CN114483271B - Vehicle exhaust waste heat recovery system based on artificial intelligence - Google Patents
Vehicle exhaust waste heat recovery system based on artificial intelligence Download PDFInfo
- Publication number
- CN114483271B CN114483271B CN202111650631.4A CN202111650631A CN114483271B CN 114483271 B CN114483271 B CN 114483271B CN 202111650631 A CN202111650631 A CN 202111650631A CN 114483271 B CN114483271 B CN 114483271B
- Authority
- CN
- China
- Prior art keywords
- automobile
- waste heat
- heat recovery
- pipe
- exhaust
- 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
- 239000002918 waste heat Substances 0.000 title claims abstract description 87
- 238000011084 recovery Methods 0.000 title claims abstract description 82
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 57
- 238000003062 neural network model Methods 0.000 claims abstract description 58
- 125000004122 cyclic group Chemical group 0.000 claims abstract description 38
- 238000001914 filtration Methods 0.000 claims abstract description 33
- 230000000306 recurrent effect Effects 0.000 claims abstract description 20
- 239000007789 gas Substances 0.000 claims description 129
- 230000007246 mechanism Effects 0.000 claims description 38
- 238000000034 method Methods 0.000 claims description 22
- 230000009471 action Effects 0.000 claims description 21
- 230000008569 process Effects 0.000 claims description 11
- 238000000605 extraction Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 10
- 239000000463 material Substances 0.000 description 4
- 238000004064 recycling Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 241000894007 species Species 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 229910002091 carbon monoxide Inorganic materials 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 235000019645 odor Nutrition 0.000 description 1
- 230000009965 odorless effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 235000019640 taste Nutrition 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N5/00—Exhaust or silencing apparatus combined or associated with devices profiting by exhaust energy
- F01N5/02—Exhaust or silencing apparatus combined or associated with devices profiting by exhaust energy the devices using heat
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N11/00—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity
- F01N11/002—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity the diagnostic devices measuring or estimating temperature or pressure in, or downstream of the exhaust apparatus
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N3/00—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust
- F01N3/02—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for cooling, or for removing solid constituents of, exhaust
- F01N3/021—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for cooling, or for removing solid constituents of, exhaust by means of filters
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N9/00—Electrical control of exhaust gas treating apparatus
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
- F01N2900/04—Methods of control or diagnosing
- F01N2900/0402—Methods of control or diagnosing using adaptive learning
-
- 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
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Mechanical Engineering (AREA)
- Combustion & Propulsion (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Exhaust Silencers (AREA)
Abstract
The invention relates to a vehicle exhaust waste heat recovery system based on artificial intelligence, which comprises: the system comprises a first analysis device, a second analysis device and a third analysis device, wherein the first analysis device is used for taking each reserve data corresponding to each motion period of a certain type of automobile before the motion period to be predicted as each input signal of a cyclic neural network model, namely an artificial intelligence model, and taking the reserve data of the type of automobile in the motion period to be predicted as a single output signal of the cyclic neural network model; the second analysis equipment is used for finishing multiple times of learning of the recurrent neural network model; and the prediction execution device is used for executing the cyclic neural network model after multiple times of learning so as to obtain the reserve data of the current automobile category in the next movement period. According to the invention, the artificial intelligence body can be adopted to complete the prediction treatment of the subsequent emission data based on the historical emission data of different types of automobiles, so that key reference information is provided for formulating an exhaust waste heat recovery strategy and an exhaust filtering strategy.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a vehicle exhaust waste heat recovery system based on artificial intelligence.
Background
Modern engine systems today have greatly improved efficiency over previous ones. However, most of the energy it produces is still converted to thermal energy rather than kinetic energy. The energy recycling technology can reduce fuel consumption and realize emission reduction, thereby achieving carbon neutralization treatment to a certain degree on the whole automobile.
The system for recovering the waste Heat of the automobile exhaust is also called EHRS (Exhaust Heat Recovery System). The background generated by the system is the pressure of the current oil vehicles produced by automobile manufacturers to reduce carbon dioxide emission mainly, so that automobile customers have certain expectations and requirements for the EHRS system. 30% of energy of the engine is discharged along with heat of a tail pipe, and a series of tail gas waste heat recovery products can be designed according to the heat of the part of energy, so that a customer can be helped to recover and utilize the part of energy, and the overall carbon emission of the automobile is reduced.
Three different technical routes are currently used to help customers recover heat exhausted from automobile exhaust, which are thermal conduction, thermal conduction and Rankine (Rankine) cycle, respectively. The heat conduction is that the heat discharged by the engine is directly recycled in the form of heat, namely the EHRS; the heat conduction is a thermoelectric material, and after heat discharged by an engine passes through the thermoelectric material, the heat generates micro-current to store the electricity, which is a second technical route to help recover the heat; the third is the Rankine (Rankine) cycle, which has two forms of output, the first being the conversion of heat into mechanical energy; the second is to bring the mechanical energy to a generator, so that the obtained mechanical energy can be converted into electric energy to be output.
However, in the practical use of the exhaust gas waste heat recovery system, as far as possible, automobile manufacturers improve and optimize the exhaust gas waste heat recovery system, but because the specific value of the exhaust gas waste heat of the automobile is a value to be predicted, the automobile cannot determine whether the exhaust gas waste heat of the next movement cycle is large to a value with an absorption value, and how much heat can be supplied for the energy of subsequent actions to be stored under a large condition, so that the management of the exhaust gas waste heat recovery system is difficult to implement effectively.
