A method, device, electronic device and storage medium for evaluating health status

Abstract

The present application discloses a health status assessment method, device, electronic device and storage medium. The method and device are applied to electronic devices and are used to assess the health status of fuel cells. Specifically, the method and device collect multiple operating parameters of the fuel cell to be assessed; standardize the multiple operating parameters respectively to obtain multiple standardized operating parameters corresponding to the operating parameters; use a global optimization algorithm to optimize the screening criteria of the multiple standardized operating parameters to obtain multiple performance indicators; filter the multiple performance indicators; calculate the multiple performance indicators after filtering to obtain the health status parameters of the fuel cell to be assessed. The present application improves the universality of the health assessment of the fuel cell by adaptively adjusting the screening threshold of the data, and then can achieve more accurate and real-time fuel cell health management and improve the system life.

Description

Technical Field

[0001] The present application relates to the field of fuel cell technology, and more specifically, to a health status assessment method, device, electronic device and storage medium. Background Art

[0002] The performance of a fuel cell is closely related to its operating conditions and corresponding working conditions. At present, the mainstream health evaluation indicators are still performance indicators under steady state, that is, system power, operating voltage or single cell voltage under constant operating current and constant working conditions. Such indicators rely on steady-state test methods, such as steady-state polarization curve tests. If performance indicators with stability and continuity under a certain operating mode can be accurately screened out from all historical operating data, its health status can be evaluated based on this.

[0003] In the process of evaluating the health status of fuel cells, the problem of inconsistent dimensions of various operating parameters of fuel cells can generally be screened by using a unified screening standard. The mainstream unified screening standard is mainly based on the statistical outlier analysis theory, and a fixed threshold such as the normal distribution standard deviation or quartile is selected as a unified screening standard to screen various operating parameters.

[0004] However, in actual applications, due to individual differences between fuel cells, the fixed threshold method is difficult to cover all application scenarios. It is possible that the screening criteria are too strict, resulting in excessive data screening and data missing; or the screening criteria are too loose, resulting in too little data screening and large fluctuations in performance indicators. As a result, the current health assessment method is not universal and it is difficult to assess the health status of different types of fuel cells. Summary of the invention

[0005] In view of this, the present application provides a health status assessment method, device, electronic device and storage medium for assessing the health status of different types of fuel cells to solve the problem that existing evaluation schemes are not universal.

[0006] In order to achieve the above objectives, the proposed solution is as follows:

[0007] A health status evaluation method is applied to an electronic device and is used to adaptively evaluate the health status of a fuel cell. The evaluation method comprises the following steps:

[0008] Collect various operating parameters of the fuel cell to be evaluated;

[0009] Standardizing the plurality of operating parameters respectively to obtain a plurality of standardized operating parameters respectively corresponding to the operating parameters;

[0010] Using a global optimization algorithm to optimize the screening criteria of the various standardized operating parameters to obtain various performance indicators;

[0011] Performing filtering processing on the multiple performance indicators;

[0012] The plurality of performance indicators after filtering are calculated to obtain health status parameters of the fuel cell to be evaluated.

[0013] Optionally, the multiple operating parameters include operating current, load change rate, hydrogen flow, air flow, coolant flow, hydrogen temperature, air temperature, coolant temperature, hydrogen pressure, air pressure, coolant pressure and some or all of their derived variables.

[0014] Optionally, the method of using a global optimization algorithm to perform screening standard optimization on a plurality of the standardized operating parameters to obtain a plurality of performance indicators comprises the steps of:

[0015] Based on the global optimization algorithm, a decision variable corresponding to each of the standardized operating parameters is selected, and based on the decision variable, each of the standardized operating parameters is screened to obtain the performance index corresponding to each parameter in the table.

[0016] Optionally, the global optimization algorithm is a probability-based or population-based heuristic algorithm.

