CN111959492B - HEV energy management hierarchical control method considering lane change behavior in internet environment - Google Patents
HEV energy management hierarchical control method considering lane change behavior in internet environment Download PDFInfo
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- 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
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
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- 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
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/12—Controlling the power contribution of each of the prime movers to meet required power demand using control strategies taking into account route information
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- 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
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/15—Control strategies specially adapted for achieving a particular effect
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- 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
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Abstract
The invention relates to a HEV energy management hierarchical control method considering lane changing behaviors in an internet environment, and belongs to the field of new energy automobiles. The method comprises the following steps of 1) an economic driving vehicle speed decision-making method considering lane changing behaviors: predicting the information of the front vehicle according to the networking information provided by vehicle-vehicle communication and vehicle-road side equipment communication, and adopting a hybrid MPC (multi-media personal computer) to decide the optimal lane changing behavior and the optimal speed; 2) the energy management optimization control method comprises the following steps: and based on the HEV model, energy distribution is carried out on the HEV by tracking the optimized speed track, and optimized control quantity is obtained. The invention optimizes two dimensions from the vehicle speed and the power system to save energy and improve traffic efficiency, and lays a foundation for designing a safe, energy-saving, efficient and intelligent hybrid electric vehicle. The invention can be widely applied to the economic driving and the energy management of different types of hybrid electric vehicles.
Description
Technical Field
The invention belongs to the field of new energy automobiles, and relates to an HEV energy management hierarchical control method considering lane change behaviors in an internet environment.
Background
Intellectualization, networking and electromotion are the development trends of future automobiles. With the rapid development of the intelligent networking technology, the networking information is fused to provide powerful support for the energy-saving control of the hybrid electric vehicle. Economical driving has received a great deal of attention as an effective way of energy-saving control. The method is integrated into energy management, and is beneficial to improving the economy and traffic efficiency of the whole vehicle.
The economic driving is to combine traffic information (such as traffic lights and traffic constraints) to plan and optimize the vehicle speed under different traffic scenes so as to improve the traffic efficiency and the vehicle economy, and the implementation modes are various, such as: economical driving ACC. And under the networking environment, more traffic information (such as surrounding vehicle information and traffic lights) can be acquired, and an effective way is provided for developing an economic driving strategy with strong adaptability.
The energy management strategy is the core technology of a Hybrid Electric Vehicle (HEV), and directly determines the economy and the dynamic property of the whole vehicle. However, due to uncertainty of actual operating conditions, designing a robust energy management strategy is extremely challenging. The performance of the vehicle-mounted power-saving control system is related to the vehicle speed and the required power, and the driving condition is influenced by external traffic environments (such as surrounding vehicles, traffic signals and the like) and presents a dynamic change characteristic. Therefore, dynamic traffic information is considered, energy management is optimized, and the economy of the whole vehicle and the adaptability of an algorithm are improved.
Therefore, under the networking environment, traffic information and vehicle states are obtained through vehicle-vehicle communication and vehicle-roadside device communication, the speed of the main vehicle is optimized, the influence of lane changing behaviors on the energy management performance of the main vehicle is considered through integrating economic driving decision and energy management strategies, two dimensions are controlled from longitudinal and transverse movement, double-layer optimization is carried out on the energy management, and the economy and the traffic efficiency of the whole vehicle are further improved.
Especially under urban conditions, if the traffic flow in front is large (the vehicle running speed is low), the traffic efficiency and the overall economy are difficult to ensure only by longitudinal control. In the prior art, the influence of the lane changing behavior (lateral control) on energy management performance is not considered, which greatly limits the further improvement of energy efficiency.
Disclosure of Invention
In view of the above, the present invention aims to provide a Hybrid Electric Vehicle (HEV) energy management hierarchical control method considering lane change behavior in an internet environment, which aims at solving the problem that the hybrid electric vehicle energy management in the current internet environment does not consider the influence of the lane change behavior on the hybrid electric vehicle, has low algorithm control freedom (only longitudinal control), cannot fully exert the energy saving potential of energy management, and is limited in improvement of energy saving effect, especially under the condition of large traffic flow (such as slow driving of a front vehicle), and integrates economic driving and energy management strategies from longitudinal and transverse movement control two dimensions, so as to realize hierarchical energy saving control on a hybrid electric vehicle power system in the internet environment, and improve traffic efficiency and vehicle economy.
In order to achieve the purpose, the invention provides the following technical scheme:
a hybrid electric vehicle energy management hierarchical control method considering lane change behavior in a network connection environment is suitable for a single-shaft parallel type hybrid electric vehicle, and comprises the following steps:
the method comprises the following steps: economic driving decision-making method considering lane changing behavior
1. The method for predicting the speed of the surrounding vehicle in the internet environment comprises the following steps: the information of the front vehicle and the traffic information are obtained through vehicle-vehicle communication and vehicle-roadside device communication, and accordingly, the states of the surrounding vehicles (such as the speed of the vehicle in front of the main lane and the speed of the vehicle in the target lane) are predicted by adopting a chain neural network. The basic principle of the chain neural network is (as shown in fig. 1): the chain neural network is formed by connecting a plurality of single-step prediction networks one by one, and first prediction output is generated after the input (such as V and u) of the first single-step prediction network is given. And the prediction is taken as the input of the next single-step prediction network, and the next prediction is carried out in sequence until the expected prediction step size is reached. In addition, the input of each single step prediction network not only retains the input quantity of the first prediction, but also gradually increases the prediction outputs of the previous times, and the input quantity of the single step prediction network increases along with the increase of the prediction step. When the network is trained, each single-step prediction network needs to be trained, and if a chain neural network for predicting p steps needs to be obtained, p different neural networks need to be trained. During prediction, the trained network is used for sequentially predicting the test data so as to complete multi-step prediction. This provides predictive information of the vehicle for economic driving decisions. The specific process is as follows:
1) a traffic flow model is established based on VISSIM, and traffic information (such as: traffic light status) and front vehicle information;
2) obtaining a training model by using the obtained multiple groups of information as training data and adopting a chain neural network;
3) and (4) predicting the vehicle speed in the front and the vehicle speed in the target lane by using the new sample as input data and utilizing the training model.
2. Economic driving vehicle speed decision method considering lane changing behavior
The method comprises the steps of simulating a multi-lane traffic scene by using a car following and lane changing model, establishing a traffic flow model by using an Intelligent Driver (IDM) model for car following and a MOBIL model for lane changing, taking traffic conditions (traffic flow and the like) into comprehensive consideration on the basis of the intelligent driver model and the MOBIL model, taking driving efficiency, car following safety and lane changing safety as objective functions, taking the speed range, the car following safety distance, the lane changing safety distance and the like of a vehicle in a lane as constraint conditions, and adopting a hybrid MPC to decide optimal lane changing behavior and optimal speed to obtain an optimal speed track. The objective function of the economic driving is shown in formula (1), and the specific steps are shown as follows.
1) Establishing a traffic flow model for simulating a multi-lane traffic scene according to the lane changing and car following models;
2) establishing an optimization problem, and adopting a hybrid MPC to construct an economic driving decision problem considering lane change behaviors, wherein the economic driving decision problem comprises setting a target function and a constraint condition, the lane change behaviors are set as discrete variables, and a state value delta belongs to {0,1}, wherein 1 represents that the main lane is changed, and 0 represents that the main lane is not changed.
The optimization objective function is shown in equation (1):
wherein the first and second terms describe vehicle driving efficiency (including minimizing fuel consumption and driving)Time), the third item describes the safety of following the car, the fourth item describes the safety of lane changing, w1、w2、w3、w4For corresponding weight factors, vrefSetting a target vehicle speed by adopting an empirical method; v. ofc,0(k) Indicating the speed of the main vehicle, ac,0(k) Indicating the acceleration of the main vehicle, λc,0Representing parameters, alpha, relating to the host vehiclefIs a constant number of times, and is,
respectively representing the headway, headway reference value, alpha of the main car and the front carcm(k) Indicating an adjustable parameter according to the current traffic flow, dm,n(k) Represents the longitudinal distance of the vehicle (m, n) from the host vehicle; the subscript m, n represents the nth vehicle on the mth lane and the value thereof remains unchanged in the prediction time domain, the value of m is { l, r, c } represents the left lane, the right lane and the current lane, and the subscript (c,0) represents the host vehicle on the current lane.
The constraint conditions are expressed by the formulas (2) to (6):
the main vehicle speed range is:
0≤vc,0(k)≤vmax (2)
the acceleration range is:
-amin≤ac,0(k)≤amax (3)
under the scene of following vehicle running, the minimum distance between the main vehicle and the front vehicle meets the following conditions:
xc,1(k)-xc,0(k)≥t0vc,0(k)+r0 (4)
when the vehicle changes lanes, for m ∈ {1, r },
wherein, thetam,n(k) It is described whether the vehicles (m, n) and (c,0) are in the same lane, as shown in equation (6):
in order to establish a multi-vehicle lane-changing traffic flow model, lane-changing and car-following models need to be established, and the method specifically comprises the following steps:
lane changing model: as shown in fig. 2, for the lane change diagram, the MOBIL model is used as the lane change model, as shown in equation (7):
in the formula, a,
Respectively changing the front acceleration and the rear acceleration of the main lane, respectively, the subscripts f and nf are respectively following vehicles in the current lane and the target lane (after lane changing), p is a lane changing polite factor, and the numerical value is [0,1]]To (c) to (d); Δ athAn acceleration safety threshold; in addition, the acceleration of the vehicle following the lane change heel needs to satisfy the following safety constraint conditions:
wherein when asafe< 0, indicating a safety limit;
following the car model: an Intelligent Driver Model (IDM) is used, as shown in equations (9) and (10):
wherein s (t), v (t) are respectively the displacement and speed of the following vehicle, amax、aminAcceleration maximum and minimum values, s, respectively0For minimum spacing during parking, vdIs a target vehicle speed, hdTime interval of the head, vp(t) is the front vehicle speed, deltadAnd (t) and delta (t) are respectively the target distance between the front vehicle and the rear vehicle, the clear distance between the tail part of the front vehicle and the head part of the vehicle, and q is an adjustable parameter.
3) And (3) solving the optimization problem formed by the steps (1) to (10) to decide the optimal lane changing behavior and the optimal vehicle speed of the main vehicle.
Step two: hybrid electric vehicle energy management optimization method considering lane changing behavior
On the basis that an upper-layer controller gives an optimal speed track, a parallel hybrid electric vehicle model is established, the tracking optimization speed track is taken as a target, the working range of each physical component is taken as a constraint condition, an optimized engine torque, a motor torque, a gear and the like are decided by utilizing a hybrid MPC (multi-control loop), and the optimized engine torque, the optimized motor torque, the optimized gear and the like are applied to a whole vehicle model of a main vehicle.
1) Establishing a whole vehicle model of the main vehicle, which comprises an engine, a motor, a battery, a transmission system and the like;
2) and calculating the required power according to the optimized speed track. Obtaining the required power of the vehicle from a vehicle dynamics model, as shown in equations (11) and (12):
Treq＝Tv/igi0 (12)
where m represents the vehicle mass, f represents the rolling resistance coefficient, V represents the vehicle speed (m/s), CDRepresenting the coefficient of air resistance, A representing the frontal area, delta representing the coefficient of conversion of the rotating mass, VaRepresenting vehicle speed (km/h), r representing wheel radius, ig、i0Respectively representing the transmission ratio of a gearbox and the transmission ratio of a main speed reducer; t isv,TreqThe torque is required at the wheel and the torque is required at the input end of the gearbox.
3) And (4) constructing an MPC optimization problem by taking the rolling time domain fuel consumption and the SOC as objective functions.
The objective function can be written as:
constraint conditions of all parts of the system are as follows:
wherein, Te_opt(t),Tm_opt(t) optimized engine torque and motor torque, respectively; t ism_min(nm(T)) is the minimum torque of the motor at the current rotational speed, Tm_max(nm(T)) is the maximum torque of the motor at the current rotational speed, Te_max(ne(t)) is the maximum engine torque at the current speed, nm_maxIs the maximum speed of the motor, ne_minIs the minimum speed of the engine, ne_maxAt maximum engine speed, SOCminIs the minimum value of the state of charge, SOCmaxIs the maximum value of the state of charge of the battery.
4) The above optimization problem is solved. And converting the energy management optimization problem into a mixed integer programming problem, and solving to obtain the optimal engine torque, motor torque and gear.
The invention has the beneficial effects that: the system structure and the hierarchical optimization method have high reliability, the influence of the lane changing behavior on the energy management optimization is considered, the hierarchical control method for the energy management of the hybrid electric vehicle in the network connection environment is provided, the problems that the degree of freedom of the energy management optimization control considering only the longitudinal movement is low and the energy-saving effect is limited are effectively solved through the two dimensions of the transverse movement and the longitudinal movement, and the economical efficiency and the traffic efficiency of the vehicle are improved. The control method is not only suitable for the single-shaft parallel hybrid electric vehicle, but also can be used for energy management optimization of other types of hybrid electric vehicles. The method specifically comprises the following steps:
1) the invention provides a vehicle speed prediction method considering multi-source information. The influence of dynamic traffic information (such as traffic signal state) on the vehicle speed is considered, the networking information is fused, the prediction of the surrounding vehicle speed is implemented, and accurate state information is provided for developing an energy management strategy for fusing economic driving.
2) The invention provides an economic driving vehicle speed decision method considering lane changing behaviors. The lane change behavior and dynamic traffic information (the front vehicle runs slowly) are considered, the sum of the driving efficiency, economy, lane change safety and vehicle following safety weight is taken as a target, the economic driving speed is planned in advance, and the optimized vehicle speed is provided for developing an efficient energy management strategy.
3) The invention provides an energy management control method considering lane change behavior in an internet environment. The lane changing behavior of the vehicle is considered, the economic driving control of the upper layer is fused with the energy management of the lower layer, the energy management is optimized from two dimensions of transverse movement and longitudinal movement, and the fuel economy and the traffic efficiency are improved. The problems of low control freedom degree, low traffic efficiency and economy caused by the fact that the lane changing behavior is not considered are effectively solved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic diagram of a chain neural network;
FIG. 2 is a lane change schematic;
FIG. 3 is a schematic structural diagram of a parallel hybrid power system of the present invention;
FIG. 4 is a diagram of a multi-lane traffic scenario in which the present invention is applied;
FIG. 5 is a diagram of an economic vehicle speed decision method that takes into account lane-change behavior;
FIG. 6 is a diagram of a hybrid vehicle energy management method that takes into account lane-change behavior.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and embodiments may be combined with each other without conflict.
As shown in fig. 3, the research object of the invention is a single-shaft parallel hybrid system, the power transmission system is composed of an engine, a dry clutch, a motor, an AMT gearbox and the like, all the components are coaxially connected, and the motor is arranged between the clutch and the transmission. The system does not need a torque coupling device, has a simple structure, and can realize switching among different working modes. There are 6 basic operating modes, which are: the system comprises a pure motor driving mode, a pure engine driving mode, an engine and motor combined driving mode, a driving charging mode, a hybrid driving regenerative braking mode and a pure electric regenerative braking mode.
As shown in fig. 4, the invention explains the hybrid electric vehicle energy management hierarchical optimization method considering lane change behavior by taking a three-lane scene as an example, so as to improve the vehicle energy efficiency and the traffic efficiency. The dark gray vehicle is a research object (main vehicle HEV), and other vehicles (which can be traditional vehicles) are arranged in a main vehicle left lane l and a main vehicle right lane r to simulate a traffic scene. When the lane is changed, the main vehicle can select to change the lane to the left side and the right side.
As shown in fig. 5, the economic driving decision method considering lane change behavior proposed by the present invention. The specific implementation process comprises the following steps: firstly, initializing a vehicle following dynamic model, a lane changing model and the like, and then deciding the optimal vehicle speed and the optimal lane changing behavior in a prediction time domain [ t, t + N ] at the current time t. Specifically, according to the vehicle prediction information of the current lane, the vehicle prediction information of the adjacent lane and the current state of the current vehicle, calculating a corresponding objective function J (formula (1)) for all possible lane changing behaviors delta E [0,1] under the condition of meeting the constraint condition of the lane changing behaviors, and solving the optimal vehicle speed corresponding to each possible lane changing behavior; and for the possible lane changing behavior corresponding to the given optimal vehicle speed, the optimal lane changing behavior corresponding to the optimal vehicle speed is obtained by minimizing the objective function J. Taking the first-step control quantity of each prediction time domain as a final optimization quantity, and recording the corresponding control quantity; and finally, repeating the steps for rolling optimization at the next moment t +1 to finish the decision of the optimal vehicle speed and the optimal lane changing behavior under the circulating working condition.
As shown in fig. 6, the hybrid electric vehicle energy management hierarchical control method considering lane change behavior in the internet environment provided by the present invention is divided into three layers, including: the system comprises a vehicle speed prediction layer, an economic driving decision layer and an energy management optimization layer. Firstly, in a vehicle speed prediction layer, the vehicle-vehicle communication and the vehicle-roadside device communication acquire the speeds of the vehicles ahead on different lanes, and a chain neural network is adopted to predict the speeds of the vehicle and the vehicles ahead, so as to provide prediction information for an economic driving decision layer. Secondly, according to the predicted speed of the surrounding vehicles, the speed range of the main vehicle, the lane change safety distance and the same-lane following distance are taken as constraint conditions, the driving efficiency, the following safety and the lane change safety in the predicted time domain are taken as target functions, wherein the following safety is taken as an evaluation index by taking the distance between the front vehicle and the rear vehicle as an index, the lane change safety is taken as an index by taking the safety distance between the main vehicle and the target vehicle as an index, and then the optimal lane change behavior delta of the main vehicle HEV is decided by adopting a hybrid MPC*And an optimum speed Vopt. The optimal speed is used as the input quantity of the lower-layer energy management optimization control. In the lower layer controller, a power transmission system model is established for the main vehicle by combining a vehicle dynamic equation according to the test data of the engine and the motor, the working range of each physical component (such as the engine and the motor) is taken as a constraint condition, and the [ t, t + N ] in a rolling time domain]The sum of the fuel consumption and the battery SOC is an objective function (formula (13)), and the hybrid MPC is used for deciding optimized engine and motor torques and gears.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (4)
1. A HEV energy management hierarchical control method considering lane change behavior in an internet environment is characterized by comprising an economic driving vehicle speed decision method and an energy management optimization control method;
1) the economic driving vehicle speed decision method considering lane change behaviors comprises the following steps: predicting front vehicle information according to networking information provided by vehicle-vehicle communication and vehicle-roadside device communication, and adopting a hybrid MPC to decide optimal lane change behavior and optimal speed; the method specifically comprises the following steps: the method comprises the steps of predicting speed in front of a current lane and predicting speed of other lanes, solving and obtaining optimal lane changing behaviors and optimal speed tracks according to a lane changing model and a following model and by combining objective functions and constraint conditions required by economic driving speed decision, and realizing decision on upper-layer economic speed;
predicting the vehicle speed by fusing networking information and adopting a chain neural network; the lane changing model and the car following model respectively adopt an MOBIL model and an intelligent driver model to simulate traffic flow and are used for determining constraint conditions of the main car; objective function of economic vehicle speed decision: in a prediction time domain, the traffic efficiency, the economy, the lane change safety and the following safety are weighted to form a weighted weight factor, and a uniform objective function considering the traffic efficiency, the safety and the economy is obtained; the constraint conditions include: speed range, acceleration range, following distance and inter-vehicle distance between main vehicle and target lane vehicle
2) The energy management optimization control method comprises the following steps: based on the HEV model, energy distribution is carried out on the HEV by tracking the optimized speed track to obtain optimized control quantity; the method specifically comprises the following steps: firstly, establishing a whole hybrid electric vehicle model to obtain expressions of different state quantities and control quantities; secondly, calculating required power by the optimal vehicle speed calculated in the economic driving vehicle speed decision method, determining a target function according to the rolling time domain fuel consumption and the SOC according to the energy management optimization target function and the constraint conditions of all parts, adopting hybrid MPC to decide the optimal engine, motor torque and gear, applying the first-step control quantity in the prediction time domain to a whole vehicle model of the hybrid electric vehicle, continuously updating the optimization control quantity in the rolling time domain, and finishing the optimization distribution of energy management.
2. The HEV energy management hierarchy control method of claim 1, wherein the economy driving decision vehicle speed method specifically includes the steps of:
s101: establishing a traffic flow model for simulating a multi-lane traffic scene according to the lane changing and car following models;
s102: establishing an optimization problem, and adopting a hybrid MPC to construct an economic driving decision problem considering lane change behaviors, wherein the economic driving decision problem comprises setting a target function and a constraint condition, the lane change behaviors are set as discrete variables, and a state value delta belongs to {0,1}, wherein 1 represents that the main lane is changed, and 0 represents that the main lane is not changed;
the optimization objective function is shown in equation (1):
wherein, w1、w2、w3、w4For corresponding weight factors, vrefA target vehicle speed; v. ofc,0(k) Indicating the speed of the main vehicle, ac,0(k) Indicating the acceleration of the main vehicle, λc,0Representing a state parameter, alpha, related to the host vehiclefIs a constant number of times, and is,
respectively representing the headway, headway reference value, alpha of the main car and the front carcm(k) Indicating an adjustable parameter according to the current traffic flow, dm,n(k) Represents the longitudinal distance of the vehicle (m, n) from the host vehicle; the subscript m, n represents the nth vehicle on the mth lane, and the value of m is kept unchanged in the prediction time domain, the value of m is { l, r, c } represents the left lane, the right lane and the current lane,subscript (c,0) indicates the host vehicle in the current lane;
the constraint conditions are expressed by the formulas (2) to (6):
the main vehicle speed range is:
0≤vc,0(k)≤vmax (2)
the acceleration range is:
-amin≤ac,0(k)≤amax (3)
under the scene of following vehicle running, the minimum distance between the main vehicle and the front vehicle meets the following conditions:
xc,1(k)-xc,0(k)≥t0vc,0(k)+r0 (4)
when the vehicle changes lanes, for m ∈ {1, r },
wherein, thetam,n(k) It is described whether the vehicles (m, n) and (c,0) are in the same lane, as shown in equation (6):
in order to establish a multi-vehicle lane-changing traffic flow model, lane-changing and car-following models need to be established, and the method specifically comprises the following steps:
lane changing model: the MOBIL model is used as a lane change model, and is shown in formula (7):
in the formula, a,
Respectively changing the front acceleration and the rear acceleration of the main lane, wherein subscripts f and nf are respectively following vehicles in the current lane and the target lane, p is a lane change polite factor, and the numerical value is [0,1]]To (c) to (d); Δ athAn acceleration safety threshold; in addition, the acceleration of the vehicle following the lane change heel needs to satisfy the following safety constraint conditions:
wherein when asafe< 0, indicating a safety limit;
following the car model: an intelligent driver model is adopted, as shown in equations (9) and (10):
wherein s (t), v (t) are respectively the displacement and speed of the following vehicle, amax、aminAcceleration maximum and minimum values, s, respectively0For minimum spacing during parking, vdTarget vehicle speed, hdTime headway, vp(t) is the front vehicle speed, deltad(t) and delta (t) are respectively the target distance between the front vehicle and the rear vehicle, the clear distance between the front vehicle and the rear vehicle, and q is an adjustable parameter;
s103: and (3) solving the optimization problem formed by the steps (1) to (10) to decide the optimal lane changing behavior and the optimal vehicle speed of the main vehicle.
3. The HEV energy management hierarchy control method of claim 1, wherein the energy management optimization control method specifically includes the steps of:
s201: establishing a whole vehicle model of the main vehicle;
s202: calculating required power according to the optimized speed track;
s203: constructing an MPC optimization problem by taking the rolling time domain fuel consumption and the SOC as a target function;
the objective function is shown in equation (11):
the constraint conditions of the components of the system are shown as the formula (12):
wherein, Te_opt(t),Tm_opt(t) optimized engine torque and motor torque, respectively; t is a unit ofm_min(nm(T)) is the minimum torque of the motor at the current rotational speed, Tm_max(nm(T)) is the maximum torque of the motor at the current rotational speed, Te_max(ne(t)) is the maximum engine torque at the current speed, nm_maxIs the maximum speed of the motor, ne_minIs the minimum speed of the engine, ne_maxAt maximum engine speed, SOCminIs the minimum value of the state of charge, SOCmaxIs the maximum value of the state of charge of the battery;
(4) solving the optimization problem; and converting the energy management optimization problem into a mixed integer programming problem, and solving to obtain the optimal engine torque, motor torque and gear.
4. The HEV energy management hierarchical control method according to claim 3, wherein in step S202, the vehicle power demand is obtained from a vehicle dynamics model, as shown in equations (13) and (14):
Treq＝Tv/igi0 (14)
where m represents the vehicle mass, f represents the rolling resistance coefficient, V represents the vehicle speed (m/s), CDRepresenting the coefficient of air resistance, A representing the frontal area, and delta representing the rotationCoefficient of mass conversion, VaRepresenting vehicle speed (km/h), r representing wheel radius, ig、i0Respectively representing the transmission ratio of the gearbox, the transmission ratio of the main reducer, Tv,TreqThe torque is required at the wheel and the torque is required at the input end of the gearbox.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112525210B (en) * | 2020-11-24 | 2022-09-16 | 同济大学 | Energy-saving-oriented global path and speed joint optimization method for electric automobile |
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| CN112937608B (en) * | 2021-03-31 | 2022-06-21 | 吉林大学 | Track prediction-based integrated rolling decision method and device for unmanned vehicle in ice and snow environment and storage medium |
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| CN113635900B (en) * | 2021-08-10 | 2024-03-19 | 吉林大学 | Channel switching decision control method based on energy management in predicted cruising process |
| CN114187759B (en) * | 2021-11-19 | 2023-01-03 | 东南大学 | Road side unit driving assistance method and device based on data driving model |
| CN114475605B (en) * | 2022-02-16 | 2024-01-19 | 东风商用车有限公司 | Double-layer prediction control method for energy conservation of heavy truck based on internet connection information |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103496366A (en) * | 2013-09-09 | 2014-01-08 | 北京航空航天大学 | Active-lane-changing collision-avoidance control method and device based on vehicle-vehicle coordination |
| JP2014078170A (en) * | 2012-10-11 | 2014-05-01 | Denso Corp | Column traveling system and column travelling device |
| CN106004867A (en) * | 2016-05-31 | 2016-10-12 | 潍柴动力股份有限公司 | Lane changing overtaking control method and controller used for hybrid bus |
| JP2017100534A (en) * | 2015-12-01 | 2017-06-08 | 本田技研工業株式会社 | Lane change control device |
| CN107507408A (en) * | 2017-07-24 | 2017-12-22 | 重庆大学 | It is a kind of consider front truck lane-change import process with the acceleration and with speeding on as modeling method of speeding |
| CN108438003A (en) * | 2018-01-05 | 2018-08-24 | 吉林大学 | A kind of hierarchical control method of road vehicle speed-optimization |
| CN109291925A (en) * | 2018-09-20 | 2019-02-01 | 厦门大学 | A kind of energy-conserving intelligent net connection hybrid vehicle follow the bus control method |
| CN109360409A (en) * | 2018-09-26 | 2019-02-19 | 江苏大学 | A kind of intelligent network connection hybrid vehicle formation control method based on driving style |
| CN110103945A (en) * | 2018-01-30 | 2019-08-09 | 通用汽车环球科技运作有限责任公司 | Expected control to hybrid vehicle propulsion system |
| CN110329263A (en) * | 2019-07-05 | 2019-10-15 | 西南交通大学 | The adaptive lane-change method for planning track of automatic driving vehicle |
| CN110568760A (en) * | 2019-10-08 | 2019-12-13 | 吉林大学 | Parameterized learning decision control system and method suitable for lane changing and lane keeping |
| CN111137288A (en) * | 2020-01-19 | 2020-05-12 | 江苏大学 | Multi-vehicle cooperative lane changing method under internet connection condition |
| CN111260956A (en) * | 2020-01-15 | 2020-06-09 | 清华大学 | Automatic vehicle lane change planning and control method based on model predictive control |
| CN111311913A (en) * | 2020-02-26 | 2020-06-19 | 清华大学 | Control method and system for improving traffic efficiency of road narrowed section |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2848487B1 (en) * | 2013-09-12 | 2020-03-18 | Volvo Car Corporation | Manoeuvre generation for automated drive |
| US10435023B2 (en) * | 2016-12-19 | 2019-10-08 | Continental Automotive Systems, Inc. | Predictive powertrain limits strategy for autonomous/automated driving vehicle |
| US20200148261A1 (en) * | 2018-11-14 | 2020-05-14 | Steering Solutions Ip Holding Corporation | Lane change and collision avoidance system |
-
2020
- 2020-08-31 CN CN202010898329.XA patent/CN111959492B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014078170A (en) * | 2012-10-11 | 2014-05-01 | Denso Corp | Column traveling system and column travelling device |
| CN103496366A (en) * | 2013-09-09 | 2014-01-08 | 北京航空航天大学 | Active-lane-changing collision-avoidance control method and device based on vehicle-vehicle coordination |
| JP2017100534A (en) * | 2015-12-01 | 2017-06-08 | 本田技研工業株式会社 | Lane change control device |
| CN106004867A (en) * | 2016-05-31 | 2016-10-12 | 潍柴动力股份有限公司 | Lane changing overtaking control method and controller used for hybrid bus |
| CN107507408A (en) * | 2017-07-24 | 2017-12-22 | 重庆大学 | It is a kind of consider front truck lane-change import process with the acceleration and with speeding on as modeling method of speeding |
| CN108438003A (en) * | 2018-01-05 | 2018-08-24 | 吉林大学 | A kind of hierarchical control method of road vehicle speed-optimization |
| CN110103945A (en) * | 2018-01-30 | 2019-08-09 | 通用汽车环球科技运作有限责任公司 | Expected control to hybrid vehicle propulsion system |
| CN109291925A (en) * | 2018-09-20 | 2019-02-01 | 厦门大学 | A kind of energy-conserving intelligent net connection hybrid vehicle follow the bus control method |
| CN109360409A (en) * | 2018-09-26 | 2019-02-19 | 江苏大学 | A kind of intelligent network connection hybrid vehicle formation control method based on driving style |
| CN110329263A (en) * | 2019-07-05 | 2019-10-15 | 西南交通大学 | The adaptive lane-change method for planning track of automatic driving vehicle |
| CN110568760A (en) * | 2019-10-08 | 2019-12-13 | 吉林大学 | Parameterized learning decision control system and method suitable for lane changing and lane keeping |
| CN111260956A (en) * | 2020-01-15 | 2020-06-09 | 清华大学 | Automatic vehicle lane change planning and control method based on model predictive control |
| CN111137288A (en) * | 2020-01-19 | 2020-05-12 | 江苏大学 | Multi-vehicle cooperative lane changing method under internet connection condition |
| CN111311913A (en) * | 2020-02-26 | 2020-06-19 | 清华大学 | Control method and system for improving traffic efficiency of road narrowed section |
Non-Patent Citations (6)
| Title |
|---|
| A Parallel Hybrid Electric Vehicle Energy Management Strategy Using Stochastic Model Predictive Control With Road Grade Preview;Xiangrui Zeng等;《IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY》;20150318;全文 * |
| 刘志强等.智能网联环境下的多车协同换道策略研究.《汽车工程》.2020, * |
| 基于车车通信的车辆并行协同自动换道控制;杨刚等;《公路交通科技》;20170115(第01期);全文 * |
| 智能网联环境下的多车协同换道策略研究;刘志强等;《汽车工程》;20200325(第03期);全文 * |
| 网联环境下基于分层式模型预测控制的车队能量控制策略研究;唐小林等;《机械工程学报》;20200720;全文 * |
| 车联网环境下并联混合动力客车控制策略优化研究;张风奇;《中国博士学位论文全文数据库 (工程科技Ⅱ辑)》;20180516;全文 * |
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