Disclosure of Invention
In order to solve the technical problems in the related field, the invention provides a vehicle exhaust waste heat recovery system based on artificial intelligence, which can predict the exhaust pipe reference temperature, the pipe body total heat, the total exhaust volume and the time axis length of the future movement cycle of an automobile based on the exhaust pipe reference temperature, the pipe body total heat, the total exhaust volume and the time axis length of a plurality of latest past movement cycles of the automobile by adopting an artificial intelligence body with a customized structure after multiple learning, so that a subsequently executed exhaust waste heat recovery strategy and an exhaust filtering strategy are more targeted, wherein the customized structure of the artificial intelligence body is based on the number of automobile types used when the artificial intelligence body completes multiple learning.
Therefore, the invention at least needs to have the following key points:
(1) the method comprises the steps of predicting total heat emitted by a pipe body in a future movement period by adopting an artificial intelligence model based on reserve data of past movement periods of different automobile types, wherein the artificial intelligence model is selected as a cyclic neural network model subjected to a targeted learning process, and the reserve data comprises an exhaust pipe reference temperature, the total heat emitted by the pipe body, a total tail gas volume and a time axis length;
(2) in the targeted learning process of the recurrent neural network model, each kind of automobile is subjected to a learning process at least once, and the total number of the selected input signals in the recurrent neural network model is positively associated with the number of the kinds of automobiles used when the recurrent neural network model completes multiple times of learning;
(3) acquiring the total heat quantity emitted by a pipe body of the current driving type in the reserve data of the next movement period, and determining a corresponding exhaust gas waste heat recovery strategy of the next movement period based on the acquired total heat quantity emitted by the pipe body, thereby completing the advanced layout of the exhaust gas waste heat recovery strategy;
(4) and acquiring the total tail gas volume of the currently driven automobile type in the reserve data of the next movement period, and determining the tail gas filtering strategy of the corresponding next movement period based on the acquired total tail gas volume, thereby finishing the advanced layout of the tail gas filtering strategy.
According to a first aspect of the present invention, there is provided an artificial intelligence based vehicle exhaust gas waste heat recovery system, the system comprising:
the temperature measuring mechanism is arranged on an exhaust pipe of each type of automobile and used for measuring the reference temperature of the exhaust pipe of each type of automobile in each motion cycle;
the heat extraction mechanism is arranged on the exhaust pipe of each type of automobile and used for accumulating the total heat emitted by the pipe body of the exhaust pipe of each type of automobile in each motion period;
the tail gas detection mechanism is arranged on an exhaust pipe of each type of automobile and used for accumulating the volume of the total tail gas discharged by the exhaust pipe of each type of automobile in each motion period;
and the cycle capturing mechanism is respectively connected with the temperature measuring mechanism and the heat extracting mechanism and is used for executing the capturing action of the reserve data of each motion cycle of each type of automobile: the reserve data comprises the reference temperature of the exhaust pipe, the total heat emitted by the pipe body, the total tail gas volume and the time axis length;
the first analysis equipment is connected with the period capturing mechanism and used for taking each reserve data corresponding to each motion period of a certain type of automobile in a preset period number before the motion period to be predicted as each input signal of a cyclic neural network model, namely an artificial intelligence model, taking the reserve data of the type of automobile in the motion period to be predicted as a single output signal of the cyclic neural network model and analyzing the cyclic neural network model;
the second analysis equipment is respectively connected with the first analysis equipment and the period capturing mechanism and is used for finishing multiple times of learning of the cyclic neural network model analyzed by the first analysis equipment by taking the reserve data of different types of automobiles in the past movement period as the input data and the output data of the cyclic neural network model analyzed by the first analysis equipment;
the prediction execution device is connected with the second analysis device and is used for taking each reserve data corresponding to each motion period with the preset number of periods before the next motion period of the currently-driven automobile type as each input signal of the cyclic neural network model completing the multiple learning so as to execute the cyclic neural network model completing the multiple learning, and obtaining a single output signal of the cyclic neural network model completing the multiple learning, namely the reserve data of the currently-driven automobile type in the next motion period;
the first countermeasure mechanism is connected with the prediction execution equipment and is used for acquiring the total heat quantity dissipated by the pipe body of the current driving type in the reserve data of the next movement period and determining a corresponding tail gas waste heat recovery strategy of the next movement period based on the acquired total heat quantity dissipated by the pipe body;
the selected numerical value of the preset period number is positively associated with the number of the automobile types used by the recurrent neural network model when multiple times of learning are completed;
wherein the time axis length occupied by the single movement period of each type of automobile is the time length consumed by the engine piston of the type of automobile when the engine piston completes the repeated movement for a fixed number of times.
According to a second aspect of the invention, an artificial intelligence-based vehicle exhaust gas waste heat recovery method is provided, and the method includes using the artificial intelligence-based vehicle exhaust gas waste heat recovery platform as described above to complete prediction of total heat emitted by a pipe body in a future movement period and total exhaust gas volume based on reserve data of past movement periods of different automobile types by using an artificial intelligence model so as to advance layout of an exhaust gas waste heat recovery strategy and an exhaust gas filtering strategy in the future movement period.
According to a third aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed, implements the steps of the artificial intelligence based vehicle exhaust gas waste heat recovery method as described above.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a technical flowchart of an artificial intelligence based vehicle exhaust waste heat recovery system according to the present invention.
Fig. 2 is a schematic structural diagram of an artificial intelligence based vehicle exhaust gas waste heat recovery system according to embodiment 1 of the present invention.
