CN112963256B - HCCI/SI combustion mode switching process control method - Google Patents
HCCI/SI combustion mode switching process control method Download PDFInfo
- Publication number
- CN112963256B CN112963256B CN202110317167.0A CN202110317167A CN112963256B CN 112963256 B CN112963256 B CN 112963256B CN 202110317167 A CN202110317167 A CN 202110317167A CN 112963256 B CN112963256 B CN 112963256B
- Authority
- CN
- China
- Prior art keywords
- combustion mode
- output
- engine
- imep
- hcci
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000002485 combustion reaction Methods 0.000 title claims abstract description 75
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000004886 process control Methods 0.000 title claims description 10
- 239000000446 fuel Substances 0.000 claims abstract description 34
- 238000013528 artificial neural network Methods 0.000 claims abstract description 32
- 230000008569 process Effects 0.000 claims abstract description 18
- 238000002347 injection Methods 0.000 claims abstract description 12
- 239000007924 injection Substances 0.000 claims abstract description 12
- 210000002569 neuron Anatomy 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 20
- 210000004027 cell Anatomy 0.000 claims description 9
- 230000004913 activation Effects 0.000 claims description 5
- 230000005284 excitation Effects 0.000 claims description 5
- 238000011478 gradient descent method Methods 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 2
- 230000003213 activating effect Effects 0.000 claims 1
- 238000012549 training Methods 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 4
- 239000000203 mixture Substances 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 239000003921 oil Substances 0.000 description 2
- 238000003915 air pollution Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011425 standardization method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D43/00—Conjoint electrical control of two or more functions, e.g. ignition, fuel-air mixture, recirculation, supercharging or exhaust-gas treatment
- F02D43/02—Conjoint electrical control of two or more functions, e.g. ignition, fuel-air mixture, recirculation, supercharging or exhaust-gas treatment using only analogue means
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/12—Improving ICE efficiencies
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Electrical Control Of Air Or Fuel Supplied To Internal-Combustion Engine (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
- Output Control And Ontrol Of Special Type Engine (AREA)
Abstract
The invention relates to a control method for an HCCI/SI combustion mode switching process, which belongs to the technical field of engine control and is characterized in that an IMEP predicted value output by a switched combustion mode at the next moment on a switched combustion mode LSTM neural network black box model prediction time sequence is established according to a rotation speed, a combustion mode, an air-fuel ratio, an oil injection quantity, an intake valve opening timing, an exhaust valve closing timing and an ignition advance angle in an SI combustion mode; designing a BP neural network controller, inputting variables of a rotating speed, a combustion mode at the next moment and an IMEP expected value, obtaining an air-fuel ratio, an oil injection quantity, an inlet valve opening timing, an exhaust valve closing timing and an ignition advance angle which enable an error between a predicted value and the expected value to be smaller than an error threshold value through training the neural network, and at the moment, modifying relevant parameters of an engine to control variable values obtained through training to realize stable switching of the combustion mode of the engine.
Description
Technical Field
The invention belongs to the field of engine control, and relates to a control method for HCCI/SI combustion mode switching process.
Background
The rapid increase of the automobile holding capacity brings environmental problems of air pollution aggravation and the likeThe problem seriously restricts the normal development of the Chinese automobile society. HCCI (Homogeneous Charge Compression Ignition), a new combustion mode that provides high thermal efficiency and low NO by compressing a Homogeneous fuel/air mixture until reaching an auto-Ignition point x And PM emission while effectively reducing fuel consumption. These advantages make it possible to satisfy both the restrictions of shortage of crude oil resources and the strict environmental emission regulations, which are the main development directions of engine technology in the future.
HCCI combustion, in addition to its almost outright advantages, presents some inherent challenges. The problem of narrow operating condition range caused by knocking at high load and misfire at low load in the HCCI combustion mode is a prominent problem in the wide application of the HCCI combustion mode in the automotive industry. For this purpose, it is necessary to operate the engine in a full operating range by means of combustion mode switching (HCCI/SI).
