Commercial vehicle multi-target coordination control method considering front vehicle safety speed effect

Abstract

The invention discloses a commercial vehicle multi-target coordination control method considering a front vehicle safety speed effect, aims to realize a safer, more comfortable and more economical coordination control process of a commercial vehicle, and specifically comprises the following steps: (1) obtaining a vehicle driving state and environment information, comprise the front vehicle position, the front vehicle speed, the front vehicle maximum deceleration, the front vehicle body length, the self-vehicle position, the self-vehicle speed and the self-vehicle maximum deceleration; (2) calculating a safety sensing distance and a safety vehicle speed in a vehicle running and following process, and establishing a safety following rule considering a safety speed effect of a front vehicle; (3) putting forward a multi-target coordination control algorithm by considering longitudinal and vertical interaction, and adopting a semi-vehicle active suspension adaptive control algorithm considering uncertainty interference in the vertical direction; and the longitudinal dimension adopts a control algorithm of a fuzzy theory. According to the method, the safety speed effect model is introduced into commercial vehicle coordination control, so that the whole vehicle system is efficient, safe, comfortable and economical.

Description

Technical Field

[0001] The invention belongs to the field of vehicle coordinated control, and in particular relates to a multi-objective coordinated control method for commercial vehicles taking into account the safety speed effect of a preceding vehicle. Background Art

[0002] Traditional energy management control strategies mostly focus on the single indicator of fuel economy. In fact, if the lowest fuel consumption is used as the single optimization goal, the control amount calculation result will make the dynamic characteristics of the transmission system worse, thus affecting the driving performance. The multi-objective coordinated control of commercial vehicles considering the safety speed effect of the leading vehicle introduces a safety speed effect model and considers the longitudinal and vertical interaction of the vehicle to further realize a safer, more comfortable and more economical coordinated control process for commercial vehicles.

[0003] Some existing patents, such as the invention patent with patent number CN108773376A, proposes a multi-objective hierarchical collaborative control and optimization method for automobiles that integrates driving intentions, and the invention patent with patent number CN106585619A proposes a dynamic coordinated control method for a planetary gear hybrid system that considers multiple objectives. The former integrates driving intentions and uses vehicle path tracking and rollover stability as objective functions for multi-objective genetic algorithm optimization, while the latter uses a dynamic programming algorithm to obtain the optimal distribution of engine and motor torque under predicted working conditions to improve economy and ride quality. Both ignore the impact of suspension and different braking intensities on vehicle driving. Summary of the invention

[0004] The present invention aims to achieve a safer, more comfortable and more economical coordinated control process for commercial vehicles, and proposes a multi-objective coordinated control method for commercial vehicles that takes into account the safety speed effect of the leading vehicle. The method obtains the vehicle's driving status and environmental information, then establishes a safe following rule that takes into account the safety speed effect of the leading vehicle, and finally proposes a multi-objective coordinated control algorithm that takes into account the longitudinal and vertical interactions, so that the entire vehicle system is efficient, safe, comfortable and economical.

[0005] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] A multi-objective coordinated control method for a commercial vehicle considering the safety speed effect of a preceding vehicle, wherein the multi-objectives are safety objectives, comfort objectives, and economic objectives, specifically comprising the following steps:

[0007] S1: Obtain vehicle driving status and environmental information, including the position of the preceding vehicle, the speed of the preceding vehicle, the maximum deceleration of the preceding vehicle, the length of the preceding vehicle, the position of the own vehicle, the speed of the own vehicle, and the maximum deceleration of the own vehicle;

[0008] S2: Calculate the safe perception distance and safe speed of the vehicle during the following process, and establish a safe following rule that takes into account the effect of the safety speed of the leading vehicle;

[0009] S3: A multi-objective coordinated control algorithm is proposed considering the interaction between the longitudinal and vertical directions. In the vertical direction, a half-vehicle active suspension adaptive control algorithm considering uncertainty interference is adopted; in the longitudinal dimension, a fuzzy theory control algorithm is adopted.

[0010] In the preferred technical solution, the safety target is achieved through the front vehicle safety speed effect model, and the safety perception distance d is defined as n_safe (t) and safe speed v n_safe (t), the specific calculation method is shown in formula (1):

[0011]

[0012] In the formula, x n+1 (t), x n (t)——position of the vehicle in front of the nth vehicle and the vehicle at time t

[0013] d——the distance between the front of the nth vehicle and the front of the vehicle

[0014] l n+1 ——Length of the front vehicle

[0015] v n (t), v n+1 (t)——the speed of the nth vehicle and the preceding vehicle at time t

[0016] τ n ——Reaction time of the driver of the nth vehicle

[0017] b n , b n+1 ——The maximum deceleration of the nth vehicle and the preceding vehicle