Fig. 3 is a schematic structural diagram of an artificial intelligence based vehicle exhaust gas waste heat recovery system according to embodiment 2 of the present invention.
Fig. 4 is a schematic structural diagram of an artificial intelligence based vehicle exhaust gas waste heat recovery system according to embodiment 3 of the present invention.
Fig. 5 is a schematic structural diagram of an artificial intelligence-based vehicle exhaust heat recovery system according to embodiment 4 of the present invention.
Fig. 6 is a schematic diagram of a computer-readable storage medium shown in embodiment 6 of the present invention.
Detailed Description
The exhaust gas refers to automobile exhaust gas, i.e. exhaust gas discharged from an exhaust pipe of an automobile. Automobile exhaust is another important factor of air pollution, and the automobile exhaust contains carbon monoxide, nitric oxide and other solid particles which have adverse effects on human bodies, especially lead-containing gasoline, so that the automobile exhaust has greater harm to human bodies. Different cars also have different odors. Automobile repair experts suggest that normal exhaust should be colorless and odorless, while automobile exhaust with different colors and tastes is likely to indicate that the vehicle itself is facing some faults. It follows that the recovery of automobile exhaust gases is of great importance for the protection of the environment and for the sustainable use of automobiles.
Meanwhile, the technology for recycling the waste heat of the exhaust gas of the automobile engine is to use related waste heat recycling equipment to recycle the heat contained in the exhaust gas of the automobile engine and then convert the heat of the exhaust gas into usable energy through a series of reactions.
At present, the waste heat of automobile exhaust is mainly applied to heating and power generation of carriages and heating of some water and food in the recycling process, although the application of the technologies improves the current automobile riding environment, the waste heat of the automobile exhaust is not fully utilized or is not enough for subsequent energy requirements because the specific emission of the waste heat of the automobile exhaust in the future cannot be accurately known. Similarly, since the volume of the exhaust gas discharged from the automobile is difficult to accurately estimate, the automobile cannot establish a corresponding exhaust filtering mechanism to reliably filter the subsequent exhaust gas, so that the automobile exhaust is not completely filtered or the existing filtering resources are greatly wasted.
To solve the above technical problems. According to the artificial intelligence based vehicle exhaust waste heat recovery system, the total amount of the automobile exhaust waste heat and the total amount of the automobile exhaust emission can be accurately evaluated based on historical data by adopting an artificial intelligence model, and then a corresponding waste heat recovery mechanism and a corresponding exhaust filtering mechanism are established, so that the effectiveness and the reliability of subsequent actions are improved.
Referring to fig. 1, a technical flow chart of an artificial intelligence based vehicle exhaust gas waste heat recovery system according to the present invention is shown.
Firstly, obtaining reserve data of past movement cycles of different automobile types, wherein the reserve data comprises total heat emitted by a pipe body, reference temperature of an exhaust pipe, total tail gas volume and time axis length, and the time axis length occupied by a single movement cycle of each type of automobile is the time length consumed by the engine piston of the type of automobile when the engine piston finishes repeated movement for a fixed number of times, so that the reserve data of the past movement cycles are various types of data acquired aiming at the time length;
secondly, establishing a cyclic neural network model for predicting reserve data of future motion cycles of the automobile to complete the construction of an artificial intelligence model, wherein as shown in fig. 1, a plurality of input data of the cyclic neural network model are a plurality of reserve data corresponding to a plurality of past motion cycles of the corresponding type of automobile respectively, and single output data of the cyclic neural network model is the reserve data of the future motion cycles of the corresponding type of automobile, wherein the used cyclic neural network model is a cyclic neural network model after multiple times of learning, and each type of automobile undergoes at least one learning process in the targeted learning process of the cyclic neural network model;
and finally, the reserve data of the future motion cycle of the automobile is used for realizing the customization of a targeted tail gas waste heat recovery strategy and a tail gas filtering strategy, and the customization comprises the steps of determining the targeted tail gas waste heat recovery strategy by using the total heat emitted by the pipe body and determining the targeted tail gas filtering strategy by using the total tail gas volume.
The method has the key points that the prediction of total heat emitted by the pipe body, the exhaust pipe reference temperature, the total tail gas volume and the time axis length of any type of automobile in the next movement period can be realized by adopting the same artificial intelligent model facing a plurality of types of automobiles, especially the extraction and acquisition of the total heat emitted by the pipe body and the total tail gas volume can provide key basic data for the tail gas waste heat recovery strategy and the tail gas filtering strategy in the next movement period, so that the treatment effect of the tail gas waste heat recovery action and the tail gas filtering action is enhanced, and the defect that the corresponding strategy cannot be customized or the customized strategy has serious deviation because the total heat emitted by the pipe body and the total tail gas volume cannot be accurately predicted in the past is overcome.
Hereinafter, the artificial intelligence based vehicle exhaust gas waste heat recovery system of the present invention will be described in detail by way of examples.
