Fuel cell voltage loss equation parameter correction method and system

Abstract

The invention provides a fuel cell voltage loss equation parameter correction method and system. The method comprises the following steps: carrying out abnormity elimination and data filling on original data of parameter variables; constructing an output voltage equation, determining a fitting contrast matrix, and determining a parameter array; the reinforcement learning network is initialized, a total loss function is determined, and the parameter array in the reinforcement learning network is iteratively updated; the method comprises the steps of calculating an evaluation value of each iteration parameter array, determining a score value based on the evaluation value, determining an optimal parameter array based on the score value, and inversely solving an output voltage equation based on the optimal parameter array. And parameters are updated in real time through online calibration, so that the model prediction precision is improved, and destructive testing on the fuel cell system is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of parameter correction, and in particular relates to a method and system for correcting parameters of a fuel cell voltage loss equation. Background Art

[0002] As an efficient and clean energy conversion device, fuel cells have shown broad application prospects in new energy vehicles, distributed power generation, aerospace and other fields. Its core advantage is that it can directly convert chemical energy into electrical energy. The theoretical energy conversion efficiency can reach more than 60%, and the product is only water, which meets the needs of low-carbon economy and sustainable development. However, fuel cells still face many technical challenges in actual operation, among which the accurate modeling and parameter correction of voltage loss directly affect its performance optimization, life prediction and system control accuracy.

[0003] The output voltage of a fuel cell is determined by the thermodynamic theoretical voltage minus various polarization losses, including activation polarization loss, ohmic polarization loss, and concentration polarization loss. Classical semi-empirical models (such as the Butler-Volmer equation, Ohm's law, and mass transfer equation) are often used to describe the mathematical relationship between these loss terms. However, the key parameters in the model (such as exchange current density, membrane resistance, mass transfer coefficient, etc.) are not fixed values, but change dynamically with operating conditions (temperature, pressure, humidity), material aging, fuel impurities, and other factors. Traditional modeling methods mostly determine parameters based on laboratory steady-state data or theoretical assumptions, which makes it difficult to adapt to the nonlinearity, time-varying nature, and uncertainty in actual dynamic operating environments.

[0004] Early parameter correction mainly relied on two types of methods: mechanism-driven method: deriving analytical expressions based on the electrochemical reaction mechanism, fitting parameters through experimental data, for example, using polarization curves to segmentally fit activation loss and ohmic loss. The advantage of this method is that the physical meaning is clear, but under complex working conditions (such as start-stop cycles, load mutations), it is easy to cause error accumulation due to parameter coupling and simplified assumptions. Offline calibration method: obtain typical operating parameters through laboratory tests, establish parameter lookup tables or empirical formulas, however, material attenuation (such as catalyst deactivation, membrane dehydration) and environmental fluctuations in the long-term operation of fuel cells will cause parameter drift, offline calibration is difficult to update parameters in real time, and the model prediction accuracy gradually decreases. Studies have shown that under dynamic conditions, the voltage prediction error of traditional methods can reach 5%-10%, which seriously affects the reliability of system control strategies (such as air supply management, water and heat balance control).

[0005] In recent years, data-driven technology has provided new ideas for parameter correction, but existing technologies still have obvious limitations. For example, some technologies use neural networks or support vector regression, such as building a purely data-driven black box model. Although it can improve short-term prediction accuracy, it is difficult to embed control systems based on traditional equations because it is separated from physical mechanisms and the results are unexplainable; other methods try to integrate mechanisms and data, but only for a single parameter and do not solve the problem of multi-parameter collaborative correction, while methods that rely on deep learning are difficult to run in real time in embedded devices due to excessive computing power requirements. In addition, existing technologies are not robust enough to real-world interference such as sensor noise and data loss, and they mostly rely on a single data source (such as voltage-current curves), and fail to fully utilize multi-dimensional information such as temperature, air pressure, and flow to improve the stability of parameter estimation. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a method and system for correcting parameters of a fuel cell voltage loss equation, which are used to solve the technical problems in the prior art.

[0007] On the one hand, the present invention provides the following technical solution, a method for correcting parameters of a fuel cell voltage loss equation, comprising: Acquire original data of parameter variables of the fuel cell, and perform abnormal elimination and data filling on the original data of the parameter variables to obtain corrected data; constructing an output voltage equation based on the correction data, determining a fitting comparison matrix based on the output voltage equation, and determining a parameter array based on the fitting comparison matrix; Initializing a reinforcement learning network based on the parameter array, determining a total loss function, and iteratively updating the parameter array in the reinforcement learning network based on the total loss function to obtain a plurality of iterative parameter arrays; Calculate the evaluation value of each iterative parameter array, determine the score value based on the evaluation value, determine the optimal parameter array based on the score value, and inversely solve the output voltage equation based on the optimal parameter array to complete the correction of the fuel cell voltage loss equation parameters.

