CN114148351B - Predictive power chain energy-saving control method applied to automatic driving - Google Patents
Predictive power chain energy-saving control method applied to automatic driving Download PDFInfo
- Publication number
- CN114148351B CN114148351B CN202210116348.1A CN202210116348A CN114148351B CN 114148351 B CN114148351 B CN 114148351B CN 202210116348 A CN202210116348 A CN 202210116348A CN 114148351 B CN114148351 B CN 114148351B
- Authority
- CN
- China
- Prior art keywords
- predictive
- vehicle speed
- vehicle
- speed
- coasting
- 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
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000005265 energy consumption Methods 0.000 claims abstract description 15
- 230000007935 neutral effect Effects 0.000 claims abstract description 14
- 230000008859 change Effects 0.000 claims abstract description 7
- 230000003213 activating effect Effects 0.000 claims abstract description 6
- 230000006870 function Effects 0.000 claims description 64
- 238000004422 calculation algorithm Methods 0.000 claims description 24
- 239000011159 matrix material Substances 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000011218 segmentation Effects 0.000 claims description 7
- 239000013598 vector Substances 0.000 claims description 6
- 230000003993 interaction Effects 0.000 claims description 4
- 230000006872 improvement Effects 0.000 description 10
- 238000005457 optimization Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 239000000446 fuel Substances 0.000 description 5
- 230000000007 visual effect Effects 0.000 description 5
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
- 229910052799 carbon Inorganic materials 0.000 description 4
- 239000003208 petroleum Substances 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 102100034112 Alkyldihydroxyacetonephosphate synthase, peroxisomal Human genes 0.000 description 2
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 101000799143 Homo sapiens Alkyldihydroxyacetonephosphate synthase, peroxisomal Proteins 0.000 description 2
- 238000000848 angular dependent Auger electron spectroscopy Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000008676 import Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000007667 floating Methods 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000005381 potential energy Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18072—Coasting
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0023—Planning or execution of driving tasks in response to energy consumption
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/40—High definition maps
-
- 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
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Human Computer Interaction (AREA)
- Control Of Transmission Device (AREA)
Abstract
The invention provides a predictive power chain energy-saving control method applied to automatic driving, which comprises the following steps: the method comprises the steps of obtaining vehicle positioning information and a target vehicle speed, obtaining map information, calculating a predictive neutral gear sliding distance and energy consumption, conducting predictive vehicle speed/gear planning, calculating an optimal energy consumption and a corresponding vehicle speed curve, and conducting arbitration on predictive sliding and predictive vehicle speed/gear planning. Wherein calculating the predictive neutral coast distance and energy consumption comprises: acquiring a vehicle position, a driving distance and driving time; checking whether the front road has speed limit change or downhill according to the map information; if so, starting to calculate a vehicle speed track curve under the condition of a neutral position of the gearbox according to a vehicle dynamic model; activating the predictive coast function if the calculated end tail speed value of the future vehicle speed trajectory falls within a sufficiently small range with respect to the target vehicle speed; when the actual coasting distance reaches the pre-estimated coasting distance, the predictive coasting is judged to be completed.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a predictive power chain energy-saving control method applied to automatic driving.
Background
With the increasing automobile holding quantity of all countries in the world, as environmental pollution and energy consumption problems caused by the huge automobile holding quantity are increased to cause high importance of all countries and related emission regulations are becoming stricter, the emerging emission regulations are gradually shifted to the reduction of greenhouse gas emission mainly comprising carbon dioxide in the exhaust from the Euro-six standard which only focuses on reducing the emission of exhaust pollutants and the U.S. EPA2010 regulation, and the short-term carbon peak-reaching target and the long-term carbon neutralization target are also proposed by the China government in 2021. Meanwhile, the petroleum sold in China highly depends on import, wherein the annual petroleum consumption of automobiles accounts for more than one third of the total annual petroleum consumption in China, and higher requirements are provided for the automobile energy-saving and emission-reducing technology no matter the external mandatory regulations aiming at the vehicle emission or the internal active reduction of the dependence on petroleum import are required. Whether it is a passenger car mainly serving individuals or a commercial car mainly serving freight, reducing carbon emissions is equivalent to reducing oil consumption by one hundred kilometers.
