1. A method for merging queues of commercial vehicles with integrated optimized oil consumption and emission is characterized in that:

s1 building vehicle speed model

The method is characterized in that diesel commercial vehicles are used as research objects, when N vehicles are ready to be formed to run on a road, the running displacement and the vehicle speed are selected as state variables, and the longitudinal dynamic system equation of the ith vehicle participating in the formation is constructed as follows

Wherein i represents the ith vehicle participating in formation, and each type of symbol is respectively of the ith vehicle: s_iVehicle displacement for the ith vehicle, v_iSpeed of the i-th vehicle, F_t,iControlling input force for the vehicle of the ith vehicle, M_iIs the vehicle mass of the ith vehicle,deceleration due to wind resistance of i-th vehicle, alpha_r,i＝gC_r,icos(θ_i) Deceleration due to rolling resistance of i-th vehicle, alpha_g,i＝gsin(θ_i) Deceleration due to gradient resistance of the i-th vehicle, ρ being air density, C_d,iIs the wind resistance coefficient of the i-th vehicle, A_f,iIs the windward area of the ith vehicle, g is the acceleration of gravity, C_r,iIs the drag friction coefficient of the i-th vehicle, theta_iIs the road grade of the ith vehicle; wherein C is_d,iIn relation to the vehicle distance:

wherein delta_(i,i+1)Is the vehicle distance delta between the ith vehicle and the (i + 1) th vehicle_(i,i+1)＝s_i+1-s_i，s_i+1Is the displacement of the (i + 1) th vehicle,for the wind resistance coefficient of the vehicle without influence of the front vehicle,andis a fitting parameter;

s2, oil consumption and emission model:

the embodiment of the kinetic energy in the time domain is the driving power of the vehicle, and the driving power formula of the ith vehicle is as follows:

P_ac,i＝F_t,i·v_i (3)

wherein P is_ac,iThe driving power of the ith vehicle;

thus, when the engine is in operation, the instantaneous fuel consumption of the vehicle of the ith vehicle is modeled as:

F_ac,i＝k_1,iP_ac,i+k_2,i (4)

wherein, F_ac,iIs the instantaneous fuel consumption of the i-th vehicle, k_1,iAnd k is_2,iIs a fitting parameter;

both temperature and residual oxygen are related to the kinetic energy of the vehicle, so the emission model for the ith vehicle is established as:

wherein N is_ac,iFor the discharge of the ith vehicle, c_1,i，c_2,i，c_3,iIs a fitting parameter;

s3, determination of control target

1) To simplify the design, several assumptions are given:

in the process of merging the following vehicles into a motorcade, only the influence of air resistance on the following vehicles is considered, and the influence of other factors is not considered;

the physical parameters of the vehicles participating in formation are completely the same;

the overtaking behavior of the vehicle does not exist in the vehicle merging process;

2) based on the formulas (3) and (4), an objective function is given when the N vehicles are ready to form a formation for driving:

where J represents the value of the objective function, and sigma represents the sum, soIn order to accumulate the oil consumption of the ith vehicle to the oil consumption of the Nth vehicle, namely the total oil consumption items of the N vehicles participating in formation,is NO from the i-th vehicle_xDischarging NO accumulated to Nth vehicle_xEmissions, i.e. NO of N vehicles participating in formation_xThe discharge term is defined as the term of discharge,in order to accumulate the comfort level of the ith vehicle to the comfort level of the Nth vehicle, namely the total comfort level item of the N vehicles participating in formation, a_iAcceleration of the i-th vehicle, ω_fFuel consumption weight parameter, ω, to be adjusted_NFor the emission weight parameter to be adjusted, ω_aWeight parameter for comfort to be adjusted, t₀Time for queue merge to begin, t_fTime to end for queue merge;

s4, determination of problem constraint

And (3) restraining the formed positions of the vehicle queues:

d_m∈[d_min,d_max] (7)

wherein d is_mPosition when the N vehicles are merged into a queue, d_minFor the minimum limit of positions when these N vehicles are merged into a queue, d_maxFor the maximum limit of the position when these N vehicles are combined into a queue, d in this patent_min0, based on engineering experienced_fThe length of a road section to be commonly driven is obtained for the vehicles participating in formation based on the intelligent networking information from the current moment;

when the vehicle is travelling alone or in a train, the speed must meet regulatory constraints:

v_i,v_p∈[v_r,min,v_r,max] (8)