[0008] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first obtains the original data of the parameter variables of the fuel cell, performs abnormal elimination and data filling on the original data of the parameter variables to obtain corrected data; then constructs an output voltage equation based on the corrected data, determines a fitting reference matrix based on the output voltage equation, and determines a parameter array based on the fitting reference matrix; then initializes a reinforcement learning network based on the parameter array, determines a total loss function, and iteratively updates the parameter array in the reinforcement learning network based on the total loss function to obtain a number of iterative parameter arrays; then calculates an evaluation value of each iterative parameter array, determines a score value based on the evaluation value, determines an optimal parameter array based on the score value, and inversely solves the output voltage equation based on the optimal parameter array to complete the correction of the parameters of the fuel cell voltage loss equation. The present invention can, in a data-driven manner, update parameters in real time through online calibration in the case of parameter drift caused by material attenuation (such as catalyst deactivation, membrane dehydration) and environmental fluctuations during long-term operation of the fuel cell, thereby improving the model prediction accuracy and avoiding destructive testing of the fuel cell system.

[0009] Preferably, the step of obtaining the original data of the parameter variables of the fuel cell, removing abnormalities and filling data on the original data of the parameter variables to obtain the corrected data includes: Acquiring raw data of parameter variables of the fuel cell, wherein the raw data includes actual current density, temperature, pressure, and output voltage; Calculate the anomaly detection value for each data point in the original data : ; In the formula, is the current density, For sampling point The corresponding current density, is the standard deviation; If the abnormal detection value of the data point in the original data If the number of consecutive abnormal data is greater than the first preset judgment value, the data point is removed as abnormal data; if the number of consecutive abnormal data is greater than the second preset judgment value, the interval corresponding to the consecutive abnormal data is taken as the abnormal interval; Starting from the abnormal interval, normal data points are searched to the left and right to obtain the left normal data point. With right normal data point , fill the abnormal interval with data based on the left normal data point and the right normal data point to obtain corrected data: ; In the formula, represents the data point after data filling, Represents the distance between the right normal data point and the first data point in the abnormal interval, Indicates the location of the first data point in the abnormal interval, express Position in the original data.

[0010] Preferably, the steps of constructing an output voltage equation based on the correction data, determining a fitting comparison matrix based on the output voltage equation, and determining a parameter array based on the fitting comparison matrix include: Splitting the total voltage loss into activation polarization , Ohmic polarization and concentration polarization , to obtain the initial voltage equation: ; In the formula, is the actual output voltage, is the temperature, is the bidirectional current density of the electrode reaction in equilibrium state, is the charge transfer coefficient, is the sum of the electrolyte, electrode and contact resistances, is the limiting current density, is the gas constant, is the number of electrons transferred in the reaction, is the Faraday constant, is a constant, is the current density; The initial voltage equation is subjected to parameter extraction and simplified fitting to obtain the output voltage equation: ; In the formula, They are the first to sixth parameters to be corrected respectively; Determine the fitting comparison matrix based on the initial voltage equation and the output voltage equation: ; In the formula, Indicates the first to twelfth conversion relationship parameters; Determine a parameter array based on the fitting control matrix .

[0011] Preferably, the step of initializing the reinforcement learning network based on the parameter array and determining the total loss function comprises: Initialize the reinforcement learning network based on the parameter array, and initialize the reward function based on the reinforcement learning network: ; In the formula, represents the value of the reward function, is the error between the theoretical output voltage and the output voltage, is the error change value, are the first and second factors respectively; Collect trajectory data for each time step , and calculate the discounted return for each of the trajectory data : ; In the formula, is the parameter array, Indicates the adjustment amount of the parameters in the parameter array, Indicates The value of the reward function, represents the step length when the trajectory ends, is the current time step; Calculate the generalized odds estimate : ; In the formula, Indicates The contribution of the TD error of the previous step to the current advantage value, is the exponential decay weight; Based on the generalized advantage estimate , the discount return Calculate the first loss separately Second loss ; Calculate Entropy Bonus : ; In the formula, Indicates in status Next select action The network output probability density of Based on the first loss The second loss , the entropy reward item Determine the total loss function : ; In the formula, are the first and second weight coefficients respectively.

