CN114783175B - Multi-signal lamp road condition internet-connected vehicle energy-saving driving control method based on pseudo-spectrum method - Google Patents
Multi-signal lamp road condition internet-connected vehicle energy-saving driving control method based on pseudo-spectrum method Download PDFInfo
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Abstract
The invention discloses an energy-saving driving control method for a multi-signal lamp road condition internet-connected vehicle based on a pseudo-spectrum method, relates to the technical field of intelligent traffic, and solves the technical problems that vehicle speed planning is not economical enough and the control method is not efficient. The speed of pseudo-spectrum planning can achieve energy saving rate similar to that of dynamic planning method, and the solving time of pseudo-spectrum is far less than that of dynamic planning method.
Description
Technical Field
The application relates to the technical field of intelligent transportation, in particular to an intelligent network-connected vehicle economical driving control technology, and especially relates to an energy-saving driving control method for a network-connected vehicle under a multi-signal lamp road condition based on a pseudo-spectrum method.
Background
In view of the climate change and environmental pollution problems caused by traffic, countries are faced with great pressure to reduce the emission of greenhouse gases associated with fuel combustion, and most of the major automotive markets have established restrictions on the emission of carbon dioxide from exhaust gases. Networking and automatic driving automobiles have great potential in improving traffic efficiency, safety and environmental sustainability, and the driving economy is improved while improving traffic flow stability, reducing unnecessary speed change and start-stop of the vehicles.
Under the urban road network, the signal lamp is an important guarantee for safety of vehicles and pedestrians. With the development of internet of things (IoT) technology, traffic facilities may share information in real-time. For intelligent network vehicles, the intelligent network vehicles have the advantages that the intelligent network vehicles can be in wireless communication with other vehicles and road side infrastructures to acquire information such as road gradient, signal lamp phase and time, traffic jam conditions and the like, and the traffic environment in which the intelligent network vehicles are positioned has the characteristic of dynamic change, so that the effects of reducing frequent start and stop and optimizing the running speed by using the road information are more remarkable. In addition, factors such as non-automatic driving vehicles and pedestrians in a dynamic traffic environment cause the vehicles to deviate from a speed curve obtained by planning, and the traditional intelligent network vehicle speed planning strategy is limited by the calculation efficiency of a speed planning algorithm, so that the existing driving strategy cannot be adjusted to meet the optimal economical efficiency and comfort. Therefore, the development of a real-time speed planning and control algorithm which can adapt to the road conditions of multiple signal lamps is a problem to be solved urgently by comprehensively considering the targets of safety, comfort, economy and the like of intelligent network vehicles.
Disclosure of Invention
The application provides a pseudo-spectrum-based energy-saving driving control method for a network-connected vehicle under a multi-signal-lamp road condition, which aims to solve the problem that an economical vehicle speed planning and control method is limited by algorithm efficiency.
The technical aim of the application is achieved through the following technical scheme:
a pseudo-spectrum method-based energy-saving driving control method for a multi-signal lamp road condition internet-connected vehicle comprises the following steps:
s1: acquiring road gradient angle, signal lamp phase and initial time t 0 And a vehicle state; wherein the vehicle state includes a current vehicleInitial speed v of vehicle 0 And an initial position s 0 ；
S2: establishing an optimal control model containing an objective function and constraint conditions; wherein, the optimization target of the optimal control model is motor energy consumption, and the constraint conditions comprise a vehicle longitudinal dynamics model and motor output torque T m Road speed limit, target time t f And a target distance s f ；t f I.e. the middle time of the green light window, s f I.e. the signal lamp intersection position;
s3: converting the optimal control model through a pseudo spectrum method to obtain a discrete nonlinear optimal control problem;
s4: solving the nonlinear optimal control problem by a sequence quadratic programming method to obtain a programmed speed track;
s5: comparing the actual speed track of the vehicle with the planned speed track to obtain an error of the actual speed track and the planned speed track, and judging whether the vehicle deviates from the planned speed track according to the error; if the error exceeds the preset range, the vehicle deviates from the planned speed track, and the steps S1 to S4 are continuously executed to re-plan the vehicle running speed track until the error of the actual speed track and the planned speed track is within the preset range.
