CN117905599B - Combustion intelligent optimization method based on CNG dual-fuel automobile engine - Google Patents
Combustion intelligent optimization method based on CNG dual-fuel automobile engine Download PDFInfo
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- 239000000446 fuel Substances 0.000 title claims abstract description 104
- 238000002485 combustion reaction Methods 0.000 title claims abstract description 64
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- 238000005457 optimization Methods 0.000 title abstract description 16
- 238000011156 evaluation Methods 0.000 claims abstract description 34
- 230000002159 abnormal effect Effects 0.000 claims abstract description 33
- 238000010606 normalization Methods 0.000 claims abstract description 15
- 238000005474 detonation Methods 0.000 claims abstract description 8
- 210000000987 immune system Anatomy 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 10
- 230000035939 shock Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 description 10
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 8
- 230000008859 change Effects 0.000 description 6
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
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- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
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- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/0025—Controlling engines characterised by use of non-liquid fuels, pluralities of fuels, or non-fuel substances added to the combustible mixtures
- F02D41/0027—Controlling engines characterised by use of non-liquid fuels, pluralities of fuels, or non-fuel substances added to the combustible mixtures the fuel being gaseous
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention relates to the technical field of engine combustion optimization, in particular to an intelligent combustion optimization method based on a CNG dual-fuel automobile engine, which comprises the following steps: collecting vibration voltage data, rotation speed data, exhaust pipe temperature and CNG concentration data of the dual-fuel engine at each moment; obtaining a neighbor vibration voltage residual error symbol sequence at each moment according to vibration voltage data and an engine abnormal vibration index; obtaining a neighboring rotating speed frequency domain amplitude sequence and a rotating speed normalization coefficient according to the rotating speed data at each moment; obtaining an engine resonance gain coefficient according to the difference of adjacent elements in the adjacent rotating speed sequence at each moment and the rotating speed regulation coefficient, and obtaining an engine knock evaluation index by combining the engine abnormal vibration index; and obtaining a detonation heat influence index according to the exhaust port temperature and CNG concentration data at each moment, constructing an objective function by combining the detonation evaluation index of the engine and the abnormal vibration index of the engine, controlling the dual-fuel engine, and improving the combustion efficiency of the engine.
Description
Technical Field
The application relates to the technical field of engine combustion optimization, in particular to an intelligent combustion optimization method based on a CNG dual-fuel automobile engine.
Background
With the gradual increase of energy crisis and environmental pollution problems, traditional fossil fuels cannot meet the current development requirements, so that the search for alternative fuels is very important. Natural gas is the most competitive alternative fuel because of the advantages of abundant reserves, economical and clean, high energy density and the like, and compared with the traditional pure natural gas spark ignition engine, the CNG compressed natural gas-based dual-fuel engine has the advantages of less improvement compared with the original diesel engine, but higher thermal efficiency.
However, the simple change of fuel does not completely solve the problems of environmental pollution and low energy utilization rate, and in a dual-fuel engine based on CNG, the combustion process of the fuel is very complex, meanwhile, the combustion speed of CNG is relatively fast, and the flame propagation speed is fast, so that the combustion process is difficult to accurately control, and therefore, the combustion process of the fuel in the dual-fuel engine based on CNG needs to be optimized. The traditional optimization algorithm, such as an AIS artificial immune system algorithm, is a biological heuristic algorithm, and due to randomness of the algorithm and diversity maintenance of immune individuals, the algorithm can search globally, so that the situation of trapping in a local optimal solution is avoided, meanwhile, the adaptability is strong, the algorithm can be adjusted in a dynamic environment, but due to the fact that the combustion process of fuel in a CNG (compressed natural gas) based dual-fuel engine is very complex, the traditional objective function is difficult to adapt to the complex combustion process in the dual-fuel engine, the optimal optimization result is difficult to obtain, the combustion efficiency of the fuel is reduced, and harmful gases such as particulate matters, carbon monoxide, nitrogen oxides and the like can be generated due to insufficient fuel combustion, and the situations of resource waste, environmental pollution and the like can occur.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent combustion optimizing method based on a CNG dual-fuel automobile engine, so as to solve the existing problems.
