A control strategy optimization method for balancing the economy and emission of a hydrogen internal combustion engine
By optimizing injection control parameters and ignition parameters using response surface methodology, the problem of high experimental workload in balancing effective thermal efficiency and nitrogen oxide emissions in hydrogen internal combustion engines was solved, achieving a balance between the economy and emissions of hydrogen internal combustion engines.
Patent Information
- Application Number
- CN202411226985.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-03
- Publication Date
- 2025-11-18
- Estimated Expiration
- 2044-09-03
AI Technical Summary
Existing technologies require extensive experimental work and lack effective optimization methods when balancing the contradictory relationship between the effective thermal efficiency and nitrogen oxide emissions of hydrogen internal combustion engines.
By combining response surface methodology with ramp tests and analysis of variance, injection control parameters and ignition parameters were optimized. A new response surface optimization equation was generated by establishing a functional relationship between the effective thermal efficiency of the hydrogen internal combustion engine and nitrogen oxide emissions, thereby reducing the number of tests.
This approach achieves a balance between the economy and emissions of hydrogen internal combustion engines while reducing the number of tests, providing a theoretical basis for performance optimization.
Smart Images
Figure CN119084165B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of hydrogen internal combustion engine technology, and in particular relates to a control strategy optimization method that takes into account both the economy and emissions of hydrogen internal combustion engines. Background Technology
[0002] Currently, hydrogen energy is one of the green and renewable energy sources for achieving "dual carbon" goals, industrial upgrading, and energy transition. Furthermore, as a zero-carbon fuel, hydrogen can replace traditional fossil fuels, reducing carbon pollutants after combustion. Therefore, hydrogen internal combustion engines have a promising future as a replacement for traditional fossil fuel internal combustion engines.
[0003] Compared to port-injected hydrogen internal combustion engines, direct-injection hydrogen internal combustion engines do not occupy intake manifold volume, resulting in higher volumetric efficiency. Furthermore, direct-injection engines, with their high injection pressure and short injection pulse width, can implement flexible injection strategies, avoiding abnormal combustion and improving brackish thermal efficiency (BTE). However, direct injection increases the maximum combustion pressure and temperature, leading to increased nitrogen oxide (NOx) emissions. Therefore, there is a trade-off between BTE and NOx. Employing a reasonable multi-injection strategy can balance this trade-off. Multi-injection strategies include parameters such as the initial injection phase (SOI), the second injection phase (SEOI), the split injection mass proportion (SIMP), and the ignition timing (IG). However, calibrating each control variable requires a significant amount of experimental work, and there is a lack of effective optimization methods to reduce this workload. This study combines ramp tests, response surface methodology, and analysis of variance to provide a control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines, while reducing the number of tests. Summary of the Invention
[0004] The purpose of this invention is to provide a control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines, and to solve the problem of the large amount of experimental work required in the existing technology when balancing the relationship between effective thermal efficiency and nitrogen oxides.
[0005] To achieve the above objectives, this invention provides a control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines, comprising the following steps:
[0006] Step 1: Based on the matching requirements of the hydrogen internal combustion engine and the engine speed, determine the target value of the effective thermal efficiency (BTE) and nitrogen oxide (NOx) concentration. xEmission limits, and simultaneously set injection parameters for the hydrogen internal combustion engine injection system;
[0007] Step 2: Conduct single-factor ramp-up tests on injection control parameters and ignition parameters (IG). The injection control parameters include the first injection start phase parameter (SOI), the second injection end phase parameter (SEOI), and the second injection mass proportion parameter (SIMP).
[0008] Step 3: After the machine has stabilized, read the BTE and NO values from the dynamometer. x Concentration values were used to obtain the optimization range for the single-factor ramp-up experiment;
[0009] Step 4: Based on the optimization interval obtained in Step 3, design the experimental conditions using the central composite design method based on response surface methodology in MINITAB.
[0010] Step 5: Based on the operating conditions obtained in Step 4, conduct research on the effective thermal efficiency and NO of the hydrogen internal combustion engine. x Emissions testing was conducted to obtain BTE and NO levels under different injection control and ignition parameters. x data;
[0011] Step 6: Input the BTE and NOx data obtained from the experiment into MINITAB software to establish a fully second-order response surface optimization equation;
[0012] Step 7: Determine the difference between BTE and NO through analysis of variance. x The P-values of each term in the response surface optimization equation are used to verify the fitting accuracy.
[0013] Step 8: Based on the fitting accuracy, obtain the new response surface optimization equation, and set BTE and NO in the response optimizer. x The weights are used to obtain new injection control parameters and ignition parameters by optimizing the new response surface equation after fitting the accuracy.
