CN115830885B - Vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types - Google Patents
Vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types Download PDFInfo
- Publication number
- CN115830885B CN115830885B CN202211509503.2A CN202211509503A CN115830885B CN 115830885 B CN115830885 B CN 115830885B CN 202211509503 A CN202211509503 A CN 202211509503A CN 115830885 B CN115830885 B CN 115830885B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- vehicles
- confluence
- energy consumption
- ramp
- 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
- 238000005265 energy consumption Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000005457 optimization Methods 0.000 claims abstract description 34
- 230000001133 acceleration Effects 0.000 claims abstract description 29
- 238000004891 communication Methods 0.000 claims description 6
- 238000005096 rolling process Methods 0.000 claims description 6
- 238000006073 displacement reaction Methods 0.000 claims description 3
- 238000013138 pruning Methods 0.000 abstract description 2
- 230000008685 targeting Effects 0.000 abstract 1
- 238000004364 calculation method Methods 0.000 description 5
- 230000002411 adverse Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention belongs to the field of intelligent vehicles and traffic, and particularly relates to a ramp converging cooperative control method combining vehicle type information. The method comprises the following steps: (1) Setting a converging control area, and distributing serial numbers to vehicles entering the control area according to an entering sequence; (2) The road side central controller receives vehicle information, including the speed, acceleration and vehicle type information of the vehicle running, and forms a corresponding data packet; (3) Taking different characteristics of mass, air resistance and the like of different vehicle types into consideration, establishing an energy consumption function, and constructing a vehicle control method targeting vehicle energy consumption, comfort and passing time; (4) reducing complexity of order optimization using pruning strategy. The invention can improve the passing efficiency when vehicles are converged through the ramp opening and reduce the energy consumption.
Description
Technical Field
The invention belongs to the field of intelligent vehicles and traffic, and particularly relates to a vehicle ramp converging cooperative control method considering energy consumption of multiple vehicles.
Background
With the intelligent development becoming one of the important development directions of automobiles, intelligent internet-connected automobiles are widely studied. The intelligent network-connected vehicle-mounted communication system realizes information exchange between vehicles and roadside infrastructure, and based on the information exchange, optimal control of multi-vehicle-type vehicle ramp converging is possible.
At present, some patents solve the problem of vehicle ramp confluence. For example, ZL202010040259.4 is an invention of a method and a system for cooperative control of ramp confluence based on confluence time optimization, the method calculates the reference time of the vehicle reaching the confluence point and broadcasts, and the actual confluence time of the vehicle is continuously updated to realize confluence optimization by solving the problem of confluence optimization; the ZL202010211956.1 invention discloses an intelligent network-connected vehicle cooperative confluence control method for a high-speed ramp entrance, which comprises the steps of uploading vehicle information to a central control system through V2I communication, carrying out confluence control optimization calculation by combining traffic management information, and reducing adverse effects of the high-speed ramp entrance on main road traffic; the ZL201810317854.0 is used for dividing a vehicle converging speed control area to determine a converging area passing rule; the ZL202010736981.1 invention discloses a ramp converging control method for an urban expressway, which constructs a vehicle converging control optimizing model to circularly optimize the motion state of a vehicle, and improves the economy and safety of converging.
The method has positive significance for safe and efficient confluence of vehicles. However, most of the related researches treat vehicles as the same node, and the influence of the dynamics characteristics of different vehicles on the merging optimization is not considered, and the energy consumption during the merging of the vehicles is greatly affected by different vehicle dynamics characteristics, quality and other factors, so that the optimal control quantity and the merging sequence of the vehicles are affected.
Disclosure of Invention
The invention aims to provide a vehicle ramp converging cooperative control method considering energy consumption of multiple vehicle types to solve the converging optimization control problem of different vehicle dynamics characteristics.
The technical solution for realizing the purpose of the invention is as follows: a vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types comprises the following steps:
step (1): and setting a converging control area, making a converging rule, and allocating a number i to new vehicles entering the main road and the ramp vehicles according to the first-in first-out rule.
