CN113669443B - Control method and control system for automatic transmission of vehicle - Google Patents
Control method and control system for automatic transmission of vehicle Download PDFInfo
- Publication number
- CN113669443B CN113669443B CN202010401870.5A CN202010401870A CN113669443B CN 113669443 B CN113669443 B CN 113669443B CN 202010401870 A CN202010401870 A CN 202010401870A CN 113669443 B CN113669443 B CN 113669443B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- gradient
- gear
- module
- control method
- 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
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
- F16H61/02—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used
- F16H61/0202—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric
- F16H61/0204—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric for gearshift control, e.g. control functions for performing shifting or generation of shift signal
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H59/00—Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
- F16H59/36—Inputs being a function of speed
- F16H59/44—Inputs being a function of speed dependent on machine speed of the machine, e.g. the vehicle
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H59/00—Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
- F16H59/50—Inputs being a function of the status of the machine, e.g. position of doors or safety belts
- F16H59/52—Inputs being a function of the status of the machine, e.g. position of doors or safety belts dependent on the weight of the machine, e.g. change in weight resulting from passengers boarding a bus
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H59/00—Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
- F16H59/60—Inputs being a function of ambient conditions
- F16H59/66—Road conditions, e.g. slope, slippery
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H59/00—Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
- F16H59/60—Inputs being a function of ambient conditions
- F16H59/66—Road conditions, e.g. slope, slippery
- F16H2059/663—Road slope
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
- F16H2061/0075—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by a particular control method
- F16H2061/0081—Fuzzy logic
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
- F16H61/04—Smoothing ratio shift
- F16H2061/0459—Smoothing ratio shift using map for shift parameters, e.g. shift time, slip or pressure gradient, for performing controlled shift transition and adapting shift parameters by learning
Abstract
The present invention relates to a control method for an automatic transmission of a vehicle and a corresponding control system. The control method comprises the following steps: acquiring basic information of a vehicle (S01); acquiring gradient information of a road ahead (S02); estimating the current total mass of the vehicle in real time (S03) to obtain an estimated current total mass of the vehicle; performing blurring processing (S04); a final gear decision is calculated and output (S05). According to the present invention, a blurring process is performed based on gradient information of a road ahead and a current total mass of a vehicle, and a final gear decision is calculated based on a result of the blurring process.
Description
Technical Field
The present invention relates to a control method for an automatic transmission of a vehicle, in particular for an automatic gear shift, and to a corresponding control system.
Background
Patent document CN103836179 discloses a GPS-based predictive shift law for automatic transmissions. The default gear shifting rule adopted is based on GPS positioning and combines vehicle information such as accelerator pedal signals, vehicle speed and the like.
Patent document CN104670211 discloses a system for controlling gear shifting of a vehicle. Wherein the vehicle shift control device is assisted by predicting the condition of the driving road.
Patent document CN107031647 discloses a predictive transmission control method using road shape recognition. A control method of performing transmission predictive control by recognizing a road shape (including a gradient and a curvature) ahead of a vehicle by high-precision map information is disclosed therein.
Patent document CN104696504 discloses a vehicle shift control method and apparatus. The method comprises the steps of extracting characteristic quantities of each gear shifting parameter by using a neural network algorithm, calculating membership values of each gear shifting mode, and finally calculating to obtain the optimal gear shifting speed under the current working condition, so that the gear shifting strategy of the transmission can be timely adjusted according to the current vehicle condition, the driving state and the change condition of road conditions, and the driving experience of a driver is enhanced.
Patent document US20100030437A1 discloses a method for adapting the shift schedule of an automatic transmission based on global positioning system/map information. The concept of road gradient factors is provided, the transmission shifting points are adjusted according to the gradient factors, the transmission shifting characteristics are improved, and the driving experience is optimized.
The technical scheme mainly adopts the front road information provided by the high-precision map to correct the gear shifting curve used by the general two-parameter gear shifting strategy so as to obtain better vehicle drivability and economic improvement. However, the above technical solutions generally do not consider the influence of the vehicle load, and are difficult to adapt to different application scenarios.