[0012] Preferably, the generalized advantage estimate , the discount return Calculate the first loss separately Second loss The steps include: Based on the generalized advantage estimate Calculate the first loss : ; ; In the formula, represents the average loss of all sampling time steps, is the probability ratio of choosing the same action by the new strategy and the old strategy, is the cutoff threshold, Represents a deep learning value; Based on the discount return Determine the second loss : ; In the formula, is the network prediction value.

[0013] Preferably, the steps of calculating the evaluation value of each of the iterative parameter arrays, determining the score value based on the evaluation value, and determining the best parameter array based on the score value include: Computes the first evaluation value of the parameter array for each of the iterations , the second evaluation value , the third evaluation value , the fourth evaluation value ; Based on the first evaluation value , the second evaluation value , the third evaluation value , the fourth evaluation value Calculate the rating value ： ; In the formula, denote the first, second, third, and fourth adjustment weights respectively; The iterative parameter array corresponding to the highest score value is taken as the optimal parameter array.

[0014] Preferably, the first evaluation value of each of the iterative parameter arrays is calculated , the second evaluation value , the third evaluation value , the fourth evaluation value The steps include: Computes the first evaluation value of the parameter array for each of the iterations : ; In the formula, For the The value of the reward function corresponding to the sampling point, is the window size; Computes the second evaluation value of the parameter array for each of the iterations : ; In the formula, is the step length when the trajectory ends, For the The amount of change in trajectory data corresponding to each sampling point; Calculates the third evaluation value of the parameter array on each of the iterations : ; In the formula, is the network prediction value, In return for a discount; Calculates the fourth evaluation value of the parameter array for each of the iterations : ; In the formula, represents the average reward under the test set distribution, represents the average reward under the distribution of the training set.

[0015] In a second aspect, the present invention provides the following technical solution, a fuel cell voltage loss equation parameter correction system, the system comprising: A filling module is used to obtain the original data of the parameter variables of the fuel cell, and perform abnormal elimination and data filling on the original data of the parameter variables to obtain corrected data; An array module, configured to construct an output voltage equation based on the correction data, determine a fitting comparison matrix based on the output voltage equation, and determine a parameter array based on the fitting comparison matrix; An iteration module, used to initialize the reinforcement learning network based on the parameter array, determine a total loss function, and iteratively update the parameter array in the reinforcement learning network based on the total loss function to obtain a plurality of iterative parameter arrays; A correction module is used to calculate the evaluation value of each of the iterative parameter arrays, determine the score value based on the evaluation value, determine the optimal parameter array based on the score value, and inversely solve the output voltage equation based on the optimal parameter array to complete the correction of the fuel cell voltage loss equation parameters.

[0016] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned fuel cell voltage loss equation parameter correction method when executing the computer program.

[0017] In a fourth aspect, the present invention provides the following technical solution: a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned method for correcting the parameters of the fuel cell voltage loss equation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flow chart of a method for correcting parameters of a fuel cell voltage loss equation provided in Embodiment 1 of the present invention; Figure 2 A structural block diagram of a fuel cell voltage loss equation parameter correction system provided in Embodiment 2 of the present invention; Figure 3 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0020] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0022] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0023] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0024] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0025] Embodiment 1 In the first embodiment of the present invention, Figure 1 As shown, a method for correcting parameters of a fuel cell voltage loss equation includes: S1. Acquire original data of parameter variables of a fuel cell, and perform abnormal elimination and data filling on the original data of the parameter variables to obtain corrected data; Wherein, the step S1 comprises: S11, obtaining original data of parameter variables of the fuel cell, wherein the original data includes actual current density, temperature, pressure, and output voltage; Specifically, the fuel cell voltage loss equation parameter correction method provided by the present invention is aimed at material attenuation and environmental fluctuations that occur during the test of the fuel cell system, so the test bench can read the original data of the parameter variables of the fuel cell through sensors.

[0026] S12: Calculate the abnormality detection value of each data point in the original data : ; In the formula, is the current density, For sampling point The corresponding current density, is the standard deviation.

[0027] S13, if the abnormal detection value of the data point in the original data If the number of consecutive abnormal data is greater than the first preset judgment value, the data point is removed as abnormal data; if the number of consecutive abnormal data is greater than the second preset judgment value, the interval corresponding to the consecutive abnormal data is taken as the abnormal interval; Specifically, the first preset judgment value here is 3, and the second preset judgment value is 2.