The intelligent combustion optimization method based on the CNG dual-fuel automobile engine adopts the following technical scheme:
the embodiment of the invention provides an intelligent combustion optimizing method based on a CNG dual-fuel automobile engine, which comprises the following steps:
collecting dual-fuel engine data of the dual-fuel engine at each moment, wherein the dual-fuel engine data comprises: vibration voltage data, rotation speed data, exhaust pipe temperature and CNG concentration data;
acquiring a neighbor vibration voltage residual error symbol sequence of each moment according to vibration voltage data of a preset number of moments before each moment; obtaining the engine abnormal vibration index at each moment according to the adjacent vibration voltage residual error symbol sequence; acquiring a neighboring rotating speed frequency domain amplitude sequence according to the rotating speed data at each moment; obtaining a rotation speed normalization coefficient at each moment according to the adjacent rotation speed frequency domain amplitude sequence; obtaining an engine resonance gain coefficient according to the difference of adjacent elements in the adjacent rotating speed sequence at each moment and the rotating speed normalization coefficient; obtaining an engine knock evaluation index according to the engine abnormal vibration index and the engine resonance gain coefficient at each moment; obtaining detonation heat influence indexes according to the exhaust port temperature and CNG concentration data at each moment; constructing an objective function according to the engine knock evaluation index, the engine abnormal shock index and the knock heat influence index at each moment;
and according to the data of the dual-fuel engine and the target function, obtaining the CNG concentration with optimal combustion efficiency, and controlling the dual-fuel engine.
Further, the obtaining the adjacent vibration voltage residual symbol sequence of each moment according to the vibration voltage data of the preset number of moments before each moment includes:
Marking any moment as moment to be analyzed, and marking a sequence formed by vibration voltage data of a preset number of moments before the moment to be analyzed as a neighbor vibration voltage sequence of the moment to be analyzed; fitting the adjacent vibration voltage sequences by using a cubic spline interpolation method to obtain fitting values corresponding to vibration voltage data at all moments in the adjacent vibration voltage sequences;
Recording the fitting value corresponding to the vibration voltage data at each moment and the difference value of the vibration voltage data as vibration voltage residual errors at each moment; when the vibration voltage residual error is negative, recording a symbol item of the vibration voltage residual error as-1; when the vibration voltage residual error is positive, recording a symbol item of the vibration voltage residual error as 1; when the vibration voltage residual error is 0, recording a symbol item of the vibration voltage residual error as 0;
and marking a sequence formed by symbol items of vibration voltage residuals at all moments in the adjacent vibration voltage sequence as an adjacent vibration voltage residual symbol sequence at the moment to be analyzed.
Further, the obtaining the engine abnormal vibration index at each moment according to the adjacent vibration voltage residual error symbol sequence includes:
carrying out connected domain analysis on the adjacent vibration voltage residual error symbol sequence at the moment to be analyzed to obtain each connected domain, and counting the number of the connected domains; calculating the ratio of the number of the connected domains to the preset number;
counting the number of elements in each connected domain, and marking the number as the number of elements in each connected domain; acquiring an exponential function taking a natural constant as a base number and taking the element number as an index; calculating the reciprocal of the exponential function; obtaining the sum of the reciprocal of all connected domains; and taking the product of the sum and the ratio as an engine abnormal vibration index at the moment to be analyzed.
Further, the obtaining the neighboring rotational speed frequency domain amplitude sequence according to the rotational speed data at each moment includes:
For the rotating speed data at the moment to be analyzed, a method identical to that of the adjacent vibration voltage sequence is adopted to obtain an adjacent rotating speed sequence at the moment to be analyzed; performing discrete Fourier transform on a neighbor rotating speed sequence at the moment to be analyzed to obtain amplitudes corresponding to frequencies of the neighbor rotating speed sequence on a frequency domain, and marking a sequence consisting of the amplitudes corresponding to all the frequencies as a neighbor rotating speed frequency domain amplitude sequence at the moment to be analyzed.
Further, the obtaining the rotation speed normalization coefficient at each moment according to the adjacent rotation speed frequency domain amplitude sequence includes:
Calculating the absolute value of the difference between the f element and the f-1 element in the frequency domain amplitude sequence of the adjacent rotating speed at the moment to be analyzed, and obtaining the sum of the absolute value of the difference and a preset harmonic factor; calculating the ratio of the maximum value in the adjacent rotating speed frequency domain amplitude sequence to the sum value; and taking the sum of the ratios of all elements contained in the adjacent rotating speed frequency domain amplitude sequence at the moment to be analyzed as a rotating speed normalization coefficient at the moment to be analyzed.
Further, the obtaining the engine resonance gain coefficient according to the difference of adjacent elements in the adjacent rotation speed sequence at each moment and the rotation speed normalization coefficient includes:
Calculating the absolute value of the difference between the d element and the d-1 element in the adjacent rotating speed sequence at the moment to be analyzed, and recording the absolute value as a first absolute value of the difference; acquiring the sum of the absolute value of the first difference value and the preset harmonic factor, and recording the sum as a first sum; calculating the reciprocal of a first sum value, obtaining the sum value of the reciprocal of all elements contained in the neighbor rotating speed sequence at the moment to be analyzed, and recording the sum value as a second sum value; and taking the product of the rotation speed normalization coefficient at the moment to be analyzed and the second sum value as the engine resonance gain coefficient at the moment to be analyzed.
