Engine fuel injection curve optimization method and device and computer storage medium

Abstract

The invention discloses an engine fuel injection curve optimization method, which comprises the following steps of: determining basic conditions of engine optimization calculation, and taking an optimization object as an engine fuel injection curve shape; establishing a fuel nozzle curve by adopting a CAD spline curve; the method comprises the following steps: firstly, setting a roughing range through a curve control point, acquiring a corresponding engine diesel injection curve according to a parameter sample point, and then redefining a fine optimization range of an initial optimization range datum point based on a parameter corresponding to the engine diesel injection curve with the maximum engine power; and a population optimization method is adopted, the optimal individual is obtained with the purpose of maximizing the engine power corresponding to the population individual, and the optimal engine fuel injection curve is output based on the optimal individual through a CAD method. The invention further discloses a corresponding optimization device and a computer storage medium. According to the method, the jet curve shape is accurately and reasonably obtained while the optimization efficiency is improved, and the output power of the engine is improved.

Description

Technical Field

[0001] The invention relates to an engine fuel injection curve optimization method, in particular to an engine fuel injection curve optimization method, device and computer storage medium. Background Art

[0002] The fuel injection curve is a curve formed by the duration of each injection of the injector calculated by the electronic control unit based on various sensor signals under different engine working conditions. It can effectively ensure the normal operation of the engine. The appropriate fuel injection curve shape can effectively improve fuel economy and increase the output power of the engine.

[0003] The current fuel injection curve is mainly obtained through experiments or selected through the experience of the designer. The shape of the curve has a nonlinear effect on the performance of the engine, which makes it impossible to obtain the best curve through experience or experiments. Summary of the invention

[0004] In view of the above-mentioned defects of the prior art, the present invention provides an engine fuel injection curve optimization method, which expresses the fuel injection curve shape through a spline curve, and obtains the optimal curve shape by continuously optimizing the control point parameters on the spline curve, so as to improve the output power of the engine and ensure the optimization efficiency. The present invention also provides an engine fuel injection curve optimization device and a computer storage medium.

[0005] The technical solution of the present invention is as follows: A method for optimizing an engine fuel injection curve comprises the following steps: Step 1, determining the basic conditions for engine optimization calculation, the object of optimization calculation is the control point parameters of the engine fuel injection curve, and the control point parameters are used to control the shape of the engine fuel injection curve; Step 2: based on the set initial optimization range of the control point parameters of the engine fuel injection curve, sample points of the control point parameters are selected, and the corresponding engine diesel injection curve is obtained by using the CAD spline curve method, and the engine diesel injection curve with the maximum engine power is used as a detailed optimization reference point by its corresponding control point parameters; Step 3, redefining the detailed optimization range of the parameters based on the detailed optimization reference points of the control point parameters; Step 4: Based on the detailed optimization range of the control point parameters, a population optimization method is used to obtain the best individual with the goal of maximizing the engine power corresponding to the population individual, and a CAD method is used based on the best individual to output the best engine fuel injection curve, wherein the population individual is composed of the control point parameters of the engine fuel injection curve.

[0006] Furthermore, the size of the control point parameter for controlling the curve deformation represents the coordinate value of the vertical direction in the two-dimensional rectangular coordinate system, and the horizontal coordinate of the control point remains fixed during the optimization process.

[0007] Furthermore, the step 2 specifically includes: 201. Determine the initial optimization range of the control point parameters of the engine fuel injection curve; 202. Generate a plurality of groups of parameter combinations belonging to the initial optimization range to form a sample set; 203. Setting boundary conditions and initial simulation parameters; 204. Setting the initial crankshaft angle, final crankshaft angle, and engine speed during simulation; 205. Set the iteration time step parameters; 206. Output the engine power corresponding to the engine diesel injection curve of each sample in the sample set through iterative calculation of the simulation software ANSYS-FORTE; 207. Select the parameter point on the engine diesel injection curve with the maximum engine power value as the reference point for the detailed optimization of the control point parameters.

[0008] Furthermore, in step 202, an optimal Latin hypercube algorithm is used to generate a parameter combination.

[0009] Furthermore, the iterative calculation in step 206 is performed using the RNG k-ε turbulence model and the KH-RT method.