Because the combustion conditions in the cylinders of the two combustion modes are completely different, the switching of the combustion modes under the same combustion conditions can cause great sudden change of the dynamic property of the output of the engine, and the stable operation of the engine in the full working condition range is seriously influenced. Therefore, the abrupt change range of the dynamic output of the engine after the combustion mode is switched is stabilized in a smaller range by changing the relevant combustion conditions at the switching moment, and the engine can be ensured to stably run in the full working condition range. The engine dynamic output is characterized by the magnitude of the output IMEP.
Considering that the combustion mode is frequently switched when the engine is always in a working state with variable working conditions in the actual running process, the engine output IMEP is greatly fluctuated, and the engine cannot normally run. The traditional control method comprises the following steps: PID control, model predictive control sliding film control and the like can realize the control of output IMEP of the HCCI engine under a constant working condition, and the control effect under a variable working condition is poor. Therefore, further investigation into the problem of HCCI engine control over varying operating conditions is needed.
Disclosure of Invention
In view of this, it is an object of the invention to provide an HCCI-Firstly, establishing an LSTM neural network engine black box model to predict the output IMEP at the next moment; secondly, designing a BP neural network controller, and adjusting an air-fuel ratio AFR and an oil injection quantity m according to the rotating speed of the engine, the combustion mode and the expected IMEP output value fuel Intake valve opening timing theta ivo Exhaust valve closing timing theta evc Angle of ignition advance alpha ign ) And the control of the dynamic and smooth output of the engine in the switching process of the HCCI/SI combustion mode is realized.
In order to achieve the purpose, the invention provides the following technical scheme:
a HCCI/SI combustion mode switching process control method, comprising the steps of:
s1: for engine speed N, combustion mode M, air-fuel ratio AFR and fuel injection quantity M fuel Intake valve opening timing theta ivo And exhaust valve closing timing theta evc Sampling as an original input data set; in the combustion mode M, the SI combustion mode is defined as M1, the HCCI combustion mode M is defined as 2, and when M is 1, the ignition advance angle α is also sampled ign ；
S2: building a black box prediction model of the switched combustion mode LSTM neural network engine, and predicting IMEP output values IMEP of the upper and lower moments of a time sequence Pre ；
S3: the rotating speed N in the running process of the engine, the combustion mode M at the next moment and the expected value IMEP of the IMEP at the next moment are calculated Exp As inputs, the air-fuel ratio AFR and the fuel injection quantity m fuel Intake valve opening timing θ ivo And exhaust valve closing timing theta evc And ignition advance angle alpha in SI combustion mode ign Designing a BP neural network controller as a control variable; obtaining IMEP predicted value and IMEP expected value IMEP of the combustion mode after switching at the next moment through learning of a neural network Exp Error e between t Less than error threshold e l Thereby controlling smooth switching of the two combustion modes.
Further, in step S1, IMEP related variables including N, M, are determined according to the characteristics of the engine combustion process and the two combustion modes,AFR、m fuel 、θ ivo 、θ evc 、α ign (ii) a Under various automobile operating conditions, data are collected once per cycle, namely, each crankshaft rotation angle rotates by 720 degrees, and an original input data set is obtained.
Further, the LSTM neural network engine black box model described in step S2 includes an input and feedback layer, a hidden layer, and an output layer; the LSTN memory unit is provided with a forgetting gate, an input gate and an output gate;
first, the output value of LSTM cells was calculated using the forward propagation algorithm: the forgetting gate outputs h according to the last moment by using sigmoid activation function t-1 And current time input x t Determining the information F of passing the forgetting gate at the last moment t 1 (ii) a The input gate determines the state of the unit which is input at the current moment and reserved to the current moment through the sigmoid activation function
The output gate obtains the output of the model through the sigmoid function and the tanh function
And then calculating an error term of each LSTM cell by using a back propagation algorithm, and updating the weight by using a gradient descent method.
Further, in step S2, according to formula F t 1 ＝σ(W f x t +U f h t-1 ) Determining the state of the unit passing through the forgetting gate; according to the formula
Calculating the unit state of the output gate; according to the formula
Calculating new memory to obtain the unit state of the current input to the current moment; according to the formula
And obtaining model output.