[0014] Step 9: Based on the new injection control parameters and ignition parameters obtained from the new response surface optimization equation after fitting accuracy, conduct verification experiments. If the obtained experimental values are within the 95% confidence interval of the new response surface optimization equation, the new response surface optimization equation is considered valid, and proceed to the next step; otherwise, return to step 7.
[0015] Step 10: Obtain calibration parameters that balance the economy and emissions of hydrogen internal combustion engines based on the effective response surface optimization equation obtained in Step 9.
[0016] Preferably, the injection parameters of the hydrogen internal combustion engine injection system in step 1 include injection pressure and injection pulse width. The injection pulse width is obtained by back-calculation based on the engine's target power, and the injection pressure depends on the engine design and the upper limit of the pressure of the on-board hydrogen nozzle. The range of the injection pressure is 6-12 MPa.
[0017] The preferred formula for calculating the effective thermal efficiency (BTE) is as follows:
[0018]
[0019] In the formula, P is the measured current output power of the hydrogen internal combustion engine, in kW; m H The measured hydrogen mass flow rate is expressed in kg / h.
[0020] Preferably, a single-factor ramp-up test is conducted on the injection control parameters and ignition parameters using the controlled variable method.
[0021] Preferably, the optimization range for the single-factor ramp test in step 3 is as follows: with the inflection point as the center value, the optimization range is ±10°CA before and after the center value of the initial phase parameter of the first injection; the optimization range is ±10°CA before and after the center value of the final phase parameter of the second injection; the optimization range is ±15% before and after the center value of the second injection ratio parameter; and the optimization range is ±2°CA before and after the center value of the ignition parameter.
[0022] Preferably, the expression for the fully second-order response surface optimization equation established in step 6 is as follows:
[0023]
[0024] In the formula, y represents BTE or NO. x The response variables are defined as follows: b0 represents a constant, b1, b2, ..., b k These are coefficients, x1, x2, ..., x k These are the corresponding injection control parameters and ignition parameters, and ∈ represents the error term.
[0025] Preferably, in step 7, BTE and NO are determined through analysis of variance. x The specific content of the P-value test for fitting accuracy is as follows: if the P-value < 0.05, the item is considered to be a good fit for BTE and NO. x For variables with significant impact, retain the terms with a p-value < 0.05 and the individual terms corresponding to the composite terms with a p-value < 0.05; otherwise, delete the corresponding terms.
[0026] Preferably, the expression for the new response surface optimization equation obtained in step 8 based on the fitting accuracy is as follows:
[0027]
[0028] In the formula, y 新 Indicates BTE or NO x The response variables of the new response surface optimization equation are given by b0, which represents a constant, and b1, b2, ..., b... m These are new coefficients, x1, x2, ..., x m These are the terms corresponding to the new injection control parameters and ignition parameters, ∈ is the error term, and m <k。
[0029] Preferably, the fitting accuracy is adjusted. The value of determines the accuracy of the new response surface optimization equation, where the adjusted fitting accuracy is denoted by . The calculation expression is as follows:
[0030] R 2 =1-SS E / SS T
[0031]
[0032]
[0033]
[0034] In the formula, R 2 To improve fitting accuracy, To adjust the fitting accuracy, SS E It is the sum of squared residuals, reflecting the degree of non-uniformity of the observed response due to random errors, y i It is the i-th response observation. SS is the model's predicted response to the i-th response observation, where n is the number of observations. T It is the total sum of squares, reflecting the degree of non-uniformity of the observed responses. It is the average of the observed response values from n trials, where p is the number of terms in the response surface model; where, if If so, the hydrogen internal combustion engine test needs to be repeated; otherwise, the new response surface optimization equation is considered to be accurate.
[0035] Therefore, this invention adopts the above-mentioned control strategy optimization method that takes into account both the economy and emissions of hydrogen internal combustion engines. Based on the response surface model optimization design method, it can establish the functional relationship between the effective thermal efficiency and nitrogen oxide emissions of hydrogen internal combustion engines and the injection control parameters and ignition parameters. Then, the fitting accuracy is improved to generate a new response surface optimization equation. The number of experiments can be reduced through the new response surface optimization equation, thereby providing a theoretical basis for the performance optimization of hydrogen internal combustion engines.
[0036] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0037] Figure 1 This is an overall flowchart of a control strategy optimization method that takes into account both the economy and emissions of hydrogen internal combustion engines according to the present invention.