Step (2): for vehicles i and i+1, the road side central controller receives vehicle related information based on the state information and the vehicle type information of the own vehicle sent by the vehicle-mounted V2X communication device.
Step (3):
Constructing a control method aiming at vehicle energy consumption and converging passage time, and establishing a comprehensive energy consumption function C (t i,ui(t),vi (t)) of a traditional vehicle and a new energy vehicle according to the wind resistance coefficient C Di, the windward area S i and the mass difference m i of different vehicles and considering the large difference of vehicle types and the energy sources of power units, wherein t i is the running time of the vehicle i, u i (t) is the control input quantity of the vehicle i, v i (t) is the minimum principle of speed of the vehicle i and the passage time when the vehicle i runs, and the overall goal of optimizing the vehicle converging cooperative control is as follows Where α 1+α2 =1 is the weight coefficient, u i (t) is the control input,/>For the time that vehicle i is traveling to the merge point,/>For the time that vehicle i enters the merge control zone, u max is the vehicle maximum control input and u min is the vehicle minimum control input. After receiving the vehicle related information, the road side central controller inputs the vehicle information into the controller, performs merging optimization of the vehicle i and the vehicle i+1 according to the current passing sequence, and calculates an energy consumption and passing time combined target in the merging process of the vehicles in the current sequence
Step (4): optimizing the vehicle passing sequence, and respectively calculating the optimal control input quantity of the vehicle confluence under the condition that the vehicle i and the vehicle i+1 pass preferentially for the vehicle i and the vehicle i+1And storing the corresponding control target quantity, and determining the vehicle merging sequence by taking the minimum sum J=J i+Ji+1 of the two-vehicle combined targets as a principle.
Step (5): traversing all vehicles entering the ramp control area until all vehicles complete the sequence and track optimization, and reading the optimal control input corresponding to the optimal sequence as the final control input of the vehicles.
Further, in the step (1), the "confluence control region" specifically includes: and establishing a converging coordinate system with a vehicle merging point as a center and a region with a radius of 500m as a converging control region, wherein a starting point of the converging control region is an origin, and a vehicle running direction is a positive direction.
Further, in the step (1), the "merging rule" specifically includes: overtaking is not allowed in the same lane, and the safety interval between vehicles is ensured by a rear vehicle.
Further, in the step (1), the "allocation number" is specifically: and uniformly numbering all vehicles on the main road and the ramp according to the time sequence of entering the confluence control area.
Further, the "vehicle-related information" in step (2) is specifically: the information comprises information such as the speed v i of the vehicle i and the vehicle i+1, the acceleration a i, the hysteresis coefficient tau i, the vehicle windward area S i, the speed v i+1 of the vehicle i+1, the acceleration a i+1, the hysteresis coefficient tau i+1, the vehicle windward area S i+1 and the like.
Further, the "control method for vehicle energy consumption and converging passage time as targets" in the step (3) is specifically:
step (31): vehicle dynamics modeling. For vehicle i, analyzing force resistance during vehicle travel
Wherein m i is the vehicle mass, f is the rolling resistance coefficient, g is the gravitational acceleration, k is the gradient, C D is the vehicle air resistance coefficient, A i is the vehicle windward area, and v i (t) is the running speed of the vehicle i; under the condition of normal running and no slip, the relation between the vehicle power and the resistance is as follows:
Wherein r i is the wheel radius, and a i (t) is the vehicle running acceleration;
step (32): vehicle dynamics model:
Where P i (T) is the displacement of vehicle i, v i (T) is the speed of vehicle i, f i is the rolling resistance coefficient of vehicle i, and T i (T) is the engine output torque;
after feedback linearization, the third-order state space equation of the longitudinal dynamics of the vehicle:
Wherein:
step (33): and establishing an optimization target.