Disclosure of Invention
The invention aims to solve the technical problem of providing a control method for an automatic transmission of a vehicle, which aims to solve the problem that the current gear shifting control method of the automatic transmission is difficult to flexibly adapt to the change of driving environment, and can improve the driving comfort of the vehicle and the economy of the vehicle.
The invention proposes a control method for an automatic transmission of a vehicle, comprising the steps of:
acquiring basic information of a vehicle;
acquiring gradient information of a road in front;
estimating the current total mass of the vehicle in real time to obtain the estimated current total mass of the vehicle;
performing fuzzy processing;
calculating a final gear decision and outputting the gear decision;
a blurring process is performed based on gradient information of a road ahead and a current total mass of the vehicle, and a final gear decision is calculated based on a result of the blurring process.
Through the comprehensive utilization of the gradient information of the road ahead and the current total mass of the vehicle, the road gradient and dynamic vehicle weight factors can be introduced under the premise of considering the parameter information of the vehicle, and the transmission gear shifting strategy can be more intelligent through an intelligent fuzzy algorithm, so that better drivability experience and more economic improvement are brought.
According to a preferred embodiment, the basic information of the vehicle includes the vehicle quality, the tire radius, the engine torque, the vehicle speed, the transmission efficiency.
According to a preferred embodiment, the current total mass of the vehicle is the real-time vehicle weight estimated by:
m is the current total mass of the vehicle,
F T is the driving force of the device,
F a is the air resistance, the air resistance is high,
J w is the moment of inertia of the wheel,
J e is the moment of inertia of the engine,
i g is the gear ratio of the current gear of the transmission,
i 0 is the main speed reduction ratio of the device,
η T is the mechanical efficiency of the drive train and,
v is the current vehicle speed and,
g is the gravitational constant of the liquid,
f is the road rolling resistance coefficient,
i is the sine of the road gradient angle.
According to a preferred embodiment, the grade information comprises one or more of the following values: the gradient value of the gradient road section, the duration of the gradient road section and the position of the starting point of the gradient road section.
According to a preferred embodiment, the gradient information is checked for validity as a function of the gradient value and/or the duration of the gradient section.
According to a preferred embodiment, the system determines that the grade value is not valid if the duration of the grade section is less than the set length.
According to a preferred embodiment, the parameter domains are determined and the fuzzy subsets are defined in a fuzzy process.
According to a preferred embodiment, in the blurring process, the Sugeno direct reasoning algorithm is used
According to a preferred embodiment, the fuzzy relation calculation is performed by utilizing the maximum and minimum synthesis rules, and then the equivalent load coefficient is obtained through the fuzzy calculation based on the gravity center method.
According to a preferred embodiment, the method calculates a target gear of the vehicle in a certain time in the future and shifts in advance based on the target gear of the vehicle in the certain time in the future.
According to a preferred embodiment, the advance of the shift ahead is derived by taking into account the current vehicle speed, the current total mass of the vehicle and the gradient information.
The invention also proposes a control system for a vehicle automatic transmission configured to perform the method according to the invention for controlling the gear of said vehicle automatic transmission.
According to a preferred embodiment, the control system comprises an input module for obtaining parameters, a predictive rules module for blurring the parameters, and a gear decision module for determining a gear.
According to a preferred embodiment, the predictive rules module includes a parameter obfuscation sub-module, an obfuscation rules sub-module, and a defuzzification sub-module.
Drawings
Fig. 1 is a flow chart of the overall control strategy of the method according to the invention.
Fig. 2 is a flowchart of the gradient information acquisition routine.
FIG. 3 is a flow chart of a real-time weight estimation algorithm.
FIG. 4 is a schematic diagram of membership functions.
Fig. 5 is a schematic block diagram of a control system.
Detailed Description
The control method and the control system according to the present invention will be described below by means of specific embodiments with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.
Fig. 1 shows a flow chart of the overall control strategy of the method according to the invention. First, in step S01, basic information of the vehicle such as, but not limited to, the entire vehicle quality, tire radius, engine torque, vehicle speed, driveline efficiency, etc. is acquired. The vehicle basic information may be retrieved from a memory of the vehicle or from a sensor of the vehicle, for example.