[0038] Figure 2 The graphs showing the influence of injection control parameters and ignition parameters on effective thermal efficiency in embodiments of the present invention are as follows: (a) is the graph showing the influence of SOI on effective thermal efficiency, (b) is the graph showing the influence of SEOI on effective thermal efficiency, (c) is the graph showing the influence of SIMP on effective thermal efficiency, and (d) is the graph showing the influence of IG on effective thermal efficiency.
[0039] Figure 3 The diagram shows the calibration parameters obtained in this embodiment of the invention, which take into account both the economy and emissions of the direct-injection hydrogen internal combustion engine. Detailed Implementation
[0040] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0041] Please see Figure 1-3 A method for optimizing control strategies that balances the economy and emissions of hydrogen internal combustion engines includes the following steps:
[0042] Step 1: Based on the matching requirements of the hydrogen internal combustion engine and the engine speed, determine the target value of the effective thermal efficiency (BTE) and nitrogen oxide (NOx) concentration. x Emission limits are set, and injection parameters for the hydrogen internal combustion engine injection system are also defined. These parameters include injection pressure and injection pulse width. The injection pulse width is calculated inversely based on the engine's target power. The injection pressure depends on the engine design and the upper pressure limit of the onboard hydrogen nozzles, with a range of 6-12 MPa. The effective thermal efficiency (BTE) is calculated using the following formula:
[0043]
[0044] In the formula, P is the measured current output power of the hydrogen internal combustion engine, in kW; m H The measured hydrogen mass flow rate is expressed in kg / h.
[0045] Step 2: Conduct single-factor ramp-up tests on injection control parameters and ignition parameters (IG). The injection control parameters include the first injection start phase parameter (SOI), the second injection end phase parameter (SEOI), and the second injection mass proportion parameter (SIMP).
[0046] Step 3: After the machine has stabilized, read the BTE and NO values from the dynamometer. x The concentration values were used to obtain the optimization range for the single-factor ramp-up test. Specifically, the optimization range for the single-factor ramp-up test is as follows: with the inflection point as the center value, the optimization range is ±10°CA before and after the center value of the initial phase parameter of the first injection; the optimization range is ±10°CA before and after the center value of the final phase parameter of the second injection; the optimization range is ±15% before and after the center value of the second injection ratio parameter; and the optimization range is ±2°CA before and after the center value of the ignition parameter.
[0047] Step 4: Based on the optimization interval obtained in Step 3, design the experimental conditions using the central composite design method based on response surface methodology in MINITAB.
[0048] Step 5: Based on the operating conditions obtained in Step 4, conduct research on the effective thermal efficiency and NO of the hydrogen internal combustion engine. x Emissions testing was conducted to obtain BTE and NO levels under different injection control and ignition parameters. x data;
[0049] Step 6: Input the BTE and NOx data obtained from the experiment into MINITAB software to establish a fully second-order response surface optimization equation; the calculation expression is as follows:
[0050]
[0051] In the formula, y represents BTE or NO. x The response variables are defined as follows: b0 represents a constant, b1, b2, ..., b k These are coefficients, x1, x2, ..., x k These are the corresponding injection control parameters and ignition parameters, and ∈ represents the error term.
[0052] Step 7: Determine the difference between BTE and NO through analysis of variance. x The P-values of each term in the response surface optimization equation are used to test the fitting accuracy; specifically, if the P-value < 0.05, the term is considered to be a good fit for BTE and NO. xFor variables with significant impact, retain the terms with a p-value < 0.05 and the individual terms corresponding to the composite terms with a p-value < 0.05; otherwise, delete the corresponding terms.
[0053] Step 8: Based on the fitting accuracy, obtain the new response surface optimization equation, and set BTE and NO in the response optimizer. x The weights are used to obtain new injection control parameters and ignition parameters through the new response surface optimization equation obtained after fitting accuracy; the expression of the new response surface optimization equation obtained after fitting accuracy is as follows:
[0054]
[0055] In the formula, y 新 Indicates BTE or NO x The response variables of the new response surface optimization equation are given by b0, which represents a constant, and b1, b2, ..., b... m These are new coefficients, x1, x2, ..., x m These are the terms corresponding to the new injection control parameters and ignition parameters, ∈ is the error term, and m <k;
[0056] In addition, by adjusting the fitting accuracy The value of determines the accuracy of the new response surface optimization equation, where the adjusted fitting accuracy is denoted by . The calculation expression is as follows:
[0057] R 2 =1-SS E / SS T
[0058]
[0059]
[0060]
[0061] In the formula, R 2 To improve fitting accuracy, To adjust the fitting accuracy, SS E It is the sum of squared residuals, reflecting the degree of non-uniformity of the observed response due to random errors, y i It is the i-th response observation. SS is the model's predicted response to the i-th response observation, where n is the number of observations. T It is the total sum of squares, reflecting the degree of non-uniformity of the observed responses. It is the average of the observed response values from n trials, where p is the number of terms in the response surface model; where, if If so, the hydrogen internal combustion engine test needs to be repeated; otherwise, the new response surface optimization equation is considered to be accurate.