Corresponding to the further development of energy conservation and emission reduction, along with the continuous development of artificial intelligence, the automatic driving gradually becomes a research hotspot in the traffic field. The American society of SAE automotive Engineers classifies autopilot into the following categories: level L0-driver manual driving, level L1 and level L2-driver assistance system, level L3-scene specific automatic driving, level L4-region specific automatic driving and level L5-full automatic driving. The automobile is equipped with self-adaptive cruise at the level of L1 and above automatic driving levels, and the automobile has the capability of automatically changing lanes at the level of L3 and above. The emergence and the progress of the automatic driving technology greatly reduce the working strength and the fatigue degree of drivers, so that the easy driving of liberating both hands and feet of the drivers becomes possible. For the freight heavy truck in the long-distance high-speed scene, the fast iteration of the automatic driving technology enables the freight driving mode to be alternately driven by two drivers, the long-distance driving by a single driver is reduced, and a solid foundation is laid for further realizing unmanned driving in the high-speed scene.
However, in the current driving assistance or automatic driving technology, one of the goals of reducing energy consumption and carbon emission is not achieved, and the current driving assistance or automatic driving technology focuses on the good realization of each function of automatic driving; under the same road section and the same vehicle flow, the oil consumption when the automatic driving is started is obviously higher than that of the manual driving of a driver. Therefore, the operation cost of drivers of operation passenger cars or commercial vehicles can be increased while the driving fatigue degree is reduced, and further, a considerable part of users can not spend extra expenses on selecting and installing the automatic driving function. In addition, vehicles equipped with automatic driving functions at present are difficult to meet increasingly strict emission regulations in real use scenes. At present, the predictive energy-saving function is not launched and applied aiming at the automatic driving vehicle in China, and the invention is designed for solving the conflict between automatic driving and energy consumption. Predictive vehicle energy consumption optimization is performed through automatic driving visual information, map information and real-time road information, so that the energy consumption level of drivers with more experience is exceeded.
Disclosure of Invention
The invention aims to provide a predictive power chain energy-saving control method applied to automatic driving.
The invention aims to reduce the relatively high oil consumption of a vehicle when automatic driving is started.
Compared with the prior art, the technical scheme and the beneficial effects of the invention are as follows:
a predictive power train energy-saving control method for autonomous driving, comprising:
acquiring vehicle positioning information and a target vehicle speed;
obtaining map information;
calculating a predictive neutral coast distance comprising: acquiring a vehicle position, a driving distance and driving time; checking whether the front road has speed limit change or downhill according to the map information; if so, starting to calculate a vehicle speed track curve under the condition of a neutral position of the gearbox according to a vehicle dynamic model; activating the predictive coasting function if the calculated end tail speed value of the future vehicle speed trajectory falls within a small numerical range with the target vehicle speed as a reference; when the actual coasting distance reaches the pre-estimated coasting distance, the predictive coasting is judged to be completed.
As a further improvement, the acquiring the vehicle positioning information and the target vehicle speed includes: accurately positioning the current position of the vehicle through a GPS and an RTK; and acquiring the target speed of the vehicle in real time through interaction with the automatic driving controller.
As a further improvement, the obtaining of the map information includes:
if the high-precision map information is available, the controller receives the high-precision map data information, wherein the map information comprises a gradient information array, a speed limit information array and a curvature information array of different lane lines within 2-3 kilometers ahead;
and if the high-precision map information IS unavailable, the corresponding map box transmits the front road data information to the controller through the CAN according to the ADAS IS map protocol.
As a further improvement, the acquiring of the vehicle position, the travel distance, and the travel time includes: and transmitting the position of the vehicle on the current road section according to a map reconstruction function or a high-precision map.
As a further improvement, the acquiring of the vehicle position, the travel distance, and the travel time includes: and calculating the current position and the running distance of the vehicle according to the vehicle speed and the vehicle running time.
As a further improvement, said starting to calculate the vehicle speed trajectory curve in the neutral condition of the transmission according to the vehicle dynamics model further comprises: in calculating the vehicle speed track of neutral coasting, a plurality of limiting conditions are provided to ensure that the deviation degree of the simulated vehicle speed track and the target vehicle speed is within a reasonable range and does not exceed the speed limit range in the whole coasting distance, and if any one of the conditions does not meet the requirement, predictive coasting is not activated.
As a further improvement, the activating the predictive taxi function further comprises: predictive coasting may be suspended when predictive coasting has been activated and the actual vehicle speed deviates too much.