[0017] A health status evaluation device is applied to electronic equipment and is used to evaluate the health status of a fuel cell. The evaluation device includes:

[0018] A parameter acquisition module, configured to acquire a plurality of operating parameters of the fuel cell to be evaluated;

[0019] A standardization processing module is configured to perform standardization processing on the plurality of operating parameters respectively to obtain a plurality of standardized operating parameters respectively corresponding to the operating parameters;

[0020] A screening processing module is configured to optimize the screening criteria of the plurality of standardized operating parameters using a global optimization algorithm to obtain a plurality of performance indicators;

[0021] A filtering processing module, configured to perform filtering processing on the multiple performance indicators;

[0022] The evaluation execution module is configured to calculate the multiple performance indicators after filtering to obtain the health status parameters of the fuel cell to be evaluated.

[0023] Optionally, the multiple operating parameters include operating current, load change rate, hydrogen flow, air flow, coolant flow, hydrogen temperature, air temperature, coolant temperature, hydrogen pressure, air pressure, coolant pressure and some or all of their derived variables.

[0024] Optionally, the screening and processing module is configured to select decision variables corresponding to each of the standardized operating parameters based on a global optimization algorithm, and is configured to screen each of the standardized operating parameters based on the decision variables to obtain the performance indicators corresponding to each parameter in the table.

[0025] Optionally, the global optimization algorithm is a probability-based or population-based heuristic algorithm.

[0026] An electronic device comprises at least one processor and a memory connected to the processor, wherein:

[0027] The memory is used to store computer programs or instructions;

[0028] The processor is used to execute the computer program or instructions so that the electronic device can implement the health status assessment method as described above.

[0029] A storage medium is applied to an electronic device, wherein the storage medium carries one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device can implement the health status assessment method as described above.

[0030] It can be seen from the above technical solutions that the present application discloses a health status assessment method, device, electronic device and storage medium. The method and device are applied to electronic devices and are used to assess the health status of fuel cells. Specifically, the method and device collect various operating parameters of the fuel cell to be assessed; standardize the various operating parameters respectively to obtain various standardized operating parameters corresponding to the operating parameters; use a global optimization algorithm to optimize the screening criteria of the various standardized operating parameters to obtain various performance indicators; filter the various performance indicators; calculate the various performance indicators after filtering to obtain the health status parameters of the fuel cell to be assessed. The present application improves the universality of the health assessment of the fuel cell by adaptively adjusting the screening threshold of the data, and then can achieve more accurate and real-time fuel cell health management and improve the system life. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A flowchart of a method for evaluating health status according to an embodiment of the present application;

[0033] Figure 2 This is an example diagram of the original data of the embodiment of the present application;

[0034] Figure 3 This is an example diagram of screening data in an embodiment of the present application;

[0035] Figure 4 This is an example diagram of filtering data in an embodiment of the present application;

[0036] Figure 5 A schematic diagram of the relationship between the fitness of the genetic algorithm and the number of iterations in the embodiment of the present application;

[0037] Figure 6 A block diagram of a health status assessment device according to an embodiment of the present application;

[0038] Figure 7 A block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] The principle of this application is that changes in fuel cell operating parameters can directly affect its real-time performance output. If the fuel cell operating current and operating parameters are used as screening conditions and the range is narrowed: in a shorter time span, its operating voltage value range will tend to be stable; in a longer time span, its operating voltage value will show a slow downward trend.

[0041] After standardization, different operating parameters can use screening criteria with a unified value range. For each operating parameter, a screening criterion can be given, and a set of criteria forms a decision variable; after the same screening method, a set of screening criteria corresponds to a screening result. If the goal is to exchange the least amount of data loss for the most stable performance indicators, the above problem can be defined as an optimization problem: that is, to find a decision variable that makes the objective function take the maximum value.

[0042] Generally speaking, the objective function of the above problem cannot be guaranteed to be differentiable everywhere in the decision space, so the traditional gradient-based mathematical optimization method cannot be used. Evolutionary algorithm is an effective method to solve this type of problem. Its basic idea is: through a finite number of iterations, several decision variables that can make the objective function take an approximate maximum value can be obtained. Therefore, by appropriately defining the objective function (or fitness function), the adaptive screening of fuel cell performance indicators on different stacks or systems can be achieved, and then it can be applied to the evaluation and prediction of health status to achieve accurate health management.

[0043] Based on the above, the following embodiments are proposed to achieve effective evaluation of the health status of the fuel cell:

[0044] Embodiment 1

[0045] Figure 1 This is a flow chart of a method for assessing health status according to an embodiment of the present application.