Further, in step S3, the BP neural network input layer includes 3 neuron nodes, the hidden layer includes 4 neuron nodes, and the output layer includes 5 neuron nodes; the excitation function of the hidden layer is
Neuron threshold of the hidden layer is a j The connection weight from the input layer to the hidden layer is w ij (ii) a The excitation function of the output layer is psi (·), and the neuron threshold is b k The connection weight from the hidden layer to the output layer is w jk 。
Further, in step S3, the BP neural network is based on a formula
Calculating output signal H of jth neuron node of hidden layer j (ii) a According to the formula
And calculating an output signal y of a neuron node of an output layer, and realizing the adjustment of engine parameters in the combustion mode switching process through an output vector to realize the stability of the combustion mode switching process.
The invention has the beneficial effects that: the invention utilizes the nonlinear function between the analog input and output of the neural network to establish the neural network controller, realizes the output IMEP control of the HCCI engine under variable working conditions, and realizes the control of the stable output of the engine dynamic property in the switching process of the HCCI/SI combustion mode.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic diagram of the steps of an LSTM neural network algorithm;
FIG. 2 is a diagram of a BP neural network structure;
FIG. 3 is a control diagram of an HCCI/SI combustion mode switching process.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustration only and not for the purpose of limiting the invention, shown in the drawings are schematic representations and not in the form of actual drawings; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Referring to fig. 1 to 3, HCCI-SI combustion mode switching process control methods provided by the present invention are illustrated.
The air-fuel ratio refers to the mass ratio of air to fuel in the mixed gas, the concentration of the mixed gas can be well represented, and within a certain range, the concentration of the mixed gas is increased, the mass of the fuel is increased, and the dynamic property of an engine is improved. Intake and exhaust valve timing can change the chemical composition of the mixture in the engine cylinder, mixture concentration, and temperature, thereby affecting engine dynamics. The timing control method defines the time when the air inlet valve of the engine is closed and the time when the exhaust valve is opened as the time when the piston moves to the bottom dead center, and realizes the timing control of the air inlet valve and the exhaust valve by controlling the opening timing of the air inlet valve and the closing timing of the exhaust valve. The volume of fuel and air in the cylinder is determined by the fuel injection quantity and the timing of the intake valve and the exhaust valve, the concentration of the mixed gas is directly influenced, and further the dynamic property of the engine is influenced. For a gasoline engine, an optimal ignition advance angle exists for obtaining optimal power performance, so when the combustion mode is switched to the SI combustion mode, the ignition advance angle is controlled to realize smooth switching from the HCCI combustion mode to the SI combustion mode. In conclusion, the air-fuel ratio, the fuel injection amount, and the intake/exhaust valve timing have a large influence on the engine dynamics, and therefore the engine dynamics output can be controlled as the control variables. It should be noted that the spark advance angle should also be added to the control variable when switching from HCCI to SI combustion mode.
The invention utilizes an LSTM neural network engine black box model to predict the output IMEP of the combustion mode after HCCI/SI switching at the next moment. A BP neural network controller is designed, and the IMEP output in the switching process is controlled according to the combustion mode, the engine speed and the IMEP expected value. The invention ensures the accuracy of the predicted value, realizes the stable output of the switching process of the two combustion modes, and ensures that the HCCI engine can operate in the full working condition range.
When the LSTM engine black box prediction model is established, a large amount of data needs to be collected to train the model so as to fit the relation between input and output. Since the magnitude and the unit of the collected sample data are different, the collected data needs to be standardized first. The invention adopts a Z-score standardization method to process data, and the formula is as follows:
wherein x is i A sequence of sample data is represented which,
obtaining a new sequence y i The mean of the new sequence is 0, the variance is 1, and there is no dimension.
The LSTM neural network engine black box prediction model is built as follows.