[0018] The safe following rules are formulated according to the safety speed effect model of the preceding vehicle. The specific rules are shown in formula (2):

[0019] In the formula, v na (t+1), v nb (t+1), v nc (t+1), v nd (t+1)——Acceleration, uniform speed, safe deceleration, deterministic deceleration rules

[0020] a n ——The acceleration of the nth car

[0021] v max ——The maximum speed of the nth vehicle

[0022] In the preferred technical solution, the comfort target and the economy target are achieved through coordinated control of the vehicle longitudinal and vertical directions, and the specific steps of step S3 include:

[0023] S31: Define x1 = z c , x5=z f2 , x7=z r2 ,

[0024] where z c , are the vertical displacement and vertical velocity of the vehicle body respectively, are the pitch angle and pitch angular velocity, z f2 , z r2 , are the vertical displacement and vertical velocity of the front and rear unsprung masses respectively. The state space equation of the half-vehicle active suspension system considering the vertical pitching motion is shown in formula (3):

[0025]

[0026] In the formula, m s , m uf 、m ur ——Sprung mass rigid body, front (f), rear (r) unsprung mass rigid body u f ,u r 、F sf , F sr 、F df , F dr ——Front and rear nonlinear stiffness active control force, spring force, damping force, u a =u f +u r , F s =F sf +F sr , F d =F df +F dr

[0027] F g ——Uncertain dynamic interference

[0028] I——Moment of inertia of half the vehicle body

[0029] a, b——the distance from the front and rear suspension systems to the center of mass of the vehicle body

[0030] ——Pitch motion controller,

[0031] z——Braking strength

[0032] h s ——Height of vehicle center of gravity

[0033] Ftf , F tr 、F bf , F br ——Front and rear linear stiffness spring force and damping force

[0034] S32: Longitudinal braking is controlled by a fuzzy optimization framework. The mechanical braking force of the front and rear axles and the motor regenerative braking force are expressed as shown in formula (4):

[0035]

[0036] In the formula, β fr (t)——Braking force distribution coefficient of front and rear axles

[0037] βrg(t)——mechanical distribution coefficient of the rear drive shaft braking force, β rg (t)=f(z(t),SOC(t),v(t))

[0038] T brk_req (t)——Required braking torque, T brk_req (t) = δmgz(t)r, δ is the rotational mass conversion factor, m is the vehicle mass, g is the gravitational acceleration, and r is the wheel radius

[0039] Considering the road restriction during braking, the motor torque limit is shown in formula (5):

[0040]

[0041] In the formula, i f 、i AMT ——Final reducer, AMT speed ratio

[0042] η AMT , η f , η w , η m_gen ——Efficiency of AMT, final reducer, wheels, and motor MG2

[0043] u0——Road adhesion coefficient

[0044] T mg2_max ——Constrain the maximum torque of the rear motor MG2

[0045] P chg_max ——Maximum battery charging power

[0046] In the fuzzy controller, z, SOC, and v meet the maximum and minimum limits of braking intensity, battery charge allowed for regenerative braking, and braking speed.

[0047] Compared with the prior art, the advantages of the present invention are:

[0048] 1. The multi-objective coordinated control method for commercial vehicles considering the safety speed effect of the leading vehicle described in the present invention introduces a safety speed effect model into the coordinated control of commercial vehicles, which can reduce the risk of vehicle collision, improve driving safety, and effectively increase the critical density of the maximum flow rate, reduce road congestion, and enhance the vehicle's road capacity.

[0049] 2. The multi-objective coordinated control method for commercial vehicles taking into account the safety speed effect of the leading vehicle described in the present invention considers uncertain dynamic interference based on suspension control, designs an adaptive active suspension control strategy taking into account the longitudinal and vertical coupling relationship, and improves the driving comfort of commercial vehicles.

[0050] 3. The multi-objective coordinated control method for commercial vehicles that takes into account the safety speed effect of the leading vehicle described in the present invention utilizes the load transfer brought about by pitching motion, designs the constraint conditions of the longitudinal drive motor, limits energy recovery, and improves the economy of the commercial vehicle during driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0052] Figure 1 It is a flow chart of a multi-objective coordinated control method for commercial vehicles considering the safety speed effect of the preceding vehicle according to the present invention;

[0053] Figure 2 It is a schematic diagram of a preceding vehicle safety speed effect model in a multi-objective coordinated control method for commercial vehicles considering preceding vehicle safety speed effect according to the present invention;

[0054] Figure 3 The present invention is a schematic diagram of the braking force distribution control logic in a multi-objective coordinated control method for commercial vehicles that takes into account the safety speed effect of a leading vehicle. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0056] The present invention will be further described below in conjunction with the accompanying drawings.

