A steady-state control method and device of a battery, a terminal device, and a storage medium

Abstract

The application discloses a kind of steady-state control method, device, terminal equipment and storage medium of battery, the method includes: the actual current of battery to be controlled is obtained, and target power at steady state is obtained, and target voltage at steady state is determined according to target power at steady state and actual current;According to actual current and target voltage, corresponding air flow, bypass air flow and fuel flow are generated;Fuel flow is iteratively adjusted until the power of battery to be controlled reaches steady state;Wherein, at each iteration adjustment, actual current, air flow, bypass air flow and fuel flow are input into the black box model of SOFC system, so that the black box model generates the predicted voltage of battery to be controlled at steady state;The difference between predicted voltage and target voltage is calculated, and the fuel flow is adjusted according to the difference, and the adjusted fuel flow is used as the fuel flow of next iteration adjustment. By implementing the application, the efficiency of battery steady-state control can be improved.

Description

Technical Field

[0001] This invention relates to the field of battery control technology, and in particular to a steady-state control method, apparatus, terminal device, and storage medium for a battery. Background Technology

[0002] Current power control methods for solid oxide fuel cells (SOFCs) primarily focus on dynamic power switching control based on mechanistic models. These methods employ modular approaches to establish mechanistic models of the fuel cell stack and other balance of power (BOP) components, based on principles of mass conservation, energy conservation, and mole fraction conservation. These physical models are then used to control issues such as fuel deficit and thermal safety during power switching in the SOFC system. However, due to the numerous coupling factors within the system, achieving effective control requires comprehensive consideration of all these factors. Existing methods for constructing mechanistic models to achieve power control require a considerable amount of time to reach a steady-state power output. Summary of the Invention

[0003] The embodiments of the present invention provide a method, apparatus, terminal device and storage medium for steady-state control of batteries, which can improve the efficiency of steady-state control of batteries.

[0004] One embodiment of the present invention provides a steady-state control method for a battery, comprising:

[0005] Obtain the actual current and steady-state target power of the battery to be controlled, and determine the steady-state target voltage based on the steady-state target power and the actual current;

[0006] The corresponding air flow rate, bypass air flow rate, and fuel flow rate are generated based on the actual current and target voltage.

[0007] The fuel flow rate is iteratively adjusted until the power of the battery to be controlled reaches a steady state. During each iteration, the actual current, air flow rate, bypass air flow rate, and fuel flow rate are input into the black-box model of the SOFC system to generate a predicted voltage of the battery to be controlled in steady state. The difference between the predicted voltage and the target voltage is calculated, and the fuel flow rate is adjusted according to the difference. The adjusted fuel flow rate is then used as the fuel flow rate for the next iteration.

[0008] Further, the step of generating corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the actual current and target voltage includes:

[0009] Based on the actual current and target voltage, determine the corresponding fuel utilization rate, air excess ratio, and bypass air valve opening on the preset fitting curve; then determine the corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the fuel utilization rate, air excess ratio, bypass air valve opening, and actual current.

[0010] The independent variables of the fitted curve are the actual current and the target voltage, and the dependent variables are the fuel utilization rate, the excess air ratio, and the bypass air valve opening.

[0011] Furthermore, the generation of the fitted curve includes:

[0012] Acquire current samples, fuel flow samples, air flow samples, bypass air flow samples, and target voltage samples in steady state;

[0013] Input the current sample, fuel flow sample, air flow sample, bypass air flow sample, and steady-state target voltage sample into the interpolation function in the Akima spline algorithm so that the interpolation function generates the corresponding interpolation table based on the input current sample, fuel flow sample, air flow sample, bypass air flow sample, and steady-state target voltage sample.

[0014] Based on current samples, fuel flow samples, air flow samples, bypass air flow samples, steady-state target voltage samples, and interpolation table data, the Akima spline algorithm is used to generate a fitted curve.

[0015] Furthermore, the target voltage sample at steady state is obtained, including:

[0016] The current sample, fuel flow sample, air flow sample, and bypass air flow sample are input into the mechanism model of the SOFC system for cyclic simulation to generate the corresponding target voltage sample in steady state.

[0017] Furthermore, constructing the black-box model includes:

[0018] Acquire current samples, fuel flow samples, air flow samples, bypass air flow samples, and target voltage samples in steady state;

[0019] Using current samples, fuel flow samples, air flow samples, and bypass air flow samples as inputs, and the target voltage sample at steady state as output, a preset neural network model is trained to generate the black box model.