Further, the obtaining the engine knock evaluation index according to the engine abnormal vibration index and the engine resonance gain coefficient at each moment includes:
The ratio of the engine knock index to the engine resonance gain coefficient at each time is taken as the engine knock evaluation index at each time.
Further, the obtaining the knock heat influence index according to the exhaust port temperature and the CNG concentration data at each moment includes:
The sequence formed by the exhaust port temperatures at the preset number of moments before the moment to be analyzed is recorded as a neighbor exhaust temperature sequence at the moment to be analyzed; the sequence formed by CNG concentration data of the preset number of moments before the moment to be analyzed is recorded as a neighbor CNG concentration sequence of the moment to be analyzed;
For the e-th moment in the neighbor discharge temperature sequence of the moment to be analyzed, calculating the absolute value of the difference between the exhaust port temperature at the e-th moment and the exhaust port temperature at the e-1 th moment, and obtaining the sum of the absolute value of the difference and CNG concentration data at the e-th moment in the neighbor CNG concentration sequence; and obtaining the reciprocal of the sum value, and taking the sum value of the reciprocal of all the moments in the neighbor discharge temperature sequence at the moment to be analyzed as the detonation heat influence index at the moment to be analyzed.
Further, the constructing an objective function according to the engine knock evaluation index, the engine knock index and the knock heat influence index at each moment includes:
calculating the sum of the engine knock evaluation index and the engine abnormal vibration index at each moment; the ratio of the knock heat influence index to the sum at each time is taken as the objective function value at each time.
Further, the obtaining the CNG concentration of the optimal combustion efficiency according to the data and the objective function of the dual-fuel engine, and controlling the dual-fuel engine comprises:
Inputting the data, the objective function value and the preset related parameters of the dual-fuel engine at each moment into an artificial immune system algorithm to obtain the CNG concentration with optimal combustion efficiency, and transmitting the CNG concentration with optimal combustion efficiency into an electronic control unit of an automobile to control the dual-fuel engine.
The invention has at least the following beneficial effects:
According to the method, the abnormal vibration index of the engine is constructed by analyzing the knocking characteristics in the dual-fuel engine cylinder, and the degree of abnormal vibration in the engine cylinder is reflected; the engine resonance gain coefficient is constructed by analyzing the difference between the engine resonance and the knocking characteristics, and the engine knocking evaluation index is constructed by combining the engine abnormal vibration index, so that the degree of knocking phenomenon in an engine cylinder is reflected, the misjudgment of normal vibration is avoided, and the recognition rate of the knocking phenomenon is improved; constructing a detonation heat influence index based on the engine detonation evaluation index and the temperature change of an engine exhaust pipe, and reflecting the combustion efficiency of fuel in a dual-fuel engine cylinder; by combining the engine abnormal vibration index, the engine knock evaluation index and the knock heat influence index to construct an objective function in an artificial immune system algorithm, the combustion condition of fuel in the CNG-based dual-fuel engine can be reflected more accurately, and the combustion efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of an intelligent combustion optimizing method of a CNG-based dual-fuel automobile engine;
Fig. 2 is a flow chart of objective function acquisition.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the intelligent combustion optimizing method based on the CNG dual-fuel automobile engine according to the invention with reference to the attached drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The specific scheme of the intelligent combustion optimizing method based on the CNG dual-fuel automobile engine provided by the invention is specifically described below with reference to the accompanying drawings.
The invention provides an intelligent combustion optimization method based on a CNG dual-fuel automobile engine, in particular to an intelligent combustion optimization method based on a CNG dual-fuel automobile engine, referring to FIG. 1, comprising the following steps:
and S001, acquiring relevant data in a dual-fuel engine cylinder and preprocessing.
Since another fuel type of the CNG-based dual fuel engine is more, the main fuel is gasoline, and the present embodiment uses the CNG and gasoline dual fuel engine as an example for analysis.
The method comprises the steps of collecting rotational speed data of a dual-fuel engine through a rotational speed sensor, collecting CNG concentration data in the dual-fuel engine through a gas sensor, collecting vibration voltage data when vibration is generated in the dual-fuel engine through a knock sensor, and collecting temperature data in an exhaust pipe of the dual-fuel engine through a temperature sensor. The acquisition interval is recorded as T, in this embodiment, 0.1s is taken, the acquisition frequency is recorded as N, and in this embodiment, 600 is taken.