Then, in step S02, gradient information of the road ahead is acquired, which may include a gradient value, a duration of the gradient link, and the like. Turning then to step S03, the current total mass of the vehicle is estimated in real time in step S03 to obtain an estimated current total mass of the vehicle. The order of step S02 and step S03 may be interchanged or may be performed in parallel.
By acquiring the basic information of the vehicle and the gradient information of the road ahead, factors affecting the vehicle resistance, such as the vehicle mass, the rolling resistance coefficient, the road gradient, and the vehicle acceleration reflecting the operation intention of the driver, are taken into consideration. In some road scenarios, the automatic transmission controller may anticipate changes in road resistance ahead by acquiring such information ahead of time and respond ahead of time. Therefore, the problems that the engine is far away from an economic working area and the like are avoided, and the drivability and the economy of the vehicle are improved.
In step S04, blurring processing is performed on each parameter. In step S05, a final gear decision is calculated and output.
Fig. 2 shows a flowchart of a gradient information acquisition routine according to the present invention. In step S21, it is determined whether the gradient information of the road ahead is updated. If it is determined in step S21 that the gradient information of the road ahead is not updated, the flow ends. Whereas if it is determined in step S21 that the gradient information of the front road is updated, gradient information of the front road is acquired in step S22, which gradient information may include a gradient value of the gradient link, a duration of the gradient link (i.e., a gradient length), absolute coordinates of a starting point of the gradient link, or an offset thereof with respect to a current vehicle position, or the like.
As an example, gradient information may be acquired from high-precision map data stored in the cloud by a receiving module at the vehicle end, and road gradient information in a certain range in front of the vehicle may be acquired by default. The high-precision map information of the cloud end is transmitted, for example, depending on the positioning information, such as longitude and latitude information, of the current vehicle, which is determined by the vehicle-mounted positioning module.
Then, the flow goes to step S23, where it is judged whether or not the gradient information is valid. Because the gradient value of the actual road has gradual change, for example, a gradient of 5% appears in front of a flat road, the gradient of the road obtained from map information is gradually increased, and corresponding processing is needed at the information receiving end, unnecessary gear shifting of the transmission caused by the gradual change of the gradient is avoided. Therefore, the strategy adopted by the invention is to comprehensively consider the gradient value and the duration of the gradient road section, and if the duration of the gradient road section is lower than the set length, the system judges that the gradient value is invalid. This strategy can also be applied to the problem of unnecessary shifting of the transmission when continuous short-distance depressions or bumps are present on the actual road.
If it is confirmed in step S23 that the gradient information is valid, the process goes to step S24 and the map gradient information verification is performed. Then, the flow goes to step S25 and gradient information is output.
In the following, a real-time weight estimation algorithm according to the present invention is described. The vehicle longitudinal dynamics equation is known:
wherein T is eng Is the output torque of the engine and,
i g is the gear ratio of the current gear of the transmission,
i 0 is the main speed reduction ratio of the device,
η T is the mechanical efficiency of the drive train and,
r is the radius of the wheel and,
C D is the air resistance coefficient, and the air resistance coefficient,
a is the frontal area of the vehicle,
v is the current vehicle speed and,
m is the mass of the vehicle and,
g is the gravitational constant of the liquid,
f is the road rolling resistance coefficient (the influence of different rolling resistance coefficients can be ignored in the current control method, and can also be considered by using the existing method),
alpha is the road gradient angle and,
J w is the moment of inertia of the wheel,
J e is the rotational inertia of the engine.
From this, it follows that
Because the actual road gradient angle a is generally small, cos a is approximately 1, sin a is approximately tan a is approximately i, i is the actual road gradient,
after the gradient information of the front road is obtained, the current total mass of the vehicle can be estimated according to a longitudinal dynamics equation of the vehicle, namely:
wherein the rolling resistance coefficient can be calculated according to an empirical formula:
wherein k is 0 ，k 1 And k 2 The parameters related to the tire can be obtained through calibration or self-learning.