As a further improvement, a predictive vehicle speed and gear planning algorithm is included, comprising: according to the gradient and speed-limiting vector information sent by map reconstruction, segmenting and identifying road sections; calculating target cost functions of different speed curves of the divided road sections; seeking an optimal energy consumption vehicle speed path of the whole section of the electronic vision road section; arbitration is performed for predictive coasting and predictive vehicle speed/gear plans.
As a further improvement, the calculation of the target cost function of the different vehicle speed curves of the divided road sections comprises the following steps: calculating the average gradient and the speed limit value of each divided road section; constructing a two-dimensional vehicle speed matrix of the segmented road sections; constructing a vehicle speed and gear integrated three-dimensional matrix; and (4) calculating an integrated three-dimensional matrix cost function.
As a further improvement, arbitrating predictive skidding and predictive vehicle speed/gear plans comprises: calculating the optimal path cost function value only with the predictive vehicle speed/gear planning; calculating an optimal path cost function value combining the predictive sliding function and the predictive vehicle speed planning; and comparing the calculated optimal cost function value only with the predictive vehicle speed and gear planning and the calculated optimal cost function value combined with the predictive sliding and the predictive vehicle speed/gear planning, and selecting a scheme with a smaller overall cost function.
The invention has the beneficial effects that:
1. the method utilizes an automatic driving system to accurately position the vehicle, combines road information provided by a high-precision map or a map box, and dynamically receives effective map data in a given distance in front of the vehicle in real time;
2. the invention can effectively reconstruct the road ahead according to the ADAS IS protocol by analyzing the effectiveness of the received map data, and identify various road conditions;
3. under the condition that automatic driving is not started, although the vehicle speed cannot be changed autonomously, economic vehicle speed and gear prompt can be performed on a driver through a man-machine interaction interface according to the recognized front road condition;
4. aiming at the automatic driving piloting working condition, the requirements of vehicle fuel saving and timeliness are integrated, and under the condition that the vehicle speed limit of a road and the psychological expectation of a driver are met, the cruising speed is autonomously adjusted to achieve the global optimum of a power chain, so that the economical performance of driving by the driver with rich experience is achieved;
5. the invention fully utilizes the capability of the automatic driving vehicle for providing future road information and future traffic flow information, adopts a global dynamic programming optimization algorithm to realize integrated programming of gears, vehicle speed and sliding, and realizes global energy optimization in an electronic visual field range;
the method fully utilizes the advantages of autonomous calculation and decision of automatic driving longitudinal and transverse acceleration, utilizes the prediction and judgment of the motion trail of the front and the surrounding vehicles by the perception function, fully utilizes the information of gradient, speed limit and curvature in map information, and utilizes navigation information to ensure the fuel saving of the vehicles, thereby meeting the triple guarantee of the expected driving performance and the time efficiency of the drivers.
Drawings
Fig. 1 is a schematic diagram of a predictive power train energy-saving control method applied to automatic driving according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of road segment identification and road segmentation provided by an embodiment of the present invention.
Fig. 3 is a diagram of a predictive taxi concept provided by an embodiment of the invention.
FIG. 4 is a schematic diagram for constructing a two-dimensional vehicle speed matrix according to an embodiment of the present invention.
Fig. 5 is a predictive shift schematic provided by an embodiment of the present invention.
Fig. 6 is a schematic diagram of a predictive vehicle speed/gear schedule provided by an embodiment of the present invention.
FIG. 7 is a schematic diagram of a combination of predictive taxi function and predictive vehicle speed planning provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Referring to fig. 1, a predictive power train energy saving control method applied to automatic driving includes:
acquiring vehicle positioning information and a target vehicle speed;
obtaining map information;
calculating a predictive neutral coast distance comprising:
acquiring a vehicle position, a driving distance and driving time;
checking whether the front road has speed limit change or downhill according to the map information; if so, starting to calculate a vehicle speed track curve under the condition of a neutral position of the gearbox according to a vehicle dynamic model;
estimating a vehicle acceleration formula:
formula for estimating vehicle speed
v(t)＝v(t0)+a(t)*Δt
Activating the predictive coasting function if the calculated end tail speed value of the future vehicle speed trajectory falls within a small numerical range with the target vehicle speed as a reference;
in the calculation of the vehicle speed track of neutral sliding, a plurality of limiting conditions are provided to ensure that the deviation degree of the simulated vehicle speed track and the target vehicle speed is within a reasonable range and cannot exceed the speed limit range in the whole sliding distance, and if any one of the conditions does not meet the requirement, predictive sliding cannot be activated;
when predictive coasting has been activated and the actual vehicle speed deviates too much, predictive coasting may be suspended;
when the real sliding distance reaches the pre-estimated sliding distance, the predictive sliding is judged to be finished;
the acquiring of the vehicle positioning information and the target vehicle speed comprises the following steps: accurately positioning the current position of the vehicle through a GPS and an RTK; and acquiring the target speed of the vehicle in real time through interaction with the automatic driving controller.