[0028] S14, starting from the abnormal interval, searching for normal data points to the left and right respectively, to obtain the left normal data point With right normal data point , fill the abnormal interval with data based on the left normal data point and the right normal data point to obtain corrected data: ; In the formula, represents the data point after data filling, Represents the distance between the right normal data point and the first data point in the abnormal interval, Indicates the location of the first data point in the abnormal interval, express Position in the original data; Specifically, in the process of searching for normal data points, the abnormal interval is used as the starting point and the search is performed toward the left. The first normal data point found on the left is used as the left normal data point. Similarly, the first normal data point found on the right is used as the right normal data point.

[0029] S2. constructing an output voltage equation based on the correction data, determining a fitting comparison matrix based on the output voltage equation, and determining a parameter array based on the fitting comparison matrix; Wherein, the step S2 comprises: S21, split the total voltage loss into activation polarization , Ohmic polarization and concentration polarization , to obtain the initial voltage equation: ; In the formula, is the actual output voltage, is the temperature, is the bidirectional current density of the electrode reaction in equilibrium state, is the charge transfer coefficient, is the sum of the electrolyte, electrode and contact resistances, is the limiting current density, is the gas constant, is the number of electrons transferred in the reaction, is the Faraday constant, is a constant, is the current density; Specifically, the total voltage loss is split according to the current density, and for the constant In terms of, it can be understood as the total voltage of the fuel cell, and the limiting current density can be regarded as a constant.

[0030] S22, extracting parameters and simplifying fitting the initial voltage equation to obtain an output voltage equation: ; In the formula, They are the first to sixth parameters to be corrected respectively.

[0031] S23, determining a fitting comparison matrix based on the initial voltage equation and the output voltage equation: ; In the formula, Indicates the first to twelfth conversion relationship parameters; The first to twelfth conversion relationship parameters represent the conversion relationship between the parameters in the initial voltage equation and the parameters in the output voltage equation.

[0032] S24, determining a parameter array based on the fitting comparison matrix .

[0033] S3, initializing a reinforcement learning network based on the parameter array, determining a total loss function, and iteratively updating the parameter array in the reinforcement learning network based on the total loss function to obtain a plurality of iterative parameter arrays; Wherein, the step S3 comprises: S31, initializing a reinforcement learning network based on the parameter array, and initializing a reward function based on the reinforcement learning network: ; In the formula, represents the value of the reward function, is the error between the theoretical output voltage and the output voltage, is the error change value, are the first and second factors respectively; Among them, the first factor and the second factor are 10 and 1 respectively.

[0034] S32. Collect trajectory data for each time step , and calculate the discounted return for each of the trajectory data : ; In the formula, is the parameter array, Indicates the adjustment amount of the parameters in the parameter array, Indicates The value of the reward function, represents the step length when the trajectory ends, is the current time step.

[0035] S33. Calculate generalized advantage estimate : ; In the formula, Indicates The contribution of the TD error of the previous step to the current advantage value, is the exponential decay weight; Specifically, the generalized advantage estimate is used to measure the superiority of an action relative to the average performance, and the exponential decay weight can make the recent errors have a greater impact and the long-term errors have a smaller impact, and its value is 0.95.

[0036] S34, based on the generalized advantage estimation , the discount return Calculate the first loss separately Second loss ; Wherein, the step S34 comprises: S341, based on the generalized advantage estimation Calculate the first loss : ; ; In the formula, represents the average loss of all sampling time steps, is the probability ratio of choosing the same action by the new strategy and the old strategy, is the cutoff threshold, Represents a deep learning value; Specifically, the first loss It can update the policy network, optimize the action probability distribution, maximize the cumulative reward, and limit the update amplitude to avoid policy mutation. Used to ensure that the optimization direction is globally consistent. Used to measure the relative magnitude of policy updates, It is used to limit the range of probability ratio changes to prevent the update amplitude from being too large.

[0037] S342: Based on the discount return Determine the second loss : ; In the formula, is the network prediction value; Specifically, the network prediction value can be obtained through dynamic curve fitting.

[0038] S35. Calculation Entropy Reward : ; In the formula, Indicates in status Next select action The network output probability density of Specifically, an entropy reward term can be used to encourage the search process.

[0039] S36. Based on the first loss The second loss , the entropy reward item Determine the total loss function : ; In the formula, are the first and second weight coefficients respectively; Specifically, the total loss function can be used to adjust different parameter arrays to perform loss minimization iterations, and each iteration uses the iteration parameter array of the previous iteration as input, so that each iteration has a corresponding iteration parameter array.