[0010] Furthermore, the step 4 specifically includes: 401. Initialize population and iteration parameters; 402. Use the CAD spline curve method to draw the fuel injection spline curve of the population individuals, and then input it into the simulation software; 403. Simulate and iterate to calculate and output the engine power corresponding to each individual in the population; 404. Grouping the population based on the engine power obtained in step 403 to form a plurality of sub-populations; 405. Update the individual positions of the subpopulation based on the inertia weight and update the inertia weight based on the number of iteration steps of the population until the number of iteration steps of the subpopulation update reaches a maximum; 406. A new population is formed from the updated sub-populations, and steps 404, 405, and 406 are repeated to iteratively update the population until the number of population iteration steps reaches a maximum; 407. Based on the individual with the largest engine power in the population after population optimization, the CAD spline curve method is used to output the optimal engine fuel injection curve.

[0011] Furthermore, the formula for updating the position of the subpopulation individuals based on the inertia weight in step 405 is as follows: , in, is the position of the i-th individual in the k-th population, s is the number of iterations for subpopulation update, is the inertia weight, is a random number between 0 and 1. is the individual with the largest power in the kth subpopulation, is the individual with the smallest power in the kth subpopulation.

[0012] Furthermore, the inertia weight is updated in step 405 as follows: , in, is the set weight coefficient, S max is the maximum number of population iteration steps.

[0013] The present invention also provides an engine fuel injection curve optimization device, comprising: A basic condition module is used to determine the basic conditions for engine optimization calculation. The object of optimization calculation is the control point parameters of the engine fuel injection curve. The control point parameters are used to control the shape of the engine fuel injection curve. The optimization reference point determination module is used to select sample points of the control point parameters based on the set initial optimization range of the engine fuel injection curve control point parameters, obtain the corresponding engine diesel injection curve using the CAD spline curve method, and use the engine diesel injection curve with the maximum engine power as the detailed optimization reference point based on the control point parameters corresponding to it; A detailed optimization range determination module, used to redefine the detailed optimization range of the parameters based on the detailed optimization reference point of the control point parameters; The iterative optimization calculation module is used to obtain the best individual based on the detailed optimization range of the control point parameters by using the population optimization method with the goal of maximizing the engine power corresponding to the population individuals, and output the best engine fuel injection curve based on the best individual by using the CAD spline curve method, wherein the population individuals are composed of the control point parameters of the engine fuel injection curve.

[0014] The present invention also provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned engine fuel injection curve optimization method is implemented.

[0015] The advantages of the technical solution provided by the present invention are: The present invention constructs a fuel injection curve through a spline curve, quickly modifies the curve shape through several control points on the curve, and uses an optimal Latin hypercube algorithm for initial optimization so as to quickly find the spatial position where the engine has the maximum power in the design space. Finally, a global optimization algorithm is used to perform a detailed search around a sample point corresponding to the maximum power to achieve detailed optimization calculation, thereby improving the optimization efficiency and accurately and reasonably obtaining a reliable injection curve shape, avoiding blind selection of the fuel injection curve, and effectively improving the output power of the engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of a method for optimizing an engine fuel injection curve according to an embodiment of the present invention.

[0017] Figure 2 The fuel injection curve shape is generated using the parameters of the j=1 sample point in the table. DETAILED DESCRIPTION

[0018] The present invention is further described below in conjunction with examples. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading this description, various equivalent modifications to this description by those skilled in the art fall within the scope defined by the claims attached to this application.

[0019] Please combine Figure 1 As shown, the engine fuel injection curve optimization method of this embodiment includes the following steps: Step 1: Determine the basic conditions for engine optimization calculation and the object of optimization calculation.

[0020] This step specifically includes: 101. Build a 3D engine geometry model. First, build a piston top geometry, and then set the relevant physical parameters of the engine, including the bore, stroke, the gap width between the piston and the cylinder wall, and the distance between the piston top and the bottom of the cylinder head when the piston moves to the top dead center.

[0021] 102. Mesh the three-dimensional engine geometry model. Use the sector mesh generator in ANSYS-FORTE software to mesh, and set the number of radial and axial meshes.

[0022] 103. Set the specific calculation method of the simulation, which includes the RNG k-ε turbulence model and the KH-RT method.