[0046] like Figure 1 As shown, the evaluation method provided in this embodiment is applied to an electronic device for evaluating the health status of a fuel cell. The electronic device can be understood as a computer or server with information processing and data computing capabilities. This application evaluates the health status of a fuel cell when it is operating at a variable load of 30 to 330A by the following evaluation method:

[0047] S1. Collect various operating parameters of the fuel cell to be evaluated.

[0048] The operating parameters include but are not limited to operating current, load rate, hydrogen flow, air flow, coolant flow, hydrogen temperature, air temperature, coolant temperature, hydrogen pressure, air pressure, coolant pressure and their derivative variables, etc. Figure 2 The derived variables here include but are not limited to temperature difference, flow resistance and metering ratio.

[0049] S2. Standardize various operating parameters separately.

[0050] By standardizing each operating parameter, a standardized operating parameter corresponding to each operating parameter is obtained. That is, the 9 operating parameters {x i}Perform Z-score standardization, that is:

[0051]

[0052] Among them, μ is the operating parameter {x i}, σ is the average value of the running parameter {x i} standard deviation.

[0053] S3. Use global optimization algorithm to optimize the screening criteria for multiple standardized operating parameters.

[0054] The corresponding screening criteria of each fuel cell operating parameter are used as decision variables, and the small data sample loss and large data fluctuation reduction are used as optimization goals. The data screening optimization problem is defined, and the fuel cell data screening criteria are optimized using a global optimization algorithm to obtain a variety of performance indicators corresponding to each standardized operating parameter, such as Figure 3 shown.

[0055] The optimization model of this scheme is defined as follows:

[0056] (1) Fuel cell operating parameters After standardization, the corresponding data screening criteria ∈ j Screening, where ∈ j ∈[0,1].

[0057] (2) Different operating parameters Corresponding to different data screening criteria∈ j , so for a set of operating parameters There is a set of screening criteria vectors {∈1,∈2,…∈ k}, which is the decision variable.

[0058] (3) The optimization goal of fuel cell performance index screening is to find a set of screening criteria so that the performance index after screening is as stable as possible (i.e., the volatility is small), and the data removed by screening is as small as possible. Based on the above goals, the mean square error between the original data of the performance index and its moving average can be used to represent the volatility of the performance index, and the logarithm of the ratio of the amount of data after screening to the amount of data before screening can be used to represent the soft constraint (or penalty) on the amount of screened data, so that the objective function (fitness function) is defined as follows:

[0059]

[0060] Among them, y obs is the observed value of the performance indicator, yavg is the moving average of the performance index, MSE(x,y) represents the mean square error between x and y, N0 is the amount of data before screening, N is the amount of data after screening, and α>0 and α≠1 are satisfied.

[0061] (4) In summary, the observed value y of the performance indicator obs , moving average y avg and the amount of data after screening N are both consistent with the data screening standard vector {∈1,∈2,…∈ k} related. Therefore, the optimization problem for data screening can be defined as follows:

[0062]

[0063] The global optimal algorithm in this scheme is a probability-based or population-based heuristic algorithm, including but not limited to simulated annealing algorithm, genetic algorithm, particle swarm algorithm, ant colony algorithm, etc.

[0064] where ∈ i ∈[0,1], select the upper bound of data screening according to the following rules:

[0065]

[0066] The lower limit of data screening is selected according to the following rules:

[0067]

[0068] That is, the result of data screening is to satisfy x i ∈[x lower ,x upper ] data. For example, for the screening standard 0.9, the upper limit of data screening is 0.05, and the lower limit of data screening is 0.95. Figure 4 shown.

[0069] This embodiment uses a genetic algorithm to select the screening standard vector {∈1,∈2,…∈ k} to optimize, such as Figure 5 As shown:

[0070] (1) Encoding. Set the precision to 0.001. Encode the decimal real number into binary, that is, a 0-1 bit encoding vector with a vector length of log2 1000, which is rounded to 9. In this way, the screening criterion 0.9 will be encoded as (1,1,1,0,0,0,0,1,0,0).

