Method and device for determining gas production rate of battery, storage medium, electronic device and program product

Abstract

The invention discloses a method and device for determining the gas production rate of a battery, a storage medium, an electronic device and a program product, and relates to the technical field of gas production calculation of the battery, and the method comprises the steps that gas production parameters of the battery are obtained from operation condition parameters of the battery, and the gas production parameters of the battery comprise storage internal pressure parameters and circulation internal pressure parameters; using the stored internal pressure parameter to calculate a first gas production rate, and using the cyclic internal pressure parameter to calculate a second gas production rate; and obtaining the gas production rate of the battery by using the first gas production rate and the second gas production rate, thereby solving the technical problem that the gas production rate of the battery cannot be accurately predicted in time, and further improving the prediction accuracy and timeliness of the gas production rate of the battery.

Description

Technical Field

[0001] This application relates to the technical field of battery gas production calculation, and more specifically, to a method, apparatus, storage medium, electronic device, and program product for determining battery gas production. Background Technology

[0002] In modern energy systems, lithium batteries play an indispensable role due to their excellent energy density and cycle performance. However, the inherent internal gas generation phenomenon in lithium batteries can cause changes in internal pressure, thereby threatening battery safety and performance. Currently, methods for predicting internal pressure due to gas generation mainly rely on traditional physicochemical models. However, these models have limited accuracy in predicting complex operating conditions, and the model construction and verification process is cumbersome and time-consuming, not only consuming resources but also failing to provide real-time predictive information during battery operation or product iteration, thus hindering the timeliness of fault diagnosis. Therefore, related technologies suffer from the technical problem of being unable to predict battery gas generation accurately and in a timely manner.

[0003] No effective solution has yet been proposed to address the technical problem of the inability to predict battery gas production in a timely and accurate manner in related technologies. Summary of the Invention

[0004] This application provides a method, apparatus, storage medium, electronic device, and program product for determining battery gas production, so as to at least solve the technical problem in the related art that it is impossible to predict battery gas production in a timely and accurate manner.

[0005] According to one embodiment of this application, a method for determining battery gas production is provided, comprising: obtaining battery gas production parameters from battery operating condition parameters, wherein the battery gas production parameters include storage internal pressure parameters and cycle internal pressure parameters; calculating a first gas production using the storage internal pressure parameters, and calculating a second gas production using the cycle internal pressure parameters; and obtaining the battery gas production using the first gas production and the second gas production.

[0006] In one exemplary embodiment, calculating a first gas production amount using the storage internal pressure parameters includes: obtaining a battery charge state, a first temperature, and a storage time from the storage internal pressure parameters; determining a storage gas production equation for calculating the first gas production amount; and using the storage gas production equation to calculate the battery charge state, the first temperature, and the storage time to obtain the first gas production amount.

[0007] In an exemplary embodiment, the first gas production amount is obtained by calculating the battery charge state, the first temperature, and the storage time using the storage gas production equation, which includes: substituting the battery charge state into the charge state factor term of the storage gas production equation, substituting the first temperature into the first temperature factor term of the storage gas production equation, and substituting the storage time into the time factor term of the storage gas production equation to obtain the first gas production amount.

[0008] In one exemplary embodiment, the gas generation equation for storage is expressed as: Δp s =A×f(SOC)×f1(T)×f(t); where A represents a constant, f(SOC) represents the charge state factor, f1(T) represents the first temperature factor, and f(t) represents the time factor.

[0009] In an exemplary embodiment, the method further includes: obtaining a historical temperature, a historical state of charge (SOC) corresponding to the historical temperature, and a historical gas production rate corresponding to the historical SOC from historical storage internal pressure parameters; fitting the historical temperature, the historical SOC, and the historical gas production rate using a fitting algorithm to obtain a first fitting equation, the first fitting equation being used to characterize the historical gas production rate corresponding to the historical SOC at the historical temperature, the first fitting equation including at least one of the following: a polynomial equation corresponding to a first SOC interval, a linear equation corresponding to a second SOC interval, wherein the SOC in the first SOC interval is all less than a preset threshold, and the SOC in the second SOC interval is equal to or greater than a preset threshold; and determining the charge state factor term and the first temperature factor term based on the fitting parameters of the first fitting equation.

[0010] In an exemplary embodiment, the fitting parameters of the first fitting equation include at least a fitting temperature parameter representing the degree of influence of the historical temperature on the historical gas production, and a fitting SOC parameter representing the degree of influence of the historical SOC on the historical gas production. Determining the charge state factor term and the first temperature factor term based on the fitting parameters of the fitting equation includes: determining a first temperature coefficient based on the fitting temperature parameter; determining the first temperature factor term based on the first temperature coefficient and a first equation corresponding to the first temperature coefficient; and determining the charge state factor term based on the fitting SOC parameter.

[0011] In an exemplary embodiment, the first equation is expressed as f1(T) = exp(-k2 / (273.15+T)), where k2 is the fitted temperature coefficient and T is the temperature; the charge state factor term is expressed as f(SOC) = k1SOC, where k1 is the fitted SOC parameter.

[0012] In one exemplary embodiment, calculating the second gas production using the cycle internal pressure parameters includes: obtaining the battery discharge depth, second temperature, and discharge rate from the cycle internal pressure parameters; determining a cycle gas production equation for calculating the second gas production; and using the cycle gas production equation to calculate the battery discharge depth, second temperature, and discharge rate to obtain the second gas production.

[0013] In an exemplary embodiment, the second gas production amount is obtained by calculating the battery discharge depth, the second temperature, and the discharge rate using the cyclic gas production equation, which includes: substituting the battery discharge depth into the discharge depth factor term of the cyclic gas production equation, substituting the second temperature into the second temperature factor term of the cyclic gas production equation, and substituting the discharge rate into the discharge rate factor term of the cyclic gas production equation to obtain the second gas production amount.

[0014] In an exemplary embodiment, the cycle gas production equation is expressed as: ΔP c =B×f(DOD)×f(Drate)×f2(T); where B represents a constant, f(DOD) represents the depth of discharge factor, f(Drate) represents the discharge rate factor, and f2(T) represents the second temperature factor.