wherein v is_pFor the queued vehicle speed, v, after merging into a queue_r,minMinimum value of vehicle speed, v, limited by regulation_r,maxMaximum vehicle speed for regulatory limits;

the distance between each vehicle and the preceding vehicle is determined by the parameters and the states of the preceding vehicle and the vehicle:

wherein v is_i+1Speed of the i +1 th vehicle, s_br,iFor emergency braking displacement of the ith vehicle, s_br,i+1For the emergency braking displacement of the (i + 1) th vehicle in front of the ith vehicle, T_react,iFor the driver emergency response time of the ith vehicle, a_bmax,iMaximum deceleration of i-th vehicle, a_bmax,i+1Maximum deceleration of the (i + 1) th vehicle;

the minimum safe vehicle distance for avoiding collision is as follows:

s_safe,(i,i+1)＝max(v_iT_react,i,s_br,i-s_br,i+1) (10)

wherein s is_safe,(i,i+1)The safe distance between the ith vehicle and the (i + 1) th vehicle closest to the front of the vehicle is obtained;

in order to minimize the wind resistance, it is therefore necessary to keep a minimum distance between the vehicles:

δ_(i,i+1)＝s_safe,(i,i+1) (11)

according to the above disclosureCalculating the safe vehicle distance range of [ s ] by combining the road rule and the vehicle speed_safe,min s_safe,max]And selecting the lower limit of the constraint as the final in-team vehicle distance s for reducing the windage and the fuel consumption at the maximum capacity_safe,(i,i+1)＝s_safe,min；

Giving the acceleration and deceleration constraint of the vehicle:

a_min,i≤a_i≤a_max,i (12)

wherein, a_iAcceleration of the i-th vehiclea_min,iIs the minimum deceleration of the ith vehicle, given directly by brake performance, a_max,iFor the maximum acceleration of the ith vehicle, given based on vehicle engine performance:

wherein eta is_t,iIs the transmission efficiency of the transmission system of the i-th vehicle, r_t,iIs the tire radius of the ith vehicle, T_e,max,iThe maximum effective torque which can be output by the engine of the ith vehicle;

determination of controller quantity

Selecting the control input force of the vehicle as a control input quantity;

optimization problem solving

Optimization problem of terminal time freedom, i.e. t in equation (6)_f∈[t_min,t_max]The time range for queue merging is given in conjunction with the preceding:

wherein t is_minMinimum value of final time for queue merging, t_maxMaximum value of final time for queue merging;

and gridding the final time region of the possible queue combination, wherein each time grid node represents one possible queue combination end time:

wherein j represents the time interval [ t ]_min,t_max]The jth time node of the grid, Δ represents the interval over which the time region is to be gridded, such that at each time node t_f,jT in the above, objective function (6)_fAll take the value of t_f,jThus, the objective function (6) is converted into an objective function with fixed terminal time, so that calculation can be carried out, the objective function with fixed terminal time is calculated, and each time node t is obtained_f,jThe value of the objective function of (a); and selecting the minimum J and the corresponding state of the vehicle and the control input force according to the comparison, namely the state of the vehicles in the formation process and the control input force required by the vehicles in the formation process, which are obtained through instantaneous speed optimization.

Background

The vehicle intelligent network (Internet of vehicles) is a huge interactive network formed by information such as vehicle position, speed, route and the like, and the vehicle can finish the collection of self environment and state information and the uploading of self information through the Internet of vehicles. Research shows that the air resistance of following vehicles can be effectively reduced when heavy freight vehicles are in queue for running at a small distance, so that the wind resistance and the oil consumption are reduced. The development of the vehicle networking technology enables vehicles to obtain road information and intelligent information such as acceleration, speed and position of vehicles on the same road through vehicle-vehicle information interaction (V2V), vehicle-network information interaction (V2N) and vehicle-traffic facility information interaction (V2I) when the vehicles are in formation driving, so that the vehicle formation behavior is optimized by using the widely advanced information, and the effect of comprehensively optimizing the overall fuel consumption and emission of the vehicles during formation is obtained. The existing research has the following defects when controlling the formation of vehicles: (1) in order to simplify the problem, the time domain problem is converted into the distance domain problem, only the average optimal speed of the vehicle is obtained, and the instantaneous speed of the vehicle is not optimized. (2) The emission is only used as an additional benefit brought by reducing the oil consumption, and the comprehensive control of the emission and the oil consumption when the vehicle runs is not considered. (3) In order to minimize the wind resistance, a distance that minimizes the wind resistance is used, but the safety of the vehicle in driving is not considered. (4) The lack of engine system information added to vehicle-level optimization may result in the engine system failing to meet the requirements of higher-level optimization or placing a burden on the engine control system.