[0071] (2) Generate an initial population. Set the population size to 100 and screen a total of 9 working variables. Generate 9 random numbers with uniform probability in the range (0,1) to form a screening standard vector, such as (0.590, 0.172, 0.602, 0.119, 0.723, 0.861, 0.363, 0.924, 0.801); then, encode each screening standard and combine all the codes together. For example, the encoding result of the above screening standard vector is (1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1 ,0,1,1,0,0,0,1,0,0,1,1,0,1,0,0,0,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,1,0,1,1,1,0,1,0,1,1,1,0,1,1,0,0,1,1,0,1,0); Repeat the above steps 100 times and the population is initialized.

[0072] (3) Selection. The “tournament selection” algorithm is used: two individuals are selected from the population with equal probability, and the individual with higher fitness is selected as the father of the next generation; the mother of the next generation is selected from the population in the same way. For example, for individual A (0.774, 0.683, 0.683, 0.684, 0.684, 0.714, 0.690, 0.902, 0.903) and individual B (0.695, 0.685, 0.686, 0.710, 0.726, 0.766, 0.700, 0.862, 0.988), the fitness is calculated to be 0.005222 and 0.004953 respectively, so individual A can be selected as the father / mother of the next generation.

[0073] (4) Crossover. With a probability of 0.8, take the father and mother individuals in step 3 as templates, for each 0-1 bit code, exchange the site with a probability of 0.5; after traversing each 0-1 bit code, a new individual is formed. For individual A(1,0,0,1,1,0,1,1,0,1,0,0,0,1,0,0,1,0,1,1,0,0,0,1,1,1,1,1,1,0,0,0,1,0,1,1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,0,0,0,1,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1) and individual B( 0,1,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,0,1,1,1,1,0,1,1,0,1,1,0,1,1,1,1,1,0,0,1,1,0,1,1,1,1,1,0,0,1,1,0,1,1,1,0,0,1,0,0,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0), if the two bodies are at the first site By performing crossover on the above, we can get the individual C(0,0,0,1,1,0,1,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,1,0,0,0,1,1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0 ,0,1) and individual B(1,1,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,0,1,1,1,1,0,1,1,0,1,1,1,1,1,0,0,1,1,0,1,0,1,1,1,1,1,0,0,1,1,0,1,1,1,0,0,1,0,0,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0).

[0074] (5) Mutation. Each 0-1 bit code in an individual undergoes a mutation of swapping between 0 and 1 with a probability of 0.1. For individual A(1,0,1,1,0,0,1,1,1,1,1,0,0,0,1,1,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,1,1,0,1,1,1,0,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,0,0,0,0,1,1,0,0,0,0,1,1,0), if the first bit The individual B is obtained by mutating the individual B(0,0,1,1,0,0,1,1,1,1,1,0,0,0,1,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,1,1,0,1,1,1,0,1,1,1,1,0,1,0,1,0,1,1,1,1,1,0,0,0,0,0,1,1,0).

[0075] (6) Traverse the individuals in the population. According to steps 3-5, the number of traversals is 50, and 2 sub-individuals are generated each time, and a population size of 100 is obtained.

[0076] (7) Termination condition judgment: If the number of iterations is less than or equal to the iteration threshold, the next generation population selection, crossover and mutation operations are restarted from step 3 based on the population after step 5; if the number of iterations is greater than the iteration threshold, the iteration is stopped and the 0-1 bit encoding vector with the highest fitness is output.

[0077] (8) Decoding. This step is the reverse operation of step (1). The binary real number code is restored to decimal, and the decoding output is the optimal individual (optimal screening standard vector) and its fitness.

[0078] S4. Filter the various performance indicators.

[0079] The operating voltage is filtered using a moving average filter with a window of 10.

[0080] S5. Perform fuel cell health assessment.

[0081] According to the specific values ​​of the screening performance indicators, the health status of the fuel cell is evaluated. Considering a longer time span, the working voltage of the fuel cell will continue to decrease under constant working current and corresponding constant working conditions. Comparing the working voltage of the fuel cell at the time of interest with the working voltage at the initial time, it can be seen that after 3500 endurance cycles, the working voltage attenuation rate of the fuel cell at 30A is 8.5%.