Example 1
Fig. 2 is a schematic structural diagram of an artificial intelligence based vehicle exhaust gas waste heat recovery system according to embodiment 1 of the present invention. As shown in fig. 2, the artificial intelligence based vehicle exhaust gas waste heat recovery system comprises the following components:
the temperature measuring mechanism is arranged on an exhaust pipe of each type of automobile and used for measuring the reference temperature of the exhaust pipe of each type of automobile in each motion cycle;
the heat extraction mechanism is arranged on the exhaust pipe of each type of automobile and used for accumulating the total heat emitted by the pipe body of the exhaust pipe of each type of automobile in each motion period;
the tail gas detection mechanism is arranged on an exhaust pipe of each type of automobile and used for accumulating the volume of the total tail gas discharged by the exhaust pipe of each type of automobile in each motion period;
and the cycle capturing mechanism is respectively connected with the temperature measuring mechanism and the heat extracting mechanism and is used for executing the capturing action of the reserve data of each motion cycle of each type of automobile: the reserve data comprises a reference temperature of the exhaust pipe, total heat emitted by the pipe body, total tail gas volume and time axis length;
the first analysis equipment is connected with the cycle capture mechanism and is used for taking each reserve data respectively corresponding to each motion cycle of a preset cycle number before the motion cycle to be predicted of a certain type of automobile as each input signal of a cyclic neural network model, namely an artificial intelligence model, taking the reserve data of the motion cycle to be predicted of the certain type of automobile as a single output signal of the cyclic neural network model, and analyzing the cyclic neural network model;
the second analysis equipment is respectively connected with the first analysis equipment and the period capturing mechanism and is used for finishing multiple times of learning of the cyclic neural network model analyzed by the first analysis equipment by taking the reserve data of different types of automobiles in the past movement period as the input data and the output data of the cyclic neural network model analyzed by the first analysis equipment;
the prediction execution device is connected with the second analysis device and is used for taking each reserve data respectively corresponding to each motion cycle with the preset cycle quantity before the next motion cycle of the currently driven automobile type as each input signal of the cyclic neural network model completing the multiple learning so as to execute the cyclic neural network model completing the multiple learning, and obtaining a single output signal of the cyclic neural network model completing the multiple learning, namely reserve data of the currently driven automobile type in the next motion cycle;
the first corresponding mechanism is connected with the prediction execution equipment and is used for acquiring the total heat quantity emitted by the pipe body of the current driving type in the reserve data of the next motion cycle and determining a corresponding tail gas waste heat recovery strategy of the next motion cycle based on the acquired total heat quantity emitted by the pipe body;
the selected numerical value of the number of the preset periods is positively associated with the number of the types of the automobiles used by the recurrent neural network model when the repeated learning is completed;
for example, the selected value of the preset number of periods and the number of the automobile types used by the recurrent neural network model when completing multiple learning can keep a nonlinear positive correlation relationship, and the following nonlinear positive correlation relationship can be selected: when the number of the automobile types used by the recurrent neural network model in the process of completing multiple times of learning is 20, the number value of the selected preset period number is 1000; when the number of the automobile types used by the recurrent neural network model in the process of completing multiple times of learning is 30, the value of the selected preset period number is 2000; when the number of the automobile types used by the recurrent neural network model after completing multiple times of learning is 40, the value of the selected preset period number is 4000;
for another example, the selected value of the number of preset periods and the number of the types of automobiles used by the recurrent neural network model when completing multiple learning may keep a linear positive correlation, and the following linear positive correlation may be selected: when the number of the automobile types used by the recurrent neural network model in completing multiple times of learning is 20, the number value of the selected preset period number is 1000; when the number of the automobile types used by the recurrent neural network model in the process of completing multiple times of learning is 30, the number value of the selected preset period number is 1500; and when the number of the automobile types used by the recurrent neural network model in completing the multiple learning is 40, the value of the selected preset period number is 2000.
Wherein the length of the time axis occupied by a single movement cycle of each type of automobile is the length of time consumed by the engine piston of the type of automobile when the fixed number of repeated movements are completed.
Example 2
Fig. 3 is a schematic structural diagram of an artificial intelligence based vehicle exhaust gas waste heat recovery system according to embodiment 2 of the present invention. As shown in fig. 3, compared to embodiment 1 of the present invention, the artificial intelligence based vehicle exhaust gas waste heat recovery system further includes:
the content storage mechanism is connected with the first corresponding mechanism and used for storing the corresponding relation between the obtained total heat emitted by the pipe body and the determined tail gas waste heat recovery strategy of the next corresponding movement period;
wherein, the tail gas waste heat recovery strategy of the next movement cycle that confirms to correspond based on the body total heat that gives off that obtains includes: when the obtained total heat emitted by the pipe body is greater than or equal to a set heat threshold value, starting the tail gas waste heat recovery action of the next movement period;
wherein, when the body that acquires gived off total heat more than or equal to and set for the heat threshold value, the tail gas waste heat recovery action that starts next movement cycle includes: determining the quantity of heat required to be drawn from the pipe body of the exhaust pipe in the tail gas waste heat recovery action of the next movement period based on the difference value of the obtained total heat emitted by the pipe body exceeding the set heat threshold value;
wherein, the heat numerical value that needs to draw from the body of blast pipe in the tail gas waste heat recovery action of next movement cycle is confirmed to the difference size that the total heat that gives off based on the body that obtains surpassed and set for the heat threshold value includes: the smaller the difference value of the obtained total heat emitted by the pipe body exceeding the set heat threshold value is, the smaller the heat value which needs to be absorbed from the pipe body of the exhaust pipe in the tail gas waste heat recovery action of the next movement period is;
wherein, the smaller the difference that the total heat that gives off based on the body of obtaining exceeds the settlement heat threshold value is, the smaller the heat numerical value that needs to draw from the body of blast pipe in the tail gas waste heat recovery action of next movement cycle of confirming includes: the adopted heat drawing mechanism for drawing the heat of the pipe body of the exhaust pipe is arranged on the pipe wall at the central position between the tail end and the head end of the exhaust pipe.