The vehicle acceleration information dv/dt can be obtained by calculating the vehicle speed signal differentially in real time and adding low-pass filtering y (n) =y (n-1) +f (y (n) -y (n-1)). If the vehicle is equipped with an acceleration sensor, the vehicle can also be obtained by directly reading the output of the acceleration sensor and checking the output with an acceleration estimation algorithm.
After the current total mass of the vehicle is estimated, the load coefficient of the vehicle can be calculated:
Δ＝m/m 0
wherein m is 0 The mass is prepared for the vehicle.
The real-time weight estimation algorithm according to the present invention may be, for example, but is not limited to, the algorithm flow diagram shown in fig. 3. As shown in fig. 3, vehicle parameters are first acquired (step S11). In step S12, it is determined whether or not the online weight calculation condition is satisfied. For example, it may be determined that the online weight calculation condition is satisfied when there are enough parameters. If it is determined in step S12 that the online weight calculation condition is satisfied, the flow proceeds to step S13. In step S13, the current propulsion or traction force is calculated. Then, the vehicle acceleration at the present time is calculated or acquired and the vehicle acceleration at the next set time is calculated (S14, S15). Finally, in step S16, the current total mass of the vehicle is calculated according to the above formula, and the vehicle load factor is determined.
The shift control strategy is described below.
First, input parameters such as, but not limited to, gradient information of a road ahead, a vehicle load factor, a vehicle speed signal, and an accelerator pedal opening signal are acquired. And then, blurring the acquired parameters, wherein parameter domains and defined fuzzy subsets are determined. For example, the parameter arguments and fuzzy subsets include:
road gradient (%):
domain of discussion: [ -20,20]
The fuzzy subset is: [ NB, NM, NS, ZO, PS, PM, PB ]
Vehicle load factor:
domain of discussion: [1,10]
The fuzzy subset is: [ PS, PM, PB ]
Vehicle speed (km/h):
the domains are: [0,120]
The fuzzy subset is: [ ZO, PS, PM, PB ]
Throttle opening (%):
the domains are: [0,100]
The fuzzy subset is: [ ZO, PS, PM, PB ]
Equivalent load factor:
the domains are: [0,10]
The fuzzy subset is: [ ZO, PS, PM, PB ]
For the determination of the equivalent load factor, the fuzzy inference decision module calculates according to a fuzzy rule (see a rule table below, for example) and according to a set membership function to obtain the equivalent load factor.
Rule table based on equivalent load factor of road gradient and vehicle load
Therefore, the establishment of the fuzzy rule directly influences the output of the equivalent load factor, and further influences the final gear decision. The membership function is formulated here mainly based on iterative optimization of simulation and test data. The membership functions formulated here are for example referred to in fig. 4.
The fuzzy algorithm adopts Sugeno direct reasoning algorithm, mainly uses the maximum and minimum synthesis rules to calculate the fuzzy relation, and then obtains the equivalent load coefficient output through the de-fuzzy calculation based on the gravity center method.
The equivalent load coefficient which is deblurred and output is input into a gear decision module. And in the gear decision module, correcting the target gear obtained by the current gear shifting control strategy by utilizing the calculated equivalent load coefficient. Because gradient information within a certain distance in front of the current vehicle position can be acquired according to the high-precision map information, and real-time vehicle weight estimation can be completed at the beginning of a single vehicle journey, the method can calculate and obtain the target gear of the vehicle within a certain future time. Therefore, gear shifting can be performed in advance, and accordingly the situation that gear shifting is performed again on a slope or circulating gear shifting occurs on the slope can be effectively avoided.
The advance amount of the advanced shift can be obtained by comprehensively considering the current vehicle speed v, the vehicle weight m, and gradient information (gradient value i and duration l of the gradient road section). Specific implementation can be realized by setting a lookup table LUT and a weight factor alpha 1 And alpha 2 The calculation results are that:
τ＝α 1 f 1 (v,m)+α 2 f 2 (i,l)
according to the control method provided by the invention, on the premise of considering the parameter information of the vehicle, road gradient and dynamic vehicle weight factors are introduced, and the transmission gear shifting strategy can be more intelligent through an intelligent fuzzy algorithm, so that better drivability experience and more economic improvement are brought.