[0015] In one exemplary embodiment, the method further includes: obtaining a historical temperature, a historical depth of discharge (DOD) corresponding to the historical temperature, and a historical cycle gas production rate corresponding to the historical DOD from historical cycle internal pressure parameters; fitting the historical temperature, the DOD, and the historical cycle gas production rate using a fitting algorithm to obtain a second fitting equation, the second fitting equation being used to characterize the historical storage gas production rate corresponding to the historical DOD at the historical temperature; and determining the depth of discharge factor term based on the fitting parameters of the second fitting equation.

[0016] In an exemplary embodiment, the fitting parameters of the second fitting equation include at least a fitted DOD parameter representing the degree of influence of the historical DOD on the historical gas production. Determining the discharge depth factor term based on the fitting parameters of the second fitting equation includes: determining the discharge depth factor term based on the second equation corresponding to the fitted DOD parameter, wherein the second equation is expressed as f(DOD) = k3DOD, and k3 is the fitted DOD parameter.

[0017] In one exemplary embodiment, the method further includes: obtaining a historical temperature, a historical discharge rate (Drate) corresponding to the historical temperature, and a historical cycle gas production volume corresponding to the historical Drate from historical storage internal pressure parameters; fitting the historical temperature, the historical Drate, and the historical cycle gas production volume using a fitting algorithm to obtain a third fitting equation, the third fitting equation being used to characterize the historical storage gas production volume corresponding to the historical Drate at the historical temperature; and determining the discharge rate factor term based on the fitting parameters of the third fitting equation.

[0018] In one exemplary embodiment, the fitting parameters of the third fitting equation include at least a fitted Drate parameter representing the degree of influence of the historical Drate on the historical gas production. Determining the discharge rate factor term based on the fitting parameters of the third fitting equation includes: determining the discharge rate factor term based on a third-party program corresponding to the fitted Drate parameter, wherein the third-party program is expressed as f(Drate) = k4Drate, and k4 is the fitted Drate parameter.

[0019] According to another aspect of the embodiments of this application, a device for determining battery gas production is also provided, comprising: an acquisition module, configured to acquire battery gas production parameters from battery operating condition parameters, wherein the battery gas production parameters include storage internal pressure parameters and cycle internal pressure parameters; a calculation module, configured to calculate a first gas production using the storage internal pressure parameters and calculate a second gas production using the cycle internal pressure parameters; and a obtaining module, configured to obtain the battery gas production using the first gas production and the second gas production.

[0020] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the above-described method for determining the amount of gas produced by the battery when it is run.

[0021] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described method for determining the amount of gas produced by the battery through the computer program.

[0022] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the above-described method for determining the amount of gas produced by the battery.

[0023] In this embodiment, battery gas production parameters are obtained from battery operating condition parameters, including storage internal pressure parameters and cycle internal pressure parameters. A first gas production amount is calculated using the storage internal pressure parameters, and a second gas production amount is calculated using the cycle internal pressure parameters. The battery gas production amount is obtained using the first and second gas production amounts. This embodiment integrates the gas production amounts from the storage internal pressure parameters and the cycle internal pressure parameters under battery operating conditions, enabling a comprehensive prediction of gas production. By considering the pressure changes during different battery usage processes, it more accurately assesses the gas production situation throughout the entire operating cycle, solving the technical problem in related technologies where timely and accurate prediction of battery gas production is impossible, thus achieving the technical effect of timely and accurate prediction of battery gas production. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the hardware environment for a method for determining the amount of gas produced by a battery according to an embodiment of this application.

[0027] Figure 2 This is a flowchart of a method for determining battery gas production according to an embodiment of this application;

[0028] Figure 3 This is a structural block diagram of a battery gas production determination device according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] The following appropriately discloses an embodiment of a battery according to this application. However, unnecessary detailed descriptions may be omitted. For example, detailed descriptions of well-known matters and repetitive descriptions of practically identical structures may be omitted. This is to avoid making the following description unnecessarily lengthy and to facilitate understanding by those skilled in the art. Furthermore, the following description is provided to enable those skilled in the art to fully understand this application and is not intended to limit the subject matter of the claims.

[0032] The battery in this application is a secondary battery, also known as a rechargeable battery or storage battery, which refers to a battery that can be used again after being discharged by recharging to activate the active materials.

[0033] Typically, a secondary battery includes an electrode assembly, an electrolyte, and an outer casing. The electrode assembly consists of a positive electrode, a negative electrode, and a separator. The electrode assembly and electrolyte are assembled inside the outer casing. During charging and discharging, active ions (such as lithium ions) move back and forth between the positive and negative electrodes, inserting and extracting. The separator, positioned between the positive and negative electrodes, primarily prevents short circuits while allowing active ions to pass through. The electrolyte, located between the positive and negative electrodes, mainly serves to conduct active ions.

[0034] During the lifespan of a lithium battery, the formation and development of expansion force is a complex process, underpinned by numerous intertwined influencing mechanisms. For example... Figure 1As shown, cell aging expansion and the battery's spatial structure are the two core factors contributing to expansion force. These factors work together within the battery to determine its safety and stability at different stages of use. Specifically, cell aging expansion can be subdivided into two types: hard expansion and soft expansion. Hard expansion is primarily driven by the relaxation effect of pre-stress, involving factors such as the compaction degree of the cell material, the initial pre-tightening force setting, and temperature changes and depth of discharge (DOD) experienced during use and aging. Soft expansion is closely related to the battery's chemical system. During charging and discharging, gases such as hydrogen, oxygen, and carbon monoxide are generated inside the cell due to electrolyte decomposition and side reactions of the positive and negative electrode materials. The accumulation of these gases leads to soft expansion. Furthermore, the battery's spatial structure (including group margin, area, and number of layers) also has a significant impact on expansion force.

