediction module for inputting the operating condition parameters into a quantitative correlation model to obtain a capacity decay prediction value output by the quantitative correlation model, wherein the quantitative correlation model is used to characterize the correlation between the operating condition parameters and the gas characteristic indicators of the battery to be assessed, and the correlation between the gas characteristic indicators and the capacity decay prediction value; and a determination module for determining the health status of the battery based on the capacity decay prediction value.

[0007] Thirdly, embodiments of this application provide a battery health status assessment apparatus, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is used to execute the battery health status assessment method described in the first aspect above.

[0008] Fourthly, embodiments of this application provide a battery system, including: a battery; a battery health status assessment device as described in the second or third aspect above; wherein the battery health status assessment device is further configured to generate an instruction based on the health status determined by the battery's operating parameters.

[0009] Fifthly, embodiments of this application provide an electric vehicle, including the battery system described in the fourth aspect above.

[0010] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing a computer program for performing the battery health status assessment method described in the first aspect above.

[0011] In a seventh aspect, embodiments of this application provide a computer program product comprising a computer program that, when executed by a processor of a computer device, enables the computer device to perform the battery health status assessment method described in the first aspect.

[0012] Eighthly, embodiments of this application provide a chip, including: a processor; and a memory for storing processor-executable instructions, wherein the processor is used to execute the battery health status assessment method described in the first aspect above.

[0013] This application provides a method, apparatus, system, and electric vehicle for assessing battery health status. By utilizing a quantitative correlation model, externally measurable operating parameters can be mapped to gaseous characteristic indicators that are difficult to directly observe inside the battery. Furthermore, these gaseous characteristic indicators are correlated with battery capacity decay, thereby obtaining a predicted value for battery capacity decay. Based on this predicted value, the battery's health status can be determined. Thus, early, rapid, non-destructive (non-invasive), and accurate assessment of battery health status can be achieved. Attached Figure Description

[0014] Figure 1 The diagram shown is a schematic diagram of a battery system provided in an exemplary embodiment of this application.

[0015] Figure 2 The diagram shown is a flowchart illustrating a battery health status assessment method provided in an exemplary embodiment of this application.

[0016] Figure 3aThe diagram shown is a schematic representation of the relationship between CO2 content and time at different temperatures, provided in an exemplary embodiment of this application.

[0017] Figure 3b The diagram shown is a schematic representation of a modified CO2 generation rate function at different temperatures provided in an exemplary embodiment of this application.

[0018] Figure 4a The diagram shown is a schematic representation of the relationship between CO2 content and time under different SOCs, provided by an exemplary embodiment of this application.

[0019] Figure 4b The diagram shown is a schematic representation of a modified CO2 generation rate function under different SOCs provided in an exemplary embodiment of this application.

[0020] Figure 5 The diagram shown is a schematic representation of the relationship between total gas production and capacity decay value provided in an exemplary embodiment of this application.

[0021] Figure 6 The diagram shown is a flowchart illustrating a battery health status assessment method provided in another exemplary embodiment of this application.

[0022] Figure 7 The diagram shown is a schematic representation of the structure of a battery health status assessment device provided in an exemplary embodiment of this application.

[0023] Figure 8 The diagram shown is a structural schematic of a battery health status assessment device provided in another exemplary embodiment of this application.

[0024] Figure 9 The diagram shown is a schematic diagram of the structure of a battery system provided in an exemplary embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Exemplary System Figure 1 The diagram shown is a schematic diagram of a battery system provided in an exemplary embodiment of this application, as follows: Figure 1As shown, the battery system 100 may include a battery 110 and a battery health status assessment device 120. The battery 110 may be a single cell or a battery module comprising multiple cells. The battery health status assessment device 120 may be a battery management system (BMS). The battery health status assessment device 120 can be used to assess or predict the health status of the battery 110.

[0027] In one example, the battery system 100 can be deployed in, for example, an electric vehicle, an energy storage station, or a portable electronic device. Specifically, the battery system 100 can be a battery pack.

[0028] In one example, the battery health assessment device 120 may include a sensor 121 and a controller 122. The sensor 121 can be used to acquire operating parameters of the battery 110. The controller 122 can input the operating parameters into a quantitative correlation model to obtain a capacity decay prediction value output by the quantitative correlation model, wherein the quantitative correlation model is used to characterize the correlation between the operating parameters and gaseous characteristic indicators of the battery 110, and the correlation between the gaseous characteristic indicators and the capacity decay prediction value. The controller 122 can determine the battery's health status based on the capacity decay prediction value.

[0029] Furthermore, the battery health status assessment device 120 may also include an actuator 123 for generating indication commands based on the health status determined by the battery's operating parameters. Exemplarily, the indication commands may include warning commands and / or battery operating parameter adjustment commands.

[0030] Furthermore, when the instruction is a warning instruction, the actuator 123 can issue a warning instruction via at least one of the following methods: voice, text, light, etc. When the instruction is a battery operating parameter adjustment instruction, the actuator 123 can control the charging or discharging parameters of the battery 110 according to the battery operating parameter adjustment instruction.

[0031] It should be understood that the above application scenario examples are only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited thereto. Rather, the embodiments of this application can be applied to any applicable scenario.

[0032] Exemplary methods Figure 2 The diagram shown is a flowchart illustrating a battery health status assessment method provided in an exemplary embodiment of this application. Figure 2 The method can be derived from Figure 1 The battery health status assessment device 120 performs the assessment. For example... Figure 2 As shown, the method for assessing the health status of the battery may include the following.

[0033] 210: Obtain the operating parameters of the battery to be evaluated.

[0034] In one example, the battery to be evaluated can be a lithium battery or other types of batteries. Further, the lithium battery can be a graphite / NMC battery, a graphite / LCO battery, a graphite / NCA battery, a silicon-carbon / NMC battery, or other types of batteries.

[0035] In one example, operating parameters may include at least one of the following: battery runtime, temperature, and battery voltage.

[0036] In one example, runtime can refer to the duration from a specified moment to the current moment. The specified moment can be the battery's factory setting or a preset moment based on actual needs. For example, runtime can be represented by a specific duration, by the number of battery cycles, or by other means.

[0037] 220: Input the operating condition parameters into the quantitative correlation model to obtain the capacity decay prediction value output by the quantitative correlation model. The quantitative correlation model is used to characterize the correlation between the operating condition parameters and the gas characteristic indicators of the battery to be evaluated, as well as the correlation between the gas characteristic indicators and the capacity decay prediction value.

[0038] In one example, a quantitative correlation model can characterize the relationship between battery operating parameters and characteristic indicators of gases generated in the battery's electrolyte (gas characteristic indicators), as well as the relationship between gas characteristic indicators and battery capacity decay. For example, the quantitative correlation model can be constructed based on sample battery operating parameters, sample gas characteristic indicators, and sample capacity decay data. Exemplarily, the quantitative correlation model can realize the transformation from the operating parameter space to the gas characteristic indicator space, and the transformation from the gas characteristic indicator space to the capacity decay space.