Fig. 5 shows a block diagram of a control system for a motor vehicle according to the invention. The control system comprises an input module for obtaining parameters, a predictive rules module 10 for fuzzifying the parameters, and a gear decision module 20 for determining the gear. In addition, the control system may also include a positioning module 05 and a communication module 06. The positioning module 05 is used to determine a vehicle, for example a GPS module. The communication module 06 is in particular a 4G/5G communication module for acquiring cloud map data 07.
The input module includes: a gradient signal acquisition and processing sub-module 01, a real-time vehicle re-estimation sub-module 02, a vehicle speed acquisition sub-module 03 and an accelerator opening acquisition sub-module 04 for acquiring gradient signals.
The input module forwards the input data to a predictive rules module 10, which includes a parameter obfuscation sub-module 11, an obfuscation rules sub-module 12, and a defuzzification sub-module 13. The data is input to the shift decision module 20 after disambiguation. The control system for a motor vehicle can carry out the above-described method and accordingly has the advantages described for this method.
Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the above disclosure of embodiments. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
Claims (9)
1. A control method for an automatic transmission of a vehicle, the control method comprising the steps of:
acquiring basic information of a vehicle (S01);
acquiring gradient information of a road ahead (S02);
estimating the current total mass of the vehicle in real time (S03) to obtain an estimated current total mass of the vehicle;
performing blurring processing (S04);
calculating a final gear decision and outputting the gear decision (S05);
it is characterized in that the method comprises the steps of,
performing a blurring process based on gradient information of a road ahead and a current total mass of the vehicle, and calculating a final gear decision based on a result of the blurring process,
the grade information includes one or more of the following values: the gradient value of the gradient road section, the duration of the gradient road section, the position of the starting point of the gradient road section,
the validity check of the gradient information is carried out on the basis of the gradient value and/or the duration of the gradient road section,
if the duration of the grade segment is less than the set length, the system determines that the grade value is invalid,
in the fuzzy processing, the Sugeno direct reasoning algorithm is adopted,
and carrying out fuzzy relation calculation by utilizing a maximum and minimum synthesis rule, and then obtaining an equivalent load coefficient through the fuzzy calculation based on a gravity center method.
2. The control method according to claim 1, wherein the basic information of the vehicle includes a vehicle preparation quality, a tire radius, an engine torque, a vehicle speed, and a driveline efficiency.
3. The control method according to claim 2, wherein the current total mass of the vehicle is a real-time vehicle weight estimated by:
m is the current total mass of the vehicle,
F T is the driving force of the device,
F a is the air resistance, the air resistance is high,
J w is the moment of inertia of the wheel,
J e is the moment of inertia of the engine,
i g is the gear ratio of the current gear of the transmission,
i 0 is the main speed reduction ratio of the device,
η T is the mechanical efficiency of the drive train and,
v is the current vehicle speed and,
g is the gravitational constant of the liquid,
f is the road rolling resistance coefficient,
i is the sine of the road gradient angle.
4. A control method according to claim 1 or 2, characterized in that in the blurring process a parameter domain is determined and a blurring subset is defined.
5. The control method according to claim 1 or 2, characterized in that the vehicle target gear in a certain time in the future is calculated by the method and shifting is advanced based on the vehicle target gear in a certain time in the future.
6. The control method according to claim 5, wherein the advance of the shift ahead is obtained by comprehensively considering the current vehicle speed, the current total mass of the vehicle, and the gradient information.
7. A control system for a vehicle automatic transmission configured to perform the method according to any one of claims 1-6 for controlling a gear of the vehicle automatic transmission.
8. Control system according to claim 7, characterized in that it comprises an input module for obtaining parameters, a predictive rules module (10) for blurring the parameters, and a gear decision module (20) for determining the gear.