The engine speed N, the combustion mode M, the air-fuel ratio AFR and the fuel injection quantity M fuel Intake valve opening timing theta ivo Exhaust valve closing timing theta evc Angle of ignition advance alpha ign ) For input, the IMEP value at the time of the combustion mode is output. The LSTN memory cell includes a forgetting gate, an input gate, and an output gate. The forgetting gate outputs h according to the last moment by utilizing sigmoid activation function t-1 And current time input x t Determining the information of passing through the forgetting door at the last moment; the input gate determines the input x at the current moment through the sigmoid activation function t The unit state of the current moment is reserved; and the output gate obtains the output of the model through the sigmoid function and the tanh function. The algorithm used to obtain the output of LSTM cells is a forward propagation algorithm. And secondly, calculating an error term of each LSTM cell by using a back propagation algorithm, and updating the weight by using a gradient descent method.
(1) The output values of LSTM cells were calculated using the forward propagation algorithm:
calculation input gate:
calculating a forgetting gate: f t 1 ＝σ(W f x t +U f h t-1 ) (3)
Calculating an output gate:
calculating new memory:
calculating the final memory:
calculating an output value:
(2) the error for each cell is calculated using a back propagation algorithm:
the loss function is defined as:
and updating the weight by adopting a gradient descent method until the error function value is smaller than a preset value.
The BP neural network controller is established as follows:
when a BP neural network controller in the HCCI/SI combustion mode switching process is established, the input variables are the engine speed N, the combustion mode M at the next moment and the expected IMEP output value Exp The control variables are air-fuel ratio AFR and fuel injection quantity m fuel Intake valve opening timing theta ivo Exhaust valve closing timing theta evc 、(α ign ) And establishing a three-layer BP neural network controller. Error e is obtained by training neural network t Less than a set error threshold e l And the time control variable value is used for modifying the engine related parameters to the trained control variable value at the next moment, so that the stable switching process can be realized.
FIG. 2 is a diagram of a BP neural network structure, x 1 Is the engine speed N, x 2 For the next combustion mode M, x 3 Expecting an output value IMEP for IMEP Exp . The hidden layer includes 4 neuron nodes (1, 2,3,4),
as a function of excitation of the hidden layer, a j Neuron threshold for the hidden layer, w ij The connection weight from the input layer to the hidden layer; the output vector is y k Phi (·) is the excitation function of the output layer, with the neuron threshold of the output layer being b k The connection weight from the hidden layer to the output layer is w jk . The training process of the BP neural network is mainly divided into two stages, wherein the first stage is forward propagation of signals from an input layer to an output layer through a hidden layer to obtain an output vector. The second stage is the back propagation of the error from the output layer to the input layer through the hidden layer, and the weight and the threshold value from the hidden layer to the output layer and the weight and the threshold value from the input layer to the hidden layer are adjusted in sequence.
The output signal of the hidden layer neuron node is:
the output signal of the output layer neuron node is:
the error formula is taken as follows:
wherein, Y k To expect the output, note:
Y k -y k ＝e k (12)
obtaining:
updating the weight by using a gradient descent method to enable the error function to reach a minimum value, and updating the weight from the hidden layer to the output layer:
the formula for updating the weights from the hidden layer to the output layer can be obtained as follows:
ω′ jk ＝ω jk +ηH j e k (15)
weight update from input layer to hidden layer:
wherein,
then the weight update formula from the input layer to the hidden layer is:
hidden layer neuron threshold update:
wherein,
the hidden layer neuron threshold update formula can be derived as:
output layer neuron threshold update:
the formula for updating the neuron threshold value of the obtained output layer is as follows:
b′ k ＝b k +ηe k (25)
where η is the learning rate.
