A fuel cell life prediction method based on working condition decomposition and automatic testing

Abstract

This invention discloses a fuel cell life prediction method based on operating condition decomposition and automated testing, belonging to the technical field of new energy power generation. Specifically, the fuel cell stack test conditions are divided into idling, start-stop, rated, and variable load conditions. Characteristic parameters for each test condition are extracted, and combined with custom test condition parameters for the fuel cell stack, test condition scripts are generated. These scripts are then imported into an intelligent fuel cell testing system to automatically complete the testing process for the corresponding test conditions, obtaining the corresponding fuel cell stack test data. Data segments with special data flags are extracted, and the predicted life of the fuel cell stack is calculated based on these data segments, corresponding characteristic parameters, and the initial voltage of the fuel cell stack. This invention can be automated through programming, minimizing the complexity of the testing process and the workload of experimental personnel, improving the reliability of test results, and conforming to the testing methods required by the national standard GB / T 38914-2020. It can be directly applied to practical engineering applications.

Description

Technical Field

[0001] This invention belongs to the field of new energy power generation technology, specifically relating to a fuel cell life prediction method based on operating condition decomposition and automatic testing. Background Technology

[0002] As a novel energy form, the lifespan of fuel cells has always been a focus of attention. With significant improvements in both cost and performance, the ability to achieve long-term, stable, and safe operation has become a key issue hindering their further development. Therefore, evaluating the lifespan of fuel cells is essential.

[0003] Life test design approaches can be broadly categorized into two types. One is the traditional method of conducting long-term cyclic operation tests on fuel cells under actual operating conditions, with the test results directly indicating battery life. The other is accelerated testing, which designs specific accelerated life test conditions to extrapolate the expected lifespan of the fuel cell in real-world operation using experimental data from shorter periods of fixed-condition testing. The first method requires significant resources and time; the testing duration of around 10,000 hours often lags behind the actual development speed of fuel cells. Therefore, most life test work focuses on researching how to quickly and efficiently predict lifespan.

[0004] In 2020, my country introduced the national standard GB / T 38914-2020 "Test and Evaluation Method for Service Life of Proton Exchange Membrane Fuel Cell Stacks for Vehicles", which specifies the experimental methods for accelerated life testing and the calculation formula for estimating service life based on the experimental results, providing a good standard for service life testing in the industry.

[0005] While accelerated testing significantly shortens the lifespan testing time, it still requires hundreds of hours of experimentation. Furthermore, the procedures for these hundreds of hours are more complex than traditional lifespan testing, and selecting valid experimental data consumes a significant amount of the researchers' time and effort. Errors in experimental results caused by human error during the process are amplified step by step through data processing, ultimately affecting the accuracy and effectiveness of the final lifespan prediction. Summary of the Invention

[0006] To address the technical problems existing in the background art, this invention proposes a fuel cell lifetime prediction method based on operating condition decomposition and automatic testing. By reducing human intervention in the testing process, standardizing the testing procedure, reducing the complexity and uncertainty of the lifetime prediction experiment, and improving the stability and reliability of lifetime prediction.

[0007] The specific technical solution of this invention is as follows:

[0008] A method for predicting the lifespan of a fuel cell based on operating condition decomposition and automated testing, characterized by the following steps:

[0009] Step 1: Divide the test conditions of the fuel cell stack into idling condition, start-stop condition, rated condition and variable load condition, and extract the characteristic parameters of each test condition;

[0010] Step 2: Based on the characteristic parameters of each test condition and the custom test condition parameters of the fuel cell stack, generate scripts for each test condition, including command sequence, instruction flags, specific instructions, test condition parameters corresponding to the instructions, specific test condition parameter values, and special data flag bits;

[0011] Step 3: Import the scripts for each test condition into the intelligent fuel cell testing system so that it can automatically complete the test process for the corresponding test condition and obtain the corresponding fuel cell stack test data;

[0012] Step 4: Extract data segments with special data flags from the fuel cell stack test data. Based on the data segments, corresponding characteristic parameters, and the initial stack voltage V0 (in volts), calculate the voltage decay rate V'1 (in volts per cycle) for each start-up and shutdown, the fuel cell voltage change rate V'2 (in volts per cycle) under varying load conditions, the fuel cell voltage change rate U'1 (in volts per hour) under idling conditions, and the fuel cell voltage change rate U'2 (in volts per hour) under rated conditions. Then, calculate the predicted lifetime t of the fuel cell stack. Lf .