[0020] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments;

[0021] One embodiment of the present invention provides a steady-state control device for a battery, comprising: a data acquisition module and a control module;

[0022] The data acquisition module is used to determine the target voltage in steady state based on the actual current and the target power in steady state of the battery to be controlled, and the target voltage in steady state based on the target power in steady state and the actual current.

[0023] The control module is used to generate corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the actual current and target voltage; iteratively adjust the fuel flow rate until the power of the battery to be controlled reaches a steady state; wherein, during each iterative adjustment, the actual current, air flow rate, bypass air flow rate, and fuel flow rate are input into the black box model of the SOFC system so that the black box model generates a predicted voltage of the battery to be controlled in steady state; the difference between the predicted voltage and the target voltage is calculated, the fuel flow rate is adjusted according to the difference, and the adjusted fuel flow rate is used as the fuel flow rate for the next iterative adjustment.

[0024] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a battery steady-state control method as described in the above-described embodiment of the invention.

[0025] Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform a battery steady-state control method as described in the above-described embodiment of the invention.

[0026] The following benefits can be obtained by implementing the present invention:

[0027] This invention provides a steady-state control method, apparatus, terminal device, and storage medium for a battery. The steady-state control method acquires the actual current of the battery to be controlled and the target power at steady state, and then determines the target voltage at steady state. Based on the target voltage, it determines the corresponding air flow rate, bypass air flow rate, and fuel flow rate, iteratively adjusts the fuel flow rate, and obtains the predicted voltage at steady state through a black-box model based on the adjusted fuel flow rate, air flow rate, bypass air flow rate, and actual current. The predicted voltage is compared with the target voltage, and the fuel flow rate is continuously adjusted based on the comparison results. This result-oriented derivation and adjustment method, compared to simulation using a mechanistic model, enables the SOFC battery system to reach steady state faster, improving the efficiency of steady-state control. Attached Figure Description

[0028] Figure 1 This is a schematic flowchart of a steady-state control method for a battery provided in an embodiment of the present invention.

[0029] Figure 2 This is an overall control block diagram of a battery steady-state control method provided in an embodiment of the present invention.

[0030] Figure 3 This is a schematic diagram of the structure of a battery steady-state control device provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] like Figure 1 As shown, an embodiment of the present invention provides a steady-state control method for a battery, comprising:

[0033] Step S1: Obtain the actual current and steady-state target power of the battery to be controlled, and determine the steady-state target voltage based on the steady-state target power and the actual current;

[0034] Step S2: Generate the corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the actual current and target voltage;

[0035] Step S3: Iteratively adjust the fuel flow rate until the power of the battery to be controlled reaches a steady state; wherein, during each iteration, the actual current, air flow rate, bypass air flow rate, and fuel flow rate are input into the black box model of the SOFC system so that the black box model generates a predicted voltage of the battery to be controlled in steady state; calculate the difference between the predicted voltage and the target voltage, adjust the fuel flow rate according to the difference, and use the adjusted fuel flow rate as the fuel flow rate for the next iteration.

[0036] For step S1, as follows Figure 2 As shown, an embodiment of the present invention provides an overall control block diagram of a battery steady-state control method. When controlling the battery to be controlled, it is necessary to first obtain the current actual current of the battery and the target power of the battery in steady state; wherein the target power of the battery in steady state can be determined by power setting. The target voltage of the battery in steady state is determined based on the target power and the actual current.

[0037] For step S2, the corresponding air flow rate, bypass air flow rate, and fuel flow rate are determined by the optimal operating point matching module based on the actual current and target voltage.

[0038] In a preferred embodiment, generating the corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the actual current and target voltage includes: determining the corresponding fuel utilization rate, air excess ratio, and bypass air valve opening based on the actual current and target voltage on a preset fitting curve; and then determining the corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the fuel utilization rate, air excess ratio, bypass air valve opening, and actual current; wherein the independent variables of the fitting curve are the actual current and target voltage, and the dependent variables are the fuel utilization rate, air excess ratio, and bypass air valve opening.

[0039] Specifically, after inputting the actual current and target voltage into the optimal operating point matching module, the fuel utilization rate FU, air excess ratio AE, and bypass air valve opening degree BP corresponding to the input actual current and target voltage are determined based on the fitting curve preset in the optimal operating point matching module. Then, the corresponding air flow rate is calculated based on the air excess ratio AE and the actual current, the corresponding bypass air flow rate is calculated based on the bypass air valve opening degree BP and the actual current, and the corresponding fuel flow rate is calculated based on the fuel utilization rate FU and the actual current.