[0035] This application emphasizes the use of a gas production prediction model (specifically including a stored gas production equation and a cyclic gas production equation) built from short-term pressure test data to predict the gas production of lithium batteries under various operating conditions. For details, please refer to the following content. This embodiment provides a method for determining battery gas production. Figure 2 This is a flowchart of a method for determining battery gas production according to an embodiment of this application. The process includes the following steps:

[0036] Step S202: Obtain battery gas generation parameters from battery operating condition parameters, wherein the battery gas generation parameters include storage internal pressure parameters and cycle internal pressure parameters;

[0037] Optionally, the process of collecting the above-mentioned battery gas generation parameters may include the following methods:

[0038] 1. Real-time data acquisition: For example, using a specially designed cover plate to connect a pressure sensor for gas generation data acquisition.

[0039] 2. Phased data collection: Five data collections are conducted within a life cycle. For example, gas generation can be collected directly using an internal pressure sensor, or through phased submersion tests, or through puncture tests.

[0040] Step S204: Calculate the first gas production using the stored internal pressure parameters, and calculate the second gas production using the circulating internal pressure parameters;

[0041] Step S206: Obtain the battery gas production using the first gas production and the second gas production.

[0042] Through the above steps, battery gas production parameters are obtained from battery operating condition parameters, including storage internal pressure parameters and cycle internal pressure parameters. A first gas production amount is calculated using the storage internal pressure parameters, and a second gas production amount is calculated using the cycle internal pressure parameters. The battery gas production amount is obtained using the first and second gas production amounts. This embodiment integrates the gas production amounts from the storage internal pressure parameters and the cycle internal pressure parameters under battery operating conditions, enabling a comprehensive prediction of gas production. By comprehensively considering the pressure changes during different battery usage processes, the gas production situation can be more accurately assessed throughout the entire operating cycle. This solves the technical problem in related technologies where it is impossible to predict battery gas production in a timely and accurate manner, thus achieving the technical effect of timely and accurate prediction of battery gas production.

[0043] Optionally, in one embodiment, based on "calculating the first gas production + the second gas production → obtaining the total gas production of the battery", a mapping relationship of "gas production → expansion force / thermal risk" can be further proposed, and then the battery safety assessment can be achieved based on the gas production.

[0044] Specifically, the calculated total gas production Q can be... total (Unit: mL) Input the preset expansion force estimation model: F exp =K×Q total ×S cell / V cell , of which F exp S is the expansion force, K is the material expansion coefficient (calibrated by the cell structure parameters), and S is the expansion force. cell V is the surface area of ​​the battery cell. cell For F, the internal cavity volume is given. exp Mechanical failure threshold F of the battery casing max By comparing the values, the difference is obtained, and the battery swelling risk level is determined based on the magnitude of the difference. The larger the difference, the higher the swelling risk level, and the greater the corresponding risk of battery swelling. The F value can be obtained through experimental experience from structural simulation experiments or crush tests. max .

[0045] At the same time, Q can be total Input the preset thermal runaway trigger probability model along with temperature T: P risk =σ(T)×exp(λ×Q total ), where P risk σ(T) represents the thermal runaway trigger probability, σ(T) represents the temperature sensitivity factor, and λ represents the gas generation acceleration factor, which can be obtained by fitting the data using an accelerating calorimeter experiment.

[0046] Based on the above steps, an alarm can be triggered by combining the bulging risk level and the probability of thermal runaway, such as "bulging risk level = high" and "P". riskIf the gas production exceeds 10%, an alarm response will be triggered simultaneously, including power limiting, shutdown, and a warning. This solution can further transform the total gas production into an intuitive safety indicator, achieving "risk quantification and graded response," improving the accuracy of battery risk prediction, and enhancing the battery's risk management capabilities.

[0047] In one exemplary embodiment, calculating the first gas production using the storage internal pressure parameters includes: obtaining the battery charge state, first temperature, and storage time from the storage internal pressure parameters; determining a storage gas production equation for calculating the first gas production; and using the storage gas production equation to calculate the battery charge state, the first temperature, and the storage time to obtain the first gas production. This embodiment refines the process of calculating the first gas production using storage internal pressure parameters. By extracting key information such as battery charge state, temperature, and storage time, and applying the storage gas production equation for calculation, the gas production of the battery under storage conditions can be effectively quantified, improving the accuracy of the calculation and ensuring the reliability of the gas production assessment.

[0048] In an exemplary embodiment, the storage gas generation equation is used to calculate the first gas generation amount based on the battery charge state, the first temperature, and the storage time. This includes substituting the battery charge state into the charge state factor term of the storage gas generation equation, substituting the first temperature into the first temperature factor term of the storage gas generation equation, and substituting the storage time into the time factor term of the storage gas generation equation to obtain the first gas generation amount. This embodiment further illustrates the specific application of the storage gas generation equation. By substituting the collected battery charge state, temperature, and storage time into the corresponding factor terms, the expected gas generation amount during the storage period can be calculated, improving the consistency of the prediction process and the comparability of the results, as well as enhancing the accuracy of the prediction.

[0049] In one exemplary embodiment, the gas generation equation for storage is expressed as: Δp s =A×f(SOC)×f1(T)×f(t); where A represents a constant, f(SOC) represents the charge state factor, f1(T) represents the first temperature factor, and f(t) represents the time factor.

[0050] This embodiment clearly defines the specific form of the storage gas generation equation. This equation combines the influence of three key factors: charge state, temperature, and storage time. By adjusting the constant, it can more scientifically predict the storage internal pressure change trend of the battery under different conditions, providing a solid foundation for the accurate assessment of gas generation.

[0051] In an exemplary embodiment, the method further includes: obtaining historical temperature, historical state of charge (SOC) corresponding to the historical temperature, and historical gas production corresponding to the historical SOC from historical storage internal pressure parameters; fitting the historical temperature, historical SOC, and historical gas production using a fitting algorithm to obtain a first fitting equation, wherein the first fitting equation characterizes the historical gas production corresponding to the historical SOC at the historical temperature, and the first fitting equation includes at least one of the following: a polynomial equation corresponding to a first SOC interval, a linear equation corresponding to a second SOC interval, wherein the SOC in the first SOC interval is less than a preset threshold, and the SOC in the second SOC interval is equal to or greater than the preset threshold; and determining the state of charge factor and the first temperature factor based on the fitting parameters of the first fitting equation. This embodiment introduces the concept of historical data fitting. By collecting historical storage internal pressure parameters and performing data analysis, a first fitting equation is obtained. This equation can distinguish the gas production variation patterns (e.g., polynomial or linear relationships) in different SOC intervals, providing a basis for determining subsequent factor terms, enabling a more accurate understanding of the influence of temperature and SOC on gas production, thereby improving the accuracy of gas production prediction.