[0040] S4, calculating the evaluation value of each of the iterative parameter arrays, determining a score value based on the evaluation value, determining an optimal parameter array based on the score value, and inversely solving the output voltage equation based on the optimal parameter array to complete the correction of the fuel cell voltage loss equation parameters; Wherein, the step S4 comprises: S41, calculating the first evaluation value of each of the iterative parameter arrays , the second evaluation value , the third evaluation value , the fourth evaluation value ; Wherein, the step S41 includes: S411, calculating the first evaluation value of each of the iterative parameter arrays : ; In the formula, For the The value of the reward function corresponding to the sampling point, is the window size; Specifically, the first evaluation value is used to determine the stability of the reward curve. If the first evaluation value rises after the iterative parameter array changes, it means that the corresponding output voltage has good stability. The window size here is 10.

[0041] S412: Calculate the second evaluation value of each of the iterative parameter arrays : ; In the formula, is the step length when the trajectory ends, For the The amount of change in trajectory data corresponding to each sampling point; Specifically, the second evaluation value is used to determine the smoothness and convergence of the parameter change trajectory, and it is necessary to maintain the control parameter after adjustment. <0.1, ensuring the stability of the strategy update direction and avoiding parameter mutations.

[0042] S413, calculating the third evaluation value of each of the iterative parameter arrays : ; In the formula, is the network prediction value, In return for a discount; Specifically, the third evaluation value is used to determine a downward trend of the prediction error.

[0043] S414, calculating the fourth evaluation value of each of the iterative parameter arrays : ; In the formula, represents the average reward under the test set distribution, represents the average reward under the distribution of the training set; Specifically, the fourth evaluation value is expressed as a generalization error, which evaluates the new state. >0, the strategy performs better in the test set than in the training set, and may overfit the training data. <0, the performance of the strategy in the new state decreases, and the diversity of training data needs to be increased.

[0044] S42, based on the first evaluation value , the second evaluation value , the third evaluation value , the fourth evaluation value Calculate the score value ： ; In the formula, denote the first, second, third, and fourth adjustment weights respectively; Specifically, in order to avoid overfitting of a single reward indicator, multi-dimensional indicators are comprehensively considered to select the optimal parameters, and the first, second, third, and fourth adjustment weights here are 0.5, 0.2, 0.2, and 0.1, respectively.

[0045] S43: taking the iterative parameter array corresponding to the highest score as the optimal parameter array.

[0046] The method for correcting the parameters of the fuel cell voltage loss equation provided in the first embodiment of the present invention first obtains the original data of the parameter variables of the fuel cell, performs abnormal elimination and data filling on the original data of the parameter variables to obtain the corrected data; then constructs the output voltage equation based on the corrected data, determines the fitting reference matrix based on the output voltage equation, and determines the parameter array based on the fitting reference matrix; then initializes the reinforcement learning network based on the parameter array, determines the total loss function, and iteratively updates the parameter array in the reinforcement learning network based on the total loss function to obtain a number of iterative parameter arrays; then calculates the evaluation value of each iterative parameter array, determines the score value based on the evaluation value, determines the optimal parameter array based on the score value, and inversely solves the output voltage equation based on the optimal parameter array to complete the correction of the parameters of the fuel cell voltage loss equation. The present invention can, in a data-driven manner, update the parameters in real time through online calibration in the case of parameter drift caused by material attenuation (such as catalyst deactivation, membrane dehydration) and environmental fluctuations during long-term operation of the fuel cell, thereby improving the model prediction accuracy and avoiding destructive testing of the fuel cell system.

[0047] Embodiment 2 like Figure 2 As shown, in the second embodiment of the present invention, a fuel cell voltage loss equation parameter correction system is provided, and the system includes: A filling module 1 is used to obtain the original data of the parameter variables of the fuel cell, and perform abnormal elimination and data filling on the original data of the parameter variables to obtain corrected data; An array module 2, used for constructing an output voltage equation based on the correction data, determining a fitting comparison matrix based on the output voltage equation, and determining a parameter array based on the fitting comparison matrix; Iteration module 3, used for initializing the reinforcement learning network based on the parameter array, determining the total loss function, and iteratively updating the parameter array in the reinforcement learning network based on the total loss function to obtain a plurality of iterative parameter arrays; The correction module 4 is used to calculate the evaluation value of each of the iterative parameter arrays, determine the score value based on the evaluation value, determine the optimal parameter array based on the score value, and inversely solve the output voltage equation based on the optimal parameter array to complete the correction of the fuel cell voltage loss equation parameters.