Disclosure of Invention

The invention aims to obtain the information of destinations, speeds, accelerations and the like of vehicles on the same road through the internet of vehicles technology, construct an optimization problem of comprehensive oil consumption and emission, and optimize the oil consumption and emission integration optimization of the instantaneous formation behaviors of the vehicles.

The method comprises the following steps:

s1 building vehicle speed model

The method is characterized in that diesel commercial vehicles are used as research objects, when N vehicles are ready to be formed to run on a road, the running displacement and the vehicle speed are selected as state variables, and the longitudinal dynamic system equation of the ith vehicle participating in the formation is constructed as follows

Wherein i represents the ith vehicle participating in formation, and each type of symbol is respectively of the ith vehicle: s_iVehicle displacement for the ith vehicle, v_iSpeed of the i-th vehicle, F_t,iControlling input force for the vehicle of the ith vehicle, M_iIs the vehicle mass of the ith vehicle,deceleration due to wind resistance of i-th vehicle, alpha_r,i＝gC_r,icos(θ_i) Deceleration due to rolling resistance of i-th vehicle, alpha_g,i＝gsin(θ_i) Deceleration due to gradient resistance of the i-th vehicle, ρ being air density, C_d,iIs the wind resistance coefficient of the i-th vehicle, A_f,iIs the windward area of the ith vehicle, g is the acceleration of gravity, C_r,iIs the drag friction coefficient of the i-th vehicle, theta_iIs the road grade of the ith vehicle; wherein C is_d,iIn relation to the vehicle distance:

wherein delta_(i,i+1)Is the vehicle distance delta between the ith vehicle and the (i + 1) th vehicle_(i,i+1)＝s_i+1-s_i，s_i+1Is the displacement of the (i + 1) th vehicle,for the wind resistance coefficient of the vehicle without influence of the front vehicle,andis a fitting parameter;

s2, oil consumption and emission model:

the embodiment of the kinetic energy in the time domain is the driving power of the vehicle, and the driving power formula of the ith vehicle is as follows:

P_ac,i＝F_t,i·v_i (3)

wherein P is_ac,iThe driving power of the ith vehicle;

thus, when the engine is in operation, the instantaneous fuel consumption of the vehicle of the ith vehicle is modeled as:

F_ac,i＝k_1,iP_ac,i+k_2,i (4)

wherein, F_ac,iIs the instantaneous fuel consumption of the i-th vehicle, k_1,iAnd k is_2,iIs a fitting parameter;

both temperature and residual oxygen are related to the kinetic energy of the vehicle, so the emission model for the ith vehicle is established as:

wherein N is_ac,iFor the discharge of the ith vehicle, c_1,i，c_2,i，c_3,iIs a fitting parameter;

s3, determination of control target

1) To simplify the design, several assumptions are given:

in the process of merging the following vehicles into a motorcade, only the influence of air resistance on the following vehicles is considered, and the influence of other factors is not considered;

the physical parameters of the vehicles participating in formation are completely the same;

the overtaking behavior of the vehicle does not exist in the vehicle merging process;

2) based on the formulas (3) and (4), an objective function is given when the N vehicles are ready to form a formation for driving:

where J represents the value of the objective function, and sigma represents the sum, soIn order to accumulate the oil consumption of the ith vehicle to the oil consumption of the Nth vehicle, namely the total oil consumption items of the N vehicles participating in formation,is NO from the i-th vehicle_xDischarging NO accumulated to Nth vehicle_xEmissions, i.e. NO of N vehicles participating in formation_xThe discharge term is defined as the term of discharge,in order to accumulate the comfort level of the ith vehicle to the comfort level of the Nth vehicle, namely the total comfort level item of the N vehicles participating in formation, a_iAcceleration of the i-th vehicle, ω_fFuel consumption weight parameter, ω, to be adjusted_NFor the emission weight parameter to be adjusted, ω_aWeight parameter for comfort to be adjusted, t₀Time for queue merge to begin, t_fTime to end for queue merge;