[0023] 104. Setting geometric parameters of the engine fuel nozzle, including nozzle position and nozzle diameter; and setting injection parameters of the engine fuel nozzle, including starting angle, duration, and total injection mass.

[0024] Step 2: Based on the set initial optimization range of the parameter control points of the engine fuel injection curve, select a specified number of sample points of the parameter control points, use the CAD spline curve method to obtain the corresponding engine diesel injection curve, and use the control point parameters corresponding to the engine diesel injection curve with the maximum engine power as the detailed optimization reference point.

[0025] This step specifically includes: 201. There are five control point parameters for the engine fuel injection curve. By modifying the values ​​of these five parameters, different shapes of fuel injection curves can be obtained. These five parameters are named a, b, c, d, and e. The ranges of the five parameters are defined as: 5≤a≤9, 6≤b≤11, 7≤c≤12, 7≤d≤12, 3≤e≤9. The size of the control point parameter represents the vertical coordinate value in the two-dimensional rectangular coordinate system, and the horizontal coordinate remains unchanged during the optimization process. The area enclosed by the generated fuel injection curve and the horizontal axis represents the total mass of the fuel injection. The amount of fuel injection at different times can be calculated by the simulation software ANSYS-FORTE.

[0026] 202. Use the optimal Latin hypercube algorithm to generate 20 groups of samples to form a set U, where U={a j , b j , c j , d j ,e j}, where 1≤j≤20. The following table lists some of the 20 groups of samples.

[0027]

[0028] The CAD method is used to draw the fuel injection spline curve corresponding to 20 sample points, passing through 1, a j 、b j 、c j ,d j 、e j and 2 are used to generate diesel curves. 1 represents the starting point of the curve, 2 represents the end point of the curve, and the positions of 1 and 2 remain unchanged. These 20 spline curves are input into the simulation software as the engine diesel injection curve. Figure 2 is the fuel injection curve constructed when j=1 in the table.

[0029] 203. Setting boundary conditions and initial simulation parameters. The boundary condition settings include parameters of the engine piston, the engine cylinder head, and the engine cylinder liner. The initial simulation parameters include parameters such as the temperature and pressure in the engine cylinder.

[0030] 204. Set corresponding simulation control parameters, including the initial crankshaft angle, the final crankshaft angle, and the engine speed during simulation.

[0031] 205. Iteration settings, including iteration time step parameters.

[0032] 206. Using the simulation software ANSYS-FORTE for iterative calculation, the engine power P corresponding to 20 engine diesel injection curves is output. j .

[0033] 207. Compare the 20 engine powers obtained, and select the control point parameters on the engine diesel injection curve with the maximum engine power as the subsequent detailed optimization reference point. In this embodiment, through calculation and comparison, it is found that: the maximum power corresponds to a j , b j , c j , d j , e j They are: 7.53, 10.47, 9.37, 7.79, 8.37 (i.e. the sample point j=18 in the table). This point is then expanded to the detailed optimization range of the control point parameters for further optimization.

[0034] Step 3: Redefine the variable range for detailed optimization based on the benchmark points obtained in step 2.

[0035] This step is based on the reference points obtained in step 207, namely 7.53, 10.47, 9.37, 7.79, 8.37, so the range of a, b, c, d, e is redefined based on this result. The specific ranges of a, b, c, d, e are defined as 7≤a≤8, 10≤b≤11, 9≤c≤10, 7≤d≤8, 8≤e≤9. Step 4: Based on the range of step 3, a population optimization method is used to maximize the engine power corresponding to the population individual to obtain the best individual, and the CAD spline curve method is used to output the best engine fuel injection curve based on the best individual.

[0036] This step specifically includes the following parts: 401. Initialize population parameters, the maximum number of iterations is T max , weight parameters ω1, ω2. Generate a set of population Y={y1, y2, y3, ..., y 6n}, contains 6n individuals, among which the i-th individual y i For {z i1 , z i2 , z i3 , z i4 , z i5}, z i1 , z i2 , z i3 , zi4 , z i5 Represents the ordinate value of the diesel curve control point, and the iteration count is t=1.

[0037] 402. Use CAD spline method to draw the line with y i The corresponding individual fuel injection spline curves pass through 1 and z respectively. i1 , z i2 , z i3 , z i4 , z i5 The diesel curve is generated by 1 and 2. 1 represents the starting point of the curve, 2 represents the end point of the curve, and the positions of 1 and 2 always remain unchanged. Then the fuel injection curve is input into the simulation software in sequence.