9. The control system according to claim 8, characterized in that the predictive rules module (10) comprises a parameter obfuscation sub-module (11), an obfuscation rules sub-module (12) and a defuzzification sub-module (13).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010401870.5A CN113669443B (en) | 2020-05-13 | 2020-05-13 | Control method and control system for automatic transmission of vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010401870.5A CN113669443B (en) | 2020-05-13 | 2020-05-13 | Control method and control system for automatic transmission of vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113669443A CN113669443A (en) | 2021-11-19 |
| CN113669443B true CN113669443B (en) | 2023-07-14 |
Family
ID=78536863
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010401870.5A Active CN113669443B (en) | 2020-05-13 | 2020-05-13 | Control method and control system for automatic transmission of vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113669443B (en) |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7499784B2 (en) * | 2007-04-09 | 2009-03-03 | General Motors Corporation | Method of selecting a transmission shift schedule |
| US8954246B2 (en) * | 2012-12-14 | 2015-02-10 | Caterpillar Inc. | Grade and payload estimate-based transmission gear selection |
| CN105691393B (en) * | 2014-11-25 | 2018-01-19 | 广州汽车集团股份有限公司 | Vehicular intelligent cruise control method and device based on real-time road |
| CN104590272B (en) * | 2014-12-23 | 2017-04-12 | 安徽江淮汽车集团股份有限公司 | Method and system for detecting ramp state of vehicle |
| CN106710269A (en) * | 2015-11-13 | 2017-05-24 | 北京奇虎科技有限公司 | Vehicle driving data processing method and device |
| CN108253134B (en) * | 2018-01-05 | 2020-05-19 | 重庆青山工业有限责任公司 | Load recognition control system for vehicle and functional module architecture |
| CN108361366B (en) * | 2018-01-17 | 2019-07-05 | 北京理工大学 | A kind of automatic mechanical transmission process for gear |
| CN110792762B (en) * | 2019-11-07 | 2021-07-30 | 吉林大学 | Method for controlling prospective gear shifting of commercial vehicle in cruise mode |
-
2020
- 2020-05-13 CN CN202010401870.5A patent/CN113669443B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113669443A (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11072329B2 (en) | Ground vehicle control techniques | |
| US10369996B2 (en) | Travel control device and travel control method | |
| US6038505A (en) | Method of controlling the drive train of a motor vehicle, and integrated drive train control system | |
| US20200346649A1 (en) | Driveline disengagement and coasting management | |
| EP2236375B1 (en) | Driving support device, driving support method, and driving support program | |
| JP4446978B2 (en) | Vehicle driving force control device | |
| EP2794379B1 (en) | Method and module for controlling a vehicle's speed based on rules and/or costs | |
| US20190378036A1 (en) | Reinforcement Learning Based Ground Vehicle Control Techniques | |
| US20140350820A1 (en) | Method and module for controlling a vehicle's speed based on rules and/or costs | |
| EP3275715B1 (en) | Cruise control device and cruise control method | |
| EP2585347A1 (en) | Method and module for controlling a vehicle's speed | |
| US20190375394A1 (en) | Ground Vehicle Control Techniques | |
| JP6700359B2 (en) | Vehicle control device | |
| WO2016152749A1 (en) | Travel control device, and travel control method | |
| CN110431058A (en) | Travel controlling system, vehicle and travel control method | |
| CN114728660A (en) | Autonomous driving function for a motor vehicle with driver intervention taken into account | |
| Obereigner et al. | A two-layer approach for ecodriving under traffic | |
| CN113669443B (en) | Control method and control system for automatic transmission of vehicle | |
| CN112428977A (en) | Method and system for controlling a vehicle | |
| CN110651140B (en) | Vehicle control device and vehicle control method | |
| CN115285120A (en) | Vehicle following hierarchical control system and method based on model predictive control | |
| CN110431297A (en) | Travel controlling system, vehicle and travel control method | |
| JP4935065B2 (en) | Vehicle driving force control device | |
| JP2017150650A (en) | Drive force control device | |
| CN110576846B (en) | Method and vehicle utilizing predicted road curvature in transmission control module |
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 |