s4, determination of problem constraint

And (3) restraining the formed positions of the vehicle queues:

d_m∈[d_min,d_max] (7)

wherein d is_mPosition when the N vehicles are merged into a queue, d_minFor the minimum limit of positions when these N vehicles are merged into a queue, d_maxFor the maximum limit of the position when these N vehicles are combined into a queue, d in this patent_min0, based on engineering experienced_fVehicles participating in formation and obtained based on intelligent networking information are to be shared from the current momentLength of the peer road segment; when the vehicle is travelling alone or in a train, the speed must meet regulatory constraints:

v_i,v_p∈[v_r,min,v_r,max] (8)

wherein v is_pFor the queued vehicle speed, v, after merging into a queue_r,minMinimum value of vehicle speed, v, limited by regulation_r,maxMaximum vehicle speed for regulatory limits;

the distance between each vehicle and the preceding vehicle is determined by the parameters and the states of the preceding vehicle and the vehicle:

wherein v is_i+1Speed of the i +1 th vehicle, s_br,iFor emergency braking displacement of the ith vehicle, s_br,i+1For the emergency braking displacement of the (i + 1) th vehicle in front of the ith vehicle, T_react,iFor the driver emergency response time of the ith vehicle, a_bmax,iMaximum deceleration of i-th vehicle, a_bmax,i+1Maximum deceleration of the (i + 1) th vehicle;

the minimum safe vehicle distance for avoiding collision is as follows:

s_safe,(i,i+1)＝max(v_iT_react,i,s_br,i-s_br,i+1) (10)

wherein s is_safe,(i,i+1)The safe distance between the ith vehicle and the (i + 1) th vehicle closest to the front of the vehicle is obtained;

in order to minimize the wind resistance, it is therefore necessary to keep a minimum distance between the vehicles:

δ_(i,i+1)＝s_safe,(i,i+1) (11)

according to the formula, the safe vehicle distance range [ s ] of the vehicle is calculated by combining the road regulation vehicle speed_safe,min s_safe,max]And selecting the lower limit of the constraint as the final in-team vehicle distance s for reducing the windage and the fuel consumption at the maximum capacity_safe,(i,i+1)＝s_safe,min；

Giving the acceleration and deceleration constraint of the vehicle:

a_min,i≤a_i≤a_max,i (12)

wherein, a_iAcceleration of the i-th vehiclea_min,iIs the minimum deceleration of the ith vehicle, given directly by brake performance, a_max,iFor the maximum acceleration of the ith vehicle, given based on vehicle engine performance:

wherein eta is_t,iIs the transmission efficiency of the transmission system of the i-th vehicle, r_t,iIs the tire radius of the ith vehicle, T_e,max,iThe maximum effective torque which can be output by the engine of the ith vehicle;

determination of controller quantity

Selecting the control input force of the vehicle as a control input quantity;

optimization problem solving the terminal time-free optimization problem, i.e. t in equation (6)_f∈[t_min,t_max]The time range for queue merging is given in conjunction with the preceding:

wherein t is_minMinimum value of final time for queue merging, t_maxMaximum value of final time for queue merging;

and gridding the final time region of the possible queue combination, wherein each time grid node represents one possible queue combination end time:

wherein j represents the time interval [ t ]_min,t_max]The jth time node of the grid, Δ represents the interval over which the time region is to be gridded, such that at each time node t_f,jT in the above, objective function (6)_fAll take the value of t_f,jThus, the objective function (6) is converted into an objective function with fixed terminal time, so that calculation can be carried out, the objective function with fixed terminal time is calculated, and each time node t is obtained_f,jThe value of the objective function of (a); and selecting the minimum J and the corresponding state of the vehicle and the control input force according to the comparison, namely the state of the vehicles in the formation process and the control input force required by the vehicles in the formation process, which are obtained through instantaneous speed optimization.

The invention firstly establishes a model of emission and oil consumption based on the speed of a single vehicle when the vehicles participate in queue merging, secondly gives out the acceleration, speed and safe distance restriction when the vehicles participate in queue merging according to the information of the relative distance between the vehicle and the front vehicle, the relative speed, the common driving distance and the like obtained from the intelligent traffic information and the performance information of the engine system at the bottom layer of the vehicle, plans the speed of the vehicles participating in queue merging, and obtains the speed of each vehicle and the control input (vehicle input control force) of the corresponding speed, which can lead the integral oil consumption and the emission of the queue to be comprehensively optimal. Finally, in order to verify the effectiveness of the method, simulation verification is carried out in Matlab.