[0040] In a preferred embodiment, the generation of the fitted curve includes: acquiring current samples, fuel flow samples, air flow samples, bypass air flow samples, and a target voltage sample at steady state; inputting the current samples, fuel flow samples, air flow samples, bypass air flow samples, and target voltage sample at steady state into the interpolation function in the Akima spline algorithm, so that the interpolation function generates a corresponding interpolation table based on the input current samples, fuel flow samples, air flow samples, bypass air flow samples, and target voltage sample at steady state; and using the Akima spline algorithm to generate a fitted curve based on the current samples, fuel flow samples, air flow samples, bypass air flow samples, target voltage sample at steady state, and the interpolation table data.

[0041] Specifically, the current value is set to a range of 20–80 with a discrete precision of 1, serving as the current sample; the fuel utilization rate (FU) is set to a range of 0.6–0.9 with a discrete precision of 0.05, serving as the fuel flow rate sample; the excess air ratio (AE) is set to a range of 6–12 with a discrete precision of 1, serving as the air flow rate sample; and the bypass valve opening (BP) is set to a range of 0–0.3 with a discrete precision of 0.05, serving as the bypass air flow rate sample.

[0042] In a preferred embodiment, obtaining the target voltage sample in steady state includes: inputting current sample, fuel flow sample, air flow sample, and bypass air flow sample into the mechanism model of the SOFC system for cyclic simulation to generate the corresponding target voltage sample in steady state.

[0043] Specifically, the above-mentioned value range and discretization precision of each sample are used as input conditions to perform cyclic simulation of the SOFC system mechanism model, thereby obtaining the target voltage sample when the system reaches steady state; in addition, the cyclic simulation of the SOFC system can also obtain relevant parameters such as battery stack temperature and battery combustion chamber temperature.

[0044] Since generating the fitted curve in the optimal operating point matching module requires a large amount of data, this embodiment uses the Akima spline interpolation algorithm to expand the acquired data samples. Four-dimensional interpolation is performed between the already obtained sample data using the Akima spline interpolation algorithm to obtain relatively accurate expanded steady-state output interpolated sample data. The system efficiency corresponding to each set of data is calculated based on the generated interpolated data, and data that does not meet the temperature constraints is deleted, finally obtaining data samples that meet the temperature constraints. The temperature constraints are used to achieve temperature safety monitoring of the SOFC system. The temperature constraint conditions include: 1. Stack temperature less than 1173K; 2. Stack temperature gradient less than 10K / cm; 3. Temperature difference between the stack anode and cathode less than 200K; 4. Exhaust gas combustion chamber temperature less than 1273K. Exceeding any of these safe temperature ranges will cause a decrease in system performance or even damage. Therefore, data that does not meet the above conditions is considered to violate the temperature constraints and is removed to provide appropriate protection for the system.

[0045] When generating a fitting curve using the Akima spline algorithm based on current samples, fuel flow samples, air flow samples, bypass air flow samples, and steady-state target voltage samples, it needs to be implemented through Simulink software. After generating the corresponding interpolation table data using the interpolation function in the Akima spline algorithm, the corresponding fitting curve is then generated based on the current samples, fuel flow samples, air flow samples, bypass air flow samples, steady-state target voltage samples, and the interpolation table data. Simulink software includes a Lookup Table module, which provides data interpolation operations and contains an interpolation function: y = F(x1, x2, ... x...). NThe module uses the following interpolation method: y represents the generated interpolation data, F is an empirical function, x represents one or more of the known current samples, fuel flow samples, air flow samples, bypass air flow samples, and steady-state target voltage samples, and N is the number of known samples. This module maps the input to the output value by looking up or inserting values ​​from a table defined using the module parameters. The module supports uniform (constant), linear (linear point-slope), Lagrange (linear Lagrange), nearest, and cubic spline interpolation methods. In this embodiment, a four-dimensional interpolation method is used, with the four dimensions being current I, fuel utilization FU, excess air ratio AE, and bypass valve opening BP. Appropriate table values ​​are interpolated using the Akima spline method. Akima spline fits an Akima spline curve between adjacent breakpoints and returns the points on the spline curve corresponding to the input. It has the following properties: less fluctuation, no overshoot, supports non-scalar signals, and is more efficient for real-time applications. Because it considers the effect of derivative values, the resulting fitted curve is smoother, more natural, and more accurate.