In order to avoid influencing the subsequent analysis process, the embodiment uses a regression filling method to fill the missing values, and in order to avoid influencing the calculation result due to different dimensions, the embodiment uses a Z-score method to normalize the filled data. The regression filling method and the Z-score normalization method are known techniques, and the specific process is not described in detail in this embodiment.
The preprocessed dual-fuel engine data are obtained, wherein the dual-fuel engine data comprise rotating speed data, vibration voltage data, exhaust pipe temperature and CNG concentration data at all times.
Step S002, initializing relevant parameters in an artificial immune system algorithm, constructing an engine abnormal vibration index by analyzing knock characteristics in a dual-fuel engine cylinder, constructing an engine knock evaluation index based on the difference of engine resonance and knock characteristics of the engine abnormal vibration index analysis, constructing a knock heat influence index based on the temperature change of an engine exhaust pipe of the engine knock evaluation index analysis, and constructing an objective function in the artificial immune system algorithm by combining the engine abnormal vibration index, the engine knock evaluation index and the knock heat influence index.
1. And initializing and setting parameters. When the artificial immune system algorithm is used for optimizing the combustion process of the dual-fuel engine, related parameters in the algorithm need to be initialized, and the method specifically comprises the following parameters:
Mutation rate: the diversity of the population is maintained by mutation operations, which ultimately converge the algorithm to a global optimum. Too large a variation rate can cause poor stability of the population, namely difficult convergence; the mutation rate is too small, the mutation effect is not obvious, the diversity of the population cannot be ensured, namely, the search space is small, the convergence speed is too high, and the global optimal solution cannot be found. The mutation rate is A 1, which is 0.3 in the embodiment;
Inhibition threshold: the combination of the inhibition threshold and the mutation rate can effectively ensure the diversity of the population and simultaneously has a certain convergence rate. Too large a suppression threshold results in searching for solutions that fall into several higher fitness levels, failing to converge; too small a suppression threshold makes it difficult to search for solutions with high fitness. The threshold for suppression is A 2, which is 0.5 in this example;
Size of solution population: i.e., the number of antibodies, the algorithm parallelism is affected by the size of the solution population. Too small a solution population size will result in too small a range of parallel searches, making it difficult to find all peaks; the solution population size results in an extended time for the search process. Thus, to improve the performance and efficiency of the algorithm, an appropriate solution population size is selected. The scale of the interpretation population was a 3, 50 in this example;
The maximum iteration number is recorded as A 4, and 100 is taken in the embodiment;
2. And constructing an engine abnormal vibration index based on the in-cylinder vibration characteristics of the dual-fuel engine. In a dual fuel engine cylinder, abnormal combustion phenomena may occur when the fuel compression ratio is too high, the ignition time is too advanced, etc. That is, during flame propagation in the combustion chamber, the air is compressed by the burnt fuel, so that the mixed fuel in the unburned part far from the spark plug is compressed, at the same time, the heat radiation effect is enhanced, the heat conduction capability is enhanced, the temperature is rapidly increased, even the self-ignition temperature of the fuel is exceeded, the fuel is self-ignited under the condition that the fuel is not ignited, a plurality of flame cores are finally formed in the combustion chamber, and a large amount of energy is released in a very short time. Because the space in the combustion chamber is limited, and the flame speed is extremely fast and is tens of times of the normal combustion flame speed, the flame can violently impact the dual-fuel engine cylinder at the moment, so that the cylinder body vibrates, namely the knocking phenomenon is generated. Based on the analysis, the embodiment constructs the engine abnormal vibration index, reflects the degree of knocking in the dual-fuel engine cylinder, and constructs the engine abnormal vibration index as follows:
Taking the a moment as an example, marking a sequence formed by vibration voltage data of b moments before the a moment as a neighbor vibration voltage sequence, taking 15 in the embodiment of the size of b, and supplementing by a regression filling method when the quantity of the moments before the a moment is smaller than b so as to enable the length of the neighbor vibration voltage sequence to be b; performing curve fitting on the adjacent vibration voltage sequence by using a cubic spline interpolation method to obtain fitting values corresponding to all moments, marking the difference between the fitting values and vibration voltage data at the corresponding moments as vibration voltage residual errors, and in order to better analyze whether the engine is in a vibration state, in the embodiment, only the symbol items of the vibration voltage residual errors are reserved, namely, the value of the vibration voltage residual errors is a negative number, the symbol items are marked as-1, the value after difference is a positive number, the symbol items are marked as 1, the value after difference is 0, the symbol items are marked as 0, and the sequence formed by the symbol items of the residual errors is marked as the adjacent vibration voltage residual error symbol sequence; carrying out connected domain analysis on the adjacent vibration voltage residual error symbol sequences to obtain a plurality of connected domains, wherein the element values belonging to the same connected domain are the same; the regression filling method, the cubic spline interpolation method and the connected domain analysis are known techniques, and the specific process is not described in detail in this embodiment. According to the method, the abnormal vibration index of the engine can be calculated, and the calculation formula is as follows:
Wherein B a represents the engine abnormal vibration index at the a-th time, C a represents the number of connected domains in the adjacent vibration voltage residual error symbol sequence at the a-th time, B represents the length of the adjacent vibration voltage residual error symbol sequence, exp () represents an exponential function based on a natural constant, and E a (C) represents the number of elements in the C-th connected domain in the adjacent vibration voltage residual error symbol sequence at the a-th time, and is recorded as the number of elements of the connected domains.