[0052] Optionally, the fitting process of this embodiment will be further described. Internal pressure is recorded under different SOC states at a fixed temperature. For each SOC interval (e.g., <20% and >20%), statistical or machine learning algorithms (e.g., least squares, linear regression, multiple regression, etc.) are used to fit the optimal equation based on the collected data. Then, temperature is gradually introduced as a variable to observe the effect of temperature changes on internal pressure, and the temperature correlation coefficient in the equation is adjusted accordingly until a model that can accurately predict internal pressure changes across the entire SOC range is obtained.

[0053] In an exemplary embodiment, the fitting parameters of the first fitting equation include at least a fitting temperature parameter representing the influence of the historical temperature on the historical gas production, and a fitting SOC parameter representing the influence of the historical SOC on the historical gas production. Determining the charge state factor and the first temperature factor based on the fitting parameters of the fitting equation includes: determining a first temperature coefficient based on the fitting temperature parameters; determining the first temperature factor based on the first temperature coefficient and a first equation corresponding to the first temperature coefficient; and determining the charge state factor based on the fitting SOC parameters. This embodiment explains how to determine the charge state and temperature coefficient through historical data fitting. Specifically, the parameters generated by the fitting algorithm are used to determine the temperature coefficient (k2) and the charge state coefficient (k1). Through in-depth analysis of historical temperature, SOC, and gas production, a first gas production calculation model that better conforms to actual patterns can be established, significantly improving the timeliness and accuracy of predictions.

[0054] In an exemplary embodiment, the first equation is expressed as f1(T) = exp(-k2 / (273.15+T)), where k2 is the fitted temperature coefficient (corresponding to the first temperature coefficient), and T is the temperature; the charge state factor term is expressed as f(SOC) = k1SOC, where k1 is the fitted SOC parameter. The first equation is the first temperature factor term. This embodiment provides explicit expressions for the charge state factor term and the first temperature factor term, enabling a quantitative assessment of the impact of temperature and SOC on gas production, thus enhancing the applicability of the model and the effectiveness of gas production prediction.

[0055] Alternatively, the gas production equation for storage can be expressed as: Δp s =A×k1SOC×exp(-k2 / (273.15+T))×t α f(t) = t α t represents storage time, and α represents the exponential coefficient.

[0056] Optionally, the aforementioned k2 can be obtained through experimental design. Specifically, the SOC is kept constant, and then the gas production rate P at different temperatures is used. test As test data, multiple sets of test data are input into the following equation for parameterization: p test =A×exp(-k2 / (273.15+T))×t α k2 is obtained by solving the equation.

[0057] In one exemplary embodiment, calculating the second gas production using the cycle internal pressure parameters includes: obtaining the battery discharge depth, second temperature, and discharge rate from the cycle internal pressure parameters; determining a cycle gas production equation for calculating the second gas production; and using the cycle gas production equation to calculate the battery discharge depth, second temperature, and discharge rate to obtain the second gas production. This embodiment proposes the concept of a cycle gas production equation for calculating gas production based on cycle internal pressure parameters. Specifically, it uses the battery discharge depth, temperature, and discharge rate as variables to predict the gas production under cyclic operating conditions. This method compensates for the shortcomings of static storage models and provides a powerful tool for prediction under dynamic usage environments.

[0058] It's important to note that the cycle pressure of a battery is primarily influenced by internal chemical reactions, temperature, depth of discharge (DOC), and discharge rate. The principles by which these factors affect the cycle pressure are as follows: Temperature has a significant impact on the cycle pressure. Generally, increased temperature accelerates the rate of internal chemical reactions, potentially leading to increased internal pressure. This is because higher temperatures make the electrolyte more active, accelerating gas generation (such as hydrogen and oxygen), thus increasing internal pressure. However, excessively high temperatures can also exacerbate side reactions, such as electrolyte decomposition, producing more gas and further increasing internal pressure. Conversely, excessively low temperatures may lead to decreased battery performance and a slower chemical reaction rate, but generally do not directly cause a significant increase in internal pressure. Depth of discharge (DOD) refers to the ratio of electrical energy released to total electrical energy when the battery is discharged to a certain SOC. High DOD can lead to increased internal pressure, especially when the battery is nearing complete discharge. This may be because the internal chemical balance is disrupted, producing more byproducts, such as gases. However, for lithium-ion batteries, a moderate discharge rate (DOD) (e.g., 20%-80%) is beneficial to battery life and performance, while excessive discharge (e.g., below 20% SOC) can damage the battery. Discharge rate is an indicator describing the rate of battery discharge, usually expressed as C, where 1C represents the current required to fully discharge the battery in one hour. An increase in discharge rate (i.e., a faster discharge rate) generally leads to an increase in internal battery pressure. This is because rapid discharge intensifies the chemical reactions inside the battery, especially between the electrode materials and the electrolyte, which can lead to the generation of more gas, thus increasing the internal pressure. Furthermore, rapid discharge can also cause heat buildup inside the battery, indirectly increasing internal pressure.

[0059] In an exemplary embodiment, the second gas production amount is obtained by calculating the battery discharge depth, the second temperature, and the discharge rate using the cyclic gas production equation. This includes substituting the battery discharge depth into the discharge depth factor term of the cyclic gas production equation, substituting the second temperature into the second temperature factor term of the cyclic gas production equation, and substituting the discharge rate into the discharge rate factor term of the cyclic gas production equation to obtain the second gas production amount. This embodiment, by substituting the discharge depth, temperature, and discharge rate into their respective factor terms in the cyclic gas production equation, can intuitively reflect the strength of their respective influence on the gas production amount, making the prediction result closer to the actual gas production amount of the battery.