[0046] After obtaining the corresponding fitting curve, when each current input condition is input to the optimal operating point matching module, the system output voltage is different under each current input condition, i.e., the output power is different. However, within each power range, there are optimal current I, fuel utilization rate FU, air excess ratio AE, and bypass valve opening BP values ​​that make the system most efficient.

[0047] For step S3, the air flow rate, bypass air flow rate, and fuel flow rate obtained in step S2 are input into the black-box model of the SOFC system to make the black-box model output a predicted voltage. The predicted voltage output by the black-box model is compared with the target voltage at steady state. Based on the difference between the predicted voltage and the target voltage, the fuel controller is controlled to adjust the fuel flow rate. Then, the adjusted fuel flow rate, bypass air flow rate, air flow rate, and actual current are input into the black-box model until the predicted voltage output by the black-box model equals the target voltage. At this point, the iterative adjustment of the fuel flow rate stops, and the power of the battery to be controlled reaches steady state. When regulating the fuel flow rate, small-amplitude control is used, and the controller used can be traditional PID control, fuzzy control, or other control methods.

[0048] It should be added that, based on the input actual current, air flow rate, bypass air flow rate, and fuel flow rate, the black-box model also outputs relevant temperature variables, including battery stack temperature and battery combustion chamber temperature. These output temperatures enable temperature monitoring of the battery system. Temperature monitoring is achieved through temperature constraints. If the temperature exceeds the constraint range, a temperature control module can be added to control the excess air ratio AE and the bypass valve opening BP to ensure the system meets the temperature constraints.

[0049] In a preferred embodiment, constructing the black box model includes: acquiring current samples, fuel flow samples, air flow samples, bypass air flow samples, and target voltage samples in steady state; using the current samples, fuel flow samples, air flow samples, and bypass air flow samples as inputs and the target voltage samples in steady state as outputs, training a preset neural network model to generate the black box model.

[0050] Specifically, the black-box model is generated by training a preset neural network model with current samples, fuel flow samples, air flow samples, and bypass air flow samples as inputs and the target voltage sample at steady state as output. In addition, during the training process, relevant temperature samples can be added as output parameters to train the neural network model.

[0051] It should be noted that, besides neural networks, black-box models can also be constructed using methods such as Support Vector Regression (SVR), Backpropagation (BP) neural networks, and Random Forests. By comparing the evaluation metrics of each model—namely, the coefficient of determination (R-squared) and Mean Squared Error (MSE)—the best model is selected for subsequent control. R-squared ranges from [0,1], with a larger value, closer to 1, indicating a better model fit. MSE evaluates the relationship between the data sequence and the true values; it has no fixed range, but a smaller value is better, indicating higher model accuracy.

[0052] By implementing this invention, the following beneficial effects can be achieved:

[0053] 1. A black-box model of the SOFC system is constructed using a data-driven approach, which directly controls the steady-state power of the system. Compared with the existing mechanism model, it does not require a lot of time to wait for the system to enter a steady state.

[0054] 2. Based on a large amount of experimental data, a current-based optimal operating point matching module was completed to achieve the highest system efficiency under actual current and target power conditions;

[0055] 3. By using the feedback of the predicted voltage to make small-scale control adjustments to the fuel flow rate, the steady-state power output of the model can be stabilized, and the optimal efficiency of the system remains basically unchanged.

[0056] 4. Add relevant temperature and temperature constraints to monitor the thermal safety of the battery system temperature.

[0057] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0058] like Figure 3 As shown, an embodiment of the present invention provides a steady-state control device for a battery, including: a data acquisition module and a control module;

[0059] The data acquisition module is used to determine the target voltage in steady state based on the actual current and the target power in steady state of the battery to be controlled, and the target voltage in steady state based on the target power in steady state and the actual current.

[0060] The control module is used to generate corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the actual current and target voltage; iteratively adjust the fuel flow rate until the power of the battery to be controlled reaches a steady state; wherein, during each iterative adjustment, the actual current, air flow rate, bypass air flow rate, and fuel flow rate are input into the black box model of the SOFC system so that the black box model generates a predicted voltage of the battery to be controlled in steady state; the difference between the predicted voltage and the target voltage is calculated, the fuel flow rate is adjusted according to the difference, and the adjusted fuel flow rate is used as the fuel flow rate for the next iterative adjustment.