In the adjacent vibration voltage residual error symbol sequence at the a-th moment, the more the number of connected domains is, namely the larger the C a is, the smaller the number of elements in the connected domains is, namely the smaller the E a (C) is, which shows that when curve fitting is carried out on the adjacent vibration voltage sequence at the moment, the more obvious positive and negative alternation conditions of residual error terms are, namely the stronger fluctuation conditions of vibration voltage are more likely to occur at the moment, so that knocking conditions of a dual-fuel engine are more likely to occur, and the calculated abnormal vibration index of the engine is larger.
3. An engine knock evaluation index is constructed based on the engine knock index. In a dual-fuel engine, when carbon deposition in the engine is excessive, gasoline sprayed out of a cold-start oil nozzle can be absorbed by a large amount of carbon deposition, so that a cold-start mixer is too thin, and starting is difficult. Under the condition, ignition and starting are easy only when the gasoline absorbed by carbon deposition is saturated, and the gasoline adsorbed on the carbon deposition can be sucked into a cylinder for combustion by the vacuum suction of the engine at the moment, so that the mixed gas is thickened, the combustible mixed gas of the engine is diluted and thickened, and idling shake after cold starting of the engine is caused, namely a resonance phenomenon occurs. When resonance occurs, the engine can vibrate, resonance is normal, each vehicle has a resonance point, the resonance point is the vehicle speed when resonance occurs, different vehicle types can have different resonance points, but the resonance condition can only occur when the engine speed is near the resonance point, and the resonance point is different from the knocking phenomenon. Based on the above analysis, in order to distinguish the resonance phenomenon from the knock phenomenon, the present embodiment constructs an engine knock evaluation index reflecting the possibility that the engine actually knocks, and the construction process of the engine knock evaluation index is as follows:
The sequence formed by the rotating speed data of b times before the a time is marked as a neighbor rotating speed sequence, and if the number of times before the a time is smaller than b, the sequence is complemented by a regression filling method, so that the length of the neighbor rotating speed sequence is b; the sequence of the neighboring rotational speed at the a-th moment is taken as the input of the discrete fourier transform, and is output as the frequency and the amplitude of the sequence in the frequency domain, and the sequence formed by the amplitudes corresponding to the frequencies of the sequence in the frequency domain is recorded as the sequence of the neighboring rotational speed frequency domain amplitudes, wherein the regression filling method and the discrete fourier transform are known techniques, and the specific process is not repeated in the embodiment. From this, an engine knock evaluation index can be calculated, whose calculation formula is as follows:
Wherein P a represents a rotation speed normalization coefficient at the a-th time, Q a represents the number of elements of the adjacent rotation speed frequency domain amplitude sequence at the a-th time, H a (max) represents the maximum value of the amplitudes in the adjacent rotation speed frequency domain amplitude sequence at the a-th time, H a(f)、Ha (f-1) represents the values of the f-th and f-1-th elements in the adjacent rotation speed frequency domain amplitude sequence at the a-th time, and e represents a harmonic factor for avoiding incapacitation caused by 0 as denominator, and 0.01 is taken in the embodiment.
F a represents the engine resonance gain coefficient at the a-th moment, b represents the number of elements contained in the adjacent rotating speed sequence, 15 is taken in the embodiment, and I a(d)、Ia (d-1) respectively represents the values of the d-th element and the d-1-th element in the adjacent rotating speed sequence at the a-th moment; g a represents the engine knock evaluation index at time a, and B a represents the engine knock index at time a.