[0048] The filling module 1 comprises: A data submodule, used to obtain raw data of parameter variables of the fuel cell, wherein the raw data includes actual current density, temperature, pressure, and output voltage; The detection submodule is used to calculate the anomaly detection value of each data point in the original data. : ; In the formula, is the current density, For sampling point The corresponding current density, is the standard deviation; The elimination submodule is used to detect abnormal values ​​of data points in the original data. If the number of consecutive abnormal data is greater than the first preset judgment value, the data point is removed as abnormal data; if the number of consecutive abnormal data is greater than the second preset judgment value, the interval corresponding to the consecutive abnormal data is taken as the abnormal interval; The filling submodule is used to search for normal data points to the left and right respectively from the abnormal interval as the starting point to obtain the left normal data point With right normal data point , fill the abnormal interval with data based on the left normal data point and the right normal data point to obtain corrected data: ; In the formula, represents the data point after data filling, Represents the distance between the right normal data point and the first data point in the abnormal interval, Indicates the location of the first data point in the abnormal interval, express Position in the original data.

[0049] The array module 2 comprises: Splitting submodule to split the total voltage loss into activation polarization , Ohmic polarization and concentration polarization , to obtain the initial voltage equation: ; In the formula, is the actual output voltage, is the temperature, is the bidirectional current density of the electrode reaction in equilibrium state, is the charge transfer coefficient, is the sum of the electrolyte, electrode and contact resistances, is the limiting current density, is the gas constant, is the number of electrons transferred in the reaction, is the Faraday constant, is a constant, is the current density; The fitting submodule is used to extract parameters and simplify the fitting of the initial voltage equation to obtain the output voltage equation: ; In the formula, They are the first to sixth parameters to be corrected respectively; A matrix submodule is used to determine a fitting comparison matrix based on the initial voltage equation and the output voltage equation: ; In the formula, Indicates the first to twelfth conversion relationship parameters; An array submodule for determining a parameter array based on the fitting control matrix .

[0050] The iteration module 3 comprises: An initialization submodule, used to initialize the reinforcement learning network based on the parameter array, and to initialize the reward function based on the reinforcement learning network: ; In the formula, represents the value of the reward function, is the error between the theoretical output voltage and the output voltage, is the error change value, are the first and second factors respectively; Trajectory submodule, used to collect trajectory data at each time step , and calculate the discounted return for each of the trajectory data : ; In the formula, is the parameter array, Indicates the adjustment amount of the parameters in the parameter array, Indicates The value of the reward function, represents the step length when the trajectory ends, is the current time step; Estimation submodule, for computing generalized advantage estimates : ; In the formula, Indicates The contribution of the TD error of the previous step to the current advantage value, is the exponential decay weight; The loss submodule is used to estimate the generalized advantage based on , the discount return Calculate the first loss separately Second loss ; Reward submodule, used to calculate the entropy reward term : ; In the formula, Indicates in status Next select action The network output probability density of A total loss submodule is used to calculate the total loss based on the first loss. The second loss , the entropy reward item Determine the total loss function : ; In the formula, are the first and second weight coefficients respectively.

[0051] The loss submodule includes: The first loss unit is used to estimate the generalized advantage based on the Calculate the first loss : ; ; In the formula, represents the average loss of all sampling time steps, is the probability ratio of choosing the same action by the new strategy and the old strategy, is the cutoff threshold, Represents a deep learning value; The second loss unit is used to return the discount based on the Determine the second loss : ; In the formula, is the network prediction value.

[0052] The correction module 4 comprises: An evaluation value submodule, used to calculate the first evaluation value of each of the iterative parameter arrays , the second evaluation value , the third evaluation value , the fourth evaluation value ; A scoring submodule is used to score based on the first evaluation value. , the second evaluation value , the third evaluation value , the fourth evaluation value Calculate the rating value ： ; In the formula, denote the first, second, third, and fourth adjustment weights respectively; The best submodule is used to take the iterative parameter array corresponding to the highest score value as the best parameter array.