As a further improvement, the obtaining of the map information includes:
if the high-precision map information is available, the controller receives the high-precision map data information, wherein the map information comprises a gradient information array, a speed limit information array and a curvature information array of different lane lines within 2-3 kilometers ahead; the map information array sent by the high-precision map is event-triggered, so the dimensionality is not fixed, namely when the information of nodes at different positions of a front road changes, the data array of the corresponding position of each road information point, the gradient information array, the speed limit information array and the curvature array can correspondingly change;
if the high-precision map information is unavailable, the corresponding map box transmits the front road data information to the controller through the CAN according to the ADASIS map protocol; the map information comprises road grade gradient information, speed limit information and curvature information, and the same node information of the road information is of an event trigger type.
If the map information is transmitted by the ADASIS protocol, the controller for realizing the algorithm needs to effectively reconstruct the road in front of the vehicle according to the ADASIS V2 protocol or ADASIS V3 protocol so as to obtain the front road gradient information, the speed limit information and the curvature information which can be used by the algorithm.
The predictive taxi aims to reduce fuel consumption of an engine by using a vehicle taxi function, and disconnect drag torque of the engine to utilize kinetic energy and potential energy of the whole vehicle to the maximum extent, so that the target vehicle speed is properly reduced on an uphill road section, and then the corresponding vehicle speed is allowed to rise on a downhill road section to ensure that the total time is not influenced.
The acquiring of the vehicle position, the travel distance and the travel time includes: and transmitting the position of the vehicle on the current road section according to a map reconstruction function or a high-precision map.
The acquiring of the vehicle position, the travel distance and the travel time includes: and calculating the current position and the running distance of the vehicle according to the vehicle speed and the vehicle running time. When the positioning information is available and accurate, the driving distance and the position of the vehicle are firstly calculated by using the position fed back by the positioning or high-precision positioning information, and if the vehicle runs into a tunnel or a mountain area and the positioning cannot work normally, the vehicle speed and the driving time are used for automatically calculating the position and the driving distance.
Referring to fig. 2 to 7, the predictive vehicle speed and gear planning algorithm mainly includes the following parts:
1. road segment identification and segmentation
According to the gradient and speed-limiting vector information sent by map reconstruction, according to the road section segmentation, the flat road, the ascending road section, the descending road section, the road section speed-limiting change, the short-distance severe change road section and the like are identified
1) When the road section segmentation algorithm function is activated for the first time, the algorithm identifies and segments all road sections with visible electronic vision
2) The algorithm is not activated for the first time, the algorithm removes the road sections which are already walked by the vehicle, the road sections which are not walked and are well segmented are reserved, and only the road information newly entering the visual field is newly identified so as to reduce repeated calculation
3) When the road is in the visible front road, if a long-distance level road condition occurs, the algorithm can also force to divide a plurality of sub-road sections so as to avoid overlong single road section
2. Calculating target cost function of different speed curves of segmented road sections
2.1 calculating the average grade and speed limit value of each divided road section
S is used for representing different road sections, S is more than or equal to 1 and less than N, N is the terminal point of the last road section after segmentation, and the average gradient of the current road section is Sslope;
j represents a start point of the divided link, j +1 represents an end point of the divided link,
j is more than or equal to 1 and less than or equal to N, and the highest speed limit of the starting point of the current road section of the vehicle is Vjmax, lowest speed limit Vj min
2.2 constructing two-dimensional vehicle speed matrix of divided road sections
The precondition is as follows:
in the whole road visual field, the initial planning speed is the target speed calculated in the fourth step
VfinalAnd at the end of the vehicle speed schedule,the speed of the whole vehicle is required to return to the initial target speed Vfinal；