[0060] In an exemplary embodiment, the cycle gas production equation is expressed as: ΔP c=B × f(DOD) × f(Drate) × f2(T); where B represents a constant, f(DOD) represents the depth of discharge factor, f(Drate) represents the rate of discharge factor, and f2(T) represents the second temperature factor. This embodiment defines the specific mathematical form of the cycle gas generation equation, which can clearly reflect the combined effect of depth of discharge, rate of discharge, and temperature on the amount of gas generated during cycle operation, thereby providing accurate numerical basis for gas pressure management during battery operation.

[0061] Furthermore, the aforementioned battery gas production can be expressed by the formula ΔP=ΔP s +△P c calculate.

[0062] In an exemplary embodiment, the method further includes: obtaining a historical temperature, a historical depth of discharge (DOD) corresponding to the historical temperature, and a historical cycle gas production corresponding to the historical DOD from historical cycle internal pressure parameters; fitting the historical temperature, the DOD, and the historical cycle gas production using a fitting algorithm to obtain a second fitting equation, the second fitting equation being used to characterize the historical storage gas production corresponding to the historical DOD at the historical temperature; and determining the depth of discharge factor term based on the fitting parameters of the second fitting equation. This embodiment expands the training data source of the model by obtaining a second fitting equation through fitting historical temperature, depth of discharge (DOD), and historical cycle gas production. This equation can quantify the gas production corresponding to the DOD, enabling the influence of the depth of discharge on the battery internal pressure to be fully considered during prediction, thus optimizing the model's predictive performance.

[0063] Optionally, some of the collected battery gas generation parameters are shown in Table 1 below.

[0064] Table 1

[0065]

[0066] Optionally, Table 2 shows some historical storage internal pressure parameters and some historical cycle internal pressure parameters.

[0067] Table 2

[0068]

[0069] In an exemplary embodiment, the fitting parameters of the second fitting equation include at least a fitted DOD parameter representing the degree of influence of the historical DOD on the historical gas production. Determining the discharge depth factor term based on the fitting parameters of the second fitting equation includes: determining the discharge depth factor term based on a second equation corresponding to the fitted DOD parameter, wherein the second equation is expressed as f(DOD) = k3DOD, and k3 is the fitted DOD parameter. The second equation is the discharge depth factor term. This embodiment can determine the specific impact of DOD on gas production based on historical DOD data. Through this equation, the direct impact of DOD on gas production is quantified, enabling more accurate parameterized control in gas production prediction under cyclic operating conditions and improving the reliability of gas production prediction.

[0070] In an exemplary embodiment, the method further includes: obtaining historical temperature, historical discharge rate (Drate) corresponding to the historical temperature, and historical cycle gas production corresponding to the historical Drate from historical storage internal pressure parameters; fitting the historical temperature, historical Drate, and historical cycle gas production using a fitting algorithm to obtain a third fitting equation, the third fitting equation being used to characterize the historical storage gas production corresponding to the historical Drate at the historical temperature; and determining the discharge rate factor term based on the fitting parameters of the third fitting equation. This embodiment can quantify the impact of discharge rate on the gas production of the cycle internal pressure. Specifically, it uses the third fitting equation obtained by fitting historical data to propose a specific mechanism of action of discharge rate on gas production, thereby refining the calculation rules of the discharge rate factor term and optimizing the prediction accuracy of the model.

[0071] In an exemplary embodiment, the fitting parameters of the third fitting equation include at least a fitted Drate parameter representing the degree of influence of the historical Drate on the historical gas production. Determining the discharge rate factor term based on the fitting parameters of the third fitting equation includes: determining the discharge rate factor term based on a third-party program corresponding to the fitted Drate parameter, wherein the third-party program is expressed as f(Drate) = k4Drate, and k4 is the fitted Drate parameter. This embodiment clarifies the mathematical expression of the discharge rate factor term: through this equation, the relationship between discharge rate and gas production can be directly quantified, providing an important reference for predicting the internal pressure of the battery under high load and enhancing the performance of the prediction model under complex operating conditions.

[0072] Furthermore, the cycle gas production equation is expressed as: ΔP c =B×k3DOD×k4Drate×exp(-k5 / (273.15+T))×N b △P cFor the internal pressure of the cycle, f1(T) = exp(-k5 / (273.15+T)) × N b k5 represents the fitted temperature coefficient, N represents the number of cycles, and b represents the exponential coefficient.

[0073] Optionally, in one embodiment, the gas generation parameters of the same battery can be shared across battery packs based on the principle of transfer learning, thereby achieving data reuse. The specific process is as follows.

[0074] First, a master model library is established, and representative battery models are selected to complete the full fitting under standard operating conditions to obtain the baseline coefficient set {A,B,k1,k2,k3,k4,k5,n}.

[0075] Then, gas production tests were conducted on the new battery pack, and key data points were collected during the gas production test process, such as: 25℃ / 100% SOC storage for 24 hours, 25℃ / 80% DOD / 1C cycling for 50 cycles, and 45℃ / 80% DOD / 2C cycling for 50 cycles. Subsequently, the measured gas production of the new battery pack was input into the corresponding transfer learning framework such as meta-learning or feature space mapping. At this point, only the temperature coefficients k2, k4, k5 and the exponential coefficient n can be optimized, while the remaining coefficients are fixed to the baseline coefficients in the main model library.

[0076] Next, through the loss function L=Σ(Δp) pred –Δp measured )²+α1×||θ new –θ base ||², achieving "small sample fit" while minimizing the loss function. Where Δp pred It predicts the internal pressure of gas production, Δp measured It is the actual measured internal pressure of the gas-producing gas, θ new θ represents the parameter vector to be optimized after the new battery pack is adapted. base This represents the baseline parameter vector in the main model library.

[0077] Here, α1 represents the regularization weight coefficient, which controls the trade-off between "fitting accuracy" and "parameter stability": if α1 is too large, the model is overly conservative, and the influence of new data is weak, so it needs to be reduced; if α1 is too small, the model may "overfit" to a small amount of new data, deviating from the physical laws of the parent model and losing generalization ability, so it needs to be increased. In practical engineering, the optimal value of α1 can be selected in the range of 0.01 to 1.0 through cross-validation (such as grid search).