Firstly, an LSTM neural network black box prediction model is established to predict IMEP output values of the switched combustion modes at the next moment. And secondly, establishing a BP neural network controller to obtain an air-fuel ratio, an oil injection quantity, an opening timing of an intake valve, a closing timing of an exhaust valve and an ignition advance angle, wherein the error between the IMEP predicted value and the expected value is smaller than an error threshold value, through training. Smooth switching of the HCCI/SI combustion mode is achieved by modifying engine-related parameters.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (6)
1. A HCCI/SI combustion mode switching process control method, characterized by: the method comprises the following steps:
s1: for engine speed N, combustion mode M, air-fuel ratio AFR and fuel injection quantity M fuel Intake valve opening timing theta ivo And exhaust valve closing timing theta evc Sampling as an original input data set; in the combustion mode M, the SI combustion mode is defined as M1, the HCCI combustion mode M is defined as 2, and when M is 1, the ignition advance angle α is also sampled ign ；
S2: building a black box prediction model of the LSTM neural network engine in the switched combustion mode, and predicting IMEP output values IMEP of the upper and lower moments of the time sequence Pre ；
S3: the rotating speed N in the running process of the engine, the combustion mode M at the next moment and the expected value IMEP of the IMEP at the next moment are calculated Exp As inputs, the air-fuel ratio AFR and the fuel injection quantity m fuel Intake valve opening timing θ ivo And exhaust valve closing timing theta evc And ignition advance angle alpha in SI combustion mode ign Designing a BP neural network controller as a control variable; obtaining IMEP predicted value and IMEP expected value IMEP of the combustion mode after switching at the next moment through learning of a neural network Exp Error e between t Less than an error threshold e l Thereby controlling smooth switching of the two combustion modes.
2. The HCCI/SI combustion mode switching process control method of claim 1, wherein: in step S1, IMEP related variables including N, M, AFR, M are determined based on characteristics of the engine combustion process and the two combustion modes fuel 、θ ivo 、θ evc 、α ign (ii) a Under various automobile operating conditions, data are collected once per cycle, namely, each crankshaft rotation angle rotates by 720 degrees, and an original input data set is obtained.
3. The HCCI/SI combustion mode switching process control method as claimed in claim 1, wherein: the LSTM neural network engine black box model in the step S2 comprises an input and feedback layer, a hidden layer and an output layer; the LSTN memory unit is provided with a forgetting gate, an input gate and an output gate;
firstly, the output value of the LSTM cell is calculated by using a forward propagation algorithm: forget to forgetThe gate outputs h according to the last moment by using sigmoid activating function t-1 And current time input x t Determining the information F of passing the forgetting gate at the last moment t 1 (ii) a The input gate determines the state of the unit which is input at the current moment and reserved to the current moment through the sigmoid activation function
The output gate obtains the output of the model through the sigmoid function and the tanh function
And then calculating an error term of each LSTM cell by using a back propagation algorithm, and updating the weight by using a gradient descent method.
4. The HCCI/SI combustion mode switching process control method of claim 3, wherein: in step S2, according to the formula
Determining the state of a unit passing through a forgetting door; according to the formula
Calculating the unit state of the output gate; according to the formula
Calculating new memory to obtain the unit state of the current input to the current moment; according to the formula
And obtaining model output.
5. The HCCI/SI combustion mode switching process control method of claim 1, wherein: in step S3, the BP neural network input layer includes 3 neuron nodes, the hidden layer includes 4 neuron nodes, and the output layerComprises 5 neuron nodes; the stimulus function of the hidden layer is
Neuron threshold of the hidden layer is a j The connection weight from the input layer to the hidden layer is