According to the calculated target vehicle speed and the vehicle speed floating range (calibratable value) allowed up and down, discretizing a vehicle speed interval according to a proper interval to form a vehicle speed node vector of the segmented road section, wherein the vector dimension is m, and connecting the vehicle speed node vectors of each segmented road section (the maximum is N-1 segmented road sections) to form an initial vehicle speed matrix shown in FIG. 4;
remarking:
Vlowwerrepresenting a value of vehicle speed allowed to float downwards
VupperRepresenting upwardly allowed float vehicle speed value
Wherein V1And VnThe starting point vehicle speed representing the whole electronic field of view and the end point vehicle speed of the last segment of the divided road section, i.e. Vfinal
vi，jRepresents the ith selectable vehicle speed point in the jth segment-divided link start point (jth-1 segment-divided link end point), and is numerically limited to the following range:
Vj min＜＝Vfinal_Vlower＜＝vi，j＜＝Vfinal+Vupper＜＝Vj max
2.3 constructing a three-dimensional matrix integrating vehicle speed and gears
1) Calculating the acceleration among all vehicle speed node connecting lines according to each speed node in the constructed vehicle speed matrix:
for example ai，j，pRepresenting slave velocity node vi，jTo velocity node vp，j+1Acceleration of (2);
2) according to the whole vehicle dynamics formula, calculating to meet the speed node vi，jAvailable gear of the vehicle speed curve to the next speed node;
2.1) according to node vi，jNode v to the next road sectioni+p，j+1The appropriate gear corresponding to the speed curve is calculated
Rg-drivelineFor the gearbox in the g gear, the speed is converted into the transmission ratio of the engine speed
Judging the effectiveness of different gears under the vehicle speed curve and eliminating ineffective gears:
minimum rotating speed of engine is less than or equal to (N)eng(i，j)And Neng(i+p，j+1))
Not more than the maximum rotating speed of the engine
2.2) secondary screening proper gears according to the engine torque required by realizing the speed curve of the section
Froll＝frmg cos βj
Fgrad＝mg sin βj
Note that:
Teng-i，j，p，k-satisfying a velocity node vi，jTo velocity node vp，j+1Engine torque required for vehicle speed curve in k gear
RTrans，k-in the current k gear, the rotational ratio of the gearbox
RFinalDriveMain reducer transmission ratio
βj-current j-th segmented road section gradient mean value
frCoefficient of rolling resistance of road surface
Judging the validity that different gears meet the engine torque under the section of the vehicle speed curve and eliminating invalid gears:
the minimum torque of the engine is less than or equal to Teng-i，j，p，kLess than or equal to the maximum torque of the engine
2.4 Integrated three-dimensional matrix cost function computation
1) Cost function calculation of energy consumption
Based on the available gears of different vehicle speed curves constructed as above and the engine rotating speed and torque corresponding to the given gear, according to the engine specific fuel consumption map BSFCmap calibrated by the pre-test, the available gears from the vehicle speed node v can be calculatedi，jTo the next vehicle speed node vi+p，j+1Fuel consumption cost value
For a hybrid power frame vehicle type, the electricity consumption is converted into equivalent oil consumption to be comprehensively considered by the oil consumption of the engine, and the formula is changed into
Fuelrateeq(Tmot，NmotT) -equivalent instantaneous oil consumption converted according to motor power
2) Calculation of an aging penalty cost function for a line
In order to prevent the energy consumption optimization algorithm from only realizing the minimum energy consumption without considering the line aging factor, an aging penalty cost function is added into the algorithm to compensate time delay, and the consistency of the average speed of the whole line and the average speed of the whole line with the cruise function is ensured
Wherein, the time penalty function TimePenaltyfac(t) is a function of road conditions and predictive plan deviation timesIn (1).
3) Calculation of penalty cost function for road speed limit
In order to prevent the planned speed of the algorithm from exceeding the road speed limit, a penalty cost function of the road speed limit is added into the algorithm so that the dynamic planning is more inclined to the speed point in the road speed limit, and the condition that the speed curve of the whole line cannot be overspeed is ensured
SpdLimPenaltyi，j，p，k
＝(VehSpdi，j-SpdLimj)*SpdLimPenaltyfac
Wherein, SpdLimPagefacIs a larger calibration value to improve the value of the cost function exceeding the road speed limit so as to avoid unreasonable vehicle speed planning
4) Construction of an objective function
According to the calculation of the two cost functions, the speed node v is driven from the speed node v in the k geari，jTo the next vehicle speed node vi+p，j+1The target cost values of (a) are as follows:
OBJCosti，j，p，k
＝FuelCosti，j，p，k+TimeCosti，j，p，k
+SpdLimPenaltyi，j，p，k
3. seeking optimal energy consumption vehicle speed path of whole section of electronic vision road section
In the whole electronic field of view, the optimal speed curve is selected by using a dynamic programming optimization algorithm, and forward optimization (namely, from v) can be used1To vnDirection of (v) to find the least cost path) or reverse optimization (i.e., from vnTo v1Finding the least cost path).