[0078] The loss function is the sum of squared prediction errors, used to measure the predicted internal pressure (Δp) of the new battery pack under extremely simplified testing conditions. pred ) and the actual measured internal pressure value (Δp) measured The deviation between ).

[0079] Finally, the parameters (temperature coefficient k2 and exponential coefficient n) from the adaptation process are synchronized to the "light quantum model" of the same battery model for use by other battery packs in the same series. Through the above process, this application can shorten the gas generation modeling cycle of new batteries, form a data reuse structure of "master model + light quantum model", and improve the reuse efficiency of battery pack parameters.

[0080] To better understand the process of determining the gas production of the battery described above, the following description will be provided in conjunction with optional embodiments, but these are not intended to limit the technical solutions of the embodiments of this application.

[0081] In one embodiment, the process of fitting the discharge rate factor and discharge depth factor is described below.

[0082] First, two sets of experiments were set up.

[0083] Experiment 1: At a fixed temperature (T), different DOD levels were changed while keeping the discharge rate and number of cycles consistent. The cycle pressure Pcvcle of each experiment was recorded.

[0084] Experiment 2: Change the discharge rate (R-rate), keep the DOD and number of cycles constant, and record the cycle pressure Pcvcle. Specifically, use a fixed DOD of 80% and conduct experiments at different discharge rates (e.g., C / 2, 1C, 2C, 3C).

[0085] Then, the two sets of experiments were repeated at different temperatures to obtain internal pressure data at different temperatures, depths of discharge, and discharge rates. For example, Experiment 1 and Experiment 2 can be repeated at different temperatures of 20, 25, and 30 degrees Celsius.

[0086] Next, the data collected from Experiment 1 will be input into regression analysis software (such as MATLAB or Python's SciPy library). Linear regression or other appropriate regression methods will be used to fit the relationship between Pcvcle and DOD to determine the optimal k3 value that minimizes the difference between the predicted internal pressure and the actual observed internal pressure. Similarly, using the data collected from Experiment 2, the linear or nonlinear relationship between Pcvcle and Drate will be fitted to find the optimal estimate of k3. Assuming a battery is operating at 25 degrees Celsius, after multiple experiments, the linear fitting results show k3 = 0.5 and k4 = 0.05. At 30 degrees Celsius, the same fitting process yields k3 = 0.7 and k4 = 0.06. This indicates that the influence of DOD and discharge rate on internal pressure increases with increasing temperature. Therefore, in the model, k3 and k4 should vary with temperature T.

[0087] Optionally, in one embodiment, based on the fitted coefficients k1, k2, k3, k4, etc., a dynamic self-calibration scheme for the coefficients can be further implemented. The specific implementation steps are as follows.

[0088] During battery operation, the current SOC, T, DOD, Drate, storage time, and real-time internal pressure Δp fed back by sensors are collected in real time. real Real-time data is then substituted into the stored gas production equation (i.e., Δp). pred =A×f(SOC)×f1(T)×f(t)) or the cyclic gas production equation (i.e., Δp) pred =B×f(DOD)×f(Drate)×f2(T)), the expected internal pressure Δp is calculated. pred .

[0089] Then calculate the residual: ε = Δp real –Δp pred If |ε| > the preset threshold (e.g., 5%), then online fitting of parameters is performed based on recursive least squares method, and only the most sensitive coefficients (e.g., temperature coefficient k2 or DOD coefficient k3) are updated.

[0090] The updated coefficients are then stored in the model parameter library and weighted together with the historical fitted coefficients to obtain the latest coefficients. These latest coefficients are then used to dynamically update the model. The weights in the weighting process can decay as the data confidence level increases.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0092] like Figure 3 As shown, an embodiment of this application provides a device for determining battery gas production, comprising:

[0093] The acquisition module 32 is used to acquire battery gas generation parameters from battery operating condition parameters, wherein the battery gas generation parameters include storage internal pressure parameters and cycle internal pressure parameters;

[0094] The calculation module 34 is used to calculate a first gas production rate using the stored internal pressure parameters, and to calculate a second gas production rate using the circulating internal pressure parameters;

[0095] Module 36 is used to obtain the battery gas production using the first gas production and the second gas production.

[0096] The aforementioned device obtains battery gas production parameters from battery operating condition parameters, including storage internal pressure parameters and cycle internal pressure parameters. A first gas production amount is calculated using the storage internal pressure parameters, and a second gas production amount is calculated using the cycle internal pressure parameters. The battery gas production amount is obtained using the first and second gas production amounts. This embodiment integrates the gas production amounts from the storage internal pressure parameters and the cycle internal pressure parameters under battery operating conditions, enabling a comprehensive prediction of gas production. By considering the pressure changes during different battery usage processes, it more accurately assesses the gas production situation throughout the entire operating cycle, solving the technical problem of not being able to predict battery gas production in a timely and accurate manner in related technologies, thus achieving the technical effect of timely and accurate prediction of battery gas production.

[0097] In an exemplary embodiment, the calculation module is further configured to: obtain the battery charge state, first temperature, and storage time from the storage internal pressure parameters; determine a storage gas generation equation for calculating the first gas generation amount; and use the storage gas generation equation to calculate the battery charge state, the first temperature, and the storage time to obtain the first gas generation amount.

[0098] In an exemplary embodiment, the calculation module is further configured to: substitute the battery charge state into the charge state factor term of the storage gas generation equation, substitute the first temperature into the first temperature factor term of the storage gas generation equation, and substitute the storage time into the time factor term of the storage gas generation equation to obtain the first gas generation amount.

[0099] In one exemplary embodiment, the gas generation equation for storage is expressed as: Δp s =A×f(SOC)×f1(T)×f(t); where A represents a constant, f(SOC) represents the charge state factor, f1(T) represents the first temperature factor, and f(t) represents the time factor.