The beneficial effects of this application lie in: according to the method, the economic driving speed of the vehicle at the multi-signal lamp intersection is planned based on the pseudo-spectrum method, unnecessary starting and stopping of the vehicle at the signal lamp intersection can be reduced, and therefore the economical efficiency, the comfort and the traffic efficiency of the vehicle are improved. In practical application, the method can be planned in cooperation with other speed planning algorithms to balance the instantaneity and economy of algorithm solution. Through simulation, the speed planning is carried out on the same road section based on the pseudo-spectrum method and the dynamic planning method, and compared with the fixed speed cruising, the electric energy of 11.39% and 11.56% is saved respectively, so that the speed planned based on the pseudo-spectrum method can achieve the energy saving rate similar to that of the dynamic planning method, and the solving time of the pseudo-spectrum method is far shorter than that of the dynamic planning method.
The pseudo-spectrum method is not only suitable for straight roads, but also suitable for roads containing gradient information and curves. The gradient information of the road is reflected in the nonlinear dynamics model constraint of the vehicle; the safety and comfort constraints of the curve can be translated into speed constraints reflected in the constraints of the speed sequence.
Meanwhile, the speed planning algorithm can adapt to complex traffic environments. In a dynamic traffic environment, a vehicle may be interfered by pedestrians or other vehicles to deviate from an original speed track, and when the error is too large, the vehicle cannot smoothly pass through a signal lamp intersection according to the original speed by comparing the error of the actual speed track of the vehicle and the planned speed track, so that speed re-planning is performed.
Drawings
FIG. 1 is a flow chart of an energy-efficient driving control method described herein;
FIG. 2 is a flow chart of a pseudo-spectral solution described herein;
FIG. 3 (a) is a schematic diagram of a simulation test of the present application, and (b) is a schematic diagram of a comparison of a planned speed trajectory and a speed trajectory planned by a fixed speed method;
FIG. 4 is a comparison of torque variation obtained by the present application and fixed speed method;
fig. 5 is a schematic diagram showing the comparison of energy consumption changes obtained by planning the present application and the fixed speed method.
Detailed Description
The technical scheme of the application will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of the energy-saving driving control method described in the present application, and as shown in fig. 1, the method for controlling energy-saving driving of a network-connected vehicle under a multi-signal road condition based on a pseudo spectrum method includes:
s1: acquiring road gradient angle, signal lamp phase and initial time t 0 And a vehicle state; wherein the vehicle state includes an initial speed v of the current vehicle 0 And an initial position s 0 。
S2: establishing an optimal control model containing an objective function and constraint conditions; wherein, the optimization target of the optimal control model is motor energy consumption, and the constraint conditions comprise a vehicle longitudinal dynamics model and motor outputTorque T m Road speed limit, target time t f And a target distance s f 。
t f I.e. the middle time of the green light window, i.e. the middle time t of the green light window f As a target time constraint for the vehicle to reach the signal intersection. s is(s) f I.e. signal crossing position, i.e. signal crossing position s f As a target distance constraint.
S3: and converting the optimal control model through a pseudo spectrum method to obtain a discrete nonlinear optimal control problem.
S4: and solving the nonlinear optimal control problem by a sequence quadratic programming method to obtain a programmed speed track.
S5: comparing the actual speed track of the vehicle with the planned speed track to obtain an error of the actual speed track and the planned speed track, and judging whether the vehicle deviates from the planned speed track according to the error; if the error exceeds the preset range, the vehicle deviates from the planned speed track, and the steps S1 to S4 are continuously executed to re-plan the vehicle running speed track until the error of the actual speed track and the planned speed track is within the preset range.
Further, in step S5, if the error is within the preset range, the vehicle does not deviate from the planned speed track, and it is determined whether the vehicle enters the next signal lamp section, if yes, steps S1-S5 are continuously performed, if not, it is determined whether the vehicle reaches the end point, the control is ended, and if not, the control returns to step S5 to determine whether the vehicle deviates from the planned speed track.
In step S2, the establishment process of the optimal control model is as follows:
firstly, taking the running distance s and the running speed v of a vehicle as state variables, and taking the output torque T of a motor m As a control variable, then a vehicle longitudinal dynamics model with two degrees of freedom is established according to an electric vehicle power system and a driving working condition, and the vehicle longitudinal dynamics model is expressed as:
wherein t represents running time, M represents whole vehicle mass, R t Represents the tire radius, ρ represents the air density, C d Representing the aerodynamic drag coefficient, A F Represents the frontal area of the vehicle, f r Representing the rolling resistance coefficient, g representing the gravitational acceleration, θ representing the road gradient angle, F R Indicating the running resistance.