[0013] Furthermore, according to the national standard GB / T 38914-2020 "Test and Evaluation Method for Service Life of Proton Exchange Membrane Fuel Cell Stacks for Vehicles", the test conditions of fuel cell stacks are divided into idling conditions, start-stop conditions, rated conditions and variable load conditions.

[0014] Furthermore, the characteristic parameters include idling time t1 per hour (in minutes), number of start-stop cycles n1 per hour (in times per hour), rated operating time t2 per hour (in minutes), and number of loading cycles n2 per hour (in times per hour).

[0015] Furthermore, the specific process of generating scripts for each test condition in step 2 is as follows: based on the characteristic parameters of each test condition, a parameterless script containing corresponding special data flags is set, and the scripts for each test condition are directly generated by inputting the custom test condition parameters of the fuel cell stack into the parameterless script.

[0016] Furthermore, the specific instructions include starting the experiment, setting, judging, jumping, waiting, looping, and stopping the experiment.

[0017] Furthermore, the fuel cell stack test data includes the total voltage, current, and power of the fuel cell stack during the test.

[0018] Furthermore, the predicted lifetime t of the fuel cell stack Lf The scope is:

[0019]

[0020] Where A represents the fuel cell stack performance degradation rate, expressed in volts per hour (V / h).

[0021]

[0022] The beneficial effects of this invention are as follows:

[0023] 1. This invention proposes a fuel cell life prediction method based on operating condition decomposition and automatic testing. It can be completed automatically through programming. The operations that require human intervention during the testing process only include determining characteristic parameters based on the test object (fuel cell stack), importing the test operating condition script into the test system, and automatically selecting test data for life calculation based on special data flag bits. This minimizes the complexity of the testing process and the amount of operation required by the experimenters. The above operations will not affect the stability of the test object during the actual testing process and reduce the possibility of deviations in test results caused by human operation.

[0024] 2. The method proposed in this invention conforms to the test method required by the national standard GB / T 38914-2020 and can be directly used in practical engineering applications to solve practical problems;

[0025] 3. This invention ensures the stability and consistency of the testing process during actual testing, and improves the reliability of the test results. Attached Figure Description

[0026] Figure 1 This is a flowchart of the fuel cell lifetime prediction method based on operating condition decomposition and automatic testing proposed in Embodiment 1 of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in conjunction with the following specific embodiments and with reference to the accompanying drawings.

[0028] The following non-limiting embodiments are intended to enable those skilled in the art to more fully understand the present invention, but do not limit the invention in any way.

[0029] Example 1

[0030] This embodiment proposes a fuel cell lifetime prediction method based on operating condition decomposition and automated testing, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0031] Step 1: According to the national standard GB / T 38914-2020 "Test and Evaluation Method for Service Life of Proton Exchange Membrane Fuel Cell Stacks for Vehicles", the test conditions of fuel cell stacks are divided into idling conditions, start-stop conditions, rated conditions and variable load conditions. The characteristic parameters of each test condition (i.e., idling characteristic parameters, start-stop characteristic parameters, rated characteristic parameters and variable load characteristic parameters) are extracted, including the idling time t1 per hour, the number of start-stop cycles n1 per hour, the rated condition running time t2 per hour and the number of load cycles n2 per hour.

[0032] Step 2: Based on the characteristic parameters of each test condition, set up parameterless scripts containing corresponding special data flags (i.e., parameterless scripts for idling, start-stop, rated, and variable load conditions). By inputting the custom test condition parameters of the fuel cell stack into the parameterless scripts, the scripts for each test condition (i.e., idling, start-stop, rated, and variable load conditions) are directly generated. The test condition parameters include load setpoint, load gradient, anode inlet flow rate, anode inlet excess ratio, anode inlet pressure, anode inlet temperature, anode inlet humidity, cathode inlet flow rate, cathode inlet excess ratio, cathode inlet pressure, cathode inlet temperature, cathode inlet humidity, circulating water inlet temperature, circulating water flow rate, and waiting time. The scripts include command sequence, instruction flags, specific instructions, corresponding test condition parameters, specific test condition parameter values, operation stage prompts, and special data flags. Specific instructions include start test, set, judge, jump, wait, loop, and stop test.