[0062] Step 9: Based on the new injection control parameters and ignition parameters obtained from the new response surface optimization equation after fitting accuracy, conduct verification experiments. If the obtained experimental values are within the 95% confidence interval of the new response surface optimization equation, the new response surface optimization equation is considered valid, and proceed to the next step; otherwise, return to step 7.
[0063] Step 10: Obtain calibration parameters that balance the economy and emissions of hydrogen internal combustion engines based on the effective response surface optimization equation obtained in Step 9.
[0064] Example
[0065] In this embodiment, a four-cylinder, four-stroke, direct-injection hydrogen internal combustion engine was tested at 2500 r / min, and the target value of effective thermal efficiency was selected as 43% and nitrogen oxide emissions were not more than 700 ppm.
[0066] Single-factor ramp-up tests were conducted on injection control parameters and ignition parameters using the controlled variable method. Data were collected after operation stabilized, and the results are as follows: Figure 2 As shown in the figure, the inflection points for each factor are SOI = -100°CA-ATDC, SEOI = -25°CA-ATDC, SIMP = 35%, and IG = -10°CA-ATDC. Based on the center point obtained from the ramp-up test and the selected optimization interval, the test conditions were designed using the central composite design method based on response surface methodology. Direct injection hydrogen internal combustion engine tests were conducted using 28 test conditions based on response surface methodology to obtain BTE and NOx data under different SOI / SEOI / SIMP / IG conditions. Based on the obtained test data, a complete second-order response surface optimization equation was established using MINITAB software. Analysis of variance was used to determine the BTE and NOx levels. x The p-value is used to test the fitting accuracy; if the p-value < 0.05, the item is considered to be a good fit for BTE and NO. x For variables with significant impact, retain terms with p-value < 0.05 and individual terms corresponding to composite terms with p-value < 0.05; otherwise, delete the corresponding terms. This yields a new response surface optimization equation with improved fitting accuracy. In the response optimizer, set BTE and NO... x The weights were determined, and new injection control parameters and ignition parameters were obtained through a new response surface optimization equation. The results were SOI = -80°CA-ATDC, SEOI = -36.1°CA-ATDC, SIMP = 15%, and IG = -5.3°CA-ATDC. Based on the new injection control parameters and ignition parameters obtained from the new response surface optimization equation, further tests were conducted on a direct injection hydrogen internal combustion engine, yielding a BTE of 43.03% and NO... xThe emissions were 640.25 ppm, meeting the target value. Furthermore, all experimental data points fell within the 95% confidence interval of the new response surface optimization equation, demonstrating its reliability. Thus, calibration parameters balancing the economy and emissions of the direct-injection hydrogen internal combustion engine were obtained: SOI = -80°CA-ATDC, SEOI = -36.1°CA-ATDC, SIMP = 15%, and IG = -5.3°CA-ATDC.
[0067] Therefore, this invention adopts the above-mentioned control strategy optimization method that takes into account both the economy and emissions of hydrogen internal combustion engines. Based on the response surface model optimization design method, it can establish the functional relationship between the effective thermal efficiency and nitrogen oxide emissions of hydrogen internal combustion engines and the injection control parameters and ignition parameters. Then, the fitting accuracy is improved to generate a new response surface optimization equation. The number of experiments can be reduced through the new response surface optimization equation, thereby providing a theoretical basis for the performance optimization of hydrogen internal combustion engines.