The following formula is the vehicle speed v from the end pointnIteration of the minimum target cost path to the starting point of the jth segmented road segment:
OBJCostn-1＝min(OBJCostn-1→n)
OBJCostn-2＝min[OBJCostn-2→n-1+OBJCostn-1→n]
…
OBJCostj＝min[OBJCostj→j+1+OBJCostj+1→n]
4. arbitration of predictive creep and predictive vehicle speed/gear schedule
According to the road condition, the predictive sliding can judge whether the front road section is suitable for starting the sliding function, the position where the sliding is to be started and the position where the sliding is to be ended are calculated in advance, and the algorithm can plan an optimal vehicle speed curve without considering the sliding and a corresponding cost function.
In this subsection, the algorithm will comprehensively judge whether the energy consumption is the lowest for starting the sliding function in the whole section of the electronic visual field range;
4.1, calculating the optimal path cost function value only with the predictive vehicle speed/gear planning;
the predictive taxi logic calculates in real time and once the taxi function starting flag position is set to 1, the represented current vehicle position can start the taxi function; once the algorithm monitors that the position of the coasting function enabling flag is 1, the predictive vehicle speed/gear planning algorithm implements all vehicle speed planning algorithms again by taking the current vehicle position as a starting point, so that the optimal vehicle speed track without the coasting function and the lowest whole-section electronic view cost function value corresponding to the optimal vehicle speed track are calculated by using the same starting point of the vehicle position, as shown in fig. 6;
4.2, calculating an optimal path cost function value combining the predictive sliding function and the predictive vehicle speed planning;
because the coasting function is started from the current vehicle position, the predictive vehicle speed planning only needs to acquire the coasting end position PSend posAnd a pre-estimated coasting end vehicle speed PSend spdThe algorithm is implemented according to the following steps, as shown in fig. 7;
1) the distance of the sliding opening, the engine is in an idling state, and the oil consumption is the product of the idling oil consumption rate and the sliding time;
2) in the section of the road where the sliding is not open, position PS is closed according to the sliding functionend posThe algorithm is reused to carry out re-road identification and segmentation on the remaining road sections, and the three-dimensional vehicle speed/gear of the remaining road sections is constructedA matrix;
3) repeating the dynamic programming optimization algorithm to find out the optimal vehicle speed path matched with the predictive sliding function;
4.3, comparing the calculated optimal cost function value only with the predictive vehicle speed and gear planning and the calculated optimal cost function value combining the predictive sliding and predictive vehicle speed/gear planning, and selecting a scheme with a smaller overall cost function to execute, thereby avoiding some non-optimal sliding functions.
The above examples are only for illustrating the technical solutions of the present invention and not for limiting the same. It will be understood by those skilled in the art that any modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention as set forth in the appended claims.
Claims (9)
1. A predictive power train energy-saving control method for autonomous driving, comprising:
acquiring vehicle positioning information and a target vehicle speed;
obtaining map information;
calculating a predictive neutral coast distance comprising:
acquiring a vehicle position, a driving distance and driving time;
checking whether the front road has speed limit change or downhill according to the map information; if so, starting to calculate a vehicle speed track curve under the condition of a neutral position of the gearbox according to a vehicle dynamic model;
activating the predictive coasting function if the calculated end tail speed value of the future vehicle speed trajectory falls within a small numerical range with the target vehicle speed as a reference;
when the real sliding distance reaches the pre-estimated sliding distance, the predictive sliding is judged to be finished;
the starting calculation of the vehicle speed trajectory curve under the condition of the neutral gear of the gearbox according to the vehicle dynamic model further comprises the following steps:
in calculating the vehicle speed track of neutral coasting, a plurality of limiting conditions are provided to ensure that the deviation degree of the simulated vehicle speed track and the target vehicle speed is within a reasonable range and does not exceed the speed limit range in the whole coasting distance, and if any one of the conditions does not meet the requirement, predictive coasting is not activated.