Drawings

FIG. 1 is a model-checking curve diagram of a wind resistance coefficient model;

FIG. 2 is a graph of a fuel consumption model correlation analysis;

FIG. 3 is NO_xA model correlation analysis graph;

FIG. 4 is a graph of vehicle trajectory, speed, windage coefficient, and inter-vehicle distance for participating in a fleet merge;

FIG. 5 is a bar graph of fuel consumption versus vehicle alone;

FIG. 6 is the individual vehicle NO_xEmission contrast columnA shape chart;

FIG. 7 is a bar graph comparing fleet fuel consumption;

FIG. 8 is a fleet NO_xEmission vs. histogram.

Detailed Description

The intelligent traffic information is used for planning the vehicle speed and the total driving force of the commercial vehicle participating in queue merging, and queue merging behavior with comprehensive optimal queue overall emission and oil consumption is obtained. According to the method, firstly, a model of emission and oil consumption based on the speed of a single vehicle during queue merging is established, secondly, the acceleration, the speed and the safety distance constraint during the queue merging of the vehicles are given according to the information of the relative distance between the vehicle and the front vehicle, the relative speed, the common driving distance and the like obtained from intelligent traffic information and the performance information of a vehicle bottom layer engine system, the speed of the vehicles participating in the queue merging is planned, and the speed of each vehicle and the control input (vehicle input control force) of the corresponding speed, which can enable the overall oil consumption and the emission of the queue to be comprehensively optimal, are obtained. Finally, in order to verify the effectiveness of the method, simulation verification is carried out in Matlab.

Research shows that the air resistance of following vehicles can be effectively reduced when heavy freight vehicles are in queue for running at a small distance, so that the wind resistance and the oil consumption are reduced. The development of the intelligent traffic system enables vehicles to obtain intelligent information such as road information and vehicle information on the same road when the vehicles are formed into a formation, so that the formation behavior of the vehicles is optimized, and the comprehensive optimal effect of overall oil consumption and emission is obtained when the vehicles are formed into a formation. According to the method, information such as destinations, speeds and accelerations of vehicles on the same road are obtained through intelligent networking information, constraint conditions are given based on wind resistance reduction and safety requirements in transient behavior optimization problems of vehicle formation, optimization problems of comprehensive oil consumption and emission are established, and formation behaviors of instantaneous speeds of vehicles are optimized. Finally, the effectiveness of the method provided by the invention is verified through a simulation experiment and compared with the conventional formation behavior for optimizing the average speed of the vehicle.

The specific method of the invention is as follows:

1. vehicle speed model building

The method is characterized in that diesel commercial vehicles are used as research objects, when N vehicles are ready to be formed to run on a road, the running displacement and the vehicle speed are selected as state variables, and the longitudinal dynamic system equation of the ith vehicle participating in the formation is constructed as follows

Wherein i represents the ith vehicle participating in formation, and each type of symbol is respectively of the ith vehicle: s_iVehicle displacement for the ith vehicle, v_iSpeed of the i-th vehicle, F_t,iControlling input force for the vehicle of the ith vehicle, M_iIs the vehicle mass of the ith vehicle,deceleration due to wind resistance of i-th vehicle, alpha_r,i＝gC_r,icos(θ_i) Deceleration due to rolling resistance of i-th vehicle, alpha_g,i＝gsin(θ_i) Deceleration due to gradient resistance of the i-th vehicle. ρ is the air density, C_d,iIs the wind resistance coefficient of the i-th vehicle, A_f,iIs the windward area of the ith vehicle, g is the acceleration of gravity, C_r,iIs the drag friction coefficient of the i-th vehicle, theta_iThe road grade of the ith vehicle.

Wherein C is_d,iIn relation to the vehicle distance:

wherein delta_(i,i+1)Is the vehicle distance delta between the ith vehicle and the (i + 1) th vehicle_(i,i+1)＝s_i+1-s_i，s_i+1Is the displacement of the (i + 1) th vehicle,for the wind resistance coefficient of the vehicle without influence of the front vehicle,andare fitting parameters.