The electric vehicle adopts 4 in-wheel motor drive, and every in-wheel motor can be according to current output torque and rotational speed computational efficiency, obtains the power model representation of motor as:
wherein P is m Representing motor power, omega m Indicating the rotation speed eta of the motor m Representing motor output torque T m As efficiency in driving torque, η e Representing motor output torque T m As efficiency in braking torque.
In order to ensure the safety of the vehicle during running, the road speed limit and the motor output torque T need to be considered m Is expressed as:
v min ≤v≤v max , (i＝1,2,...,N)； (3)
T min ≤T m ≤T max , (i＝1,2,...,N)； (4)
and if the optimization target of the optimal control model is that the energy consumption is optimal, the objective function is expressed as:
the optimal control model established according to the formulas (1) to (5) is expressed as:
in order to ensure that the vehicle passes through the signal lamp intersection, the middle time of the signal lamp green light window is selected as the target time of the vehicle in the road section, namely:
wherein t is r2g Indicating the switching time from red light to green light, t g2r And the switching time from green light to red light in the same period is represented.
Step 3 is to convert the above optimal control model into discrete nonlinear programming problem based on the Legendre pseudo-spectrum method, and the specific conversion process is shown in FIG. 2, including:
s31: discrete interval [ -1,1 ] defined according to Legendre pseudospectrometry]The time t of the interval will be planned 0 ,t f ]The conversion relationship is expressed as:
wherein t is 0 Representing the initial time.
S32: n distribution points tau are obtained according to the Legendre pseudo-spectrum method i The state variable and the control variable in the optimal control model are discretized to obtain:
where i=1, 2, …, N.
S33: after the state variable and the control variable obtain N interpolation points, approximating the state variable and the control variable by using a Lagrange interpolation polynomial, and then respectively representing the state variable and the control variable as follows:
wherein L is i (τ) represents an nth order lagrangian interpolation basis function defined as:
s34: the approximate state equation of the travel distance s and the travel speed v is differentiated according to the formula (10):
wherein D is i (τ k ) Representing a pseudo-spectrum differential matrix, expressed as:
wherein P is N Representing the Legend polynomial of order N.
S35: converting equation (1) according to equation (12) yields the equation constraint for the converted nonlinear optimal control problem:
wherein V is k Representing the vehicle speed after dispersion, T k Representing the discrete motor torque.
S36: the integral term of the objective function in the formula (5) is obtained by converting the Gauss-Lobatto integral method:
wherein S is k Representing the discrete vehicle position; omega k Represents the integral weight, and:
s37: converting the optimal control model into a discrete nonlinear optimal control problem according to S31 to S36, expressed as:
solving the formula (17) by a sequence quadratic programming method to obtain a speed sequence (V) representing a planned speed track 1 ,V 2 ,...,V N ) According to the velocity sequence (V 1 ,V 2 ,...,V N ) And obtaining a running time error delta t of the actual speed track and the planned speed track, wherein the error delta t is expressed as:
Δt＝|t i -t|； (18)
when (when)
When the error exceeds the range of the green light window, the running speed track of the vehicle is re-planned, so that the vehicle is ensured not to stop and pass through the signal lamp intersection. Wherein t is i And the time corresponding to the ith position in the planned speed track is represented, and t represents the current time of vehicle running.
The energy-saving driving control method of the embodiment is verified by a simulation test, a 2000m road with gradient information collected from a real traffic environment is selected, wherein a first signal lamp is positioned at 800m of the road, a second signal lamp is positioned at 1600m, each signal lamp period consists of 20s red light and 30s green light, and the speed limit interval of the whole road section is [5,40] km/h.