2. The predictive power-train energy-saving control method applied to automatic driving of claim 1, wherein the obtaining of the vehicle positioning information and the target vehicle speed comprises:
accurately positioning the current position of the vehicle through a GPS and an RTK;
and acquiring the target speed of the vehicle in real time through interaction with the automatic driving controller.
3. The predictive power-train energy-saving control method applied to automatic driving of claim 1, wherein the obtaining of the map information comprises:
if the high-precision map information is available, the controller receives the high-precision map data information, wherein the map information comprises a gradient information array, a speed limit information array and a curvature information array of different lane lines within 2-3 kilometers ahead;
and if the high-precision map information is unavailable, the corresponding map box transmits the front road data information to the controller through the CAN according to the ADASIS map protocol.
4. The predictive power-train energy-saving control method applied to automatic driving according to claim 1, wherein the obtaining of the vehicle position, the driving distance and the driving time comprises:
and transmitting the position of the vehicle on the current road section according to a map reconstruction function or a high-precision map.
5. The predictive power-train energy-saving control method applied to automatic driving according to claim 1, wherein the obtaining of the vehicle position, the driving distance and the driving time comprises:
and calculating the current position and the running distance of the vehicle according to the vehicle speed and the vehicle running time.
6. The predictive power-train energy-saving control method applied to automatic driving of claim 1, wherein the activating the predictive coasting function further comprises:
predictive coasting may be suspended when predictive coasting has been activated and the actual vehicle speed deviates too much.
7. The predictive power train energy saving control method for autonomous driving of claim 1 further comprising a predictive vehicle speed and gear planning algorithm comprising:
according to the gradient and speed-limiting vector information sent by map reconstruction, road section identification and segmentation are carried out;
calculating target cost functions of different speed curves of the divided road sections;
seeking an optimal energy consumption vehicle speed path of the whole section of the electronic vision road section;
arbitration is performed for predictive coasting and predictive vehicle speed/gear plans.
8. The predictive power-train energy-saving control method applied to automatic driving of claim 7, wherein the calculation of the target cost function of the different vehicle speed curves of the divided road sections comprises the following steps:
calculating the average gradient and the speed limit value of each divided road section;
constructing a two-dimensional vehicle speed matrix of the segmented road sections;
constructing a vehicle speed and gear integrated three-dimensional matrix;
and (4) calculating an integrated three-dimensional matrix cost function.
9. The predictive power train energy saving control method for autonomous driving of claim 7 wherein arbitrating predictive coasting and predictive vehicle speed/gear plans comprises:
calculating the optimal path cost function value only with the predictive vehicle speed/gear planning;
calculating an optimal path cost function value combining the predictive sliding function and the predictive vehicle speed planning;
and comparing the calculated optimal cost function value only with the predictive vehicle speed and gear planning and the calculated optimal cost function value combined with the predictive sliding and the predictive vehicle speed/gear planning, and selecting a scheme with a smaller overall cost function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210116348.1A CN114148351B (en) | 2022-02-07 | 2022-02-07 | Predictive power chain energy-saving control method applied to automatic driving |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210116348.1A CN114148351B (en) | 2022-02-07 | 2022-02-07 | Predictive power chain energy-saving control method applied to automatic driving |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114148351A CN114148351A (en) | 2022-03-08 |
| CN114148351B true CN114148351B (en) | 2022-04-29 |
Family
ID=80450301
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210116348.1A Active CN114148351B (en) | 2022-02-07 | 2022-02-07 | Predictive power chain energy-saving control method applied to automatic driving |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114148351B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114889596A (en) * | 2022-06-13 | 2022-08-12 | 浙江吉利控股集团有限公司 | Predictive cruise control method, device, device and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006226178A (en) * | 2005-02-17 | 2006-08-31 | Hino Motors Ltd | Auto cruise control device |