2. Oil consumption and emission model:

according to the heat energy and mechanical principle, the energy generated by the fuel combustion of the engine drives the vehicle to run through the transmission of the transmission system, so that the kinetic energy of the vehicle and the oil consumption of the engine have a direct proportional relation, the reflection of the kinetic energy in a time domain is the driving power of the vehicle, and the driving power calculation formula of the ith vehicle is as follows:

P_ac,i＝F_t,i·v_i (3)

wherein P is_ac,iIs the driving power of the ith vehicle.

Thus, when the engine is in operation, the instantaneous fuel consumption of the vehicle of the ith vehicle can be modeled as:

F_ac,i＝k_1,iP_ac,i+k_2,i (4)

wherein, F_ac,iIs the instantaneous fuel consumption of the i-th vehicle, k_1,iAnd k is_2,iAre fitting parameters.

The engine will produce emissions, NO, as long as it is running_xThe exhaust gas is accelerated to be generated in a high-temperature rich environment in the engine, the temperature and the residual oxygen are related to the kinetic energy of the vehicle, and therefore the exhaust model of the ith vehicle can be established as follows:

wherein N is_ac,iFor the discharge of the ith vehicle, c_1,i，c_2,i，c_3,iAre fitting parameters.

3. Description of control problems

3.1 determination of control targets

When N vehicles are formed into a team, the front vehicles can be properly decelerated, and meanwhile, the rear vehicles are properly accelerated to accelerate the merging process of the team, so that after the vehicle team is formed, the vehicles in the team can run for a longer distance with smaller wind resistance, and more wind resistance and oil consumption are reduced. Therefore, compared with the mode that the speed of the front vehicle is unchanged or accelerated, the mode that the rear vehicle is accelerated to form the chasing behind to form the motorcade has larger fuel-saving potential. Under the formation of the queue, the front vehicles can bring energy loss of converting kinetic energy into heat energy for dissipation when running in a deceleration mode, and the rear vehicles can generate extra oil consumption and emission when running in an acceleration mode, because the rear vehicles run in an acceleration mode, and the engine is in a high-temperature and rich-nutrient working environment, the emission increase is inevitable, so the emission optimization is placed in the process of controlling the vehicles to be merged into the queue, and the vehicles participating in formation are controlled to form the fleet by taking the comprehensive optimal oil consumption and emission as a target. To simplify the design, several assumptions are given:

in the process of merging the following vehicles into a motorcade, only the influence of air resistance on the following vehicles is considered, and the influence of other factors is not considered;

the physical parameters of the vehicles participating in formation are completely the same;

and the overtaking behavior of the vehicle does not exist in the vehicle merging process.

Based on the formulas (3) and (4), an objective function is given when the N vehicles are ready to form a formation for driving:

where J represents the value of the objective function. Since Σ represents the sum, soThe fuel consumption of the ith vehicle is always added to the fuel consumption of the Nth vehicle, namely the total fuel consumption item of the N vehicles participating in formation.Is NO from the i-th vehicle_xDischarging NO accumulated to Nth vehicle_xEmissions, i.e. NO of N vehicles participating in formation_xAnd (4) discharging items.The comfort level of the ith vehicle is added to the comfort level of the Nth vehicle, namely the total comfort level item of the N vehicles participating in formation. a is_iAcceleration of the i-th vehicle, ω_fFuel consumption weight parameter, ω, to be adjusted_NFor the emission weight parameter to be adjusted, ω_aWeight parameter for comfort to be adjusted, t₀Time for queue merge to begin, t_fTime to end for queue merge.

3.2 determination of problem constraints

After the vehicles participating in formation are determined, the vehicles have the potential of saving oil only when forming a queue within a certain range, otherwise, the oil consumption increased in the process of forming the vehicles exceeds the wind resistance oil consumption saved by running in the queue, so that the position formed by the vehicle queue needs to be restrained:

d_m∈[d_min,d_max] (7)

wherein d is_mPosition when the N vehicles are merged into a queue, d_minFor the minimum limit of positions when these N vehicles are merged into a queue, d_maxFor the maximum limit of the position when these N vehicles are combined into a queue, d in this patent_min0, based on engineering experienced_fThe vehicles participating in formation, which are obtained based on the intelligent networking information, are to travel the length of the road section together from the current time.

When the vehicle is travelling alone or in a train, the speed must meet regulatory constraints:

v_i,v_p∈[v_r,min,v_r,max] (8)

wherein v is_pFor the queued vehicle speed, v, after merging into a queue_r,minMinimum value of vehicle speed, v, limited by regulation_r,maxIs the maximum value of the vehicle speed limited by the legislation.