[0053] The evaluation value submodule includes: A first evaluation value unit, used to calculate the first evaluation value of each of the iterative parameter arrays : ; In the formula, For the The value of the reward function corresponding to the sampling point, is the window size; A second evaluation value unit is used to calculate the second evaluation value of each of the iteration parameter arrays. : ; In the formula, is the step length when the trajectory ends, For the The amount of change in trajectory data corresponding to each sampling point; A third evaluation value unit, used to calculate the third evaluation value of each of the iterative parameter arrays : ; In the formula, is the network prediction value, In return for a discount; A fourth evaluation value unit, used to calculate a fourth evaluation value of each of the iterative parameter arrays : ; In the formula, represents the average reward under the test set distribution, represents the average reward under the distribution of the training set.

[0054] In some other embodiments of the present invention, the embodiments of the present invention provide the following technical solution: a computer, comprising a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101, wherein the processor 101 implements the fuel cell voltage loss equation parameter correction method as described above when executing the computer program.

[0055] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0056] Among them, the memory 102 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 102 may be inside or outside the data processing device. In a specific embodiment, the memory 102 is a non-volatile memory. In a specific embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0057] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0058] The processor 101 implements the above-mentioned fuel cell voltage loss equation parameter correction method by reading and executing the computer program instructions stored in the memory 102 .

[0059] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 3 As shown, the processor 101, the memory 102, and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0060] The communication interface 103 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present invention. The communication interface 103 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0061] The bus 100 includes hardware, software or both, and couples the components of the computer device to each other. The bus 100 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 100 may include one or more buses. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0062] The computer can execute the fuel cell voltage loss equation parameter correction method of the present invention based on the obtained fuel cell voltage loss equation parameter correction system, thereby realizing the fuel cell voltage loss equation parameter correction.

[0063] In some further embodiments of the present invention, in combination with the above-mentioned method for correcting the parameters of the fuel cell voltage loss equation, the embodiments of the present invention provide the following technical solution: a storage medium having a computer program stored thereon, and the computer program implements the above-mentioned method for correcting the parameters of the fuel cell voltage loss equation when executed by a processor.

[0064] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0065] More specific examples of readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0066] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0067] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for correcting parameters of a fuel cell voltage loss equation, characterized in that: include: Acquire original data of parameter variables of the fuel cell, and perform abnormal elimination and data filling on the original data of the parameter variables to obtain corrected data; constructing an output voltage equation based on the correction data, determining a fitting comparison matrix based on the output voltage equation, and determining a parameter array based on the fitting comparison matrix; Initializing a reinforcement learning network based on the parameter array, determining a total loss function, and iteratively updating the parameter array in the reinforcement learning network based on the total loss function to obtain a plurality of iterative parameter arrays; Calculate the evaluation value of each iterative parameter array, determine the score value based on the evaluation value, determine the optimal parameter array based on the score value, and inversely solve the output voltage equation based on the optimal parameter array to complete the correction of the fuel cell voltage loss equation parameters.

2. The method for correcting fuel cell voltage loss equation parameters according to claim 1, characterized in that: The steps of obtaining the original data of the parameter variables of the fuel cell, removing abnormalities and filling data on the original data of the parameter variables to obtain corrected data include: Acquiring raw data of parameter variables of the fuel cell, wherein the raw data includes actual current density, temperature, pressure, and output voltage; Calculate the anomaly detection value for each data point in the original data : ； In the formula, is the current density, For sampling point The corresponding current density, is the standard deviation; If the abnormal detection value of the data point in the original data If the number of consecutive abnormal data is greater than the first preset judgment value, the data point is removed as abnormal data; if the number of consecutive abnormal data is greater than the second preset judgment value, the interval corresponding to the consecutive abnormal data is taken as the abnormal interval; Starting from the abnormal interval, normal data points are searched to the left and right to obtain the left normal data point. With right normal data point , fill the abnormal interval with data based on the left normal data point and the right normal data point to obtain corrected data: ； In the formula, represents the data point after data filling, Represents the distance between the right normal data point and the first data point in the abnormal interval, Indicates the location of the first data point in the abnormal interval, express Position in the original data.

3. The method for correcting fuel cell voltage loss equation parameters according to claim 1, characterized in that: The steps of constructing an output voltage equation based on the correction data, determining a fitting comparison matrix based on the output voltage equation, and determining a parameter array based on the fitting comparison matrix include: Splitting the total voltage loss into activation polarization , Ohmic polarization and concentration polarization , to obtain the initial voltage equation: ； In the formula, is the actual output voltage, is the temperature, is the bidirectional current density of the electrode reaction in equilibrium state, is the charge transfer coefficient, is the sum of the electrolyte, electrode and contact resistances, is the limiting current density, is the gas constant, is the number of electrons transferred in the reaction, is the Faraday constant, is a constant, is the current density; The initial voltage equation is subjected to parameter extraction and simplified fitting to obtain the output voltage equation: ； In the formula, They are the first to sixth parameters to be corrected respectively; Determine the fitting comparison matrix based on the initial voltage equation and the output voltage equation: ； In the formula, Indicates the first to twelfth conversion relationship parameters; Determine a parameter array based on the fitting control matrix .