The excitation function of the output layer is psi (·), and the neuron threshold is b k The connection weight from the hidden layer to the output layer is w jk 。
6. The HCCI/SI combustion mode switching process control method of claim 5, wherein: in step S3, the BP neural network is based on a formula
Calculating output signal H of jth neuron node of hidden layer j (ii) a According to the formula
And calculating an output signal y of a neuron node of an output layer, and realizing the adjustment of engine parameters in the combustion mode switching process through an output vector to realize the stability of the combustion mode switching process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110317167.0A CN112963256B (en) | 2021-03-22 | 2021-03-22 | HCCI/SI combustion mode switching process control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110317167.0A CN112963256B (en) | 2021-03-22 | 2021-03-22 | HCCI/SI combustion mode switching process control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112963256A CN112963256A (en) | 2021-06-15 |
| CN112963256B true CN112963256B (en) | 2022-09-30 |
Family
ID=76278838
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110317167.0A Active CN112963256B (en) | 2021-03-22 | 2021-03-22 | HCCI/SI combustion mode switching process control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112963256B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11459962B2 (en) * | 2020-03-02 | 2022-10-04 | Sparkcognitton, Inc. | Electronic valve control |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007096064A1 (en) * | 2006-02-21 | 2007-08-30 | Fev Motorentechnik Gmbh | Direct-injection spark ignition internal combustion engine with scr catalyst |
| EP1990527A2 (en) * | 2007-05-09 | 2008-11-12 | Hitachi, Ltd. | Engine controller |
| CN103085649A (en) * | 2007-04-12 | 2013-05-08 | 福特环球技术公司 | Control Strategy For Multi-mode Vehicle Propulsion System |
| CN107472038A (en) * | 2017-07-21 | 2017-12-15 | 重庆邮电大学 | Hybrid vehicle energy management method based on hcci engine |
| CN110259590A (en) * | 2019-06-28 | 2019-09-20 | 重庆邮电大学 | Hcci engine burning timing control system under complex working condition |
| CN110552804A (en) * | 2019-09-05 | 2019-12-10 | 重庆邮电大学 | method for estimating combustion timing of mixed fuel HCCI (homogeneous charge compression ignition) engine based on LSTM (localized fuel transfer model) |
| WO2020040014A1 (en) * | 2018-08-21 | 2020-02-27 | 日立オートモティブシステムズ株式会社 | Internal combustion engine control device and internal combustion engine control method |
| JP6708292B1 (en) * | 2019-08-30 | 2020-06-10 | トヨタ自動車株式会社 | Internal combustion engine state determination device, internal combustion engine state determination system, data analysis device, and internal combustion engine control device |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1416143A1 (en) * | 2002-10-29 | 2004-05-06 | STMicroelectronics S.r.l. | Virtual sensor for the exhaust emissions of an endothermic motor and corresponding injection control system |
| US20040182359A1 (en) * | 2003-03-17 | 2004-09-23 | Stewart Daniel W. | Individual cylinder-switching in a multi-cylinder engine |
| FR2905142B1 (en) * | 2006-08-28 | 2008-10-24 | Inst Francais Du Petrole | REAL-TIME ESTIMATION METHOD OF INDICATORS OF COMBUSTION CONDITION OF AN INTERNAL COMBUSTION ENGINE |
| JP5514601B2 (en) * | 2010-03-25 | 2014-06-04 | 本田技研工業株式会社 | Control device for internal combustion engine |
| US8276363B2 (en) * | 2010-08-10 | 2012-10-02 | General Electric Company | Method for compensating for combustion efficiency in fuel control system |
| US10030602B2 (en) * | 2014-07-22 | 2018-07-24 | The Regents Of The University Of Michigan | Adaptive machine learning method to predict and control engine combustion |
| US10358983B2 (en) * | 2016-04-19 | 2019-07-23 | General Electric Company | Asset degradation model baselinening system and method |
| CA2957693A1 (en) * | 2017-02-10 | 2018-08-10 | Motrec International Inc. | Range extender for industrial electric vehicle |
| JP6547992B1 (en) * | 2019-04-18 | 2019-07-24 | トヨタ自動車株式会社 | Oxygen storage amount estimation device, oxygen storage amount estimation system, control device for internal combustion engine, data analysis device, and oxygen storage amount estimation method |
-
2021
- 2021-03-22 CN CN202110317167.0A patent/CN112963256B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007096064A1 (en) * | 2006-02-21 | 2007-08-30 | Fev Motorentechnik Gmbh | Direct-injection spark ignition internal combustion engine with scr catalyst |