[0082] It can be seen from the above technical solution that this embodiment provides a health status assessment method, which is applied to electronic equipment and is used to assess the health status of fuel cells. Specifically, it collects multiple operating parameters of the fuel cell to be evaluated; standardizes the multiple operating parameters respectively to obtain multiple standardized operating parameters corresponding to the operating parameters; uses a global optimization algorithm to optimize the screening criteria of the multiple standardized operating parameters to obtain multiple performance indicators; filters the multiple performance indicators; calculates the multiple performance indicators after filtering to obtain the health status parameters of the fuel cell to be evaluated. This application improves the universality of the health assessment of the fuel cell by adaptively adjusting the screening threshold of the data, and then can achieve more accurate and real-time fuel cell health management, thereby improving the system life.

[0083] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] Although operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0085] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0086] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Python, Julia, Java, Smalltalk, C++, and the like, as well as conventional procedural programming languages, such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0087] Embodiment 2

[0088] Figure 6 This is a block diagram of a health status assessment device according to an embodiment of the present application.

[0089] like Figure 6 As shown, the evaluation device provided in this embodiment is applied to an electronic device for evaluating the health status of a fuel cell. The electronic device can be understood as a computer or server with information processing and data computing capabilities. The evaluation device of the present application includes a parameter acquisition module 10, a standardization processing module 20, a screening processing module 30, a filtering processing module 40, and an evaluation execution module 50.

[0090] The parameter acquisition module is used to acquire various operating parameters of the fuel cell to be evaluated.

[0091] The operating parameters include but are not limited to operating current, load rate, hydrogen flow, air flow, coolant flow, hydrogen temperature, air temperature, coolant temperature, hydrogen pressure, air pressure, coolant pressure and their derivative variables, etc. Figure 2 The derived variables here include but are not limited to temperature difference, flow resistance and metering ratio.

[0092] The standardization processing module is used to standardize various operating parameters respectively.

[0093] By standardizing each operating parameter, a standardized operating parameter corresponding to each operating parameter is obtained. That is, the 9 operating parameters {x i}Perform Z-score standardization, that is:

[0094]

[0095] Among them, μ is the operating parameter {xi}, σ is the average value of the running parameter {x i} standard deviation.

[0096] The screening processing module is used to optimize the screening criteria for a variety of standardized operating parameters using a global optimization algorithm.

[0097] The corresponding screening criteria of each fuel cell operating parameter are used as decision variables, and the small data sample loss and large data fluctuation reduction are used as optimization goals. The data screening optimization problem is defined, and the fuel cell data screening criteria are optimized using a global optimization algorithm to obtain a variety of performance indicators corresponding to each standardized operating parameter, such as Figure 3 shown.

[0098] The optimization model of this scheme is defined as follows:

[0099] (1) Fuel cell operating parameters After standardization, the corresponding data screening criteria ∈ j Screening, where ∈ j ∈[0,1].

[0100] (2) Different operating parameters Corresponding to different data screening criteria∈ j , so for a set of operating parameters There is a set of screening criteria vectors {∈1,∈2,…∈ k}, which is the decision variable.

[0101] (3) The optimization goal of fuel cell performance index screening is to find a set of screening criteria so that the performance index after screening is as stable as possible (i.e., the volatility is small), and the data removed by screening is as small as possible. Based on the above goals, the mean square error between the original data of the performance index and its moving average can be used to represent the volatility of the performance index, and the logarithm of the ratio of the amount of data after screening to the amount of data before screening can be used to represent the soft constraint (or penalty) on the amount of screened data, so that the objective function (fitness function) is defined as follows:

[0102]

[0103] Among them, y obs is the observed value of the performance indicator, y avg is the moving average of the performance index, MSE(x,y) represents the mean square error between x and y, N0 is the amount of data before screening, N is the amount of data after screening, and α>0 and α≠1 are satisfied.

[0104] (4) In summary, the observed value y of the performance indicator obs , moving average y avgand the amount of data after screening N are both consistent with the data screening standard vector {∈1,∈2,…∈ k} related. Therefore, the optimization problem for data screening can be defined as follows:

[0105]

[0106] The global optimal algorithm in this scheme is a probability-based or population-based heuristic algorithm, including but not limited to simulated annealing algorithm, genetic algorithm, particle swarm algorithm, ant colony algorithm, etc.