[0038] 403. Based on the basic conditions of the engine optimization calculation determined in step 1 and the boundary conditions, initial simulation parameters, simulation control parameters and iterative time step parameters determined in steps 203 to 205, iterative calculation is performed to output the engine power P corresponding to the i-th individual in population Y. i , i ranges from 1 to 6n.

[0039] 404. According to the power P i The 6n individuals are sorted from large to small according to their size, and then the population is divided into 6 sub-populations, and the sorted individuals are assigned to the 6 sub-populations in turn. k ={Q k1 , Q k2 , Q k3 , ...,Q kn}, where 1≤k≤6. Let s=1, the maximum number of iterations is S max .

[0040] 405a. For each subpopulation Q k Update individuals, where individual positions are updated as follows: , in, is the position of the i-th individual in the k-th population, s is the number of iterations for subpopulation update, is the inertia weight, is a random number between 0 and 1. is the individual with the largest power in the kth subpopulation, is the individual with the smallest power in the kth subpopulation.

[0041] 405b. The updating method of inertia weight is: , Among them, s is the number of iterations of subpopulation update, S maxis the maximum number of population iteration steps.

[0042] 405c. If the power of the updated individual is greater than the original one, the new individual is used to replace the individual with the smallest power. Otherwise, a new individual is randomly generated to replace the individual with the smallest power.

[0043] 405d. Determine s=S max Is it true? If so, execute step 406; otherwise, set s=s+1 and return to step 405a to continue iterative calculation and update the 6 sub-populations.

[0044] 406. Combine the six sub-species groups obtained in step 405d into a new set YY, and determine whether t=T max Is it true? If so, execute step 407; otherwise, set t=t+1 and return to step 404 for iterative calculation.

[0045] 407. Based on the individual with the largest engine power in the population after population optimization, the CAD spline curve method is used to output the optimal engine fuel injection curve.

[0046] The engine fuel injection curve optimization device of the embodiment includes modules: A basic condition module is used to determine the basic conditions for engine optimization calculation. The object of optimization calculation is the control point parameters of the engine fuel injection curve. The control point parameters are used to control the shape of the engine fuel injection curve. The optimization reference point determination module is used to select sample points of the control point parameters based on the set initial optimization range of the engine fuel injection curve control point parameters, obtain the corresponding engine diesel injection curve using the CAD spline curve method, and use the engine diesel injection curve with the maximum engine power as the detailed optimization reference point based on the control point parameters corresponding to it; A detailed optimization range determination module, used to redefine the detailed optimization range of the parameters based on the detailed optimization reference point of the control point parameters; The iterative optimization calculation module is used for the detailed optimization range based on the control point parameters. The population optimization method is adopted to obtain the best individual with the goal of maximizing the engine power corresponding to the population individual. The CAD spline curve method is used to output the best engine fuel injection curve based on the best individual. The population individual is composed of the control point parameters of the engine fuel injection curve.

[0047] The above modules are combined to realize the above-mentioned engine fuel injection curve optimization method.

[0048] It should be noted that the specific methods of the above embodiments can form a computer program product. Therefore, the computer program product implemented by the present application can be stored on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.). In addition, the present application can be implemented in the form of hardware, software, or a combination of hardware and software, or constitute a computer device including at least one processor and a memory, the memory storing a computer program for implementing the above process steps, and the processor executing the computer program on the memory to form the method steps of the above embodiments.

Claims

1. A method for optimizing an engine fuel injection curve, characterized in that: The following steps are involved: Step 1, determining the basic conditions for engine optimization calculation, the object of optimization calculation is the control point parameters of the engine fuel injection curve, and the control point parameters are used to control the shape of the engine fuel injection curve; Step 2: based on the set initial optimization range of the control point parameters of the engine fuel injection curve, sample points of the control point parameters are selected, and the corresponding engine diesel injection curve is obtained by using the CAD spline curve method, and the engine diesel injection curve with the maximum engine power is used as a detailed optimization reference point by its corresponding control point parameters; Step 3, redefining the parameter detailed optimization range based on the detailed optimization reference point of the control point parameter; Step 4: Based on the detailed optimization range of the control point parameters, a population optimization method is used to obtain the best individual with the goal of maximizing the engine power corresponding to the population individual. Based on the best individual, a CAD spline curve method is used to output the best engine fuel injection curve, and the population individual is composed of the control point parameters of the engine fuel injection curve.