[0057] See also Figure 1 The present invention provides a multi-objective coordinated control method for commercial vehicles considering the safety speed effect of the preceding vehicle, wherein the multi-objectives are safety objectives, comfort objectives, and economic objectives, and specifically comprises the following steps:

[0058] S1: Obtain vehicle driving status and environmental information, including the position of the preceding vehicle, the speed of the preceding vehicle, the maximum deceleration of the preceding vehicle, the length of the preceding vehicle, the position of the own vehicle, the speed of the own vehicle, and the maximum deceleration of the own vehicle;

[0059] S2: Calculate the safe perception distance and safe speed of the vehicle during the following process, and establish a safe following rule that takes into account the effect of the safety speed of the leading vehicle;

[0060] S3: A multi-objective coordinated control algorithm is proposed considering the interaction between the longitudinal and vertical directions. In the vertical direction, a half-vehicle active suspension adaptive control algorithm considering uncertainty interference is adopted; in the longitudinal dimension, a fuzzy theory control algorithm is adopted.

[0061] See also Figure 2 The safety target is achieved through the front vehicle safety speed effect model, and the safety perception distance d is defined n_safe (t) and safe speed v n_safe (t), the specific calculation method is shown in formula (1):

[0062]

[0063] In the formula, x n+1 (t), x n (t)——position of the vehicle in front of the nth vehicle and the vehicle at time t

[0064] d——the distance between the front of the nth vehicle and the front of the previous vehicle

[0065] l n+1 ——Length of the front vehicle

[0066] v n (t), v n+1 (t)——the speed of the nth vehicle and the preceding vehicle at time t

[0067] τ n ——Reaction time of the driver of the nth vehicle

[0068] b n , b n+1 ——The maximum deceleration of the nth vehicle and the preceding vehicle

[0069] The safe following rules are formulated according to the safety speed effect model of the preceding vehicle. The specific rules are shown in formula (2):

[0070] In the formula, v na (t+1), v nb (t+1), v nc (t+1), v nd (t+1)——Acceleration, uniform speed, safe deceleration, deterministic deceleration rules

[0071] a n ——The acceleration of the nth car

[0072] v max ——The maximum speed of the nth vehicle

[0073] The comfort target and the economy target are achieved through the coordinated control of the longitudinal and vertical directions of the vehicle. The specific steps of step S3 include:

[0074] S31: Define x1 = z c , x5=z f2 , x7=z r2 ,

[0075] where z c , are the vertical displacement and vertical velocity of the vehicle body respectively, are the pitch angle and pitch angular velocity, z f2 , z r2 , are the vertical displacement and vertical velocity of the front and rear unsprung masses respectively. The state space equation of the half-vehicle active suspension system considering the vertical pitching motion is shown in formula (3):

[0076]

[0077] In the formula, m s , m uf 、m ur ——Sprung mass rigid body, front (f), rear (r) unsprung mass rigid body u f ,u r 、F sf , F sr 、F df , F dr ——Front and rear nonlinear stiffness active control force, spring force, damping force, u a =u f +u r , F s =F sf +F sr , F d =F df +F dr

[0078] F g ——Uncertain dynamic interference

[0079] I——Moment of inertia of half the vehicle body

[0080] a, b——the distance from the front and rear suspension systems to the center of mass of the vehicle body

[0081] ——Pitch motion controller,

[0082] z——Braking strength

[0083] h s ——Height of vehicle center of gravity

[0084] F tf , F tr 、F bf , F br ——Front and rear linear stiffness spring force and damping force

[0085] S32: See Figure 3 The longitudinal braking is controlled by a fuzzy optimization framework. The mechanical braking force of the front and rear axles and the motor regenerative braking force are expressed as shown in formula (4):

[0086]

[0087] In the formula, β fr (t)——Braking force distribution coefficient of front and rear axles

[0088] βrg(t)——mechanical distribution coefficient of the rear drive shaft braking force, β rg (t)=f(z(t),SOC(t),v(t))

[0089] T brk_req (t)——Required braking torque, T brk_req (t) = δmgz(t)r, δ is the rotational mass conversion factor, m is the vehicle mass, g is the gravitational acceleration, and r is the wheel radius

[0090] Considering the road restriction during braking, the motor torque limit is shown in formula (5):

[0091]

[0092] In the formula, i f 、i AMT ——Final reducer, AMT speed ratio

[0093] η AMT , η f , η w , η m_gen ——Efficiency of AMT, final reducer, wheels, and motor MG2

[0094] u0——Road adhesion coefficient

[0095] T mg2_max ——Constrain the maximum torque of the rear motor MG2

[0096] P chg_max——Maximum battery charging power

[0097] In the fuzzy controller, z, SOC, and v meet the maximum and minimum limits of braking intensity, battery charge allowed for regenerative braking, and braking speed.