Example 3
Fig. 4 is a schematic structural diagram of an artificial intelligence based vehicle exhaust gas waste heat recovery system according to embodiment 3 of the present invention. As shown in fig. 4, compared to embodiment 3 of the present invention, the artificial intelligence based vehicle exhaust gas waste heat recovery system further includes:
the second corresponding mechanism is connected with the prediction execution equipment and is used for acquiring the total tail gas volume of the current driving type in the reserve data of the next motion cycle and determining a tail gas filtering strategy of the corresponding next motion cycle based on the acquired total tail gas volume;
wherein determining an exhaust filtration strategy for a corresponding next movement cycle based on the obtained total exhaust volume comprises: the smaller the value of the obtained total exhaust volume is, the smaller the number of the exhaust filter mesh bodies used in the exhaust filter strategy corresponding to the next movement period is.
Wherein, the smaller the value of the obtained total exhaust volume is, the smaller the number of the exhaust filter net bodies used in the exhaust filter strategy of the corresponding next movement period is, the smaller the number of the exhaust filter net bodies comprises: the number of exhaust gas filter mesh bodies used in the determined exhaust gas filtering strategy for the corresponding next movement period is directly proportional to the value of the obtained total exhaust gas volume.
Example 4
Fig. 5 is a schematic structural diagram of an artificial intelligence based vehicle exhaust gas waste heat recovery system according to embodiment 4 of the present invention. As shown in fig. 5, compared with embodiment 4 of the present invention, the artificial intelligence based vehicle exhaust heat recovery system further includes:
the exhaust gas filtering device comprises a filtering net body array and a control system, wherein the filtering net body array comprises a plurality of exhaust gas filtering net bodies, each exhaust gas filtering net body is positioned at the tail end of a pipe body of the exhaust pipe in a use state, and the plurality of exhaust gas filtering net bodies have the same structure;
wherein, each tail end that tail gas filter screen body lies in the body of blast pipe under the user state includes: when a plurality of exhaust gas filter net bodies are used, the exhaust gas filter net bodies are simultaneously positioned at the tail end of the pipe body of the exhaust pipe in a uniform interval mode;
illustratively, the plurality of exhaust gas filter screen bodies are arranged at the tail end of the pipe body of the exhaust pipe in a pattern that maintains a space of 15 mm therebetween in a direction from the front end to the tail end of the pipe body
In any of the above embodiments, optionally, in the artificial intelligence based vehicle exhaust gas waste heat recovery system:
the multiple learning of the cyclic neural network model analyzed by the first analysis device is completed by taking the reserve data of different types of automobiles in the past movement period as the input data and the output data of the cyclic neural network model analyzed by the first analysis device, and the multiple learning comprises the following steps: in the multiple learning, each kind of automobile is subjected to at least one learning process.
In any of the above embodiments, optionally, in the artificial intelligence based vehicle exhaust gas waste heat recovery system:
measuring the exhaust pipe reference temperature of the species vehicle at each motion cycle comprises: respectively acquiring a plurality of pipe wall temperatures of the exhaust pipe of the automobile of the same type at uniform time intervals in each motion period, and taking a middle value for the plurality of pipe wall temperatures to acquire the reference temperature of the exhaust pipe of the automobile of the same type in each motion period;
the method for obtaining the reference temperature of the exhaust pipe of the automobile of the type in each motion period by obtaining the plurality of pipe wall temperatures of the exhaust pipe of the automobile of the type at uniform time intervals in each motion period and taking the intermediate value of the plurality of pipe wall temperatures to obtain the reference temperature of the exhaust pipe of the automobile of the type in each motion period comprises the following steps: uniformly dividing a time axis occupied by each motion cycle to obtain a plurality of equal time periods, and collecting the temperature of a single pipe wall of the exhaust pipe of the automobile type at the tail end of each time period;
wherein evenly dividing the time axis occupied by each motion cycle to obtain a plurality of equal time segments, and acquiring a single wall temperature of the automobile-like exhaust pipe at the end of each time segment comprises: a contact thermometer may be employed for collecting the individual wall temperatures of the exhaust pipes of the generic car at the end of each time period;
and alternatively, evenly dividing the time axis occupied by each movement cycle to obtain a plurality of equal time segments, the acquiring of the single wall temperature of the exhaust pipe of the species of automobile at the end of each time segment comprising: a non-contact thermometer may be employed for collecting the individual wall temperatures of the exhaust pipes of the generic car at the end of each time period.
In any of the above embodiments, optionally, in the artificial intelligence based vehicle exhaust gas waste heat recovery system:
the tail gas waste heat recovery strategy for determining the corresponding next movement period based on the obtained total heat emitted by the pipe body further comprises the following steps: and when the obtained total heat emitted by the pipe body is smaller than the set heat threshold value, the tail gas waste heat recovery action of the next motion cycle is suspended.
Example 5
In this embodiment, the invention builds an artificial intelligence-based vehicle exhaust waste heat recovery method, and the method includes that the artificial intelligence-based vehicle exhaust waste heat recovery platform is used to predict the total heat emitted by the pipe body and the total exhaust volume of the future motion cycle based on the stored data of the past motion cycles of different automobile types by adopting an artificial intelligence model so as to arrange the exhaust waste heat recovery strategy and the exhaust filtering strategy of the future motion cycle in advance.
Example 6
Fig. 6 is a schematic diagram of a computer-readable storage medium shown in embodiment 6 of the present invention. As shown in fig. 6, a computer-readable storage medium 60, having non-transitory computer-readable instructions 61 stored thereon, in accordance with an embodiment of the present disclosure. When the non-transitory computer readable instructions 61 are executed by the processor, all or part of the steps of the artificial intelligence based vehicle exhaust gas waste heat recovery method of embodiment 5 of the present invention described above are performed.