[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines, characterized in that, Includes the following steps: Step 1: Based on the matching requirements of the hydrogen internal combustion engine and the engine speed, determine the target value of the effective thermal efficiency (BTE) and nitrogen oxide (NOx) concentration. x Emission limits, and simultaneously set injection parameters for the hydrogen internal combustion engine injection system; Step 2: Conduct a single-factor ramp-up test on the injection control parameters and ignition parameters. The injection control parameters include the initial phase parameter of the first injection, the final phase parameter of the second injection, and the proportion parameter of the second injection. Step 3: After the machine has stabilized, read the BTE and NO values from the dynamometer. x Concentration values were used to obtain the optimization range for the single-factor ramp-up experiment; Step 4: Based on the optimization interval obtained in Step 3, design the experimental conditions using the central composite design method based on response surface methodology in MINITAB. Step 5: Based on the operating conditions obtained in Step 4, conduct research on the effective thermal efficiency and NO of the hydrogen internal combustion engine. x Emissions testing was conducted to obtain BTE and NO levels under different injection control and ignition parameters. x data; Step 6: Input the BTE and NOx data obtained from the experiment into MINITAB software to establish a fully second-order response surface optimization equation; Step 7: Determine the difference between BTE and NO through analysis of variance. x The P-values of each term in the response surface optimization equation are used to verify the fitting accuracy. Step 8: Based on the fitting accuracy, obtain the new response surface optimization equation, and set BTE and NO in the response optimizer. x The weights are used to obtain new injection control parameters and ignition parameters by optimizing the new response surface equation after fitting the accuracy. Step 9: Based on the new injection control parameters and ignition parameters obtained from the new response surface optimization equation after fitting accuracy, conduct verification experiments. If the obtained experimental values are within the 95% confidence interval of the new response surface optimization equation, the new response surface optimization equation is considered valid, and proceed to the next step; otherwise, return to step 7. Step 10: Obtain calibration parameters that balance the economy and emissions of hydrogen internal combustion engines based on the effective response surface optimization equation obtained in Step 9.
2. The control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines according to claim 1, characterized in that: In step 1, the injection parameters of the hydrogen internal combustion engine injection system include injection pressure and injection pulse width. The injection pulse width is obtained by back-calculation based on the engine's target power. The injection pressure depends on the engine design and the upper limit of the pressure of the on-board hydrogen nozzle. The range of injection pressure is 6-12 MPa.
3. The control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines according to claim 2, characterized in that, The formula for calculating the effective thermal efficiency (BTE) is as follows: In the formula, P is the measured current output power of the hydrogen internal combustion engine, in kW; m H The measured hydrogen mass flow rate is expressed in kg / h.
4. The control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines according to claim 3, characterized in that: Single-factor ramp-up tests were conducted on injection control parameters and ignition parameters using the controlled variable method.
5. The control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines according to claim 4, characterized in that, The optimization interval for the single-factor ramp test in step 3 is as follows: taking the inflection point as the center value, the optimization interval is ±10°CA before and after the center value of the initial phase parameter of the first injection; the optimization interval is ±10°CA before and after the center value of the final phase parameter of the second injection; and the optimization interval is ±15% before and after the center value of the proportion parameter of the second injection. The ignition parameters were selected with an optimization range of ±2°CA before and after the center value.
6. The control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines according to claim 5, characterized in that, The expression for the fully second-order response surface optimization equation established in step 6 is as follows: In the formula, y represents BTE or NO. x The response variables are defined as follows: b0 represents a constant, b1, b2, ..., b k These are coefficients, x1, x2, ..., x k These are the corresponding injection control parameters and ignition parameters, and ∈ represents the error term.
7. The control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines according to claim 6, characterized in that, In step 7, analysis of variance is used to determine BTE and NO. x The specific content of the P-value test for fitting accuracy is as follows: if the P-value < 0.05, the item is considered to be a good fit for BTE and NO. x For variables with significant impact, retain the terms with a p-value < 0.05 and the individual terms corresponding to the composite terms with a p-value < 0.05; otherwise, delete the corresponding terms.
8. The control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines according to claim 7, characterized in that, The expression for the new response surface optimization equation obtained in step 8 based on the fitting accuracy is as follows: In the formula, y 新 Indicates BTE or NO x The response variables of the new response surface optimization equation are given by b0, which represents a constant, and b1, b2, ..., b... m These are the new coefficients, x1, x2, ..., x m These are the terms corresponding to the new injection control parameters and ignition parameters, ∈ is the error term, and m <k。 9. The control strategy optimization method that balances the economy and emissions of hydrogen internal combustion engines according to claim 8, characterized in that: By adjusting the fitting accuracy The value of determines the accuracy of the new response surface optimization equation, where the adjusted fitting accuracy is denoted by . The calculation expression is as follows: R 2 =1-SS E / SS T In the formula, R 2 To improve fitting accuracy, To adjust the fitting accuracy, SS E It is the sum of squared residuals, reflecting the degree of non-uniformity of the observed response due to random errors, y i It is the i-th response observation. SS is the model's predicted response to the i-th response observation, where n is the number of observations. T It is the total sum of squares, reflecting the degree of non-uniformity of the observed responses. It is the average of the observed response values from n trials, where p is the number of terms in the response surface model; where, if If so, the hydrogen internal combustion engine test needs to be repeated; otherwise, the new response surface optimization equation is considered to be accurate.