[0039] Specifically, chemical side reactions that lead to battery aging, such as the growth of the solid electrolyte interface (SEI) film, electrolyte decomposition, and lithium plating, are generally accompanied by the generation of specific gases. By using quantitative correlation models based on operating parameters, gas characteristic indicators reflecting the severity of chemical side reactions inside the battery can be obtained. In this way, the quantitative correlation model can evaluate the state of the battery based on gas characteristic indicators and obtain the predicted value of capacity decay.

[0040] In one example, gas characteristic indicators may include the content, concentration, or generation rate of a target gas in the battery's electrolyte. The target gas may include gases associated with chemical side reactions during battery aging, such as gases produced by these reactions. The target gas may include one or more gases.

[0041] The quantitative correlation model may include any of the following: a machine learning model trained based on the sample operating parameters, sample gas characteristic indicators and sample capacity decay data of the sample battery; or a mechanistic model fitted by the sample operating parameters, sample gas characteristic indicators and sample capacity decay data of the sample battery.

[0042] In one example, the capacity degradation prediction can be used to characterize the difference between the battery's initial capacity and its current maximum capacity as a percentage of the battery's initial capacity, thus characterizing the degree of aging within the battery.

[0043] 230: Determine the battery's health status based on the predicted capacity decay value.

[0044] In one example, the sum of the predicted capacity degradation value and the battery's state of health could be 1. For instance, the predicted capacity degradation value output by the quantitative correlation model might be... The formula for calculating the State of Health (SOH) of a battery is as follows: Furthermore, in some examples, after obtaining the battery's health status, an instruction can be generated based on that health status. Specifically, different health statuses can correspond to different instruction commands.

[0045] For example, multiple thresholds can be pre-set, and these thresholds can correspond to multiple levels of instructions. For instance, the multiple thresholds may include a first threshold and a second threshold, where the second threshold is greater than the first threshold. Generating indication instructions based on the battery's health status may include: generating a warning instruction when the health status is greater than or equal to the first threshold but less than the second threshold, to remind the user to pay attention to the battery's current health status, facilitating appropriate usage strategies and ensuring safe use; and generating a battery operating parameter adjustment instruction when the health status is greater than or equal to the second threshold, to adjust the battery's operating parameters in a timely manner, ensuring battery safety and extending battery life as much as possible.

[0046] In one example, a warning command can be executed through at least one of several warning methods, such as voice, text, or light.

[0047] In one example, battery operating parameter adjustment instructions can characterize the subsequent use strategy of the battery, such as the battery charging strategy and / or discharging strategy.

[0048] Furthermore, in some examples, the quantitative correlation model can also output gas characteristic indicators, allowing users to determine the causes of battery aging based on these indicators. For example, different gas characteristic indicators correspond to different aging mechanisms (e.g., CO2 is often associated with carbonate decomposition, H2 with moisture contamination, and C2H4 with lithium plating). Based on these gas characteristic indicators, gases with high content or high generation rates can be identified, thus determining the main causes of battery aging. For instance, a high CO2 content or high generation rate suggests that the main cause of battery aging is related to carbonate decomposition, allowing users to adopt appropriate maintenance or usage strategies. Alternatively, the gas characteristic indicators output by the quantitative correlation model can be input into an aging cause determination model, which can then output the cause of battery aging. Here, the aging cause determination model can be a model derived from sample gas characteristic indicators and sample aging causes of a sample battery. This model can be a machine learning model or a mechanistic model.

[0049] Optionally, the quantitative correlation model can directly output the causes of battery aging. For example, based on operating parameters, the quantitative correlation model can determine gas characteristic indicators, and based on these gas characteristic indicators, it can output predicted capacity decay values ​​and the causes of battery aging. For example, the quantitative correlation model can be a model derived from sample operating parameters, sample gas characteristic indicators, sample capacity decay data, and sample causes of aging of a sample battery.

[0050] This application provides a method for assessing battery health status. By utilizing a quantitative correlation model, externally measurable operating parameters can be mapped to gaseous characteristic indicators that are difficult to directly observe inside the battery. Furthermore, these gaseous characteristic indicators are correlated with battery capacity decay, thereby obtaining a predicted value for battery capacity decay. Based on this predicted value, the battery's health status can be determined. This enables early, rapid, non-destructive (non-invasive), and accurate assessment of battery health status.

[0051] According to one embodiment of this application, the gas characteristic indicators include at least one of the following: the content, concentration, or generation rate of at least one gas in the electrolyte of the battery to be evaluated; and the total gas production of at least one gas.

[0052] In one example, the quantity and type of at least one gas are related to the type of battery and the mechanism of battery aging. Different types of batteries correspond to different quantities and types of gases, and different aging mechanisms may also correspond to different quantities and types of gases. For example, for a specific battery model, the gases generated in the electrolyte during the aging process include at least one of CO2, CO, H2, CH4, C2H6, and C2H4.

[0053] In one example, the gas characteristic index includes the total gas production of at least one gas. For example, a quantitative correlation model directly obtains the total gas production of at least one gas based on operating parameters and outputs a capacity decay prediction based on the total gas production. Alternatively, the gas characteristic index includes the content or concentration of at least one gas, and the total gas production of at least one gas. For example, a quantitative correlation model directly obtains the content or concentration of at least one gas based on operating parameters, obtains the total gas production based on the content or concentration of at least one gas, and then outputs a capacity decay prediction based on the total gas production. Alternatively, the gas characteristic index includes the generation rate of at least one gas, and the total gas production of at least one gas. For example, a quantitative correlation model directly obtains the generation rate of at least one gas based on operating parameters, obtains the total gas production based on the generation rate of at least one gas, and then outputs a capacity decay prediction based on the total gas production.

[0054] In one example, the battery's operating parameters can be monitored in real time at certain intervals, and a quantitative correlation model can be used to output a capacity decay prediction value based on the operating parameters. In turn, the battery's health status can be determined in real time based on the capacity decay prediction value.

[0055] In this embodiment, the content, concentration, or generation rate of at least one gas in the electrolyte of the battery can be determined based on operating parameters, or the total gas production of at least one gas in the electrolyte can be determined. In this way, by monitoring the characteristics of gases related to chemical side reactions of battery aging inside the battery, the capacity decay of the battery can be predicted, thereby improving the foresight and reliability of the prediction results.

[0056] According to one embodiment of this application, the gas characteristic indicators include the generation rate of at least one gas in the electrolyte of the battery to be evaluated and the total gas production of at least one gas. The at least one gas includes CO2, CO, H2, CH4, C2H6, and C2H4. The quantitative correlation model includes a computational model for the total gas production, which includes the following formula: in, Total gas production For time, The CO2 generation rate, For CO generation rate, CH4 formation rate, For the C2H6 formation rate, The C2H4 formation rate, The H2 generation rate.

[0057] In one example, multiple gases may be generated inside the battery during aging. Gas characteristics may include the generation rate of each gas and the total amount of gas produced.

[0058] In one example, a quantitative correlation model can determine the generation rate of each gas based on operating parameters, and determine the total gas production based on the generation rate of each gas and time. Exemplarily, operating parameters may include temperature, battery voltage, and time; or, operating parameters may include temperature and battery voltage, the quantitative correlation model may obtain time from the battery system, or the quantitative correlation model itself may have time statistics functionality.