[0107] where ∈ i ∈[0,1], select the upper bound of data screening according to the following rules:

[0108]

[0109] The lower limit of data screening is selected according to the following rules:

[0110]

[0111] That is, the result of data screening is to satisfy x i ∈[x lower ,x upper ] data. For example, for the screening standard 0.9, the upper limit of data screening is 0.05, and the lower limit of data screening is 0.95. Figure 4 shown.

[0112] This embodiment uses a genetic algorithm to select the screening standard vector {∈1,∈2,…∈ k} to optimize, such as Figure 5 As shown:

[0113] (1) Encoding. Set the precision to 0.001. Encode the decimal real number into binary, that is, a 0-1 bit encoding vector with a vector length of log2 1000, which is rounded to 9. In this way, the screening criterion 0.9 will be encoded as (1,1,1,0,0,0,0,1,0,0).

[0114] (2) Generate an initial population. Set the population size to 100 and screen a total of 9 working variables. Generate 9 random numbers with uniform probability in the range (0,1) to form a screening standard vector, such as (0.590, 0.172, 0.602, 0.119, 0.723, 0.861, 0.363, 0.924, 0.801); then, encode each screening standard and combine all the codes together. For example, the encoding result of the above screening standard vector is (1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1 ,0,1,1,0,0,0,1,0,0,1,1,0,1,0,0,0,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,1,0,1,1,1,0,1,0,1,1,1,0,1,1,0,0,1,1,0,1,0); Repeat the above steps 100 times and the population is initialized.

[0115] (3) Selection. The “tournament selection” algorithm is used: two individuals are selected from the population with equal probability, and the individual with higher fitness is selected as the father of the next generation; the mother of the next generation is selected from the population in the same way. For example, for individual A (0.774, 0.683, 0.683, 0.684, 0.684, 0.714, 0.690, 0.902, 0.903) and individual B (0.695, 0.685, 0.686, 0.710, 0.726, 0.766, 0.700, 0.862, 0.988), the fitness is calculated to be 0.005222 and 0.004953 respectively, so individual A can be selected as the father / mother of the next generation.

[0116] (4) Crossover. With a probability of 0.8, take the father and mother individuals in step 3 as templates, for each 0-1 bit code, exchange the site with a probability of 0.5; after traversing each 0-1 bit code, a new individual is formed. For individual A(1,0,0,1,1,0,1,1,0,1,0,0,0,1,0,0,1,0,1,1,0,0,0,1,1,1,1,1,1,0,0,0,1,0,1,1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,0,0,0,1,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1) and individual B( 0,1,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,0,1,1,1,1,0,1,1,0,1,1,0,1,1,1,1,1,0,0,1,1,0,1,1,1,1,1,0,0,1,1,0,1,1,1,0,0,1,0,0,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0), if the two bodies are at the first site By performing crossover on the above, we can get the individual C(0,0,0,1,1,0,1,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,1,0,0,0,1,1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0 ,0,1) and individual B(1,1,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,0,1,1,1,1,0,1,1,0,1,1,1,1,1,0,0,1,1,0,1,0,1,1,1,1,1,0,0,1,1,0,1,1,1,0,0,1,0,0,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0).

[0117] (5) Mutation. Each 0-1 bit code in an individual undergoes a mutation of swapping between 0 and 1 with a probability of 0.1. For individual A(1,0,1,1,0,0,1,1,1,1,1,0,0,0,1,1,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,1,1,0,1,1,1,0,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,0,0,0,0,1,1,0,0,0,0,1,1,0), if the first bit The individual B is obtained by mutating the individual B(0,0,1,1,0,0,1,1,1,1,1,0,0,0,1,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,1,1,0,1,1,1,0,1,1,1,1,0,1,0,1,0,1,1,1,1,1,0,0,0,0,0,1,1,0).

[0118] (6) Traverse the individuals in the population. According to steps 3-5, the number of traversals is 50, and 2 sub-individuals are generated each time, and a population size of 100 is obtained.