[0098] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-objective coordinated control method for commercial vehicles considering the safety speed effect of the preceding vehicle, characterized in that: The multiple objectives are safety objectives, comfort objectives and economic objectives, and specifically include the following steps: S1: Obtain vehicle driving status and environmental information, including the position of the preceding vehicle, the speed of the preceding vehicle, the maximum deceleration of the preceding vehicle, the length of the preceding vehicle, the position of the own vehicle, the speed of the own vehicle, and the maximum deceleration of the own vehicle; S2: Calculate the safe perception distance and safe speed of the vehicle during the following process, and establish a safe following rule that takes into account the effect of the safety speed of the leading vehicle; S3: A multi-objective coordinated control algorithm is proposed considering the interaction between the longitudinal and vertical directions. In the vertical direction, a half-vehicle active suspension adaptive control algorithm considering uncertainty interference is adopted; in the longitudinal dimension, a fuzzy theory control algorithm is adopted.

2. The multi-objective coordinated control method for commercial vehicles considering the safety speed effect of the preceding vehicle according to claim 1 is characterized in that: The safety target is achieved through the front vehicle safety speed effect model, defining the safety perception distance d n_safe (t) and safe speed v n_safe (t), the specific calculation method is shown in formula (1): In the formula, x n+1 (t), x n (t)——position of the vehicle in front of the nth vehicle and the vehicle at time t d——the distance between the front of the nth vehicle and the front of the previous vehicle l n+1 ——Length of the front vehicle v n (t), v n+1 (t)——the speed of the nth vehicle and the preceding vehicle at time t τ n ——Reaction time of the driver of the nth vehicle b n , b n+1 ——The maximum deceleration of the nth vehicle and the preceding vehicle The safe following rules are formulated according to the safety speed effect model of the preceding vehicle. The specific rules are shown in formula (2): In the formula, v na (t+1), v nb (t+1), v nc (t+1), v nd (t+1)——Acceleration, uniform speed, safe deceleration, deterministic deceleration rules a n ——The acceleration of the nth car v max ——The maximum speed of the nth vehicle.

3. The multi-objective coordinated control method for commercial vehicles considering the safety speed effect of the preceding vehicle according to claim 1 is characterized in that: The comfort target and the economy target are achieved through the coordinated control of the longitudinal and vertical directions of the vehicle. The specific steps of step S3 include: S31: Define x1 = z c , x5=z f2 , x7=z r2 , where z c , are the vertical displacement and vertical velocity of the vehicle body respectively, are the pitch angle and pitch angular velocity, z f2 , z r2 , are the vertical displacement and vertical velocity of the front and rear unsprung masses respectively. The state space equation of the half-vehicle active suspension system considering the vertical pitching motion is shown in formula (3): In the formula, m s , m uf 、m ur ——Sprung mass rigid body, front (f), rear (r) unsprung mass rigid body u f ,u r 、F sf , F sr 、F df , F dr ——Front and rear nonlinear stiffness active control force, spring force, damping force, u a =u f +u r , F s =F sf +F sr , F d =F df +F dr F g ——Uncertain dynamic interference I——Moment of inertia of half the vehicle body a, b——the distance from the front and rear suspension systems to the center of mass of the vehicle body ——Pitch motion controller, z——Braking strength h s ——Height of vehicle center of gravity F tf , F tr 、F bf , F br ——Front and rear linear stiffness spring force and damping force S32: Longitudinal braking is controlled by a fuzzy optimization framework. The mechanical braking force of the front and rear axles and the motor regenerative braking force are expressed as shown in formula (4): In the formula, β fr (t)——Braking force distribution coefficient of front and rear axles β rg (t)——mechanical distribution coefficient of the braking force on the rear drive shaft, β rg (t)=f(z(t),SOC(t),v(t)) T brk_req (t)——Required braking torque, T brk_req (t) = δmgz(t)r, δ is the rotational mass conversion factor, m is the vehicle mass, g is the gravitational acceleration, and r is the wheel radius Considering the road restriction during braking, the motor torque limit is shown in formula (5): In the formula, i f 、i AMT ——Final reducer, AMT speed ratio η AMT , η f , η w , η m_gen ——AMT, main reducer, wheels, motor MG2 power generation efficiency; u0——road adhesion coefficient T mg2_max ——Constrain the maximum torque of the rear motor MG2 P chg_max ——Maximum battery charging power In the fuzzy controller, z, SOC, and v meet the maximum and minimum limits of braking intensity, battery charge allowed for regenerative braking, and braking speed.

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