[0033] Table 1 shows excerpts of the scripts for each test condition;

[0034] Table 1. Partial scripts for each test condition.

[0035]

[0036]

[0037] Step 3: Import the scripts for each test condition into the intelligent fuel cell testing system, so that it can automatically complete the test process for the corresponding test condition according to the script mode and obtain the corresponding fuel cell stack test data (i.e., fuel cell stack test data under idle condition, fuel cell stack test data under start-stop condition, fuel cell stack test data under rated condition, and fuel cell stack test data under variable load condition), which mainly includes the total voltage, current and power of the fuel cell stack.

[0038] Step 4: Based on the special data flags in the script, extract the data segments with special data flags from the fuel cell stack test data. Based on the data segments, corresponding characteristic parameters, and the initial voltage V0 of the fuel cell stack, calculate the voltage decay rate V'1 for each start-up and shutdown, the fuel cell voltage change rate V'2 under varying load conditions, the fuel cell voltage change rate U'1 under idling conditions, and the fuel cell voltage change rate U'2 under rated conditions. Then, calculate the predicted lifespan t of the fuel cell stack using the fuel cell decay rate evaluation module. Lf :

[0039]

[0040] Where A is the fuel cell stack performance degradation rate.

[0041]

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for predicting the lifespan of a fuel cell based on operating condition decomposition and automated testing, characterized in that, Includes the following steps: Step 1: Divide the test conditions of the fuel cell stack into idling condition, start-stop condition, rated condition, and variable load condition, and extract the characteristic parameters of each test condition, including the idling time per hour. Number of starts and stops per hour Hourly rated operating time and number of loads per hour ; Step 2: Generate scripts for each test condition based on the characteristic parameters and the custom test condition parameters of the fuel cell stack, including command sequence, instruction flags, specific instructions, test condition parameters corresponding to the instructions, specific test condition parameter values, and special data flag bits; Step 3: Import the scripts for each test condition into the intelligent fuel cell testing system so that it can automatically complete the test process for the corresponding test condition and obtain the corresponding fuel cell stack test data; Step 4: Extract data segments with special data flags from the fuel cell stack test data, and then analyze the data segments, corresponding characteristic parameters, and initial voltage of the fuel cell stack. The voltage decay rate for each start-up and shutdown was calculated. Variable load conditions causing fuel cell voltage variation rate Idle operation causes fuel cell voltage change rate Rate of change of fuel cell voltage under rated operating conditions Then, the predicted lifetime of the fuel cell stack can be calculated. Its scope is: ； in, The performance degradation rate of the fuel cell stack is expressed in volts per hour. 。 2. The fuel cell lifetime prediction method based on operating condition decomposition and automatic testing according to claim 1, characterized in that, According to the national standard GB / T 38914-2020 "Test and Evaluation Method for Service Life of Proton Exchange Membrane Fuel Cell Stacks for Vehicles", the test conditions for fuel cell stacks are divided into idling conditions, start-stop conditions, rated conditions, and variable load conditions.

3. The fuel cell lifetime prediction method based on operating condition decomposition and automatic testing according to claim 1, characterized in that, The specific process of generating scripts for each test condition in step 2 is as follows: Based on the characteristic parameters of each test condition, set up a parameterless script containing corresponding special data flags, and directly generate scripts for each test condition by inputting custom test condition parameters of the fuel cell stack into the parameterless script.

4. The fuel cell lifetime prediction method based on operating condition decomposition and automatic testing according to claim 1, characterized in that, The specific instructions include start experiment, set, judge, jump, wait, loop, and stop experiment.

5. The fuel cell lifetime prediction method based on operating condition decomposition and automatic testing according to claim 1, characterized in that, The fuel cell stack test data includes the total voltage, current, and power of the fuel cell stack during the test.

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