In addition, the multiple learning of the recurrent neural network model analyzed by the first analysis device by using the reserve data of different types of automobiles in the past movement period as the input data and the output data of the recurrent neural network model analyzed by the first analysis device comprises the following steps: in the one-time learning of the recurrent neural network model aiming at a certain past motion period of a certain kind of automobile, respectively corresponding reserve data of each motion period with a preset period number before the certain past motion period of the certain kind of automobile are used as input signals of the recurrent neural network model, and the reserve data of the certain past motion period of the certain kind of automobile are used as single input signals of the recurrent neural network model;
and alternatively, measuring the exhaust pipe reference temperature of the species vehicle at each cycle of motion comprises: and respectively acquiring a plurality of pipe wall temperatures of the exhaust pipe of the automobile of the same type at uniform time intervals in each motion period, and taking an arithmetic mean value of the plurality of pipe wall temperatures to obtain the reference temperature of the exhaust pipe of the automobile of the same type in each motion period.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the embodiments of the present application, "a plurality" means two or more unless specifically defined otherwise. In the description herein, references to the description of the terms "one embodiment," "certain embodiments," "an illustrative embodiment," "an example," "a specific example" or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. The utility model provides a vehicle exhaust waste heat recovery system based on artificial intelligence which characterized in that, the system includes:
the temperature measuring mechanism is arranged on an exhaust pipe of each type of automobile and used for measuring the reference temperature of the exhaust pipe of each type of automobile in each motion cycle;
the heat extraction mechanism is arranged on the exhaust pipe of each type of automobile and used for accumulating the total heat emitted by the pipe body of the exhaust pipe of each type of automobile in each motion period;
the tail gas detection mechanism is arranged on an exhaust pipe of each type of automobile and used for accumulating the volume of the total tail gas discharged by the exhaust pipe of each type of automobile in each motion period;
and the cycle capturing mechanism is respectively connected with the temperature measuring mechanism and the heat extracting mechanism and is used for executing the capturing action of the reserve data of each motion cycle of each type of automobile: the reserve data comprises the reference temperature of the exhaust pipe, the total heat emitted by the pipe body, the total tail gas volume and the time axis length;
the first analysis equipment is connected with the cycle capture mechanism and is used for taking each reserve data respectively corresponding to each motion cycle of a preset cycle number before the motion cycle to be predicted of a certain type of automobile as each input signal of a cyclic neural network model, namely an artificial intelligence model, taking the reserve data of the motion cycle to be predicted of the certain type of automobile as a single output signal of the cyclic neural network model, and analyzing the cyclic neural network model;
the second analysis equipment is respectively connected with the first analysis equipment and the period capturing mechanism and is used for finishing multiple learning of the cyclic neural network model analyzed by the first analysis equipment by taking the reserve data of different types of automobiles in the past movement period as the input data and the output data of the cyclic neural network model analyzed by the first analysis equipment;
the prediction execution device is connected with the second analysis device and is used for taking each reserve data corresponding to each motion period with the preset number of periods before the next motion period of the currently-driven automobile type as each input signal of the cyclic neural network model completing the multiple learning so as to execute the cyclic neural network model completing the multiple learning, and obtaining a single output signal of the cyclic neural network model completing the multiple learning, namely the reserve data of the currently-driven automobile type in the next motion period;
the first countermeasure mechanism is connected with the prediction execution equipment and is used for acquiring the total heat quantity dissipated by the pipe body of the current driving type in the reserve data of the next movement period and determining a corresponding tail gas waste heat recovery strategy of the next movement period based on the acquired total heat quantity dissipated by the pipe body;
the selected numerical value of the preset period number is positively associated with the number of the automobile types used by the recurrent neural network model when multiple times of learning are completed;
wherein the length of the time axis occupied by a single movement cycle of each type of automobile is the length of time consumed by the engine piston of the type of automobile when the fixed number of repeated movements are completed.
2. The artificial intelligence based vehicle exhaust gas waste heat recovery system of claim 1, further comprising:
the content storage mechanism is connected with the first corresponding mechanism and used for storing the corresponding relation between the obtained total heat emitted by the pipe body and the determined tail gas waste heat recovery strategy of the next corresponding movement period;
wherein, the tail gas waste heat recovery strategy of the next movement cycle that confirms to correspond based on the body total heat that gives off that obtains includes: when the obtained total heat emitted by the pipe body is greater than or equal to a set heat threshold value, starting the tail gas waste heat recovery action of the next movement period;
wherein, when the body that acquires gived off total heat more than or equal to and set for the heat threshold value, the tail gas waste heat recovery action that starts next movement cycle includes: determining the quantity of heat which needs to be drawn from the pipe body of the exhaust pipe in the tail gas waste heat recovery action of the next movement period based on the difference value of the obtained total heat emitted by the pipe body exceeding the set heat threshold value;
wherein, the heat numerical value that needs to draw from the body of blast pipe in the tail gas waste heat recovery action of next movement cycle is confirmed to the difference size that the total heat that gives off based on the body that obtains surpassed and set for the heat threshold value includes: the smaller the difference value of the obtained total heat emitted by the pipe body exceeding the set heat threshold value is, the smaller the heat value which needs to be absorbed from the pipe body of the exhaust pipe in the tail gas waste heat recovery action of the next movement period is;
wherein, the smaller the difference that total heat that gives off based on the body that obtains surpasss the setting heat threshold value, the smaller the heat numerical value that needs to draw from the body of blast pipe in the tail gas waste heat recovery action of the next movement cycle of confirming includes: the adopted heat drawing mechanism for drawing the heat of the pipe body of the exhaust pipe is arranged on the pipe wall at the central position between the tail end and the head end of the exhaust pipe.