Starting from time 0, the vehicle has an initial velocity v 0 =15 km/h, terminal speed constraint of [15,20]km/h. According to the energy-saving driving control method, a road is divided into three sections, namely: starting point to first signal light crossing, first signal light crossing to second signal light crossing, second signal light crossing to terminal point. As shown in fig. 3, the economic speed planning is performed for three roads respectively, and the time of the vehicle at the green light window is ensured. According to the figure1, when a vehicle starts, an optimal control model is established, an economic speed track is planned based on a pseudo-spectrum method, and the speed track of the whole road section is planned, so that the vehicle can pass through a signal lamp intersection at a green light window; the deviation of the tracking speed track of the vehicle is monitored in real time in the tracking process, if the deviation is too large, the deviation exceeds half of the time of a green light window with the original time, and if the deviation exceeds 15s, the vehicle is regarded as deviating from the planned track, and then the subsequent road section is re-planned; and then detecting whether the vehicle enters the next signal lamp section, if the vehicle is still in the current signal lamp section, continuing to track the existing economical speed track, and if not, planning the next signal lamp section.
The solving time of the speed planning algorithm based on the pseudo-spectrum method for three paths is 1.3s,0.4s and 0.1s respectively, the total planning time is 1.8s, and the speed track planning for the same road section by adopting dynamic planning with global optimal characteristics needs 27.9s. Therefore, the solving time of the energy-saving driving control method based on the pseudo-spectrum method is only 6.5% of that of dynamic planning, the real-time performance is good, and the existing speed track can be updated in real time according to the road condition.
In consideration of the dynamic traffic environment, the intelligent network-connected vehicle deviates from the original speed track due to factors such as other social vehicles, pedestrians and the like, so that the original speed track cannot be installed to reach the signal lamp intersection, and therefore when the deviation between the actual running time and the planned running time is too large, the vehicle re-plans the speed track of the subsequent road section. Assuming that the vehicle is forced to slow down due to pedestrian interference at 200m, a pseudo-spectrum method is used for re-planning a subsequent road section, and the obtained results are shown in fig. 3, 4 and 5. Simulation results show that after re-planning, the vehicle can pass through the first signal lamp intersection and the second signal lamp intersection at the green light window, so that the vehicle can reach the road end without stopping, and meanwhile, the traffic efficiency is improved. Compared with the fixed speed cruising, the energy-saving driving control method of the network-connected vehicle under the multi-signal-lamp road condition based on the pseudo spectrum method can achieve 9.91% of energy consumption saving rate.
The control system for implementing the network-connected vehicle energy-saving driving control method of the embodiment comprises V2X communication equipment, a network-connected vehicle, vehicle-mounted positioning equipment and a vehicle-mounted controller; the V2X communication equipment and the vehicle-mounted positioning equipment are used for acquiring road gradient information in front of a vehicle, signal lamp phase and time information and for solving the speed planning of the vehicle-mounted controller; the vehicle-mounted controller utilizes the energy-saving driving control method according to the acquired vehicle road information to plan the economic speed of the signal lamp road section where the current vehicle is positioned and provide a reference speed track for the network-connected vehicle; and the network-connected vehicle runs through the front signal lamp intersection according to the economical speed track until reaching the end point.
According to the method, the economic speed of the vehicle under the working condition of multiple signal lamps is marked by using pseudo-spectrum regulations, so that the vehicle can avoid starting and stopping at the signal lamp intersection, and the energy consumption is reduced. The vehicle can update the existing speed track in real time under the condition of being interfered by other traffic participants due to the good real-time performance of the algorithm, so that the economical efficiency of the vehicle under the working condition of multiple signal lamps is ensured.
The foregoing is an exemplary embodiment of the present application, the scope of which is defined by the claims and their equivalents.