[0119] (7) Termination condition judgment: If the number of iterations is less than or equal to the iteration threshold, the next generation population selection, crossover and mutation operations are restarted from step 3 based on the population after step 5; if the number of iterations is greater than the iteration threshold, the iteration is stopped and the 0-1 bit encoding vector with the highest fitness is output.

[0120] (8) Decoding. This step is the reverse operation of step (1). The binary real number code is restored to decimal, and the decoding output is the optimal individual (optimal screening standard vector) and its fitness.

[0121] The filtering processing module is used to filter a variety of performance indicators.

[0122] The operating voltage is filtered using a moving average filter with a window of 10.

[0123] The evaluation execution module is used to perform fuel cell health evaluation.

[0124] According to the specific values ​​of the screening performance indicators, the health status of the fuel cell is evaluated. Considering a longer time span, the working voltage of the fuel cell will continue to decrease under constant working current and corresponding constant working conditions. Comparing the working voltage of the fuel cell at the time of interest with the working voltage at the initial time, it can be seen that after 3500 endurance cycles, the working voltage attenuation rate of the fuel cell at 30A is 8.5%.

[0125] It can be seen from the above technical solution that this embodiment provides a health status assessment device, which is applied to electronic equipment and is used to assess the health status of a fuel cell, specifically collecting a variety of operating parameters of the fuel cell to be assessed; standardizing the various operating parameters respectively to obtain a variety of standardized operating parameters corresponding to the operating parameters; optimizing the screening criteria of the various standardized operating parameters using a global optimization algorithm to obtain a variety of performance indicators; filtering the various performance indicators; calculating the various performance indicators after filtering to obtain the health status parameters of the fuel cell to be assessed. This application improves the universality of the health assessment of the fuel cell by adaptively adjusting the screening threshold of the data, and then can achieve more accurate and real-time fuel cell health management, thereby improving the system life.

[0126] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0127] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0128] Embodiment 3

[0129] Figure 7 A block diagram of an electronic device according to an embodiment of the present application.

[0130] refer to Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0131] like Figure 7As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 606 into a random access memory (RAM) 603. In RAM 603, various programs and data required for the operation of the electronic device are also stored. The processing device 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0132] Typically, the following devices may be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 606 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 609. The communication devices 609 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 7 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0133] Embodiment 4

[0134] This embodiment provides a computer-readable storage medium that carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method for evaluating the health status of a fuel cell in the first embodiment.

[0135] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0136] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0137] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0138] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0139] The technical solution provided by the present invention is introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A health status evaluation method, applied to electronic equipment, for evaluating the health status of a fuel cell, characterized in that: The evaluation method comprises the steps of: Collect various operating parameters of the fuel cell to be evaluated; Standardizing the plurality of operating parameters respectively to obtain a plurality of standardized operating parameters respectively corresponding to the operating parameters; Using a global optimization algorithm to optimize the screening criteria of the various standardized operating parameters to obtain various performance indicators; Performing filtering processing on the multiple performance indicators; Calculating the plurality of performance indicators after filtering to obtain health status parameters of the fuel cell to be evaluated; The method of using a global optimization algorithm to perform screening standard optimization on a plurality of the standardized operating parameters to obtain a plurality of performance indicators comprises the following steps: Based on the global optimization algorithm, a decision variable corresponding to each of the standardized operating parameters is selected, and each of the standardized operating parameters is screened based on the decision variable to obtain the performance index corresponding to each of the standardized operating parameters; the specific process is as follows: When the fuel cell operating parameters After standardization, according to the data screening criteria ∈ j Screening, where ∈ j ∈[0,1]; where different operating parameters Corresponding to different data screening criteria∈ j , for a set of operating parameters There is a set of screening criteria vectors {∈1,∈2,…∈ k }, is the decision variable; The volatility of the performance indicator is represented by the mean square error between the original data of the performance indicator and its moving average, and the soft constraint on the amount of filtered data is represented by the logarithm of the ratio of the amount of data after filtering to the amount of data before filtering, so that the objective function is defined as follows: Among them, y obs is the observed value of the performance indicator, y avg is the moving average of the performance index, MSE(x,y) represents the mean square error between x and y, N0 is the amount of data before screening, N is the amount of data after screening, and α>0 and α≠1 are satisfied; Observed value y of performance indicator obs , moving average y avg and the amount of data N after screening are consistent with the data screening standard vector {∈1,∈2,…∈ k }, the optimization problem for data screening is defined as follows: s.t.∈ i ∈[0,1],i=1,2,……k where ∈ i ∈[0,1], select the upper bound of data screening according to the following rules: The lower limit of data screening is selected according to the following rules: That is, the result of data screening is to satisfy x i ∈[x lower ,x upper ] data.