3. The artificial intelligence based vehicle exhaust gas waste heat recovery system of claim 2, further comprising:
the second corresponding mechanism is connected with the prediction execution equipment and is used for acquiring the total tail gas volume of the current driving type in the reserve data of the next movement period and determining a tail gas filtering strategy of the corresponding next movement period based on the acquired total tail gas volume;
wherein determining an exhaust filtration strategy for a corresponding next movement cycle based on the obtained total exhaust volume comprises: the smaller the value of the obtained total exhaust gas volume, the smaller the number of exhaust gas filter mesh bodies used in the exhaust gas filtering strategy corresponding to the next movement period.
4. The artificial intelligence based vehicle exhaust gas waste heat recovery system of claim 3, further comprising:
the exhaust gas filtering device comprises a filtering net body array and a tail gas filtering net body array, wherein the filtering net body array comprises a plurality of exhaust gas filtering net bodies, each exhaust gas filtering net body is positioned at the tail end of a pipe body of the exhaust pipe in a use state, and the structures of the plurality of exhaust gas filtering net bodies are the same;
wherein, each tail end that tail gas filter screen body lies in the body of blast pipe under the user state includes: when a plurality of exhaust gas filter mesh bodies are used, the plurality of exhaust gas filter mesh bodies are simultaneously located at the tail end of the pipe body of the exhaust pipe in an evenly spaced mode.
5. The artificial intelligence based vehicle exhaust gas waste heat recovery system of any one of claims 1 to 4, wherein:
the multiple learning of the cyclic neural network model analyzed by the first analysis device is completed by taking the reserve data of different types of automobiles in the past movement period as the input data and the output data of the cyclic neural network model analyzed by the first analysis device, and the multiple learning comprises the following steps: in the multiple learning, each kind of automobile is subjected to at least one learning process.
6. The artificial intelligence based vehicle exhaust gas waste heat recovery system of any one of claims 1 to 4, wherein:
measuring the exhaust pipe reference temperature of the species vehicle at each motion cycle comprises: and respectively acquiring a plurality of pipe wall temperatures of the exhaust pipe of the automobile of the same type at uniform time intervals in each motion period, and taking a middle value for the plurality of pipe wall temperatures to acquire the reference temperature of the exhaust pipe of the automobile of the same type in each motion period.
7. The artificial intelligence based vehicle exhaust gas waste heat recovery system of claim 6, wherein:
respectively acquiring a plurality of pipe wall temperatures of an exhaust pipe of the automobile of the same type at uniform time intervals in each motion cycle, and obtaining a middle value of the pipe wall temperatures to acquire a reference temperature of the exhaust pipe of the automobile of the same type in each motion cycle comprises the following steps: the time axis occupied by each movement cycle is evenly divided to obtain a plurality of equal time segments, and the single wall temperature of the exhaust pipe of the automobile type is collected at the end of each time segment.
8. The artificial intelligence based vehicle exhaust gas waste heat recovery system of any one of claims 2 to 4, wherein:
the tail gas waste heat recovery strategy for determining the corresponding next movement period based on the obtained total heat emitted by the pipe body further comprises the following steps: and when the obtained total heat emitted by the pipe body is smaller than the set heat threshold value, the tail gas waste heat recovery action of the next motion cycle is suspended.
9. An artificial intelligence based vehicle exhaust gas waste heat recovery method, which comprises the step of using the artificial intelligence based vehicle exhaust gas waste heat recovery system according to any one of claims 1 to 8 to predict the total heat dissipated by the pipe body and the total exhaust volume of a future motion cycle based on the stored data of past motion cycles of different automobile types by adopting an artificial intelligence model so as to arrange an exhaust gas waste heat recovery strategy and an exhaust gas filtering strategy of the future motion cycle in advance.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed, performs the steps of the method of claim 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111650631.4A CN114483271B (en) | 2021-12-30 | 2021-12-30 | Vehicle exhaust waste heat recovery system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111650631.4A CN114483271B (en) | 2021-12-30 | 2021-12-30 | Vehicle exhaust waste heat recovery system based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114483271A CN114483271A (en) | 2022-05-13 |
| CN114483271B true CN114483271B (en) | 2022-09-30 |
Family
ID=81507513
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111650631.4A Active CN114483271B (en) | 2021-12-30 | 2021-12-30 | Vehicle exhaust waste heat recovery system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114483271B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2523990A (en) * | 2014-03-10 | 2015-09-16 | Gm Global Tech Operations Inc | Method of controlling a diesel particulate filter |
| EP3004583A1 (en) * | 2013-05-27 | 2016-04-13 | Volvo Truck Corporation | Method for timing of a regeneration process |