After converting the adjacent rotating speed sequence at the a-th moment into the frequency domain, the larger the maximum value of the elements in the obtained adjacent rotating speed frequency domain amplitude sequence, namely H a (max), is, the more stable the rotating speed at the moment is, the smaller the amplitude difference of the adjacent frequency domains is, namely |H a(f)-Ha (f-1) | is, the smaller the amplitude of the low-frequency component is, namely the smoother and stable the rotating speed change is, so that the calculated rotating speed normalization coefficient is larger; meanwhile, the smaller the difference between adjacent elements in the adjacent rotating speed sequence is, namely the smaller the I a(d)-Ia (d-1) is, the smaller the change of the rotating speed of the engine is, namely the more stable the vehicle speed is, the more the vibration of the dual-fuel engine is caused by the fact that the vehicle speed reaches a resonance point instead of a knocking phenomenon, so that the calculated resonance gain coefficient is larger; meanwhile, the smaller the engine knock index at the a-th moment, namely the smaller the B a, the more likely the knocking phenomenon does not occur at the moment, so the smaller the calculated engine knock evaluation index.
4. A knock heat impact index is constructed based on the engine knock evaluation index. In a dual fuel engine, when knocking occurs, a plurality of flame cores appear in the engine cylinder, resulting in insufficient combustion of fuel, reduced thermal efficiency, and unstable variation of engine outlet temperature. According to this embodiment, a knock heat influence index is constructed based on the engine knock evaluation index, reflecting the combustion efficiency in the dual-fuel engine cylinder, and the knock heat influence index is constructed as follows:
And (3) marking a sequence formed by the exhaust port temperature of the engine at b times before the a time as a neighbor exhaust temperature sequence, marking a sequence formed by CNG concentration data in the engine at b times before the a time as a neighbor CNG concentration sequence, and supplementing by a regression filling method if the number of times before the a time is less than b, so that the length of the neighbor exhaust temperature sequence and the neighbor CNG concentration sequence is b, wherein the regression filling method is a known technology, and the specific process is not repeated in the embodiment. The knock heat influence index can be calculated as follows:
where K a denotes the knock heat influence index at the a-th time, b denotes the length of the neighbor discharge temperature sequence, 15 is taken in this embodiment, g a (e) denotes the engine knock evaluation index at the time corresponding to the e-th element in the neighbor discharge temperature sequence at the a-th time, J a(e)、Ja (e-1) respectively denotes the values of the e-th and e-1-th elements in the neighbor discharge temperature sequence at the a-th time, and Ma ( e) denotes the value of the e-th element in the neighbor CNG concentration sequence at the a-th time.
The larger the temperature change of the exhaust pipe of the dual-fuel engine at the adjacent moment, i.e. the larger the I J a(e)-Ja (e-1) I, the more insufficient and unstable the combustion of the fuel in the engine cylinder at the moment, the lower the combustion efficiency of the fuel is caused, the larger the knock evaluation index of the engine is, i.e. the larger the G a (e) is, the more likely to generate knocking phenomenon in the cylinder of the dual-fuel engine at the moment, the temperature and the pressure in the combustion chamber are rapidly increased, part of the fuel is discharged without being fully combusted, the combustion efficiency of the fuel is reduced, the higher the CNG concentration is, i.e. the higher the M a (e) is, the higher the total discharged fuel amount is, the serious the waste of the fuel is caused, the degree of the reduction of the combustion efficiency is greater, and the calculated knocking heat influence index is greater.
5. An objective function is constructed based on the engine knock index, the engine knock evaluation index, and the knock heat influence index, and a specific flow is shown in fig. 2. Through the engine abnormal vibration index, the engine knock evaluation index and the knock heat influence index at each moment obtained by the steps, an objective function in an artificial immune system algorithm can be constructed, and the objective function can be specifically expressed as follows:
wherein L a represents the objective function value at time a, and B a、Ga、Ka represents the engine knock index, the engine knock evaluation index, and the knock heat influence index at time a, respectively.
The larger the engine abnormal shock index and the engine knock evaluation index at the a time, namely the larger the B a、Ga is, the more abnormal shock and knocking phenomena are likely to occur in the cylinder of the dual-fuel engine at the moment, meanwhile, the smaller the knock heat influence index at the a time, namely the smaller the K a is, the lower the combustion efficiency of fuel in the cylinder of the dual-fuel engine is likely to be, and the smaller the calculated objective function value is.
And step S003, optimizing the combustion of the dual-fuel engine through an artificial immune system algorithm based on the objective function.