Target 1: minimum transit time. Moment when vehicle i enters the confluence control regionTime to exit merge point/>The time required for the process is the smallest: /(I)
Target 2: minimum energy consumption. I.e., energy expenditure index J i(ti,ui(t),vi (t)) is minimized.
Wherein C (t i,ui(t),vi (t)) is an energy consumption function:
C(ti,ui(t),vi(t))＝Cacc(ti,ui(t),vi(t))+Ccruse(ti,ui(t),vi(t))
The vehicle energy consumption is divided into acceleration consumption and constant-speed running consumption, and the energy consumption rate C acc at the time of vehicle acceleration is calculated as follows:
Cacc(ti,ui(t),vi(t))＝miai(t)vi(t)
The energy consumption rate C cruse when the vehicle is traveling at a constant speed is as follows:
wherein C Di is the air resistance coefficient of the vehicle i.
Step (34): in order to ensure that the running safety and control input of the vehicle meet the vehicle performance requirements, the following constraint conditions are established:
Constraint 1: the vehicle confluence control should ensure the running safety of the vehicle at first, and in the confluence process, the running safety is ensured by means of the vehicle safety time, and for the vehicles in the same lane, the vehicles keeping the same lane are required to have enough safety time interval t s:
ti-ti+1＞ts
Constraint 2: and (5) safe merging. The vehicle should have a certain safety time t ms when it is traveling to the merging point.
Constraint 3: because the self-driven braking capability of the vehicle is limited and the running speed of the vehicle is limited, the speed is constrained to meet the acceleration and deceleration capability of the vehicle:
vmin≤vi≤vmax
control input constraints:
umin≤ui(t)≤umax
The acceleration and braking capabilities are different for different vehicles, and therefore the maximum acceleration and deceleration will also be different.
Step (35): the multi-vehicle confluence problem is converted into an optimal control problem in multi-vehicle speed planning, and the state equation is converted into the following constraint equation form:
The control optimization targets are as follows:
Where α 1+α2 =1 is a weight coefficient.
Constructing a Hamilton function:
The regular equation is:
The Euler-Lagrangian equation is:
the optimal solution is as follows:
pi(t)＝c1t+c2
Wherein c 1,c2,c3,c4 are constant. The optimal solution of the vehicle track can be obtained according to the following conditions Initial conditions/>Terminal conditions/>Boundary conditionsAnd cross-sectional conditions/>
Further, "optimizing the vehicle passing sequence" in the step (4) specifically includes:
Order optimization between the main road vehicle i and the ramp vehicle i+1 is carried out, and optimal control input under the condition of preferential passing of the main road vehicle is calculated respectively And the optimal control input/>, under the condition of converging traffic time t i and ramp vehicle preferential trafficAnd the converging passage time t i+1 is used for storing the optimal control input and determining the passage sequence according to the minimum converging passage performance index principle.
The sequence between the host road vehicle i and the host road vehicle i+1 is optimized, and the first-in first-out sequence is kept unchanged according to the rule that overtaking is not allowed by the same lane when the vehicles are converged. Optimal control input in computational orderAnd a confluence transit time t i,ti+1. Storing optimal control inputs/>
The sequence between the ramp vehicle i and the ramp vehicle i+1 is optimized, and the first-in first-out sequence is kept unchanged according to the rule that overtaking is not allowed by the same lane when the vehicles are converged. Optimal control input in computational orderAnd a confluence transit time t i,ti+1. Storing optimal control inputs/>
And (5) traversing all vehicles entering the ramp control zone until all vehicles finish the sequence and track optimization.
Compared with the prior art, the invention has the remarkable advantages that:
(1) The multi-vehicle-type information ramp converging cooperative control method based on the multi-vehicle-type information ramp converging cooperative control considers the vehicle dynamics characteristics of different vehicle types and performs multi-vehicle cooperative control under the condition of considering different vehicle acceleration and braking capacities.