| CN108594895A (en) * | 2018-05-31 | 2018-09-28 | 江苏大学 | Energy recovery device of automobile exhaust electric-control system based on thermo-electric generation and method |
| CN208205047U (en) * | 2017-10-27 | 2018-12-07 | 北京中电联节能技术有限公司 | The hardware structure of feedforward neural network control system suitable for flue gas waste heat recovery |
| JP2019133610A (en) * | 2018-02-03 | 2019-08-08 | アレグロスマート株式会社 | Data orchestration platform management |
| JP6935837B1 (en) * | 2020-08-20 | 2021-09-15 | トヨタ自動車株式会社 | Machine learning device and machine learning system |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5941918A (en) * | 1997-07-30 | 1999-08-24 | Engelhard Corporation | Automotive on-board monitoring system for catalytic converter evaluation |
| JP2007332932A (en) * | 2006-06-19 | 2007-12-27 | Toyota Motor Corp | Abnormality diagnosis device for internal combustion engine |
| US8301356B2 (en) * | 2008-10-06 | 2012-10-30 | GM Global Technology Operations LLC | Engine out NOx virtual sensor using cylinder pressure sensor |
| DE102012006632A1 (en) * | 2012-03-31 | 2013-10-02 | Volkswagen Aktiengesellschaft | Method and system for heat transfer for a vehicle |
| US9909481B2 (en) * | 2015-12-10 | 2018-03-06 | GM Global Technology Operations LLC | System and method for determining target actuator values of an engine using model predictive control while satisfying emissions and drivability targets and maximizing fuel efficiency |
| CN105715318B (en) * | 2016-04-12 | 2017-04-26 | 浙江大学 | Comprehensive waste heat utilization evaluation method on basis of industrial steam turbine and comprehensive waste heat utilization device |
| JP6477951B1 (en) * | 2018-04-05 | 2019-03-06 | トヨタ自動車株式会社 | In-vehicle electronic control unit |
| US20200063633A1 (en) * | 2018-08-22 | 2020-02-27 | GM Global Technology Operations LLC | Method and system for compensating nox sensor measurement error |
| CN212843004U (en) * | 2020-09-18 | 2021-03-30 | 川崎重工业株式会社 | Waste heat recovery boiler and dust removal operation planning device thereof |
-
2021
- 2021-12-30 CN CN202111650631.4A patent/CN114483271B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3004583A1 (en) * | 2013-05-27 | 2016-04-13 | Volvo Truck Corporation | Method for timing of a regeneration process |
| GB2523990A (en) * | 2014-03-10 | 2015-09-16 | Gm Global Tech Operations Inc | Method of controlling a diesel particulate filter |
| CN208205047U (en) * | 2017-10-27 | 2018-12-07 | 北京中电联节能技术有限公司 | The hardware structure of feedforward neural network control system suitable for flue gas waste heat recovery |
| JP2019133610A (en) * | 2018-02-03 | 2019-08-08 | アレグロスマート株式会社 | Data orchestration platform management |
| CN108594895A (en) * | 2018-05-31 | 2018-09-28 | 江苏大学 | Energy recovery device of automobile exhaust electric-control system based on thermo-electric generation and method |
| JP6935837B1 (en) * | 2020-08-20 | 2021-09-15 | トヨタ自動車株式会社 | Machine learning device and machine learning system |
Non-Patent Citations (1)
| Title |
|---|
| 一种基于神经网络的氧化催化器出口温度控制方法;李延红等;《汽车电器》;20200120(第01期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114483271A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109707494A (en) | A kind for the treatment of method and apparatus post-processing sulfur poisoning | |
| CN103225534B (en) | Soot sensor is monitored | |
| CN106014568A (en) | Systems and methods for monitoring the health of a selective catalytic reduction catalyst | |
| CN100419229C (en) | A deterioration diagnostic device for an exhaust gas purifier | |
| JP2010241263A (en) | Diagnosis device for assisting failure reproduction, and output method for failure reproduction data | |
| CN101963800A (en) | Remote real-time monitoring system and monitoring method for diesel vehicle road emission | |
| CN102061973A (en) | Method for diagnosing a catalytic device by estimating an aging index | |
| DE102018127550A1 (en) | METHOD FOR OPERATING A HYBRID VEHICLE | |
| CN103399820A (en) | Fault diagnosis system and fault diagnosis method based on sequence and consequence analysis of event tree | |
| CN111197517B (en) | Fault diagnosis method and device for urea tank, vehicle and storage medium | |
| CN114483271B (en) | Vehicle exhaust waste heat recovery system based on artificial intelligence | |
| DE112018004701T5 (en) | CONTROL DEVICE AND CONTROL METHOD FOR AN EXHAUST GAS PURIFICATION SYSTEM IN A VEHICLE | |
| CN103147830B (en) | A kind of SCR exhaust gas aftertreatment system failure diagnostic apparatus | |
| CN112199145A (en) | Intelligent diagnosis method, system and diagnosis equipment for vehicle | |
| Canova et al. | Model-based fault detection and isolation for a diesel lean NOx trap aftertreatment system | |
| KR102185572B1 (en) | Method and device for performing a reference measurement on a sensor of an internal combustion engine | |
| CN102678242A (en) | Hydrocarbon conversion diagnostic | |
| CN113962825A (en) | Household carbon footprint monitoring system | |
| US8783097B2 (en) | System for sensing soot of diesel vehicle | |
| CN115079663A (en) | Vehicle power system monitoring method and device based on digital twin technology | |
| CN110259552B (en) | Method and device for detecting vehicle exhaust treatment result | |
| CN201503349U (en) | Automobile remote monitoring device | |
| CN112253294B (en) | Remote online monitoring and optimizing method for SCR efficiency of diesel engine | |
| Rodgers et al. | Analysing the energy consumption of the BMW ActiveE field trial vehicles with application to distance to empty algorithms | |
| CN110738754B (en) | Vehicle kinematic segment extraction method, vehicle working condition analysis method and corresponding device |
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 |