Based on the objective function obtained in the above steps, the objective function is used as an objective function of an artificial immune system algorithm, data of the dual-fuel engine, the objective function and related parameters at each moment are used as inputs of the optimization algorithm, the related parameters are set in the above steps, the related parameters comprise a variation rate A 1, a suppression threshold A 2, a solution group scale A 3 and a maximum iteration number A 4, in the embodiment, 0.3, 0.5, 50 and 100 are respectively taken, an output result is an optimization result under the optimal combustion efficiency, specifically, CNG concentration under the optimal combustion efficiency is achieved, the obtained optimization result is transmitted to an electronic control unit of an ECU (electronic control unit) automobile, and the ECU controls the dual-fuel engine according to the optimal result to achieve the optimal combustion efficiency, so that combustion optimization of the dual-fuel automobile engine is achieved. The optimization process of the artificial immune system algorithm is a well-known technology, and the specific process is not described in detail in this embodiment.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; the technical solutions described in the foregoing embodiments are modified or some of the technical features are replaced equivalently, so that the essence of the corresponding technical solutions does not deviate from the scope of the technical solutions of the embodiments of the present application, and all the technical solutions are included in the protection scope of the present application.
Claims (10)
1. The intelligent combustion optimizing method based on the CNG dual-fuel automobile engine is characterized by comprising the following steps of:
collecting dual-fuel engine data of the dual-fuel engine at each moment, wherein the dual-fuel engine data comprises: vibration voltage data, rotation speed data, exhaust pipe temperature and CNG concentration data;
acquiring a neighbor vibration voltage residual error symbol sequence of each moment according to vibration voltage data of a preset number of moments before each moment; obtaining the engine abnormal vibration index at each moment according to the adjacent vibration voltage residual error symbol sequence; acquiring a neighboring rotating speed frequency domain amplitude sequence according to the rotating speed data at each moment; obtaining a rotation speed normalization coefficient at each moment according to the adjacent rotation speed frequency domain amplitude sequence; obtaining an engine resonance gain coefficient according to the difference of adjacent elements in the adjacent rotating speed sequence at each moment and the rotating speed normalization coefficient; obtaining an engine knock evaluation index according to the engine abnormal vibration index and the engine resonance gain coefficient at each moment; obtaining detonation heat influence indexes according to the exhaust port temperature and CNG concentration data at each moment; constructing an objective function according to the engine knock evaluation index, the engine abnormal shock index and the knock heat influence index at each moment;
and according to the data of the dual-fuel engine and the target function, obtaining the CNG concentration with optimal combustion efficiency, and controlling the dual-fuel engine.
2. The intelligent combustion optimizing method based on the CNG dual-fuel automobile engine according to claim 1, wherein the obtaining the adjacent vibration voltage residual symbol sequence of each moment according to the vibration voltage data of the preset number of moments before each moment comprises:
Marking any moment as moment to be analyzed, and marking a sequence formed by vibration voltage data of a preset number of moments before the moment to be analyzed as a neighbor vibration voltage sequence of the moment to be analyzed; fitting the adjacent vibration voltage sequences by using a cubic spline interpolation method to obtain fitting values corresponding to vibration voltage data at all moments in the adjacent vibration voltage sequences;
Recording the fitting value corresponding to the vibration voltage data at each moment and the difference value of the vibration voltage data as vibration voltage residual errors at each moment; when the vibration voltage residual error is negative, recording a symbol item of the vibration voltage residual error as-1; when the vibration voltage residual error is positive, recording a symbol item of the vibration voltage residual error as 1; when the vibration voltage residual error is 0, recording a symbol item of the vibration voltage residual error as 0;
and marking a sequence formed by symbol items of vibration voltage residuals at all moments in the adjacent vibration voltage sequence as an adjacent vibration voltage residual symbol sequence at the moment to be analyzed.
3. The intelligent combustion optimizing method based on the CNG dual-fuel automobile engine as claimed in claim 2, wherein the obtaining the engine abnormal vibration index at each moment according to the adjacent vibration voltage residual error symbol sequence comprises the following steps:
carrying out connected domain analysis on the adjacent vibration voltage residual error symbol sequence at the moment to be analyzed to obtain each connected domain, and counting the number of the connected domains; calculating the ratio of the number of the connected domains to the preset number;
counting the number of elements in each connected domain, and marking the number as the number of elements in each connected domain; acquiring an exponential function taking a natural constant as a base number and taking the element number as an index; calculating the reciprocal of the exponential function; obtaining the sum of the reciprocal of all connected domains;
and taking the product of the sum and the ratio as an engine abnormal vibration index at the moment to be analyzed.