2. The engine fuel injection curve optimization method according to claim 1, characterized in that: The control point parameter size that controls the curve deformation represents the vertical coordinate value in the two-dimensional rectangular coordinate system. The horizontal coordinate of the control point remains fixed during the optimization process.

3. The engine fuel injection curve optimization method according to claim 1, characterized in that: The step 2 specifically includes:

201. Determine the initial optimization range of the control point parameters of the engine fuel injection curve; 202. Generate a plurality of groups of parameter combinations belonging to the initial optimization range to form a sample set; 203. Setting boundary conditions and initial simulation parameters; 204. Setting the initial crankshaft angle, final crankshaft angle, and engine speed during simulation; 205. Set the iteration time step parameters; 206. Output the engine power corresponding to the engine diesel injection curve of each sample in the sample set through iterative calculation of the simulation software ANSYS-FORTE; 207. Select the parameter point on the engine diesel injection curve with the maximum engine power value as the reference point for the detailed optimization of the control point parameters.

4. The engine fuel injection curve optimization method according to claim 3, characterized in that: In step 202, the optimal Latin hypercube algorithm is used to generate a parameter combination.

5. The engine fuel injection curve optimization method according to claim 3, characterized in that: The iterative calculation in step 206 is performed using the RNG k-ε turbulence model and the KH-RT method.

6. The engine fuel injection curve optimization method according to claim 1, characterized in that: The step 4 specifically includes:

401. Initialize population and iteration parameters; 402. Use the CAD spline curve method to draw the fuel injection spline curve of the population individuals, and then input it into the simulation software; 403. Simulate and iterate to calculate and output the engine power corresponding to each individual in the population; 404. Grouping the population based on the engine power obtained in step 403 to form a plurality of sub-populations; 405. Update the individual positions of the subpopulation based on the inertia weight and update the inertia weight based on the number of iteration steps of the population until the number of iteration steps of the subpopulation update reaches a maximum; 406. A new population is formed from the updated sub-populations, and steps 404, 405, and 406 are repeated to iteratively update the population until the number of population iteration steps reaches a maximum; 407. Based on the individual with the largest engine power in the population after population optimization, the CAD spline curve method is used to output the optimal engine fuel injection curve.

7. The method for optimizing the engine fuel injection curve according to claim 6, characterized in that: The formula for updating the position of the subpopulation individuals based on the inertia weight in step 405 is as follows: ， in, is the position of the i-th individual in the k-th population, s is the number of iterations for subpopulation update, is the inertia weight, is a random number between 0 and 1. is the individual with the largest power in the kth subpopulation, is the individual with the smallest power in the kth subpopulation.

8. The method for optimizing the engine fuel injection curve according to claim 7, characterized in that: The inertia weight is updated in step 405 as follows: ， in, is the set weight coefficient, S max is the maximum number of population iteration steps.

9. An engine fuel injection curve optimization device, characterized in that: include: A basic condition module is used to determine the basic conditions for engine optimization calculation. The object of optimization calculation is the control point parameters of the engine fuel injection curve. The control point parameters are used to control the shape of the engine fuel injection curve. The optimization reference point determination module is used to select sample points of the control point parameters based on the set initial optimization range of the engine fuel injection curve control point parameters, obtain the corresponding engine diesel injection curve using the CAD spline curve method, and use the engine diesel injection curve with the maximum engine power as the detailed optimization reference point based on the control point parameters corresponding to it; A detailed optimization range determination module, used to redefine the detailed optimization range of the parameters based on the detailed optimization reference point of the control point parameters; The iterative optimization calculation module is used to obtain the best individual based on the detailed optimization range of the control point parameters by using the population optimization method with the goal of maximizing the engine power corresponding to the population individuals, and output the best engine fuel injection curve based on the best individual by using the CAD spline curve method, wherein the population individuals are composed of the control point parameters of the engine fuel injection curve.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the engine fuel injection curve optimization method according to any one of claims 1 to 8 is implemented.

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