(2) The controller calculates the corresponding energy consumption function according to the vehicle information, so that the optimal confluence control and the optimal confluence sequence of the comprehensive vehicle type factors are obtained, the dynamic performance among different vehicles and the large difference in quality are fully considered in the optimal calculation of the vehicle control, the acceleration and deceleration of the heavy-duty vehicle means the factor of larger energy consumption, the running efficiency under the confluence control is improved, and the energy consumption is further reduced.
(3) According to the method, order optimization calculation is reduced, for general vehicle order optimization, a common method is to traverse all vehicle merging orders, the optimal control quantity corresponding to the merging process is calculated in each traverse, and the calculation complexity is exponential.
Drawings
Fig. 1 is a sectional view of a confluence control region according to the present invention.
FIG. 2 is a flowchart of the optimization control of the present invention.
FIG. 3 is a flow chart of overall optimization of vehicle merging according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
The invention relates to a vehicle ramp converging cooperative control method considering energy consumption of multiple vehicle types, and converging optimization control is shown in figure 1. The confluence control method comprises the following steps: establishing a vehicle ramp converging model; and setting a ramp converging control region, and setting a region with a radius of 500m from a converging point as a converging control region.
Establishing a vehicle driving rule, wherein the rule 1 is as follows: vehicles in the same lane are not allowed to overtake; rule 2: the safety interval of the vehicle is ensured by the rear vehicle. The vehicle transmits the vehicle running state including speed, acceleration, vehicle type information, etc. through the V2I communication device. The roadside infrastructure receives vehicle information that enters the confluence control region. And counting and calculating information, and numbering the vehicles entering the confluence control area according to a first-in first-out principle. For the vehicle i, the road side central controller receives relevant information of the vehicle i based on the state information and the vehicle type information of the vehicle sent by the vehicle-mounted V2X communication equipment. The vehicle related information specifically includes: the information includes the information such as the speed v i of the vehicle i and the vehicle i+1, the acceleration a i, the hysteresis coefficient τ i, the wheel output torque T i, the vehicle air resistance coefficient C Di, the vehicle windward area S i, the speed i+1 of the vehicle i+1, the acceleration a i+1, the hysteresis coefficient τ i+1, the wheel output torque T i+1, the vehicle air resistance coefficient C Di+1, the vehicle windward area S i+1 and the like.
After receiving the vehicle-related information, the road side central controller starts to perform multi-vehicle speed optimization as shown in fig. 2, inputs the vehicle information into the controller, performs merging optimization of the vehicles i and i+1 according to the current passing sequence, and calculates energy consumption and merging passing time in the merging process of the vehicles in the current passing sequence. For vehicle i, analyzing force resistance during vehicle travel
Wherein m i is the vehicle mass, f is the rolling resistance coefficient, g is the gravitational acceleration, k is the gradient, C D is the vehicle air resistance coefficient, S i is the vehicle windward area, and v i (t) is the running speed of the vehicle i; under the condition of normal running and no slip, the relation between the vehicle power and the resistance is as follows:
Vehicle dynamics model:
Where P i (T) is the displacement of vehicle i, v i (T) is the speed of vehicle i, f i is the rolling resistance coefficient of vehicle i, and T i (T) is the engine output torque;
after feedback linearization, the third-order state space equation of the longitudinal dynamics of the vehicle:
Wherein:
To ensure that the running safety and control input of the vehicle are suitable for the performance requirement of the vehicle, a constraint condition 1 is established: the vehicle confluence control firstly ensures the running safety of vehicles, in the confluence process, the running safety is ensured by means of the vehicle safety time, and the vehicles in the same lane are required to keep the same lane with enough safety time interval t s：ti-ti+1＞ts; constraint 2: when the vehicles reach the junction point, in order to realize safe junction, all vehicles should meet a safe time interval t ms; constraint 3: because the vehicle has limited self-driven braking capability and limited vehicle running speed, the control input is constrained to meet the acceleration and deceleration capability of the vehicle: v min≤vi≤vmax, control input constraints: u min≤ui(t)≤umax; the acceleration and braking capabilities are different for different vehicles, and therefore the maximum acceleration and deceleration will also be different.