[0059] In one example, the quantitative correlation model includes a total gas production calculation model. This model calculates the total gas production of various gases using formulas, thus improving computational efficiency.

[0060] In one example, the quantitative correlation model may also include a gas generation rate calculation model. The quantitative correlation model can first determine the generation rate of each gas based on operating parameters using the gas generation rate calculation model, and then determine the total gas production based on the generation rate of each gas and time using a total gas production calculation mechanism model. Here, the gas generation rate calculation model can be a machine learning model or a mechanism model.

[0061] In this embodiment, the generation rate of at least one gas in the electrolyte of the battery can be determined based on the operating parameters, and the total gas production can be determined based on the generation rate of at least one gas using a mechanism model (total gas production calculation mechanism model). This can improve the calculation efficiency of the total gas production and the stability of the calculation results.

[0062] According to one embodiment of this application, the operating parameters of the battery to be evaluated include voltage and temperature, and the generation rate expression for any one of the gases CO, H2, CH4, C2H6, and C2H4 is as follows: ,in, in, Pre-exponential factor, For activation energy, For temperature, This is the universal gas constant. For voltage, For voltage reference factor, It is an acceleration factor.

[0063] In one example, the formation rate of each of at least one gas may be temperature-dependent. Exemplarily, a formula for the gas formation rate can be constructed based on the Arrhenius equation for electrochemical reactions, such as... . The gas generation rate is denoted as .

[0064] Furthermore, the formation rate of some gases is related to temperature and battery voltage. For example, a formula for the gas formation rate can be constructed based on the Arrhenius equation for electrochemical reactions and the voltage acceleration effect, such as... .

[0065] In one example, for a specific battery model, various gases are generated during battery aging, and the generation rate of each gas can be related to temperature and battery voltage. Therefore, for each gas, the above formula can be used... Calculate the generation rate. The pre-exponential factor in this formula... ,activation energy Accelerator It can be obtained by fitting sample data (such as sample operating parameters, sample gas characteristic indicators, and sample volume decay data). Voltage reference factor It could be a voltage reference standard based on the battery system, for example, for a specific battery model. It can serve as a stable voltage reference point, used to measure and define the potential of all other electrodes. Different battery models may have different... .

[0066] In one example, the pre-exponential factors in the generation rate formulas for different gases ,activation energy and acceleration factors These can differ. Optionally, for the same type of battery, the voltage reference factor in the formula for the generation rate of different gases can vary. They can be the same.

[0067] In one example, the generation rate of some gases is related to temperature and battery voltage when specific conditions are met, and primarily to temperature when those specific conditions are not met. For instance, the generation rate of CO2 gas can be related to temperature and battery voltage when the voltage is greater than or equal to a voltage threshold, and can be expressed by the formula... Calculate the generation rate; when the voltage is below the voltage threshold, the generation rate of gaseous CO2 is mainly related to temperature and can be calculated using the formula... Calculate the formation rate. The formation rate of any of the gases CO, H2, CH4, C2H6, and C2H4 is related to temperature and cell voltage, and can be calculated using the formula... Calculate the generation rate.

[0068] The formula for calculating the gas generation rate can also be called the gas generation rate calculation model.

[0069] In this embodiment, a model (gas generation rate calculation model) can be established based on the Arrhenius equation of electrochemical reactions and the voltage acceleration effect. This allows the derivation of the generation rate to have a clear physical or chemical meaning, making it easy to understand and verify. Furthermore, setting this model as a parameterized mathematical expression can improve calculation speed, making it suitable for real-time or periodic calculations in vehicle-mounted BMS.

[0070] According to one embodiment of this application, the operating parameters of the battery to be evaluated include voltage and temperature. When the voltage is below a voltage threshold, the CO2 generation rate... The expression is When the voltage is greater than or equal to the voltage threshold, the CO2 generation rate The expression is ,in, in, It is the first pre-exponential factor. The first activation energy, For temperature, This is the universal gas constant. It is the second pre-exponential factor. The second activation energy, For voltage, For voltage reference factor, It is an acceleration factor.

[0071] In one example, the factors influencing the generation rate of each gas may differ. For each gas, a suitable gas generation rate calculation model, i.e., a generation rate calculation formula, can be constructed based on the specific influencing factors.

[0072] For example, regarding gaseous CO2, its generation rate is affected by factors including temperature and battery voltage. When the voltage is below a certain threshold, the CO2 generation rate primarily changes with temperature, while the effect of voltage changes on the generation rate is negligible. Therefore, the expression for the CO2 generation rate in this case is: When the voltage is greater than or equal to a voltage threshold, the CO2 generation rate varies with both temperature and voltage. The expression for the CO2 generation rate in this case is: .

[0073] In one example, the voltage threshold can be set according to the actual situation, and different battery models may have different voltage thresholds.

[0074] In one example, the function can be obtained by fitting the first sample data (such as sample operating parameters and sample gas characteristic indicators) obtained under conditions where the voltage is less than the voltage threshold. That is, the first pre-exponential factor is obtained. First activation energy .

[0075] In one example, a function can be obtained by fitting second sample data (such as sample operating parameters or sample gas characteristic indicators) obtained under conditions where the voltage is greater than or equal to a voltage threshold. That is, to obtain the second pre-exponential factor. Second activation energy Acceleration factor Voltage reference factor It can be a voltage reference standard based on the battery system.

[0076] For example, for a gas whose generation rate is influenced by factors including temperature and battery voltage, the voltage can be kept constant first to obtain a function relating the generation rate to temperature, thus yielding the pre-exponential factor and activation energy. Then, the temperature can be kept constant to obtain a function relating the generation rate to voltage, thus yielding the acceleration factor.

[0077] Specifically, taking CO2 as an example, the CO2 content can be obtained using sample data (second sample data) obtained under conditions where the voltage is greater than or equal to a voltage threshold. Figure 3a (vertical axis) and time ( Figure 3a The relationship between the x-axis and y-axis is the change curve, such as... Figure 3a As shown. Specifically, for sample data at specific temperatures (such as 35℃, 45℃, 60℃, 70℃, etc.), curve fitting can be performed using sample gas characteristic indicators (such as CO2 content) and time (Time / day) to obtain the change curve of CO2 content with time at that temperature. Figure 3a The variation curves can be obtained under the same battery voltage.

[0078] Furthermore, the function of CO2 generation rate can be applied. A log-linear transformation yields the deformation function (the function relating the generation rate to temperature): Available from Figure 3a The parameters relating temperature and generation rate at different temperatures are extracted from the curves, and these parameters are then substituted into the above formula or used for curve fitting. Because... Figure 3a The variation curves in the above formula were obtained under the same battery voltage. Therefore, the variation with temperature in the above formula is... It can remain unchanged; it is a constant. Therefore, through fitting, the slope can be obtained. as well as Thus, the second activation energy and the second pre-exponential factor can be obtained, i.e., the curve corresponding to the deformable function is as follows: Figure 3b As shown, where, . Figure 3b The x-coordinate in the figure is 1 / T.