Claims (1)
1. The utility model provides a many signal lamps road conditions are networking vehicle energy-conserving driving control method down based on pseudo-spectral method which characterized in that includes:
s1: acquiring road gradient angle, signal lamp phase and initial time t 0 And a vehicle state; wherein the vehicle state includes an initial speed v of the current vehicle 0 And an initial position s 0 ；
S2: establishing an optimal control model containing an objective function and constraint conditions; wherein, the optimization target of the optimal control model is motor energy consumption, and the constraint conditions comprise a vehicle longitudinal dynamics model and motor output torque T m Road speed limit, target time t f And a target distance s f ；t f I.e. the middle time of the green light window, s f I.e. the signal lamp intersection position;
wherein, the driving distance s and the driving speed v of the vehicle are taken as state variables, and the motor output is takenTorque T m As a control variable, then a vehicle longitudinal dynamics model is established according to an electric vehicle power system and a driving working condition, and the vehicle longitudinal dynamics model is expressed as:
wherein t represents running time, M represents whole vehicle mass, R t Represents the tire radius, ρ represents the air density, C d Representing the aerodynamic drag coefficient, A F Represents the frontal area of the vehicle, f r Representing the rolling resistance coefficient, g representing the gravitational acceleration, θ representing the road gradient angle, F R Representing the running resistance;
according to the current output matrix and the rotating speed calculation efficiency of each hub motor of the electric vehicle, the power model of the obtained motor is expressed as follows:
wherein P is m Representing motor power, omega m Indicating the rotation speed eta of the motor m Representing motor output torque T m As efficiency in driving torque, η e Representing motor output torque T m Efficiency as braking torque;
the constraint conditions of road speed limit are expressed as follows: v min ≤v≤v max ； (3)
Motor output torque T m The constraint of (2) is expressed as: t (T) min ≤T m ≤T max ； (4)
Wherein v is min Representing minimum road limit, v max Representing a maximum road speed limit; t (T) min Representing minimum motor output torque, T max Representing a maximum motor output torque;
the optimization target of the optimal control model is motor energy consumption, and then the objective function is expressed as:
the optimal control model established according to the formulas (1) to (5) is expressed as:
wherein N represents the number of distribution points;
wherein t is r2g Indicating the switching time from red light to green light, t g2r The green light to red light conversion time in the same period is represented;
s3: converting the optimal control model by a pseudo-spectrum method to obtain discrete nonlinear optimal control problems, wherein the method comprises the following steps of:
s31: discrete interval [ -1,1 ] defined according to pseudo-spectrometry]The time t of the interval will be planned 0 ,t f ]The conversion relationship is expressed as:
wherein t is 0 Representing an initial time;
s32: obtaining N distribution points tau according to a pseudo-spectrum method i And discretizing the state variable and the control variable in the optimal control model to obtain:
wherein S is i Representing the distribution point tau i A location of the site; v (V) i Representing the distribution point tau i Speed at (c); t (T) i Representing the distribution point tau i The motor at the position outputs torque; i=1, 2,. -%, N;
s33: after the state variable and the control variable obtain N interpolation points, approximating the state variable and the control variable by using a Lagrange interpolation polynomial, and then respectively representing the state variable and the control variable as follows:
wherein L is i (τ) represents an nth order lagrangian interpolation basis function defined as:
s34: the approximate state equation of the travel distance s and the travel speed v is differentiated according to the formula (10):
wherein D is i (τ k ) Representing a pseudo-spectrum differential matrix, expressed as:
wherein P is N Representing an N-th order Legend polynomial;
s35: the conversion of formula (1) according to formula (12) yields:
wherein V is k Representing the vehicle speed after dispersion, T k Representing the discrete motor torque;
s36: the integral term of the objective function in the formula (5) is obtained by converting the Gauss-Lobatto integral method:
wherein S is k Representing the discrete vehicle position; omega k Represents the integral weight, and:
s37: converting the optimal control model into a discrete nonlinear optimal control problem according to S31 to S36, expressed as:
s4: solving the nonlinear optimal control problem by a sequence quadratic programming method to obtain a programmed speed track; wherein the speed sequence representing the planned speed trajectory is (V 1 ,V 2 ,...,V N )；
S5: comparing the actual speed track of the vehicle with the planned speed track to obtain an error of the actual speed track and the planned speed track, and judging whether the vehicle deviates from the planned speed track according to the error; if the error exceeds the preset range, the vehicle deviates from the planned speed track, and the steps S1 to S4 are continuously executed to re-plan the vehicle running speed track until the error of the actual speed track and the planned speed track is within the preset range; if the error is within the preset range, the vehicle does not deviate from the planned speed track, whether the vehicle enters the next signal lamp road section is judged, if yes, the steps S1-S5 are continuously executed, if not, whether the vehicle reaches the end point is judged, the control is ended, if not, the control is ended, and if not, the step S5 is returned to for judging whether the vehicle deviates from the planned speed track;
wherein, according to the velocity sequence (V 1 ,V 2 ,...,V N ) And obtaining a running time error delta t of the actual speed track and the planned speed track, wherein the error delta t is expressed as:
Δt＝|t i -t|； (18)
when (when)
When the vehicle is in a running state, the running speed track of the vehicle is re-planned;
wherein t is i And the time corresponding to the ith position in the planned speed track is represented, and t represents the current time of vehicle running.
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| CN114783175A (en) | 2022-07-22 |
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