4. The intelligent combustion optimizing method based on the CNG dual-fuel automobile engine as claimed in claim 2, wherein the obtaining the adjacent rotating speed frequency domain amplitude sequence according to the rotating speed data of each moment comprises the following steps:
For the rotating speed data at the moment to be analyzed, a method identical to that of the adjacent vibration voltage sequence is adopted to obtain an adjacent rotating speed sequence at the moment to be analyzed; performing discrete Fourier transform on a neighbor rotating speed sequence at the moment to be analyzed to obtain amplitudes corresponding to frequencies of the neighbor rotating speed sequence on a frequency domain, and marking a sequence consisting of the amplitudes corresponding to all the frequencies as a neighbor rotating speed frequency domain amplitude sequence at the moment to be analyzed.
5. The intelligent combustion optimizing method based on CNG dual-fuel automobile engine as claimed in claim 4, wherein the obtaining the rotation speed normalization coefficient of each moment according to the adjacent rotation speed frequency domain amplitude sequence comprises:
Calculating the absolute value of the difference between the f element and the f-1 element in the frequency domain amplitude sequence of the adjacent rotating speed at the moment to be analyzed, and obtaining the sum of the absolute value of the difference and a preset harmonic factor; calculating the ratio of the maximum value in the adjacent rotating speed frequency domain amplitude sequence to the sum value; and taking the sum of the ratios of all elements contained in the adjacent rotating speed frequency domain amplitude sequence at the moment to be analyzed as a rotating speed normalization coefficient at the moment to be analyzed.
6. The intelligent combustion optimizing method based on CNG dual-fuel automobile engine as claimed in claim 5, wherein the obtaining the engine resonance gain coefficient according to the difference of adjacent elements in the adjacent rotating speed sequence at each moment and the rotating speed normalization coefficient comprises the following steps:
Calculating the absolute value of the difference between the d element and the d-1 element in the adjacent rotating speed sequence at the moment to be analyzed, and recording the absolute value as a first absolute value of the difference; acquiring the sum of the absolute value of the first difference value and the preset harmonic factor, and recording the sum as a first sum; calculating the reciprocal of a first sum value, obtaining the sum value of the reciprocal of all elements contained in the neighbor rotating speed sequence at the moment to be analyzed, and recording the sum value as a second sum value; and taking the product of the rotation speed normalization coefficient at the moment to be analyzed and the second sum value as the engine resonance gain coefficient at the moment to be analyzed.
7. The intelligent combustion optimizing method based on the CNG dual-fuel automobile engine as claimed in claim 1, wherein the obtaining the engine knock evaluation index according to the engine knock index and the engine resonance gain coefficient at each moment comprises the following steps:
The ratio of the engine knock index to the engine resonance gain coefficient at each time is taken as the engine knock evaluation index at each time.
8. The intelligent combustion optimizing method based on CNG dual-fuel automobile engine according to claim 2, wherein the obtaining knock heat influence index according to the exhaust port temperature and CNG concentration data at each moment comprises:
The sequence formed by the exhaust port temperatures at the preset number of moments before the moment to be analyzed is recorded as a neighbor exhaust temperature sequence at the moment to be analyzed; the sequence formed by CNG concentration data of the preset number of moments before the moment to be analyzed is recorded as a neighbor CNG concentration sequence of the moment to be analyzed;
For the e-th moment in the neighbor discharge temperature sequence of the moment to be analyzed, calculating the absolute value of the difference between the exhaust port temperature at the e-th moment and the exhaust port temperature at the e-1 th moment, and obtaining the sum of the absolute value of the difference and CNG concentration data at the e-th moment in the neighbor CNG concentration sequence; and obtaining the reciprocal of the sum value, and taking the sum value of the reciprocal of all the moments in the neighbor discharge temperature sequence at the moment to be analyzed as the detonation heat influence index at the moment to be analyzed.
9. The intelligent combustion optimizing method based on the CNG dual-fuel automobile engine as claimed in claim 1, wherein the constructing the objective function according to the engine knock evaluation index, the engine knock index and the knock heat influence index at each moment comprises the following steps:
calculating the sum of the engine knock evaluation index and the engine abnormal vibration index at each moment; the ratio of the knock heat influence index to the sum at each time is taken as the objective function value at each time.
10. The intelligent combustion optimizing method based on the CNG dual-fuel automobile engine as claimed in claim 1, wherein the CNG concentration for obtaining the optimal combustion efficiency according to the dual-fuel engine data and the objective function, the control of the dual-fuel engine comprises the following steps:
Inputting the data, the objective function value and the preset related parameters of the dual-fuel engine at each moment into an artificial immune system algorithm to obtain the CNG concentration with optimal combustion efficiency, and transmitting the CNG concentration with optimal combustion efficiency into an electronic control unit of an automobile to control the dual-fuel engine.
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