[0079] Similarly, using sample data obtained under conditions where the voltage is greater than or equal to the voltage threshold (second sample data), the characteristic indicators of the sample gas (such as CO2 content) at the same temperature can be used to obtain the relationship between CO2 content and time (Time / day) under different battery voltages, i.e., the change curve. Figure 4a As shown. Then, based on these variation curves, the function relating the generation rate to the battery voltage can be obtained, thus yielding the acceleration factor. For example, for sample data at a specific battery voltage (which can be equivalent to a specific SOC, or a specific SOC characterizing the battery voltage; SOC can be 100% SOC, 90% SOC, 45% SOC, etc.), curve fitting can be performed using sample gas characteristic indicators (such as CO2 content) and time (Time / day) to obtain the variation curve of CO2 content versus time at that battery voltage. Figure 4a The variation curves can be obtained at the same temperature.

[0080] Furthermore, the function of CO2 generation rate can be applied. A log-linear transformation yields the deformation function (the function relating the generation rate to temperature): Available from Figure 4a The parameters of battery voltage and generation rate at different battery voltages (or SOCs) are extracted from the curves. These parameters are then substituted into the above formula or used for curve fitting. Because... Figure 4a The variation curves in the above formula were obtained at the same temperature; therefore, the variation with voltage in the above formula is... , as well as It can remain unchanged; it is a constant. Therefore, through fitting, the slope can be obtained. This yields the acceleration factor, which is the curve corresponding to the deformation function, as shown in the figure. Figure 4b As shown, where, . Figure 4b The x-coordinate in the middle is .

[0081] In this embodiment, based on the different effects of voltage and temperature on the gas generation rate, multiple generation rate calculation formulas can be set for a single gas to improve the accuracy of the generation rate calculation, thereby improving the accuracy of the subsequent capacity decay prediction value and health status.

[0082] According to one embodiment of this application, the quantitative correlation model further includes a capacity decay prediction mechanism model, which includes the following formula: in, This is the predicted value for capacity decay. For coefficients, It is an exponential factor. In some cases, It can be 1, that is and The change is linear.

[0083] Step 230 may include: determining the health status of the battery based on the predicted capacity decay value using the following formula: .

[0084] In one example, the total gas production and sample capacity decay data (capacity decay value) can be used as the basis for the analysis. Construct a capacity decay prediction mechanism model, that is, obtain the coefficients. and exponential factors .For example, Figure 5 The total gas production and capacity decay values ​​are shown. The curve showing the change between them.

[0085] In practical applications, after obtaining the total gas production, the capacity decay prediction value can be obtained based on the total gas production using a capacity decay prediction mechanism model. The battery's health status can then be determined based on the predicted capacity decay value.

[0086] In this embodiment, the capacity decay prediction mechanism model can improve the calculation efficiency of the capacity decay prediction value, which is suitable for real-time or periodic calculation of vehicle-mounted BMS.

[0087] According to one embodiment of this application, the quantitative correlation model is a mechanistic model, which is constructed based on the functional relationship between the sample battery's sample operating parameters, sample gas characteristic indicators, and sample capacity decay data.

[0088] In one example, the sample condition parameters of the sample battery may include the sample temperature and sample voltage of the sample battery during the test.

[0089] In one example, the sample gas characteristic indicators may include the total gas production of the sample, and the quantitative correlation model may include a computational model of the total gas production and a capacity decay prediction mechanism model. The quantitative correlation model can use the computational model of the total gas production to obtain the total gas production based on the current operating parameters of the battery, and use the capacity decay prediction mechanism model to obtain the predicted capacity decay value based on the total gas production.

[0090] Optionally, the sample gas characteristic indicators may include the sample content, sample concentration, or sample generation rate of at least one gas, and the total sample gas production of at least one gas. The quantitative correlation model may include a gas generation rate calculation model, a total gas production calculation mechanism model, and a capacity decay prediction mechanism model. In one example, when the sample gas characteristic indicators include the sample generation rate of at least one gas, the gas generation rate calculation model can be directly obtained based on the sample generation rate. Alternatively, when the sample gas characteristic indicators include the sample content or sample concentration of at least one gas, the sample generation rate can be obtained based on the sample content, or the sample content can be obtained based on the sample concentration, and then the sample generation rate can be obtained based on the sample content, and further, the gas generation rate calculation model can be obtained based on the sample generation rate. The quantitative correlation model can use the gas generation rate calculation model to obtain the generation rate of at least one gas based on the current operating parameters of the battery, use the total gas production calculation mechanism model to obtain the total gas production based on the generation rate of at least one gas, and use the capacity decay prediction mechanism model to obtain the predicted capacity decay value based on the total gas production.

[0091] In this embodiment, the quantitative correlation model is a mechanistic model, which can improve the model construction efficiency, improve the calculation efficiency of capacity decay prediction values, and improve the stability of prediction results to a certain extent.

[0092] According to one embodiment of this application, the mechanism model includes a first function and a second function. Step 220 may include: using the first function to determine the gas characteristic indicators of the battery to be evaluated based on the operating condition parameters, wherein the first function is constructed based on the sample operating condition parameters and sample gas characteristic indicators of the sample battery; and using the second function to output a capacity decay prediction value based on the gas characteristic indicators, wherein the second function is constructed based on the sample gas characteristic indicators and sample capacity decay data of the sample battery.

[0093] In one example, the first function may include the gas generation rate function described above. (or or ), and the total gas production computer theoretical model, i.e., the formula .

[0094] In one example, the second function may include the capacity decay prediction mechanism model described above, i.e., formula .

[0095] In this embodiment, the first and second functions can be obtained by fitting sample data, which improves the efficiency of model construction, the computational efficiency of capacity decay prediction, and the stability of prediction results to a certain extent. Furthermore, calculating gas characteristic indicators through the first function facilitates the output of these indicators, enabling the identification of the main causes of battery aging and providing support for subsequent battery use and maintenance.

[0096] According to one embodiment of this application, the quantitative correlation model is a machine learning model trained based on the sample battery's operating parameters, sample gas characteristic indicators, and sample capacity decay data.

[0097] In one example, the quantitative correlation model can be trained using sample data (sample operating parameters, sample gas characteristic indicators, and sample volume decay data). That is, this quantitative correlation model is a data-driven machine learning model, such as a neural network or support vector machine. For example, this machine learning model may include a regression model constructed using the random forest algorithm. Machine learning models can learn complex nonlinear mapping relationships from large amounts of sample data, offering the advantage of high accuracy in evaluating results.

[0098] In one example, the sample data for the machine learning model is similar to that for the mechanistic model described above. For instance, the sample operating condition parameters of the sample battery may include the sample temperature and sample voltage during testing. Sample gas characteristic indicators may include the total gas production. The machine learning model may include a total gas production calculation model and a capacity decay prediction model. During training, the sample operating condition parameters, total gas production, and sample capacity decay data can be input into the machine learning model. The machine learning model can use the total gas production calculation model to predict the total gas production based on the sample operating condition parameters, and adjust the parameters of the total gas production calculation model based on the difference between the predicted total gas production and the sample total gas production. Furthermore, after the total gas production calculation model meets the training requirements, the machine learning model can use the capacity decay prediction model to obtain the predicted capacity decay data based on the predicted total gas production output by the total gas production calculation model, and adjust the parameters of the capacity decay prediction model based on the difference between the predicted capacity decay data and the sample capacity decay data. In practical applications, the quantitative correlation model can use the total gas production calculation model to obtain the total gas production based on the current operating parameters of the battery, and use the capacity decay prediction model to obtain the capacity decay prediction value based on the total gas production.