| CN110509922A (en) * | 2019-08-20 | 2019-11-29 | 一汽解放汽车有限公司 | A kind of vehicle prediction cruise control method based on high-precision map |
| CN112660130A (en) * | 2020-12-31 | 2021-04-16 | 联合汽车电子有限公司 | New energy automobile sliding control system and method based on intelligent networking information and new energy automobile |
| WO2021136130A1 (en) * | 2019-12-31 | 2021-07-08 | 华为技术有限公司 | Trajectory planning method and apparatus |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102016215421A1 (en) * | 2016-08-17 | 2018-02-22 | Robert Bosch Gmbh | Method and device for operating an automated motor vehicle |
| WO2019146013A1 (en) * | 2018-01-24 | 2019-08-01 | 日産自動車株式会社 | Method for automatically operating vehicle and automatic control apparatus |
| JP7243589B2 (en) * | 2019-11-15 | 2023-03-22 | トヨタ自動車株式会社 | vehicle controller |
-
2022
- 2022-02-07 CN CN202210116348.1A patent/CN114148351B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006226178A (en) * | 2005-02-17 | 2006-08-31 | Hino Motors Ltd | Auto cruise control device |
| CN110509922A (en) * | 2019-08-20 | 2019-11-29 | 一汽解放汽车有限公司 | A kind of vehicle prediction cruise control method based on high-precision map |
| WO2021136130A1 (en) * | 2019-12-31 | 2021-07-08 | 华为技术有限公司 | Trajectory planning method and apparatus |
| CN112660130A (en) * | 2020-12-31 | 2021-04-16 | 联合汽车电子有限公司 | New energy automobile sliding control system and method based on intelligent networking information and new energy automobile |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114148351A (en) | 2022-03-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11072329B2 (en) | Ground vehicle control techniques | |
| CN112660130B (en) | New energy vehicle coasting control system, method and new energy vehicle based on intelligent network information | |
| Kamal et al. | Ecological vehicle control on roads with up-down slopes | |
| Huang et al. | Speed trajectory planning at signalized intersections using sequential convex optimization | |
| CN108438003B (en) | Layered control method for optimizing speed of road vehicle | |
| US9821803B2 (en) | Vehicle speed and coasting control method and system | |
| US20190375394A1 (en) | Ground Vehicle Control Techniques | |
| US9714704B2 (en) | Transmission control system | |
| CN111002993B (en) | Automatic driving low-oil-consumption movement planning method and system based on scene recognition | |
| CN101994584A (en) | Road grade coordinated engine control systems | |
| Zhai et al. | Cooperative look-ahead control of vehicle platoon for maximizing fuel efficiency under system constraints | |
| CN113734175B (en) | A real-time optimization method for energy-saving driving strategy between two intersections under time-varying conditions | |
| US20230341237A1 (en) | Systems and methods for vehicle cruise speed recommendation | |
| US11866061B2 (en) | Systems and methods for vehicle propulsion recommendation | |
| US20250095408A1 (en) | System and method for estimating a remaining energy range of a vehicle and reducing driver range anxiety | |
| CN114148351B (en) | Predictive power chain energy-saving control method applied to automatic driving | |
| Kaku et al. | Model predictive control for ecological vehicle synchronized driving considering varying aerodynamic drag and road shape information | |
| Obereigner et al. | A two-layer approach for ecodriving under traffic | |
| D'Amato et al. | Development and on-board testing of an ADAS-based methodology to enhance cruise control features towards CO2 reduction | |
| US20240308387A1 (en) | System and method for estimating a remaining energy range of a vehicle | |
| EP3974272B1 (en) | Intelligent driving passive pedal control | |
| US11904880B2 (en) | Systems and methods for vehicle coasting recommendation | |
| Hesami et al. | Proactive Eco-Driving Control of an Autonomous Electric Vehicle in Presence of Signalized Intersections and Preceding Vehicles | |
| Hu et al. | Minimum fuel consumption strategy in autonomous adaptive cruise control scenarios | |
| Bentaleb et al. | Enhanced energy efficiency of two-layer control approach for connected vehicles |
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 | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A predictive powertrain energy-saving control method for autonomous driving Effective date of registration: 20220904 Granted publication date: 20220429 Pledgee: Hangzhou High-tech Financing Guarantee Co.,Ltd. Pledgor: Hangzhou Hongjing Zhijia Technology Co.,Ltd. Registration number: Y2022330002087 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Date of cancellation: 20230907 Granted publication date: 20220429 Pledgee: Hangzhou High-tech Financing Guarantee Co.,Ltd. Pledgor: Hangzhou Hongjing Zhijia Technology Co.,Ltd. Registration number: Y2022330002087 |
|
| PC01 | Cancellation of the registration of the contract for pledge of patent right |