[0100] In an exemplary embodiment, the calculation module is further configured to: obtain historical temperature, historical state of charge (SOC) corresponding to the historical temperature, and historical gas production corresponding to the historical SOC from historical storage internal pressure parameters; fit the historical temperature, the historical SOC, and the historical gas production using a fitting algorithm to obtain a first fitting equation, the first fitting equation being used to characterize the historical gas production corresponding to the historical SOC at the historical temperature, the first fitting equation including at least one of the following: a polynomial equation corresponding to a first SOC interval, a linear equation corresponding to a second SOC interval, wherein the SOC in the first SOC interval is less than a preset threshold, and the SOC in the second SOC interval is equal to or greater than a preset threshold; and determine the charge state factor term and the first temperature factor term based on the fitting parameters of the first fitting equation.

[0101] In an exemplary embodiment, the fitting parameters of the first fitting equation include at least a fitting temperature parameter representing the degree of influence of the historical temperature on the historical gas production, and a fitting SOC parameter representing the degree of influence of the historical SOC on the historical gas production. The calculation module is further configured to: determine a first temperature coefficient based on the fitting temperature parameters; determine a first temperature factor term based on the first temperature coefficient and a first equation corresponding to the first temperature coefficient; and determine the charge state factor term based on the fitting SOC parameters.

[0102] In an exemplary embodiment, the first equation is expressed as f1(T) = exp(-k2 / (273.15+T)), where k2 is the fitted temperature coefficient and T is the temperature; the charge state factor term is expressed as f(SOC) = k1SOC, where k1 is the fitted SOC parameter.

[0103] In an exemplary embodiment, the calculation module is further configured to: obtain the battery discharge depth, second temperature, and discharge rate from the cycle internal pressure parameters; determine a cycle gas generation equation for calculating the second gas generation amount; and use the cycle gas generation equation to calculate the battery discharge depth, the second temperature, and the discharge rate to obtain the second gas generation amount.

[0104] In an exemplary embodiment, the calculation module is further configured to: substitute the battery discharge depth into the discharge depth factor term of the cyclic gas generation equation, substitute the second temperature into the second temperature factor term of the cyclic gas generation equation, and substitute the discharge rate into the discharge rate factor term of the cyclic gas generation equation to obtain the second gas generation amount.

[0105] In an exemplary embodiment, the cycle gas production equation is expressed as: ΔP c=B×f(DOD)×f(Drate)×f2(T); where B represents a constant, f(DOD) represents the depth of discharge factor, f(Drate) represents the discharge rate factor, and f2(T) represents the second temperature factor.

[0106] In an exemplary embodiment, the calculation module is further configured to: obtain a historical temperature, a historical depth of discharge (DOD) corresponding to the historical temperature, and a historical cycle gas production rate corresponding to the historical DOD from historical cycle internal pressure parameters; fit the historical temperature, the DOD, and the historical cycle gas production rate using a fitting algorithm to obtain a second fitting equation, the second fitting equation being used to characterize the historical storage gas production rate corresponding to the historical DOD at the historical temperature; and determine the depth of discharge factor term based on the fitting parameters of the second fitting equation.

[0107] In an exemplary embodiment, the fitting parameters of the second fitting equation include at least a fitted DOD parameter representing the degree of influence of the historical DOD on the historical gas production. The calculation module is further configured to: determine the discharge depth factor term based on the second equation corresponding to the fitted DOD parameter, wherein the second equation is expressed as f(DOD) = k3DOD, and k3 is the fitted DOD parameter.

[0108] In an exemplary embodiment, the calculation module is further configured to: obtain a historical temperature, a historical discharge rate Drate corresponding to the historical temperature, and a historical cycle gas production corresponding to the historical Drate from historical storage internal pressure parameters; fit the historical temperature, the historical Drate, and the historical cycle gas production using a fitting algorithm to obtain a third fitting equation, the third fitting equation being used to characterize the historical storage gas production corresponding to the historical Drate at the historical temperature; and determine the discharge rate factor term based on the fitting parameters of the third fitting equation.

[0109] In an exemplary embodiment, the fitting parameters of the third fitting equation include at least a fitted Drate parameter representing the degree of influence of the historical Drate on the historical gas production. The calculation module is further configured to: determine the discharge rate factor term based on a third-party program corresponding to the fitted Drate parameter, wherein the third-party program is expressed as f(Drate) = k4Drate, and k4 is the fitted Drate parameter.

[0110] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0111] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0112] S1, Obtain battery gas generation parameters from battery operating condition parameters, wherein the battery gas generation parameters include storage internal pressure parameters and cycle internal pressure parameters;

[0113] S2, calculate the first gas production using the stored internal pressure parameters, and calculate the second gas production using the circulating internal pressure parameters;

[0114] S3, the battery gas production is obtained using the first gas production and the second gas production.

[0115] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0116] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0117] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0118] S1, Obtain battery gas generation parameters from battery operating condition parameters, wherein the battery gas generation parameters include storage internal pressure parameters and cycle internal pressure parameters;

[0119] S2, calculate the first gas production using the stored internal pressure parameters, and calculate the second gas production using the circulating internal pressure parameters;

[0120] S3, the battery gas production is obtained using the first gas production and the second gas production.

[0121] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0122] Optionally, embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0123] Optionally, embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0124] Optionally, embodiments of this application also provide a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in any of the above method embodiments.

[0125] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0126] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuits, or multiple modules or steps can be fabricated as a single integrated circuit. Thus, this application is not limited to any particular hardware and software combination.

[0127] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining the gas production of a battery, characterized in that, At least including: The battery gas production parameters are obtained from the battery operating condition parameters, wherein the battery gas production parameters include storage internal pressure parameters and cycle internal pressure parameters; The first gas production rate is calculated using the stored internal pressure parameters, and the second gas production rate is calculated using the circulating internal pressure parameters; The battery gas production is obtained using the first gas production rate and the second gas production rate.

2. The method for determining the gas production of a battery according to claim 1, characterized in that, The calculation of the first gas production using the stored internal pressure parameters includes: The battery charge state, first temperature, and storage time are obtained from the storage internal pressure parameters; A storage gas production equation is determined for calculating the first gas production amount. The storage gas production equation is then used to calculate the battery charge state, the first temperature, and the storage time to obtain the first gas production amount.