2. The evaluation method according to claim 1, characterized in that: The multiple operating parameters include operating current, load change rate, hydrogen flow, air flow, coolant flow, hydrogen temperature, air temperature, coolant temperature, hydrogen pressure, air pressure, coolant pressure and some or all of their derived variables.

3. The evaluation method according to claim 1, characterized in that: The global optimization algorithm is a probability-based or population-based heuristic algorithm.

4. A health status evaluation device, applied to electronic equipment, for evaluating the health status of a fuel cell, characterized in that: The evaluation device comprises: A parameter acquisition module, configured to acquire a plurality of operating parameters of the fuel cell to be evaluated; A standardization processing module is configured to perform standardization processing on the plurality of operating parameters respectively to obtain a plurality of standardized operating parameters respectively corresponding to the operating parameters; A screening processing module is configured to optimize the screening criteria of the plurality of standardized operating parameters using a global optimization algorithm to obtain a plurality of performance indicators; A filtering processing module, configured to perform filtering processing on the multiple performance indicators; An evaluation execution module is configured to calculate the plurality of performance indicators after filtering to obtain a health status parameter of the fuel cell to be evaluated; The method of using a global optimization algorithm to perform screening standard optimization on a plurality of the standardized operating parameters to obtain a plurality of performance indicators comprises the following steps: Based on the global optimization algorithm, a decision variable corresponding to each of the standardized operating parameters is selected, and each of the standardized operating parameters is screened based on the decision variable to obtain the performance index corresponding to each of the standardized operating parameters; the specific process is as follows: When the fuel cell operating parameters After standardization, according to the data screening criteria ε j Screening, where ∈ j ∈[0,1]; where different operating parameters Corresponding to different data screening criteriaε j , for a set of operating parameters There is a set of screening criteria vectors {ε1,ε2,…ε k }, is the decision variable; The volatility of the performance indicator is represented by the mean square error between the original data of the performance indicator and its moving average, and the soft constraint on the amount of filtered data is represented by the logarithm of the ratio of the amount of data after filtering to the amount of data before filtering, so that the objective function is defined as follows: Among them, y obs is the observed value of the performance indicator, y avg is the moving average of the performance index, MSE(x,y) represents the mean square error between x and y, N0 is the amount of data before screening, N is the amount of data after screening, and α>0 and α≠1 are satisfied; Observed value y of performance indicator obs , moving average y avg and the amount of data N after screening are consistent with the data screening standard vector {∈1,∈2,…∈ k }, the optimization problem for data screening is defined as follows: s.t.∈ i ∈[0,1],i=1,2,……k where ∈ i ∈[0,1], select the upper bound of data screening according to the following rules: The lower limit of data screening is selected according to the following rules: That is, the result of data screening is to satisfy x i ∈[x lower ,x upper ] data.

5. The evaluation device according to claim 4, characterized in that The multiple operating parameters include operating current, load change rate, hydrogen flow, air flow, coolant flow, hydrogen temperature, air temperature, coolant temperature, hydrogen pressure, air pressure, coolant pressure and some or all of their derived variables.

6. The evaluation device according to claim 4, characterized in that The global optimization algorithm is a probability-based or population-based heuristic algorithm.

7. An electronic device, characterized in that: The method comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instruction so that the electronic device can implement the health status assessment method as described in any one of claims 1 to 3.

8. A storage medium, applied to an electronic device, characterized in that: The storage medium carries one or more computer programs, and when the one or more computer programs are executed by the electronic device, the electronic device can implement the health status assessment method as described in any one of claims 1 to 3.

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