4. The method for correcting fuel cell voltage loss equation parameters according to claim 1, characterized in that: The step of initializing the reinforcement learning network based on the parameter array and determining the total loss function comprises: Initialize the reinforcement learning network based on the parameter array, and initialize the reward function based on the reinforcement learning network: ； In the formula, represents the value of the reward function, is the error between the theoretical output voltage and the output voltage, is the error change value, are the first and second factors respectively; Collect trajectory data for each time step , and calculate the discounted return for each of the trajectory data : ； In the formula, is the parameter array, Indicates the adjustment amount of the parameters in the parameter array, Indicates The value of the reward function, represents the step length when the trajectory ends, is the current time step; Calculate the generalized odds estimate : ； In the formula, Indicates The contribution of the TD error of the previous step to the current advantage value, is the exponential decay weight; Based on the generalized advantage estimate , the discount return Calculate the first loss separately Second loss ; Calculate Entropy Bonus : ； In the formula, Indicates in status Next select action The network output probability density of Based on the first loss The second loss , the entropy reward item Determine the total loss function : ； In the formula, are the first and second weight coefficients respectively.

5. The method for correcting the fuel cell voltage loss equation parameters according to claim 4, characterized in that: The generalized advantage estimate , the discount return Calculate the first loss separately Second loss The steps include: Based on the generalized advantage estimate Calculate the first loss : ； ； In the formula, represents the average loss of all sampling time steps, is the probability ratio of choosing the same action by the new strategy and the old strategy, is the cutoff threshold, Represents a deep learning value; Based on the discount return Determine the second loss : ； In the formula, is the network prediction value.

6. The method for correcting fuel cell voltage loss equation parameters according to claim 1, characterized in that: The steps of calculating the evaluation value of each of the iterative parameter arrays, determining the score value based on the evaluation value, and determining the optimal parameter array based on the score value include: Computes the first evaluation value of the parameter array for each of the iterations , the second evaluation value , the third evaluation value , the fourth evaluation value ; Based on the first evaluation value , the second evaluation value , the third evaluation value , the fourth evaluation value Calculate the rating value ： ； In the formula, denote the first, second, third, and fourth adjustment weights respectively; The iterative parameter array corresponding to the highest score value is taken as the optimal parameter array.

7. The method for correcting the fuel cell voltage loss equation parameters according to claim 6, characterized in that: The first evaluation value of the parameter array is calculated for each iteration , the second evaluation value , the third evaluation value , the fourth evaluation value The steps include: Computes the first evaluation value of the parameter array for each of the iterations : ； In the formula, For the The value of the reward function corresponding to the sampling point, is the window size; Computes the second evaluation value of the parameter array for each of the iterations : ； In the formula, is the step length when the trajectory ends, For the The amount of change in trajectory data corresponding to each sampling point; Calculates the third evaluation value of the parameter array on each of the iterations : ； In the formula, is the network prediction value, In return for a discount; Calculates the fourth evaluation value of the parameter array for each of the iterations : ； In the formula, represents the average reward under the test set distribution, represents the average reward under the distribution of the training set.

8. A fuel cell voltage loss equation parameter correction system, characterized in that: The system comprises: A filling module is used to obtain the original data of the parameter variables of the fuel cell, and perform abnormal elimination and data filling on the original data of the parameter variables to obtain corrected data; An array module, configured to construct an output voltage equation based on the correction data, determine a fitting comparison matrix based on the output voltage equation, and determine a parameter array based on the fitting comparison matrix; An iteration module, used to initialize the reinforcement learning network based on the parameter array, determine a total loss function, and iteratively update the parameter array in the reinforcement learning network based on the total loss function to obtain a plurality of iterative parameter arrays; A correction module is used to calculate the evaluation value of each of the iterative parameter arrays, determine the score value based on the evaluation value, determine the optimal parameter array based on the score value, and inversely solve the output voltage equation based on the optimal parameter array to complete the correction of the fuel cell voltage loss equation parameters.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for correcting the parameters of the fuel cell voltage loss equation as described in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for correcting the parameters of the fuel cell voltage loss equation according to any one of claims 1 to 7 is implemented.

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