And establishing an optimization target. Target 1: minimum transit time. Moment when vehicle i enters the confluence control regionTime to exit merge point/>The time required for the process is the smallest: /(I)Target 2: minimum energy consumption.
I.e., energy expenditure index J i(ti,ui(t),vi (t)) is minimized.
Wherein C (t i,ui(t),vi (t)) is an energy consumption function:
C(ti,ui(t),vi(t))＝Cacc(ti,ui(t),vi(t))+Ccruse(ti,ui(t),vi(t))
The vehicle energy consumption is divided into acceleration consumption and constant-speed running consumption, and the energy consumed when the vehicle accelerates C acc is calculated as follows:
Cacc(ti,ui(t),vi(t))＝miai(t)vi(t)ti
Where a i is the acceleration of the vehicle while it is traveling.
The energy C cruse consumed when the vehicle is traveling at a constant speed is as follows:
wherein C Di is the air resistance coefficient of the vehicle i.
The problem of multi-vehicle confluence is converted into an optimally controlled multi-vehicle speed planning problem, and the state equation is converted into the following constraint equation form:
The control optimization targets are as follows:
Where α 1+α2 =1 is a weight coefficient.
Constructing a Hamilton function:
The regular equation is:
The Euler-Lagrangian equation is:
the optimal solution is as follows:
pi(t)＝c1t+c2
Wherein c 1,c2,c3,c4 are constant. The optimal solution of the vehicle track can be obtained according to the following conditions Initial conditions/>Terminal conditions/>Boundary conditions/>And cross-sectional conditions/>
Order optimization between the main road vehicle i and the ramp vehicle i+1 is carried out, and optimal control input under the condition of preferential passing of the main road vehicle is calculated respectivelyAnd the optimal control input/>, under the condition of converging traffic time t i and ramp vehicle preferential trafficAnd the converging passage time t i+1 is used for storing the optimal control input and determining the passage sequence according to the minimum converging passage performance index principle.
The sequence between the host road vehicle i and the host road vehicle i+1 is optimized, and the first-in first-out sequence is kept unchanged according to the rule that overtaking is not allowed by the same lane when the vehicles are converged. Optimal control input in computational orderAnd a confluence transit time t i,ti+1. Storing optimal control inputs/>
The sequence between the ramp vehicle i and the ramp vehicle i+1 is optimized, and the first-in first-out sequence is kept unchanged according to the rule that overtaking is not allowed by the same lane when the vehicles are converged. Optimal control input in computational orderAnd a confluence transit time t i,ti+1. Storing optimal control inputs/>
If the adjacent vehicles are vehicles in the same lane, the confluence control optimization is carried out according to the rule of not allowing overtaking, if the adjacent vehicles are in different lanes, the adjacent serial number vehicle passing sequence is exchanged, and in order to reduce the confluence optimization calculation under the meaningless vehicle sequence, the pruning optimization is carried out on the vehicle confluence sequence, as shown in fig. 3.
Let S i (m, n) be the i-th step optimizing traffic order, m be the priority traffic vehicle, n be the rear traffic vehicle. Step 1, performing control optimization on the vehicles 1 and 2 in different orders to obtain a merging order S 1 (2, 1) of the vehicles 1 and 2 with smaller objective function, and then storing corresponding control inputsAccording to the optimizing result of step 1, step 2 is carried out, step 2 is added to the vehicle 3 for analysis, the merging order of the vehicle 1 and the vehicle 3 is analyzed because the vehicle 1 is the rear vehicle in the optimizing result of the previous step, and the merging order is kept unchanged because the vehicle 1 and the vehicle 3 are the same-lane vehicles, namely, the vehicle order S 2 (1, 3), and the corresponding control input/> isstoredStep 3 and step2, the merging sequence of the same-lane vehicles is kept unchanged, and an optimized sequence S 3 (3, 4) and an optimal control input/> areobtainedStep 4 and step 1, the optimization order is S 4 (5, 4), and the optimal control input/>, is stored
And for i vehicles, traversing all vehicles entering the ramp control area until all vehicles complete the sequence and track optimization, and reading the optimal control input corresponding to the optimal sequence as the final control input of the vehicles.