[0099] Optionally, the sample gas characteristic indicators may include the sample content, sample concentration, or sample generation rate of at least one gas, and the total sample gas production of at least one gas. The machine learning model may include a gas generation rate calculation model, a total gas production calculation model, and a capacity decay prediction model. The training process of this machine learning model is similar to that of the machine learning model in the previous example, and will not be repeated here. In one example, when the sample gas characteristic indicators include the sample generation rate of at least one gas, the gas generation rate calculation model can be obtained directly based on the sample generation rate. Alternatively, when the sample gas characteristic indicators include the sample content or sample concentration of at least one gas, the sample generation rate can be obtained based on the sample content, or the sample content can be obtained based on the sample concentration, and then the sample generation rate can be obtained based on the sample content, and then the gas generation rate calculation model can be obtained based on the sample generation rate. The quantitative correlation model can use the gas generation rate calculation model to obtain the generation rate of at least one gas based on the current operating parameters of the battery, use the total gas production calculation model to obtain the total gas production based on the generation rate of at least one gas, and use the capacity decay prediction mechanism model to obtain the capacity decay prediction value based on the total gas production.

[0100] In this embodiment, the quantitative correlation model is a machine learning model, which can improve the model's adaptability and the accuracy of the prediction results.

[0101] According to an embodiment of this application, the quantitative correlation model includes a first model and a second model. The first model is a machine learning model trained based on the sample operating condition parameters, sample gas characteristic indicators, and sample capacity decay data of the sample battery. The second model is a mechanistic model, which is constructed based on the functional relationship between the sample operating condition parameters, sample gas characteristic indicators, and sample capacity decay data of the sample battery. Step 220 may include: inputting the operating condition parameters into the first model to obtain a first candidate capacity decay prediction value output by the first model; inputting the operating condition parameters into the second model to obtain a second candidate capacity decay prediction value output by the second model; performing a weighted calculation on the first candidate capacity decay prediction value and the second candidate capacity decay prediction value, and outputting a capacity decay prediction value.

[0102] In one example, a first model determines the first candidate gas characteristic index of the battery under evaluation based on operating condition parameters, and outputs a first candidate capacity decay prediction value based on the first candidate gas characteristic index. A second model determines the second candidate gas characteristic index of the battery under evaluation based on the operating condition parameters, and outputs a second candidate capacity decay prediction value based on the second candidate gas characteristic index. Furthermore, the first and second candidate capacity decay prediction values ​​can be weighted and calculated to output a final capacity decay prediction value. This improves the accuracy of the capacity decay prediction value.

[0103] In one example, the first model is a machine learning model. The specific details of the training and actual use of the machine learning model can be found in the descriptions of the above embodiments; to avoid repetition, they will not be repeated here. Similarly, the second model is a mechanistic model. The specific details of the construction and actual use of the mechanistic model can be found in the descriptions of the above embodiments; to avoid repetition, they will not be repeated here.

[0104] In this embodiment, the first candidate capacity decay prediction value and the second candidate capacity decay prediction value are obtained based on the operating condition parameters by the first model and the second model in parallel. The first candidate capacity decay prediction value and the second candidate capacity decay prediction value are weighted and calculated to output the capacity decay prediction value, which can improve the accuracy and precision of the capacity decay prediction value.

[0105] According to one embodiment of this application, the battery health status assessment method further includes: conducting aging tests under various conditions on a sample battery of the target model to obtain sample operating parameters, sample gas characteristic indicators and sample capacity decay data, wherein the aging tests under various conditions include at least one of the following: storage tests at different temperatures; and cycle tests at different rates and depths of discharge.

[0106] In one example, operating condition parameters may include time, temperature, and voltage. Similarly, sample operating condition parameters may include sample time, sample temperature, and sample voltage.

[0107] For example, parameters such as battery aging temperature, storage SOC, cycle rate, and depth of discharge (DOD) indicate the battery's usage conditions. Time (or number of cycles) can be a measure of how long the battery has been used; it is a cumulative quantity, equivalent to the X-axis. In some cases, the time in an experiment (sample time) generally refers to the time during which the experiment was conducted. For instance, if a battery is stored at 60°C and then returned to 25°C, the time in between is not cumulative. Moreover, in some cases, the battery's temperature and SOC are adjusted at the factory to a state where battery side reactions are very slow, and this damage is essentially negligible. In practical applications, the actual operating conditions at the vehicle can be broken down. For example, how long did the vehicle run at 90-87 DOD, approximately how much gas was produced, and how long has the vehicle been parked in the garage, and what is the gas production during this period? Each operating condition can be broken down and superimposed to obtain the corresponding total gas production and SOH.

[0108] Optionally, time can be independent of operating condition parameters, i.e., operating condition parameters may include temperature and voltage. Operating condition parameters and time can be input into a quantitative correlation model to obtain capacity decay predictions. Similarly, sample operating condition parameters may include sample temperature and sample voltage, and a quantitative correlation model can be obtained based on sample time and sample operating condition parameters.

[0109] In one example, storage tests can be performed on the sample battery at different temperatures. For instance, at a specific temperature and a specific battery voltage (or a specific state of charge), sample gas characteristics and sample capacity decay data can be measured at different times. Each storage test at each temperature corresponds to a set of sample operating parameters, a set of sample gas characteristics, and a set of sample capacity decay data.

[0110] For example, Table 1 shows the sample operating parameters for storage testing of the sample batteries. The blank spaces can be used to record test results, such as sample gas characteristic indicators and corresponding sample capacity decay data.

[0111] Table 1. Sample operating parameters for storage testing of sample batteries. For example, in storage testing, at a given temperature and SOC, a curve showing the change in gas content (e.g., CO2 content) versus time can be obtained. That is, each blank space in Table 1 above corresponds to a curve showing the change in gas content versus time. Multiple curves can be obtained as follows... Figure 3a As shown, different curves can be obtained at different battery voltages when the temperature remains constant. For example, the activation energy and pre-exponential factor can be obtained based on the sample operating parameters obtained under storage testing. Furthermore, the activation energy, pre-exponential factor, and acceleration factor can be obtained based on the sample operating parameters obtained under storage testing.

[0112] In one example, the sample battery can be subjected to cyclic testing at different rates and depths of discharge. Furthermore, the sample battery can be subjected to cyclic testing at different rates and depths of discharge at various temperatures. For example, during cyclic testing at a specific temperature, sample gas characteristics and sample capacity decay data can be measured at different times. Each cyclic test at each temperature (which may have a specific rate and depth of discharge, and a specific cutoff state of equilibrium (SOH)) corresponds to a set of sample operating parameters, a set of sample gas characteristics, and a set of sample capacity decay data.

[0113] For example, Table 2 shows the sample operating parameters for cyclic testing of the sample batteries. The blank spaces can be used to record test results, such as sample gas characteristic indicators and corresponding sample capacity decay data.