| CN103085649A (en) * | 2007-04-12 | 2013-05-08 | 福特环球技术公司 | Control Strategy For Multi-mode Vehicle Propulsion System |
| EP1990527A2 (en) * | 2007-05-09 | 2008-11-12 | Hitachi, Ltd. | Engine controller |
| CN107472038A (en) * | 2017-07-21 | 2017-12-15 | 重庆邮电大学 | Hybrid vehicle energy management method based on hcci engine |
| WO2020040014A1 (en) * | 2018-08-21 | 2020-02-27 | 日立オートモティブシステムズ株式会社 | Internal combustion engine control device and internal combustion engine control method |
| CN110259590A (en) * | 2019-06-28 | 2019-09-20 | 重庆邮电大学 | Hcci engine burning timing control system under complex working condition |
| JP6708292B1 (en) * | 2019-08-30 | 2020-06-10 | トヨタ自動車株式会社 | Internal combustion engine state determination device, internal combustion engine state determination system, data analysis device, and internal combustion engine control device |
| CN110552804A (en) * | 2019-09-05 | 2019-12-10 | 重庆邮电大学 | method for estimating combustion timing of mixed fuel HCCI (homogeneous charge compression ignition) engine based on LSTM (localized fuel transfer model) |
Non-Patent Citations (4)
| Title |
|---|
| HCCI/SI燃烧模式转换过程关键因素的研究;田国弘等;《2007年APC联合学术年会论文集》;20090901;全文 * |
| Study of Fuel Film Parameter Identification in Gasoline Engine Transient Conditions Based on Weighted Recursive Instrumental Variable Method;郑太雄等;《2016 12th World Congress on Intelligent Control and Automation (WCICA)》;20160615;全文 * |
| 基于神经网络的汽油HCCI发动机空燃比控制策略;周能辉等;《农业机械学报》;20090625(第06期);全文 * |
| 基于黑箱模型和RBF-PID的HCCI发动机燃烧正时控制;郑太雄等;《仪器仪表学报》;20151115;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112963256A (en) | 2021-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Aliramezani et al. | Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions | |
| Zareei et al. | Optimization and study of performance parameters in an engine fueled with hydrogen | |
| Bendu et al. | Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO | |
| Rezaei et al. | Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks | |
| Zhao et al. | Optimization and matching for range-extenders of electric vehicles with artificial neural network and genetic algorithm | |
| Yap et al. | Exhaust emissions control and engine parameters optimization using artificial neural network virtual sensors for a hydrogen-powered vehicle | |
| Hellstrom et al. | Reducing cyclic variability while regulating combustion phasing in a four-cylinder HCCI engine | |
| Duan et al. | Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN) | |
| US20110308503A1 (en) | Egr control apparatus for internal combustion engine | |
| Tong et al. | Experiment analysis and computational optimization of the Atkinson cycle gasoline engine through NSGA Ⅱ algorithm using machine learning | |
| JP2012002123A (en) | Egr control apparatus for internal combustion engine | |
| Yu et al. | Internal combustion engine calibration using optimization algorithms | |
| CN112963256B (en) | HCCI/SI combustion mode switching process control method | |
| CN107472038B (en) | Hybrid electric vehicle energy management method based on HCCI engine | |
| CN110259590B (en) | HCCI (homogeneous charge compression ignition) engine combustion timing control system under complex working conditions | |
| CN110552804B (en) | Method for estimating combustion timing of mixed fuel HCCI (homogeneous charge compression ignition) engine based on LSTM (localized fuel transfer model) | |
| Xu et al. | Model-free reinforcement learning approach to optimal speed control of combustion engines in start-up mode | |
| Jansri et al. | Enhanced model and fuzzy strategy of air to fuel ratio control for spark ignition engines | |
| Bozza et al. | Steady-state and transient operation simulation of a “downsized” turbocharged SI engine | |
| Peng et al. | A comparison of neural network-based strategies for diesel engine air handling control | |
| Chiang et al. | Constrained control of homogeneous charge compression ignition (HCCI) engines | |
| CN114934848B (en) | Fuzzy neural network modeling method for optimizing control of combustion performance of diesel engine | |
| Zhu et al. | A nonlinear model predictive control strategy with a disturbance observer for spark ignition engines with external EGR | |
| Chiang et al. | Adaptive control of homogeneous charge compression ignition (HCCI) engines | |
| Kang et al. | Control-oriented cyclic modeling method for spark ignition engines |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |