Heterogeneous vehicle queue cooperative control method based on hierarchical control architecture

Abstract

The invention relates to the field of vehicle queue cooperative control, in particular to a heterogeneous vehicle queue cooperative control method based on a hierarchical control architecture. The heterogeneous vehicle queue comprises a pure electric vehicle, a hybrid electric vehicle and a fuel cell vehicle; the cooperative control method comprises the steps of establishing a heterogeneous vehicle queue model, performing vehicle queue cooperative control based on a top-layer centralized controller, and performing vehicle queue energy-saving control based on a bottom-layer distributed controller. According to the invention, by focusing the longitudinal following error of the vehicle queue at the top layer and focusing on energy management optimization at the bottom layer, layered decoupling of queue control and energy management is realized, and the real-time performance and expansibility of the system are improved. The algorithm structure is clearer through the independent design of the top layer and the bottom layer, implementation and expansion in an actual vehicle control system are easy, high real-time performance and applicability are achieved in motorcade environments of different scales and complexity, pure electric vehicles, hybrid electric vehicles and fuel cell vehicles are compatible, and optimal energy-saving control is achieved.

Description

Technical Field

[0001] The present invention relates to the field of cooperative control of vehicle platoons, and particularly to a cooperative control method for heterogeneous vehicle platoons based on a hierarchical control architecture. Background Art

[0002] The rapid development of new energy vehicle technology has promoted the wide application of intelligent connected transportation systems. Among them, pure electric vehicles, hybrid electric vehicles, and fuel cell vehicles have become important components of intelligent transportation systems. However, in a heterogeneous vehicle platoon composed of them, due to differences in power response, energy compensation, endurance, and control strategies among different power systems, how to achieve stable cooperative control and optimal energy allocation has become a key technical problem.

[0003] Currently, vehicle platoon control methods mainly include fixed-spacing control, adaptive cruise control, cooperative adaptive cruise control, etc. Among them, cooperative adaptive cruise control combines vehicle networking technology and can achieve information sharing and cooperative optimization. However, existing methods mostly target homogeneous vehicle platoons, that is, it is assumed that all vehicles have the same power system. However, in practical applications, different types of new energy vehicles often drive in a mixed formation, and existing control strategies are difficult to take into account the dynamic response characteristics, energy management requirements, and cooperative control methods of heterogeneous power systems, resulting in mismatched responses between vehicles in the platoon, large following errors, and thus affecting the platoon stability. In addition, traditional energy management strategies mainly optimize for individual vehicles and lack a global energy consumption optimal control mechanism, resulting in too high overall energy consumption of the platoon. Considering the above factors, how to achieve global optimality of energy management while ensuring stable following of a vehicle platoon with different power systems is still a technical problem that urgently needs to be solved in the field of intelligent connected new energy vehicles. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a cooperative control method for heterogeneous vehicle platoons based on a hierarchical control architecture. The heterogeneous vehicle platoon includes at least two of pure electric vehicles, hybrid electric vehicles, and fuel cell vehicles. The cooperative control method includes the following steps:

[0005] Step 1: Establish a heterogeneous vehicle platoon model:

[0006] The heterogeneous vehicle platoon model includes a vehicle platoon dynamics model, a pure electric vehicle power system model, a hybrid electric vehicle power system model, and a fuel cell vehicle power system model.

[0007] The method for establishing the vehicle platoon dynamics model is as follows:

[0008] For the i-th vehicle, its longitudinal dynamics equation is:

[0009]

[0010] where m i is the mass of the i-th vehicle, v i is the longitudinal speed of the i-th vehicle, F T,i is the driving force of the i-th vehicle, F R,i is the total driving resistance of the i-th vehicle. The total driving resistance includes rolling resistance, air resistance, and gradient resistance:

[0011]

[0012] where C r is the rolling resistance coefficient, C d is the air resistance coefficient, A is the frontal surface area of the vehicle, ρ is the air density, g is the acceleration due to gravity, and θ is the ramp angle.

[0013] The distance error and speed error between the i-th vehicle and its preceding vehicle are defined as:

[0014]

[0015] where e s,i is the distance error, e v,i is the speed error, s i is the position of the i-th vehicle, and h is the headway time.

[0016] The power system model of the pure electric vehicle is:

[0017]

[0018] where F T,BEV is the driving force of the pure electric vehicle, T mot is the motor torque, η gear is the transmission system efficiency, r w is the wheel radius, P mot is the motor output power, ω mot is the motor speed, P bat is the net discharge power of the battery, and η mot is the motor efficiency.

[0019] The driving power P drive,BEV of the pure electric vehicle is:

[0020] P drive,BEV = P mot

[0021] The power system model of the hybrid vehicle is:

[0022]

[0023] where F T,HEVIs the driving force of a hybrid vehicle, P eng Is the power output by the engine through the generator, ω eng Is the engine speed, T eng Is the engine torque, η gen Is the generator efficiency, P bat Is the net discharge power of the battery.

[0024] The driving power of the hybrid vehicle is provided by the motor. The driving power P of the hybrid vehicle drive,HEV Is:

[0025] P drive,HEV = P mot

[0026] The described fuel cell vehicle power system model is:

[0027]

[0028] Among them, F T,FCEV Is the driving force of the fuel cell vehicle, P FC Is the fuel cell output power, Is the hydrogen consumption rate, LHV H Is the lower heating value of hydrogen, η FC Is the fuel cell efficiency.

[0029] The driving power P of the fuel cell vehicle drive,FCEV Is:

[0030] P drive,FCEV = P mot

[0031] Step 2: Vehicle platoon cooperative control based on the top-level centralized controller:

[0032] The described top-level centralized controller focuses on reducing the following distance error between vehicles and ensuring the cooperative stability of the vehicle platoon. Define the state vector x as:

[0033]

[0034] Among them, s is the position, v is the speed, and a is the acceleration.

[0035] Define the control input u as the acceleration, that is:

[0036] u = a

[0037] Define the objective function J of the top-level control as:

[0038]

[0039] Among them, Q s Is the following distance error weight, Qv is the speed error weight. The speed error weight and the distance error weight are determined according to the tendencies of the distance error and the speed error in the objective function. T is the control time domain.

[0040] The Hamiltonian function constructed based on the Pontryagin minimum principle is:

[0041] H(x,u,λ) = Q s (e s ) 2 + Q v (e v ) 2 + λ s v + λ v a + λ a u

[0042] where H(x,u,λ) is the Hamiltonian function, λ = [λ s , λ v , λ a T is the co - state vector, where λ s , λ v , λ a are the co - state quantities of position, speed, and acceleration in the Hamiltonian function respectively;

[0043] The co - state vector satisfies the co - state update equation:

[0044]

[0045] Therefore, the optimal control input is:

[0046]

[0047] Step 3: Energy - saving control of the vehicle queue based on the underlying distributed controller:

[0048] The objective of the described underlying distributed controller is to minimize the overall energy consumption of the vehicle fleet while satisfying the power demands and operation constraints of each vehicle type. For the i - th vehicle, the state vector X i is defined as:

[0049] X i = SOC i

[0050] where SOC i is the state of charge of the battery of the i - th vehicle.

[0051] For the i - th vehicle, the control input U BDC,i is defined as: ​

[0052]

[0053] Among them, P bat,i is the net discharge power of the battery of the i-th vehicle, P FC,i is the output power of the fuel cell of the i-th vehicle, P eng,i is the power output by the engine of the i-th vehicle through the generator;

[0054] For the i-th vehicle, the objective function J of the underlying control ECMS,i is:

[0055]

[0056] Among them, w bat is the battery energy consumption weight, w FC is the hydrogen consumption weight of the fuel cell vehicle, w f is the fuel consumption weight of the hybrid vehicle. The battery energy consumption weight, the hydrogen consumption weight of the fuel cell vehicle, and the fuel consumption weight of the hybrid vehicle are determined according to the tendency of the battery energy consumption, the hydrogen consumption of the fuel cell vehicle, and the fuel consumption of the hybrid vehicle in the objective function. P bat,i is the net discharge power of the battery of the i-th vehicle, C H,i is the hydrogen consumption cost of the fuel cell vehicle, C f,i is the fuel consumption cost of the hybrid vehicle.

[0057] The local Hamiltonian function constructed based on the Pontryagin minimum principle is:

[0058] H E,i (X i , U BDC,i , μ i ) = w bat P bat,i + w FC C H,i + w f C f,i + μ i T g i (X i , U BDC,i )

[0059] Among them, H E,i (X i , U BDC,i , μ i ) is the local Hamiltonian function, g i (X i , U BDC,i ) is the function describing the state dynamics of the i-th vehicle, μi is the local co-state vector of the i-th vehicle, and the local co-state vector satisfies the co-state update equation:

[0060]

[0061] The constraint conditions for pure electric vehicles are:

[0062]

[0063] The constraint conditions for fuel cell vehicles are:

[0064]

[0065] The constraint conditions for hybrid vehicles are:

[0066]

[0067] where, P drive,i is the driving force of the i-th vehicle, P mot,i is the motor output power of the i-th vehicle, SOC i (t) is the state of charge of the battery of the i-th vehicle, P FC,i is the fuel cell output power of the i-th vehicle, P eng,i is the engine output power of the i-th vehicle, SOC min is the lower limit of the state of charge of the battery, SOC max is the upper limit of the state of charge of the battery, is the lower limit of the fuel cell output power of the i-th vehicle, is the upper limit of the fuel cell output power of the i-th vehicle, is the lower limit of the engine output power of the i-th vehicle, is the upper limit of the engine output power of the i-th vehicle;

[0068] The local optimal control input is:

[0069]

[0070] The beneficial effects of the present invention:

[0071] 1. By focusing on the longitudinal following error of the vehicle queue at the top layer and concentrating on the energy management optimization at the bottom layer, the present invention realizes the hierarchical decoupling of queue control and energy management, improving the real-time performance and scalability of the system. The top layer only considers the car-following performance and does not consider the energy-saving control, and the personalized energy-saving control of multi-energy-source vehicles (heterogeneous vehicles) is realized through the bottom layer controller. The independent design of the top layer and the bottom layer makes the algorithm structure clearer, easier to implement and expand in the actual vehicle control system, and has high real-time performance and applicability in fleet environments of different scales and complexities.

[0072] 2. The top - level centralized controller constructed in the present invention realizes precise modeling and optimal control of the platoon following error by acquiring and fusing the dynamic information between vehicles in real time and adopting advanced prediction and optimal control algorithms, so that high - level coordination and rapid response are achieved among vehicles, effectively breaking through the limitations of traditional single - vehicle control strategies in heterogeneous platoon cooperative control, and improving the overall stability and safety of the platoon.

[0073] 3. The present invention is compatible with heterogeneous vehicle platoons including pure electric vehicles, hybrid electric vehicles and fuel cell vehicles, and realizes optimal energy scheduling. The constructed bottom - level distributed controller enables each vehicle to independently solve local energy optimization problems and achieve global coordination through real - time information exchange, thus significantly enhancing the flexibility, scalability and energy - saving performance of the system under multi - scenario and multi - requirement conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a schematic diagram of a heterogeneous vehicle platoon according to an embodiment of the present invention;

[0075] Figure 2 is a schematic diagram of the framework of a cooperative control method for a heterogeneous vehicle platoon based on a hierarchical control architecture according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] A cooperative control method for a heterogeneous vehicle platoon based on a hierarchical control architecture provided in this embodiment, wherein the leading vehicle of the heterogeneous vehicle platoon is a pure electric vehicle, the first following vehicle is a hybrid electric vehicle, and the second following vehicle is a fuel cell vehicle, as Figure 1 shown;

[0077] First, a heterogeneous vehicle platoon model is established:

[0078] The heterogeneous vehicle platoon model includes a vehicle platoon dynamics model, a pure electric vehicle power system model, a hybrid electric vehicle power system model, and a fuel cell vehicle power system model.

[0079] The method for establishing the vehicle platoon dynamics model is as follows:

[0080] For the i - th vehicle, its longitudinal dynamics equation is:

[0081]

[0082] where m i is the mass of the i - th vehicle (kg), v i is the longitudinal speed of the i - th vehicle (m / s), F T,i is the driving force of the i - th vehicle (N), F R,i is the total driving resistance of the i - th vehicle (N), and the total driving resistance includes rolling resistance, air resistance and gradient resistance:

[0083]

[0084] Among them, C r is the rolling resistance coefficient, C d is the air resistance coefficient, A is the frontal surface area of the vehicle (m 2 ), ρ is the air density (kg / m 3 ), g is the acceleration due to gravity (9.81 m / s 2 ), and θ is the ramp angle (rad).

[0085] To describe the following performance in a vehicle platoon, the distance error and speed error between the i-th vehicle and its preceding vehicle are defined as follows:

[0086]

[0087] Among them, e s,i is the distance error (m), e v,i is the speed error (m / s), s i is the position of the i-th vehicle (m), and h is the headway time (s).

[0088] The power system model of the pure electric vehicle is as follows:

[0089]

[0090] Among them, F T,BEV is the driving force of the pure electric vehicle (N), T mot is the motor torque (Nm), η gear is the transmission system efficiency, r w is the wheel radius (m), P mot is the motor output power (W), ω mot is the motor speed (rad / s), P bat is the net discharge power of the battery (W), and η mot is the motor efficiency.

[0091] Therefore, the driving power of the pure electric vehicle is:

[0092] P drive,BEV = P mot

[0093] Among them, P drive,BEV is the driving power of the pure electric vehicle (W).

[0094] The power system model of the hybrid electric vehicle is as follows:

[0095]

[0096] Among them, FT,HEV is the driving force (N) of a hybrid vehicle, P eng is the power (W) output by the engine through the generator, ω eng is the engine speed (rad / s), T eng is the engine torque (Nm), η gen is the generator efficiency, P bat is the net discharge power (W) of the battery.

[0097] The driving power of the hybrid vehicle is provided by the motor, i.e.:

[0098] P drive,HEV = P mot

[0099] where P drive,HEV is the driving power (W) of the hybrid vehicle.

[0100] The power system model of the fuel cell vehicle is as follows:

[0101]

[0102] where F T,FCEV is the driving force (N) of the fuel cell vehicle, P FC is the output power (W) of the fuel cell, is the hydrogen consumption rate (kg / s), LHV H is the lower heating value of hydrogen (J / kg), η FC is the fuel cell efficiency.

[0103] Therefore, the driving power of the fuel cell vehicle is:

[0104] P drive,FCEV = P mot

[0105] where P drive,FCEV is the driving power (W) of the fuel cell vehicle.

[0106] As Figure 2 shown, the collaborative control method includes a vehicle platoon collaborative control method based on a top-level centralized controller and a vehicle platoon energy-saving control method based on a bottom-level distributed controller;

[0107] In the vehicle platoon collaborative control method based on the top-level centralized controller, the top-level centralized controller focuses on reducing the following error between vehicles and ensuring the collaborative stability of the vehicle platoon. The state vector x is defined as:

[0108]

[0109] where s is the position (m), v is the velocity (m / s), and a is the acceleration (m / s 2 ).

[0110] Define the control input u as the acceleration, i.e.:

[0111] u = a

[0112] Define the objective function J of the top-level control as:

[0113]

[0114] where Q s is the weight of the distance error (N 2 / m 2 ), Q v is the weight of the velocity error ((m / s)2). The weights of the distance error and the velocity error are determined according to the tendency of the distance error and the velocity error in the objective function. T is the control time domain (s).

[0115] The Hamiltonian function constructed based on the Pontryagin minimum principle is:

[0116] H(x, u, λ) = Q s (e s ) 2 + Q v (e v ) 2 + λ s v + λ v a + λ a u

[0117] In the formula, H(x, u, λ) is the Hamiltonian function, λ = [λ s , λ v , λ a T is the co-state vector, where λ s , λ v , λ a are the co-state quantities of position, velocity, and acceleration respectively;

[0118] Define the constraint condition: The co-state vector satisfies the co-state update equation:

[0119]

[0120] Therefore, the optimal control input is:

[0121]

[0122] ​In the described vehicle queue energy-saving control method based on the underlying distributed controller, the goal of the underlying distributed controller is to minimize the overall energy consumption of the vehicle fleet while meeting the power requirements and operating constraints of each vehicle type. For the i-th vehicle, the state vector X i is defined as:

[0123] X i = SOC i

[0124] where SOC i is the state of charge of the battery of the i-th vehicle.

[0125] For the i-th vehicle, the control input U BDC,i is defined as:

[0126]

[0127] where P bat,i is the net discharge power of the battery of the i-th vehicle (W), P FC,i is the output power of the fuel cell of the i-th vehicle (W), P eng,i is the power output by the engine of the i-th vehicle through the generator (W);

[0128] For the i-th vehicle, the objective function J ECMS,i of the underlying control is:

[0129]

[0130] where w bat is the battery energy consumption weight (W -1 ), w FC is the hydrogen consumption weight of the fuel cell vehicle (kg -1 / s -1 ), w f is the fuel consumption weight of the hybrid vehicle (kg -1 / s -1 ). The battery energy consumption weight, the hydrogen consumption weight of the fuel cell vehicle, and the fuel consumption weight of the hybrid vehicle are determined according to the tendency of the battery energy consumption, the hydrogen consumption of the fuel cell vehicle, and the fuel consumption of the hybrid vehicle in the objective function. P bat,i is the net discharge power of the battery of the i-th vehicle (W), C H,i is the hydrogen consumption cost of the fuel cell vehicle (J / s), and C f,i is the fuel consumption cost of the hybrid vehicle (J / s).

[0131] The local Hamiltonian function constructed based on the Pontryagin minimum principle is:

[0132] HE,i (X i , U BDC,i , μ i ) = w bat P bat,i + w FC C H,i + w f C f,i + μ i T g i (X i , U BDC,i )

[0133] Among them, H E,i (X i , U BDC,i , μ i ) is the local Hamiltonian function, g i (X i , U BDC,i ) is the function describing the state dynamics of the i-th vehicle, μ i is the local co-state vector of the i-th vehicle, and the local co-state vector satisfies the co-state update equation:

[0134]

[0135] The constraint conditions for pure electric vehicles are:

[0136]

[0137] The constraint conditions for fuel cell vehicles are:

[0138]

[0139] The constraint conditions for hybrid vehicles are:

[0140]

[0141] Among them, P drive,i is the driving force (N) of the i-th vehicle, P mot,i is the motor output power (W) of the i-th vehicle, SOC i (t) is the state of charge of the battery of the i-th vehicle, P FC,i is the fuel cell output power (W) of the i-th vehicle, P eng,i is the engine output power (W) of the i-th vehicle, SOC min is the lower limit of the state of charge of the battery, SOC max is the upper limit of the state of charge of the battery, is the lower limit of the fuel cell output power of the i-th vehicle, is the upper limit of the fuel cell output power of the i-th vehicle, is the lower limit of the engine output power of the i-th vehicle, is the upper limit of the engine output power of the i-th vehicle.

[0142] Therefore, the local optimal control input is:

[0143]

Claims

1. A method for cooperative control of heterogeneous vehicle platoons based on a hierarchical control architecture, wherein the heterogeneous vehicle platoons include at least two of pure electric vehicles, hybrid electric vehicles, and fuel cell vehicles; characterized in that: The collaborative control method comprises the following steps: Step 1: Establish a heterogeneous vehicle platoon model: The heterogeneous vehicle platoon model includes a vehicle platoon dynamics model, a pure electric vehicle power system model, a hybrid vehicle power system model and a fuel cell vehicle power system model; Step 2: Vehicle platoon collaborative control based on top-level centralized controller: The top-level centralized controller defines the state vector x as: Among them, s is the position, v is the velocity, and a is the acceleration; Define the control input u as acceleration, that is: u=a The objective function J of the top-level control is defined as: Among them, Q s is the vehicle distance error weight, Q v is the speed error weight, T is the control time domain, e s,i is the vehicle distance error, e v,i is the speed error; The Hamiltonian function constructed based on the Pontryagin minimum principle is: H(x,u,λ)=Q s (e s ) 2 +Q v (e v ) 2 +λ s v+λ v a+λ a you Where H(x,u,λ) is the Hamiltonian function, λ=[λ s ,λ v ,λ a ] T is the co-state vector, where λ s , v , a are the co-state quantities of position, velocity and acceleration respectively; The co-state vector satisfies the co-state update equation: Optimal control input for: Step 3: Vehicle platoon energy-saving control based on underlying distributed controller: The underlying distributed controller defines the state vector X for the i-th vehicle: i for: X i =SOC i Among them, SOC i is the battery state of charge of the i-th vehicle; For the i-th vehicle, define the control input U BDC,i for: Among them, P bat,i is the net discharge power of the battery of the i-th vehicle, P FC,i is the fuel cell output power of the i-th vehicle, P eng,i is the power output of the engine of the i-th vehicle through the generator; For the i-th vehicle, the objective function J of the underlying control is ECMS,i for: Among them, w bat is the battery energy consumption weight, w FC is the hydrogen consumption weight of fuel cell vehicles, w f is the fuel consumption weight of the hybrid vehicle, P bat,i is the net discharge power of the battery of the i-th vehicle, C H,i is the hydrogen consumption cost of fuel cell vehicles, C f,i The fuel consumption cost of hybrid vehicles; The local Hamiltonian function constructed based on the Pontryagin minimum principle is: Among them, H E,i (X i ,U BDC,i ,μ i ) is the local Hamiltonian function, g i (X i ,U BDC,i ) is a function describing the state dynamics of the i-th vehicle, μ i is the local co-state vector of the i-th vehicle, and the local co-state vector satisfies the co-state update equation: The constraints for pure electric vehicles are: The constraints for fuel cell vehicles are: The constraints for hybrid electric vehicles are: Among them, P drive,i is the driving force of the i-th vehicle, P mot,i is the motor output power of the i-th vehicle, SOC i (t) is the battery charge state of the i-th vehicle, P FC,i is the fuel cell output power of the i-th vehicle, P eng,i is the engine output power of the i-th vehicle, SOC min The lower limit of the battery state of charge, SOC max is the upper limit of the battery state of charge, is the lower limit of the fuel cell output power of the i-th vehicle, is the upper limit of the fuel cell output power of the i-th vehicle, is the lower limit of the engine output power of the i-th vehicle, is the upper limit of the engine output power of the i-th vehicle; Locally optimal control input for:

2. The method for cooperative control of heterogeneous vehicle platoons based on a hierarchical control architecture according to claim 1, characterized in that: In step 1, the vehicle platoon dynamics model is established as follows: For the i-th vehicle, its longitudinal dynamic equation is: Among them, m i is the mass of the i-th vehicle, v i is the longitudinal velocity of the ith vehicle, F T,i is the driving force of the i-th vehicle, F R,i is the total driving resistance of the i-th vehicle, which includes rolling resistance, air resistance and slope resistance: Among them, C r is the rolling resistance coefficient, C d is the air resistance coefficient, A is the front surface area of ​​the vehicle, ρ is the air density, g is the acceleration due to gravity, and θ is the ramp angle; The distance error and speed error between the i-th vehicle and the vehicle in front of it are defined as: Among them, e s,i is the vehicle distance error, e v,i is the speed error, s i is the position of the i-th vehicle, and h is the headway time.

3. The method for cooperative control of heterogeneous vehicle platoons based on a hierarchical control architecture according to claim 1, characterized in that: In step 1, the pure electric vehicle power system model is: Among them, F T,BEV As the driving force of pure electric vehicles, T mot is the motor torque, η gear is the transmission system efficiency, r w is the wheel radius, P mot is the motor output power, ω mot is the motor speed, P bat is the net discharge power of the battery, η mot is the motor efficiency; The driving power P of pure electric vehicles drive,BEV for: P drive,BEV =P mot 。 4. The method for cooperative control of heterogeneous vehicle platoons based on a hierarchical control architecture according to claim 1, characterized in that: In step 1, the hybrid vehicle power system model is: Among them, F T,HEV is the driving force of the hybrid vehicle, P eng is the power output of the engine through the generator, ω eng is the engine speed, T eng is the engine torque, η gen is the generator efficiency, P bat is the net discharge power of the battery; The driving power P of a hybrid vehicle drive,HEV for: P drive,HEV =P mot 。 5. The method for cooperative control of heterogeneous vehicle platoons based on a hierarchical control architecture according to claim 1, characterized in that: The fuel cell vehicle power system model is: Among them, F T,FCEV As the driving force of fuel cell vehicles, P FC is the fuel cell output power, is the hydrogen consumption rate, LHV H is the lower heating value of hydrogen, η FC for fuel cell efficiency; Fuel cell vehicle driving power P drive,FCEV for: P drive,FCEV =P mot 。

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