[0114] Table 2. Sample operating parameters for cyclic testing of the sample batteries. For example, in cyclic testing, an equivalent battery voltage can be obtained based on the SOC range. In other words, there is a correspondence between battery voltage and SOC range; a specific SOC range corresponds to a specific equivalent battery voltage. At a given temperature and an equivalent battery voltage (a specific SOC range), a curve showing the change in gas content (e.g., CO2 content) versus time can be obtained. That is, each blank space in Table 2 above corresponds to a curve showing the change in gas content versus time. Multiple curves can be obtained as follows... Figure 4a As shown, Figure 4a The SOC in the figure can be the upper limit of the SOC range. When the temperature remains constant, different curves can be obtained under different SOC ranges (or different expansion rates, different SOH). For example, the acceleration factor can be obtained based on the sample operating parameters obtained under cyclic testing. Furthermore, the activation energy, pre-exponential factor, and acceleration factor can be obtained based on the sample operating parameters obtained under cyclic testing.

[0115] According to one embodiment of this application, aging tests are performed on a sample battery of a target model under various conditions to obtain sample operating parameters, sample gas characteristic indicators, and sample capacity decay data. This includes: after the aging test under each condition is completed, the sample battery is placed under standardized gas production measurement conditions; the concentration of the gas produced by the sample battery under the standardized gas production measurement conditions is measured to obtain sample gas characteristic indicators.

[0116] In one example, standardized gas production measurement conditions can provide a uniform benchmark for measuring gas content, thus ensuring the reliability of the data.

[0117] In one example, the sample battery can be placed in a sealed chamber and subjected to standardized gas generation measurement conditions. These standardized conditions may include measuring the sample battery after it has been left to stand at a specific SOC and temperature for a specific duration. For example, the sample battery could be left to stand at 0% SOC and 25°C for 4 hours, or it could be measured under charge-discharge conditions, thus ensuring the accuracy of the online measurement.

[0118] In one example, a batch of new batteries of the same model can be selected as sample batteries, and a series of accelerated aging tests covering their normal operating range can be performed on the sample batteries.

[0119] In one example, the sample battery can be placed in a sealed chamber and subjected to standardized gas production measurement conditions. The composition and concentration of the gas in the sealed chamber can then be analyzed using a gas analysis instrument (such as a gas chromatograph, GC). The gas may include CO2, CO, H2, CH4, C2H4, etc.

[0120] For example, the sample cell can be punctured in a vacuum-sealed chamber, causing a pressure change. The total gas content can be calculated using the pressure change based on the ideal equation pV=nRT. Furthermore, the gas can be extracted and its concentration measured using a gas chromatography (GC). For a specific gas, its content is equal to the total gas content multiplied by its concentration.

[0121] Optionally, a gas sensor is placed inside the sample battery, which can measure the total gas content and / or the gas concentration.

[0122] Furthermore, sample gas characteristic indicators (GFI) can be calculated. For example, sample gas characteristic indicators may include: total gas production, content or concentration of each gas, etc.

[0123] Furthermore, the sample batteries can be calibrated to obtain the actual capacity decay value (sample capacity decay data) or state of health (SOH) of the sample batteries.

[0124] In this embodiment, by measuring the concentration of gas produced by the sample battery under standardized gas production measurement conditions, the characteristic indicators of the sample gas can be obtained, which can improve the consistency and comparability of the sample data, and thus improve the reliability of the subsequent quantitative correlation model.

[0125] Figure 6 The diagram shown is a flowchart illustrating a battery health status assessment method provided in another exemplary embodiment of this application. Figure 6 The example is Figure 2 Examples of the embodiments are provided below; to avoid repetition, the similarities can be referred to the descriptions in the above embodiments, and will not be repeated here. For example... Figure 6 As shown, the method for assessing the health status of the battery may include the following.

[0126] 610: Aging tests were conducted on sample batteries of the target model under various conditions to obtain sample operating parameters, sample gas characteristic indicators, and sample capacity decay data.

[0127] In one example, aging tests under various conditions include at least one of the following: storage tests at different temperatures; and cycle tests at different rates and depths of discharge.

[0128] 620: A quantitative correlation model was obtained based on the sample battery's operating parameters, sample gas characteristic indicators, and sample capacity decay data.

[0129] In one example, a quantitative correlation model is used to characterize the relationship between operating parameters and gaseous characteristics of the battery under evaluation, as well as the relationship between gaseous characteristics and predicted capacity decay.

[0130] In one example, the quantitative correlation model may include a machine learning model and / or a mechanistic model. For instance, the machine learning model may be trained based on the sample battery's operating parameters, sample gas characteristic indicators, and sample capacity decay data to obtain a trained machine learning model, and / or a mechanistic model may be constructed based on the sample battery's operating parameters, sample gas characteristic indicators, and sample capacity decay data, the mechanistic model including a first function and a second function.

[0131] 630: Obtain the operating parameters of the battery to be evaluated.

[0132] In one example, the quantitative correlation model established in the offline phase can be applied to batteries of the same model with unknown health status.

[0133] In one example, for the battery being evaluated, its temperature and voltage can be obtained at any point during its lifespan. Operating parameters may include temperature and voltage.

[0134] In one example, the voltage in the operating condition parameters may include the positive electrode potential of the battery, the average voltage of the battery during charging and discharging, the operating voltage, or other parameters that characterize the voltage state of the battery.

[0135] 640: Input the operating parameters into the quantitative correlation model to obtain the capacity decay prediction value output by the quantitative correlation model.

[0136] 650: Determine the battery's health status based on the predicted capacity decay value.

[0137] 660: Can generate instruction commands based on the battery's health status.

[0138] In one example, the instruction may include a warning instruction or a battery operating parameter adjustment instruction.

[0139] In one example, a warning command can be used to indicate the current degree of battery aging (or health status), serving as a warning. For example, the warning command can be executed through at least one of several warning methods, such as voice, text, or light. Battery operating parameter adjustment commands may include at least one of the following: limiting the battery's charging cut-off voltage, reducing the battery's maximum permissible charge / discharge current, or adjusting the operating intensity of the battery's thermal management system.

[0140] In one example, the warning and control module in the BMS can generate warning commands or battery operating parameter adjustment commands based on the battery's health status. For instance, a quantitative correlation model can be embedded in the BMS.

[0141] It should be understood that the specific details of aging tests, sample operating parameters, sample gas characteristic indicators, sample capacity decay data, quantitative correlation models, operating parameters, capacity decay prediction values, health status, etc., can be found in the relevant descriptions in the above embodiments. To avoid repetition, they will not be repeated here.

[0142] In this embodiment, a quantitative correlation model is used to obtain a capacity decay prediction value based on the operating parameters of the battery to be evaluated. Based on this prediction, the battery's health status can be determined. This allows for direct detection of the products (gases) of internal chemical reactions that lead to aging, enabling earlier warning and assessment of battery degradation before significant deterioration occurs in external electrical properties (such as capacity and internal resistance). Furthermore, the quantitative correlation model is based on direct measurement of the battery's internal chemical mechanisms, avoiding the complex and indirect correlation between external electrical parameters and aging, resulting in more accurate and reliable assessment results. Moreover, the quantitative correlation model shortens the overall assessment process, eliminating the need for lengthy charge-discharge capacity calibration or destructive disassembly of the battery, achieving rapid and non-destructive assessment. Furthermore, different gas characteristic indicators correspond to different aging mechanisms (e.g., CO2 is often associated with carbonate decomposition, H2 with moisture contamination, and C2H4 with lithium plating). Analyzing the gas composition not only assesses SOH but also helps diagnose the main causes of aging. Furthermore, the method provided in this application provides a theoretical basis and technical path for developing a miniaturized online life detection module integrated inside a battery pack, and has great potential for engineering applications.