3. The method for determining battery gas production according to claim 2, characterized in that, The storage gas generation equation is used to calculate the first gas generation amount by considering the battery state of charge, the first temperature, and the storage time, including: Substituting the battery charge state into the charge state factor term of the storage gas generation equation, substituting the first temperature into the first temperature factor term of the storage gas generation equation, and substituting the storage time into the time factor term of the storage gas generation equation, the first gas generation amount is obtained.

4. The method for determining the gas production of a battery according to claim 3, characterized in that, The gas generation equation for storage is expressed as: △p s =A×f(SOC)×f1(T)×f(t); Where A represents a constant, f(SOC) represents the charge state factor, f1(T) represents the first temperature factor, and f(t) represents the time factor.

5. The method for determining the gas production of a battery according to claim 4, characterized in that, The method further includes: The historical temperature, the historical state of charge (SOC) corresponding to the historical temperature, and the historical gas production rate corresponding to the historical SOC are obtained from the historical storage internal pressure parameters. The historical temperature, the historical SOC, and the historical storage gas production are fitted using a fitting algorithm to obtain a first fitting equation. The first fitting equation is used to characterize the historical storage gas production corresponding to the historical SOC at the historical temperature. The first fitting equation includes at least one of the following: a polynomial equation corresponding to a first SOC interval, a linear equation corresponding to a second SOC interval, wherein the SOC in the first SOC interval is less than a preset threshold, and the SOC in the second SOC interval is equal to or greater than a preset threshold. The charge state factor and the first temperature factor are determined based on the fitting parameters of the first fitting equation.

6. The method for determining the gas production of a battery according to claim 5, characterized in that, The fitting parameters of the first fitting equation include at least a fitting temperature parameter representing the influence of the historical temperature on the historical gas production, and a fitting SOC parameter representing the influence of the historical SOC on the historical gas production. The charge state factor term and the first temperature factor term are determined based on the fitting parameters of the fitting equation, including: A first temperature coefficient is determined based on the fitted temperature parameters, and a first temperature factor term is determined based on the first temperature coefficient and the first equation corresponding to the first temperature coefficient. The charge state factor term is determined based on the fitted SOC parameters.

7. The method for determining battery gas production according to claim 6, characterized in that, The first equation is expressed as f1(T) = exp(-k2 / (273.15+T)), where k2 is the fitted temperature coefficient and T is the temperature; the charge state factor term is expressed as f(SOC) = k1SOC, where k1 is the fitted SOC parameter.

8. The method for determining the gas production of a battery according to claim 1, characterized in that, The calculation of the second gas production rate using the aforementioned internal pressure parameters includes: The battery discharge depth, second temperature, and discharge rate are obtained from the cycle internal pressure parameters. A cyclic gas generation equation is determined for calculating the second gas generation amount. The cyclic gas generation equation is then used to calculate the battery discharge depth, the second temperature, and the discharge rate to obtain the second gas generation amount.

9. The method for determining the gas production of a battery according to claim 8, characterized in that, The second gas production amount is obtained by calculating the battery discharge depth, the second temperature, and the discharge rate using the cycle gas production equation, including: Substituting the battery discharge depth into the discharge depth factor term of the cycle gas generation equation, substituting the second temperature into the second temperature factor term of the cycle gas generation equation, and substituting the discharge rate into the discharge rate factor term of the cycle gas generation equation, the second gas generation amount is obtained.

10. The method for determining the gas production of a battery according to claim 9, characterized in that, The gas production cycle equation is expressed as follows: △P c =B×f(DOD)×f(Drate)×f2(T); Where B represents a constant, f(DOD) represents the depth of discharge factor, f(Drate) represents the discharge rate factor, and f2(T) represents the second temperature factor.

11. The method for determining the gas production of a battery according to claim 10, characterized in that, The method further includes: The historical temperature, the historical depth of discharge (DOD) corresponding to the historical temperature, and the historical gas production rate corresponding to the historical DOD are obtained from the historical cycle internal pressure parameters. A fitting algorithm is used to fit the historical temperature, the DOD, and the historical cycle gas production to obtain a second fitting equation. The second fitting equation is used to characterize the historical storage gas production corresponding to the historical DOD at the historical temperature. The discharge depth factor is determined based on the fitting parameters of the second fitting equation.

12. The method for determining the gas production of a battery according to claim 11, characterized in that, The fitting parameters of the second fitting equation include at least a fitted DOD parameter representing the degree of influence of the historical DOD on the historical gas production. The depth of discharge factor term is determined based on the fitting parameters of the second fitting equation, including: The discharge depth factor is determined based on the second equation corresponding to the fitted DOD parameters, wherein the second equation is expressed as f(DOD) = k3DOD, and k3 is the fitted DOD parameter.

13. The method for determining the gas production of a battery according to claim 10, characterized in that, The method further includes: Obtain the historical temperature, the historical discharge rate Drate corresponding to the historical temperature, and the historical cycle gas production corresponding to the historical Drate from the historical storage internal pressure parameters. A fitting algorithm is used to fit the historical temperature, the historical Drate, and the historical cycle gas production to obtain a third fitting equation, which is used to characterize the historical storage gas production corresponding to the historical Drate at the historical temperature. The discharge rate factor term is determined based on the fitting parameters of the third fitting equation.

14. The method for determining the gas production of a battery according to claim 13, characterized in that, The fitting parameters of the third fitting equation include at least a fitting Drate parameter representing the degree of influence of the historical Drate on the historical gas production. The discharge rate factor term is determined based on the fitting parameters of the third fitting equation, including: The discharge rate factor is determined based on a third-party program corresponding to the fitted Drate parameter, wherein the third-party program is expressed as f(Drate) = k4Drate, and k4 is the fitted Drate parameter.

15. A device for determining the amount of gas produced by a battery, characterized in that, include: The acquisition module is used to acquire battery gas generation parameters from battery operating condition parameters, wherein the battery gas generation parameters include storage internal pressure parameters and cycle internal pressure parameters; A calculation module is used to calculate a first gas production rate using the stored internal pressure parameters, and to calculate a second gas production rate using the circulating internal pressure parameters; The module is used to obtain the battery gas production using the first gas production and the second gas production.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method according to any one of claims 1 to 14.

17. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 14 through the computer program.

18. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1 to 14.

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