Claims (3)
1. A vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types is characterized by comprising the following steps:
Step (1): setting a converging control area, formulating a converging rule, and allocating a number i to new vehicles entering the main road and the ramp vehicles according to a first-in first-out rule;
Step (2): for the vehicle i, based on the state information and the vehicle type information of the vehicle sent by the vehicle-mounted V2X communication equipment, the road side central controller receives vehicle related information, wherein the vehicle related information comprises a vehicle number i and the moment of entering the confluence control region Speed v i, acceleration a i, hysteresis coefficient tau i, wheel output torque T i, vehicle windward area S i;
Step (3): constructing a control method aiming at vehicle energy consumption and converging passage time, aiming at wind resistance coefficient C Di, vehicle windward area S i and mass m i of different vehicles, taking large differences of vehicle types and different energy sources of power units into consideration, establishing a comprehensive energy consumption function C (t i,ui(t),vi (t)) of a traditional vehicle and a new energy vehicle, wherein t i is the running time of the vehicle i, u i (t) is the control input quantity of the vehicle i, v i (t) is the speed of the vehicle i, and combining the minimum passage time principle when running, and optimizing the overall goal of vehicle converging cooperative control is as follows Where α 1+α2 =1 is the weight coefficient, u i (t) is the control input,/>For the time that vehicle i is traveling to the merge point,/>For the time when vehicle i enters the merge control zone, u max is the vehicle maximum control input, and u min is the vehicle minimum control input; after receiving the vehicle related information, the road side central controller inputs the vehicle information into the controller, performs merging optimization of the vehicle i and the vehicle i+1 according to the current passing sequence, and calculates an energy consumption and passing time combined target in the merging process of the vehicles in the current sequence
The "confluence control method" in the step (3) specifically includes:
step (31): modeling vehicle dynamics; for vehicle i, analyzing force resistance during vehicle travel
Wherein m i is the vehicle mass, f is the rolling resistance coefficient, g is the gravitational acceleration, k is the gradient, v i (t) is the running speed of the vehicle i; under the condition of normal running and no slip, the relation between the vehicle power and the resistance is as follows:
Wherein r i is the wheel radius, and a i (t) is the vehicle running acceleration;
step (32): vehicle dynamics model:
Where p i (T) is the displacement of vehicle i, v i (T) is the speed of vehicle i, f i is the rolling resistance coefficient of vehicle i, and T i (T) is the engine output torque;
after feedback linearization, the third-order state space equation of the longitudinal dynamics of the vehicle:
Wherein:
Step (33): establishing an optimization target;
target 1: minimum transit time; moment when vehicle i enters the confluence control region Time to exit merge point/>The time required for the process is the smallest: /(I)
Target 2: minimum energy consumption; i.e., energy expenditure index J i(ti,ui(t),vi (t)) is minimal;
Wherein C (t i,ui(t),vi (t)) is an energy consumption function:
C(ti,ui(t),vi(t))＝Cacc(ti,ui(t),vi(t))+Ccruse(ti,ui(t),vi(t))
the energy consumption of the vehicle is divided into acceleration consumption and uniform driving consumption;
the energy consumption rate C acc at the time of vehicle acceleration is calculated as follows:
Cacc(ti,ui(t),vi(t))＝miai(t)vi(t)
The energy consumption rate C cruse when the vehicle is traveling at a constant speed is as follows:
Step (34): in order to ensure that the running safety and control input of the vehicle meet the vehicle performance requirements, the following constraint conditions are established:
constraint 1: the vehicle confluence control should ensure the running safety of the vehicle at first, and in the confluence process, the running safety is guaranteed by means of the vehicle safety time, and for the adjacent vehicles in the same lane, the vehicles which need to keep the same lane have enough safety time interval t s:
ti-ti+1>ts
Constraint 2: safe merging; the vehicle has a certain safety time t ms when running to the merging point;
Constraint 3: because the self-driving braking capability of the vehicle is limited and the running speed of the vehicle is limited, in order to meet the acceleration and deceleration capability of the vehicle,
Speed is constrained:
vmin≤vi≤vmax
control input constraints:
umin≤ui(t)≤umax
the acceleration capacity and braking capacity are different for different vehicles, so that the maximum acceleration and deceleration are also different;
Step (35): the multi-vehicle confluence problem is converted into an optimal control problem in multi-vehicle speed planning, and the state equation is converted into the following constraint equation form:
The control optimization targets are as follows:
wherein α 1+α2 =1 is a weight coefficient;
constructing a Hamilton function:
The regular equation is:
The Euler-Lagrangian equation is:
the optimal solution is as follows:
pi(t)＝c1t+c2
Wherein c 1,c2,c3,c4 are constant; the optimal solution of the vehicle track can be obtained according to the following conditions Initial conditionsTerminal conditions/>Boundary conditions/> Cross-sectional conditions/>
Step (4): optimizing the vehicle passing sequence, and respectively calculating the optimal control input quantity of the vehicle confluence under the condition that the vehicle i and the vehicle i+1 pass preferentially for the vehicle i and the vehicle i+1Storing the corresponding control target quantity, and determining the vehicle merging sequence by taking the minimum sum J=J i+Ji+1 of the two-vehicle combined targets as a principle; the step (4) is specifically as follows:
Order optimization between the main road vehicle i and the ramp vehicle i+1 is carried out, and optimal control input under the condition of preferential passing of the main road vehicle is calculated respectively And the optimal control input/>, under the condition of converging traffic time t i and ramp vehicle preferential trafficAnd converging passage time t i+1, storing optimal control input, and determining a passage order according to the minimum converging passage performance index principle;
The sequence between the main road vehicle i and the main road vehicle i+1 is optimized, and the first-in first-out sequence is kept unchanged according to the rule that overtaking is not allowed by the same lane when the vehicles are converged; calculating optimal control input in current first-in first-out order Confluence transit time t i,ti+1; storing optimal control inputs/>
The sequence between the ramp vehicle i and the ramp vehicle i+1 is optimized, and the first-in first-out sequence is kept unchanged according to the rule that overtaking is not allowed by the same lane when the vehicles are converged; calculating optimal control input in current first-in first-out order Confluence transit time t i,ti+1; storing optimal control inputs/>
Step (5): traversing all vehicles entering the ramp control area until all vehicles complete the sequence and track optimization, and reading the optimal control input corresponding to the optimal sequence as the final control input of the vehicles.
2. The cooperative control method for vehicle ramp merging taking into account energy consumption of multiple vehicle types according to claim 1, wherein the "merging rule" in the step (1) is specifically: and (3) not allowing overtaking in the same lane, and establishing a confluence coordinate system with the starting point of a confluence control area as an origin and the running direction of the vehicle as a positive direction.