[0061] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0062] Those skilled in the art will clearly understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0063] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0064] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a battery steady-state control method according to any one of the present invention.

[0065] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0066] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0067] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0068] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0069] One embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to execute a steady-state control method for a battery as described in any one of the present invention.

[0070] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0071] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A steady-state control method for a battery, characterized in that, include: Obtain the actual current and steady-state target power of the battery to be controlled, and determine the steady-state target voltage based on the steady-state target power and the actual current; The corresponding air flow rate, bypass air flow rate, and fuel flow rate are generated based on the actual current and target voltage. The step of generating corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the actual current and target voltage includes: determining the corresponding fuel utilization rate, air excess ratio, and bypass air valve opening based on the actual current and target voltage on a preset fitting curve; and then determining the corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the fuel utilization rate, air excess ratio, bypass air valve opening, and actual current; wherein, the independent variables of the fitting curve are the actual current and target voltage, and the dependent variables are the fuel utilization rate, air excess ratio, and bypass air valve opening. The fuel flow rate is iteratively adjusted until the power of the battery to be controlled reaches a steady state. During each iteration, the actual current, air flow rate, bypass air flow rate, and fuel flow rate are input into the black-box model of the SOFC system to generate a predicted voltage of the battery to be controlled in steady state. The difference between the predicted voltage and the target voltage is calculated, and the fuel flow rate is adjusted according to the difference. The adjusted fuel flow rate is then used as the fuel flow rate for the next iteration.

2. The steady-state control method for a battery as described in claim 1, characterized in that, The generation of the fitted curve includes: Acquire current samples, fuel flow samples, air flow samples, bypass air flow samples, and target voltage samples in steady state; Input the current sample, fuel flow sample, air flow sample, bypass air flow sample, and steady-state target voltage sample into the interpolation function in the Akima spline algorithm so that the interpolation function generates the corresponding interpolation table based on the input current sample, fuel flow sample, air flow sample, bypass air flow sample, and steady-state target voltage sample. Based on current samples, fuel flow samples, air flow samples, bypass air flow samples, steady-state target voltage samples, and interpolation table data, the Akima spline algorithm is used to generate a fitted curve.

3. The steady-state control method for a battery as described in claim 2, characterized in that, Obtain the target voltage sample in steady state, including: The current sample, fuel flow sample, air flow sample, and bypass air flow sample are input into the mechanism model of the SOFC system for cyclic simulation to generate the corresponding target voltage sample in steady state.

4. The steady-state control method for a battery as described in claim 2, characterized in that, Constructing the black-box model includes: Acquire current samples, fuel flow samples, air flow samples, bypass air flow samples, and target voltage samples in steady state; Using current samples, fuel flow samples, air flow samples, and bypass air flow samples as inputs, and the target voltage sample at steady state as output, a preset neural network model is trained to generate the black box model.

5. A steady-state control device for a battery, characterized in that, include: Data acquisition module and control module; The data acquisition module is used to determine the target voltage in steady state based on the actual current and the target power in steady state of the battery to be controlled, and the target voltage in steady state based on the target power in steady state and the actual current. The control module is used to generate corresponding air flow rate, bypass air flow rate and fuel flow rate based on the actual current and target voltage; The fuel flow rate is iteratively adjusted until the power of the battery under control reaches a steady state. During each iteration, the actual current, air flow rate, bypass air flow rate, and fuel flow rate are input into the black-box model of the SOFC system to generate a predicted voltage for the battery under control in steady state. The difference between the predicted voltage and the target voltage is calculated, and the fuel flow rate is adjusted based on this difference. The adjusted fuel flow rate is then used as the fuel flow rate for the next iteration. Generating the corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the actual current and target voltage includes: determining the corresponding fuel utilization rate, air excess ratio, and bypass air valve opening based on the actual current and target voltage on a preset fitting curve; and then determining the corresponding air flow rate, bypass air flow rate, and fuel flow rate based on the fuel utilization rate, air excess ratio, bypass air valve opening, and actual current. The independent variables of the fitting curve are the actual current and target voltage, and the dependent variables are the fuel utilization rate, air excess ratio, and bypass air valve opening.

6. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a steady-state control method for a battery as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform a steady-state control method for a battery as described in any one of claims 1 to 4.

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