[0143] Exemplary device Figure 7 The diagram shown is a structural schematic of a battery health status assessment device provided in an exemplary embodiment of this application. Figure 7 As shown, the battery health status assessment device 700 includes: an acquisition module 710, a prediction module 720, and a determination module 730.

[0144] The acquisition module 710 is used to acquire the operating condition parameters of the battery to be evaluated; the prediction module 720 is used to input the operating condition parameters into the quantitative correlation model to obtain the capacity decay prediction value output by the quantitative correlation model. The quantitative correlation model is used to characterize the correlation between the operating condition parameters and the gas characteristic indicators of the battery to be evaluated, as well as the correlation between the gas characteristic indicators and the capacity decay prediction value; the determination module 730 is used to determine the health status of the battery based on the capacity decay prediction value.

[0145] This application provides a battery health status assessment device. By utilizing a quantitative correlation model, externally measurable operating parameters can be mapped to gaseous characteristic indicators that are difficult to directly observe inside the battery. Furthermore, these gaseous characteristic indicators are correlated with battery capacity decay, thereby obtaining a predicted value for battery capacity decay. Based on this predicted value, the battery's health status can be determined. Thus, early, rapid, non-destructive (non-invasive), and accurate assessment of battery health status can be achieved.

[0146] According to one embodiment of this application, the gas characteristic indicators include at least one of the following: the content, concentration, or generation rate of at least one gas in the electrolyte of the battery to be evaluated; and the total gas production of at least one gas.

[0147] According to one embodiment of this application, at least one gas includes at least one of CO2, CO, H2, CH4, C2H6, and C2H4.

[0148] According to one embodiment of this application, the gas characteristic indicators include the generation rate of at least one gas in the electrolyte of the battery to be evaluated and the total gas production of at least one gas. The at least one gas includes CO2, CO, H2, CH4, C2H6, and C2H4. The quantitative correlation model includes a computational model for the total gas production, which includes the following formula: in, Total gas production For time, The CO2 generation rate, For CO generation rate, CH4 formation rate, For the C2H6 formation rate, The C2H4 formation rate, The H2 generation rate.

[0149] According to one embodiment of this application, the operating parameters of the battery to be evaluated include voltage and temperature. When the voltage is below a voltage threshold, the CO2 generation rate... The expression is When the voltage is greater than or equal to the voltage threshold, the CO2 generation rate The expression is ,in, in, It is the first pre-exponential factor. The first activation energy, For temperature, This is the universal gas constant. It is the second pre-exponential factor. The second activation energy, For voltage, For voltage reference factor, It is an acceleration factor.

[0150] According to one embodiment of this application, the operating parameters of the battery to be evaluated include voltage and temperature, and the generation rate expression for any one of the gases CO, H2, CH4, C2H6, and C2H4 is as follows: ,in, in, Pre-exponential factor, For activation energy, For temperature, This is the universal gas constant. For voltage, For voltage reference factor, It is an acceleration factor.

[0151] According to one embodiment of this application, the quantitative correlation model further includes a capacity decay prediction mechanism model, which includes the following formula: in, This is the predicted value for capacity decay. For coefficients, As an exponential factor, The determination module 730 is used to determine the battery's health status based on the predicted capacity decay value using the following formula: .

[0152] According to one embodiment of this application, the quantitative correlation model is a machine learning model trained based on the sample battery's operating parameters, sample gas characteristic indicators, and sample capacity decay data.

[0153] According to one embodiment of this application, the quantitative correlation model is a mechanistic model, which is constructed based on the functional relationship between the sample battery's sample operating parameters, sample gas characteristic indicators, and sample capacity decay data.

[0154] According to one embodiment of this application, the mechanism model includes a first function and a second function. The prediction module 720 is used to: determine the gas characteristic indicators of the battery to be evaluated based on the operating condition parameters using the first function, wherein the first function is constructed based on the sample operating condition parameters and sample gas characteristic indicators of the sample battery; and output the capacity decay prediction value based on the gas characteristic indicators using the second function, wherein the second function is constructed based on the sample gas characteristic indicators and sample capacity decay data of the sample battery.

[0155] According to one embodiment of this application, the quantitative correlation model includes a first model and a second model. The first model is a machine learning model trained based on the sample operating condition parameters, sample gas characteristic indicators, and sample capacity decay data of the sample battery. The second model is a mechanistic model, which is constructed based on the functional relationship between the sample operating condition parameters, sample gas characteristic indicators, and sample capacity decay data of the sample battery. The prediction module 720 is used to: input the operating condition parameters into the first model to obtain a first candidate capacity decay prediction value output by the first model; input the operating condition parameters into the second model to obtain a second candidate capacity decay prediction value output by the second model; perform a weighted calculation on the first candidate capacity decay prediction value and the second candidate capacity decay prediction value, and output a capacity decay prediction value.

[0156] According to one embodiment of this application, the operating parameters include the voltage and temperature of the battery to be evaluated.

[0157] According to one embodiment of this application, the battery health status assessment device 700 further includes a test module 740, which is used to: perform aging tests under various conditions on a sample battery of a target model to obtain sample operating parameters, sample gas characteristic indicators and sample capacity decay data, wherein the aging tests under various conditions include at least one of the following: storage tests at different temperatures; and cycle tests at different rates and depths of discharge.

[0158] According to one embodiment of this application, the test module 740 is used to: after the aging test under each condition is completed, place the sample battery under standardized gas production measurement conditions; measure the concentration of gas produced by the sample battery under the standardized gas production measurement conditions, and obtain the sample gas characteristic index.

[0159] It should be understood that the operation and function of the acquisition module 710, prediction module 720, determination module 730, and testing module 740 in the above embodiments can be referred to the above. Figure 2 or Figure 6 The description of the battery health status assessment method provided in the embodiments will not be repeated here to avoid repetition.

[0160] Figure 8 The diagram shown is a structural schematic of a battery health status assessment device provided in another exemplary embodiment of this application. (Refer to...) Figure 8The battery health status assessment apparatus 800 includes a processing component 810, which further includes one or more processors, and memory resources represented by a memory 820 for storing instructions executable by the processing component 810, such as application programs. The application programs stored in the memory 820 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 810 is configured to execute instructions to perform the aforementioned battery health status assessment method.

[0161] The battery health assessment device 800 may further include a power supply component configured to perform power management of the battery health assessment device 800, a wired or wireless network interface configured to connect the battery health assessment device 800 to a network, and an input / output (I / O) interface. The battery health assessment device 800 can be operated based on an operating system stored in memory 820, such as Windows Server. TM Mac OS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0162] like Figure 9 As shown, this application embodiment also provides a battery system 900, including: a battery 910; a battery health status assessment device 920; wherein the battery health status assessment device 920 is further configured to generate an indication command based on the health status determined by the battery's operating parameters. Exemplarily, the battery health status assessment device 920 may be the aforementioned... Figure 7 or Figure 8 The device shown is for assessing the health status of a battery.