3. The cooperative control method for vehicle ramp merging considering energy consumption of multiple vehicle types according to claim 2, wherein the "allocation number" in the step (1) is specifically: all vehicles on the main road and the ramp are numbered uniformly according to the time of entering the confluence control area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211509503.2A CN115830885B (en) | 2022-11-29 | 2022-11-29 | Vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211509503.2A CN115830885B (en) | 2022-11-29 | 2022-11-29 | Vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115830885A CN115830885A (en) | 2023-03-21 |
| CN115830885B true CN115830885B (en) | 2024-05-07 |
Family
ID=85532532
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211509503.2A Active CN115830885B (en) | 2022-11-29 | 2022-11-29 | Vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115830885B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116189461B (en) * | 2023-04-28 | 2023-07-21 | 安徽大学 | Intersection traffic control method, system and storage medium considering carbon emission |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112750318A (en) * | 2020-12-30 | 2021-05-04 | 山东高速信息集团有限公司 | Ramp confluence control method and system based on edge cloud |
| CN113362619A (en) * | 2021-06-04 | 2021-09-07 | 东南大学 | Intelligent network vehicle ramp cooperative confluence control method and device under mixed traffic environment |
| CN114973650A (en) * | 2022-04-13 | 2022-08-30 | 东南大学 | Vehicle ramp entrance confluence control method, vehicle, electronic device, and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10108197B2 (en) * | 2015-12-08 | 2018-10-23 | Ford Global Technologies, Llc | Deceleration determination of a vehicle |
-
2022
- 2022-11-29 CN CN202211509503.2A patent/CN115830885B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112750318A (en) * | 2020-12-30 | 2021-05-04 | 山东高速信息集团有限公司 | Ramp confluence control method and system based on edge cloud |
| CN113362619A (en) * | 2021-06-04 | 2021-09-07 | 东南大学 | Intelligent network vehicle ramp cooperative confluence control method and device under mixed traffic environment |
| CN114973650A (en) * | 2022-04-13 | 2022-08-30 | 东南大学 | Vehicle ramp entrance confluence control method, vehicle, electronic device, and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 智能网联汽车协同生态驾驶策略综述;杨澜 等;《交通运输工程学报》;20201031;第20卷(第5期);第58-72页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115830885A (en) | 2023-03-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108877268B (en) | Unmanned-oriented traffic-light-free crossroad intelligent scheduling method | |
| CN108973998B (en) | Heterogeneous vehicle queue distributed energy-saving control method based on MPC | |
| CN114241778B (en) | Multi-objective optimization control method and system for expressway internet of vehicles cooperating with ramp junction | |
| CN109410561B (en) | Uniform and heterogeneous formation driving control method for vehicles on highway | |
| CN110910663A (en) | Multi-intelligent-vehicle intersection passing coordination control method under cooperative vehicle-road environment | |
| CN113299107B (en) | Multi-target fusion intersection dynamic vehicle internet speed guiding control method | |
| CN110363986B (en) | Centralized confluence area vehicle speed optimization method | |
| CN110435655B (en) | Electric automobile fleet self-adaptive cruise optimization method considering regenerative braking | |
| Wang et al. | Research on speed optimization strategy of hybrid electric vehicle queue based on particle swarm optimization | |
| CN115830885B (en) | Vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types | |
| CN109559499B (en) | Vehicle queue running management platform, control method and vehicle-mounted terminal | |
| CN114265398A (en) | Trajectory planning method and device for automatic driving vehicle, electronic equipment and storage medium | |
| CN111243296B (en) | Ramp confluence cooperative control method and system based on confluence time optimization | |
| CN112164217B (en) | Automatic driving vehicle queue running management system and control method thereof | |
| CN110838229A (en) | Vehicle queue formation method | |
| CN112767715B (en) | Intersection traffic signal lamp and intelligent networked automobile cooperative control method | |
| CN110260872A (en) | The dynamic based on GPS is overtaken other vehicles Trajectory Planning System under a kind of bus or train route cooperative surroundings | |
| CN113489793A (en) | Expressway double-lane cooperative control method in mixed traffic scene | |
| CN111532264A (en) | Intelligent internet automobile cruising speed optimization method for variable-gradient and variable-speed-limit traffic scene | |
| CN114973733A (en) | Method for optimizing and controlling track of networked automatic vehicle under mixed flow at signal intersection | |
| CN115662131B (en) | Multi-lane collaborative lane changing method for road accident section in network environment | |
| CN112581747A (en) | Vehicle-road cooperative cargo vehicle fleet organization method, device and system | |
| CN112731806B (en) | Intelligent networking automobile random model prediction control real-time optimization method | |
| CN112750298A (en) | Truck formation dynamic resource allocation method based on SMDP and DRL | |
| CN113993760B (en) | Method for controlling a vehicle |
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 |