[0163] This application also provides an electric vehicle including the battery system described above.

[0164] This application embodiment also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of the battery health status assessment device 800, enables the battery health status assessment device 800 to perform a battery health status assessment method.

[0165] This application also provides a computer program product, which includes a computer program. When the computer program is executed by the processor of a computer device, it enables the computer device to perform the battery health status assessment method provided in any of the above embodiments.

[0166] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0167] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0168] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0169] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0170] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0171] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0172] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program verification codes, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0173] It should be noted that in the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0174] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0175] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for assessing the health status of a battery, characterized in that, include: Obtain the operating parameters of the battery to be evaluated; The operating condition parameters are input into the quantitative correlation model to obtain the capacity decay prediction value output by the quantitative correlation model. The quantitative correlation model is used to characterize the correlation between the operating condition parameters and the gas characteristic indicators of the battery to be evaluated, as well as the correlation between the gas characteristic indicators and the capacity decay prediction value. The health status of the battery is determined based on the predicted capacity decay value.

2. The method for assessing battery health status according to claim 1, characterized in that, The gas characteristic indicators include at least one of the following: The content, concentration, or generation rate of at least one gas in the electrolyte of the battery to be evaluated; The total gas production of at least one of the gases.

3. The method for assessing battery health status according to claim 2, characterized in that, The at least one gas includes at least one of CO2, CO, H2, CH4, C2H6, and C2H4.

4. The method for assessing battery health status according to claim 2, characterized in that, The gas characteristic indicators include the generation rate of at least one gas in the electrolyte of the battery under evaluation and the total gas production of at least one gas. The at least one gas includes CO2, CO, H2, CH4, C2H6, and C2H4. The quantitative correlation model includes a computational model for the total gas production, which includes the following formula: in, The total gas production is... For time, The CO2 generation rate, For CO generation rate, CH4 formation rate, For the C2H6 formation rate, The C2H4 formation rate, The H2 generation rate.

5. The method for assessing battery health status according to claim 4, characterized in that, The operating parameters of the battery to be evaluated include voltage and temperature. When the voltage is below a voltage threshold, the CO2 generation rate... The expression is When the voltage is greater than or equal to the voltage threshold, the CO2 generation rate The expression is ,in, in, It is the first pre-exponential factor. The first activation energy, For temperature, This is the universal gas constant. It is the second pre-exponential factor. The second activation energy, For voltage, For voltage reference factor, It is an acceleration factor.

6. The method for assessing battery health status according to claim 4, characterized in that, The operating parameters of the battery to be evaluated include voltage and temperature, and the generation rate expression for any one of the gases CO, H2, CH4, C2H6, and C2H4 is: ,in, in, Pre-exponential factor, For activation energy, For temperature, This is the universal gas constant. For voltage, For voltage reference factor, It is an acceleration factor.

7. The method for assessing battery health status according to claim 4, characterized in that, The quantitative correlation model also includes a capacity decay prediction mechanism model, which comprises the following formula: in, The predicted capacity decay value is... For coefficients, As an exponential factor, The step of determining the health status of the battery based on the predicted capacity decay value includes: Based on the predicted capacity decay value, the health status of the battery is determined using the following formula: 。 8. The method for assessing battery health status according to claim 1, characterized in that, The quantitative correlation model is a machine learning model trained based on the sample battery's operating parameters, sample gas characteristic indicators, and sample capacity decay data.

9. The method for assessing battery health status according to claim 1, characterized in that, The quantitative correlation model is a mechanistic model, which is constructed based on the functional relationship between the sample battery's operating parameters, sample gas characteristic indicators, and sample capacity decay data.

10. The method for assessing battery health status according to claim 9, characterized in that, The mechanistic model includes a first function and a second function. The step of inputting the operating parameters into the quantitative correlation model to obtain the capacity decay prediction value output by the quantitative correlation model includes: The gas characteristic index of the battery to be evaluated is determined based on the operating condition parameters using the first function, wherein the first function is constructed based on the sample operating condition parameters and sample gas characteristic index of the sample battery. The second function is used to output the predicted capacity decay value based on the gas characteristic index, wherein the second function is constructed based on the sample gas characteristic index and sample capacity decay data of the sample battery.

11. The method for assessing battery health status according to claim 1, characterized in that, The quantitative correlation model includes a first model and a second model. The first model is a machine learning model trained based on the sample operating parameters, sample gas characteristic indicators, and sample capacity decay data of the sample battery. The second model is a mechanistic model, which is constructed based on the functional relationship between the sample operating parameters, sample gas characteristic indicators, and sample capacity decay data of the sample battery. The process of inputting the operating parameters into the quantitative correlation model to obtain the capacity decay prediction value output by the quantitative correlation model includes: Input the operating parameters into the first model to obtain the first candidate capacity decay prediction value output by the first model; Input the operating parameters into the second model to obtain the second candidate capacity decay prediction value output by the second model; The first candidate capacity decay prediction value and the second candidate capacity decay prediction value are weighted and calculated to output the capacity decay prediction value.

12. The method for assessing battery health status according to claim 1, characterized in that, The operating parameters include the voltage and temperature of the battery to be evaluated.

13. The method for assessing battery health status according to any one of claims 1 to 12, characterized in that, Also includes: Aging tests were conducted on sample batteries of the target model under various conditions to obtain sample operating parameters, sample gas characteristic indicators, and sample capacity decay data. The aging tests under various conditions include at least one of the following: Storage tests at different temperatures; Cyclic testing at different rates and depths of discharge.

14. The method for assessing battery health status according to claim 13, characterized in that, The sample batteries of the target model were subjected to aging tests under various conditions to obtain sample operating parameters, sample gas characteristic indicators, and sample capacity decay data, including: After the aging test under each condition was completed, the sample battery was placed under standardized gas production measurement conditions. The concentration of the gas produced by the sample battery under the standardized gas production measurement conditions is measured to obtain the characteristic indicators of the sample gas.

15. A device for assessing the health status of a battery, characterized in that, include: The acquisition module is used to acquire the operating parameters of the battery to be evaluated. The prediction module is used to input the operating condition parameters into the quantitative correlation model to obtain the capacity decay prediction value output by the quantitative correlation model. The quantitative correlation model is used to characterize the correlation between the operating condition parameters and the gas characteristic indicators of the battery to be evaluated, as well as the correlation between the gas characteristic indicators and the capacity decay prediction value. The determination module is used to determine the health status of the battery based on the predicted capacity decay value.

16. A device for assessing the health status of a battery, characterized in that, include: processor; Memory used to store the processor's executable instructions. The processor is used to execute the battery health status assessment method according to any one of claims 1 to 14.

17. A battery system, characterized in that, include: Battery; The battery health status assessment device as described in claim 15 or 16 above; The battery health status assessment device is also used to generate indication instructions based on the health status determined by the battery's operating parameters.

18. An electric vehicle, characterized in that, Includes the battery system described in claim 17.

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