Because the inter-vehicle distances in the queue are not equal, the inter-vehicle distance between each vehicle and the preceding vehicle is determined by the parameters and the states of the preceding vehicle and the vehicle:

wherein v is_i+1Speed of the i +1 th vehicle, s_br,iFor emergency braking displacement of the ith vehicle, s_br,i+1For the emergency braking displacement of the (i + 1) th vehicle in front of the ith vehicle, T_react,iFor the driver emergency response time of the ith vehicle, a_bmax,iMaximum deceleration of i-th vehicle, a_bmax,i+1Maximum deceleration of the (i + 1) th vehicle.

The minimum safe headway to avoid a collision is therefore:

s_safe,(i,i+1)＝max(v_iT_react,i,s_br,i-s_br,i+1) (10)

wherein s is_safe,(i,i+1)The safe distance between the ith vehicle and the (i + 1) th vehicle closest to the front of the vehicle is obtained.

In order to minimize the wind resistance, it is therefore necessary to keep a minimum distance between the vehicles:

δ_(i,i+1)＝s_safe,(i,i+1) (11)

according to the formula, the safe vehicle distance range [ s ] of the vehicle is calculated by combining the road regulation vehicle speed_safe,min s_safe,max]In order to reduce the wind resistance and oil consumption at the maximum capacity, the patent selects the lower constraint limit as the final in-team vehicle distance s_safe,(i,i+1)＝s_safe,min。

Since the acceleration of the vehicle depends on the power performance of the underlying engine and the deceleration of the vehicle depends on the performance of the brake, the acceleration and deceleration constraints of the vehicle are given according to the power performance of the underlying engine and the performance of the brake:

a_min,i≤a_i≤a_max,i (12)

wherein, a_iAcceleration of the i-th vehiclea_min,iFor minimum deceleration of i-th vehicle, directly from brake performanceGiven a_max,iThe maximum acceleration of the ith vehicle.

Based on vehicle engine performance:

wherein eta is_t,iIs the transmission efficiency of the transmission system of the i-th vehicle, r_t,iIs the tire radius of the ith vehicle, T_e,max,iThe maximum effective torque that the engine of the ith vehicle can output.

The controller amount is determined by directly representing the states of displacement, speed and acceleration in the process of the vehicle on the road. And displacement is integral of speed, acceleration is differential of speed, so the state of the vehicle is selected as speed, the influence on the vehicle speed is the control input force of the vehicle according to the formula (1b), the control input force is positive, the engine works to drive the vehicle to move forwards, the control input force is negative, the engine does not work, the brake works, and the control input force of the vehicle is selected as the control input quantity.

Optimization problem solving because only the final queue forming position is subjected to constraint requirements and the final queue combining time is not subjected to requirements during queue combining, the optimization problem of free terminal time, namely t in formula (6)_f∈[t_min,t_max]。

The time range for queue merging is given in conjunction with the foregoing:

wherein t is_minMinimum value of final time for queue merging, t_maxIs the maximum value of the final time of queue merging.

But t is not known_fTool (A)The volume value, and therefore the objective function (6), cannot be calculated. To calculate this terminal time-free objective function, the final time region of possible queue merges is gridded, so that each time grid node represents a possible queue merge end time:

wherein j represents the time interval [ t ]_min,t_max]The jth time node of the grid, Δ represents the interval over which the time region is to be gridded, such that at each time node t_f,jT in the above, objective function (6)_fAll take the value of t_f,jThus, the objective function (6) is converted into an objective function with fixed terminal time, so that calculation can be carried out. Calculating the objective function with fixed terminal time, we can obtain each time node t_f,jThe value J of the objective function at (a). And selecting the minimum J and the corresponding state of the vehicle and the control input force according to the comparison, namely the state of the vehicles in the formation process and the control input force required by the vehicles in the formation process, which are obtained through instantaneous speed optimization.

4. Simulation verification and analysis

Acquiring data of an initial vehicle during actual running from GT-Power, constructing a model for controlling vehicle emission according to a modeling method of chapter 2, identifying and obtaining model parameters according to the GT data, obtaining a final vehicle oil consumption and emission control model under an intelligent networking environment, and performing correlation analysis on the GT data and output data of the established model under the same vehicle working condition, as shown in FIGS. 1,2 and 3, it can be seen that a wind resistance coefficient model, an oil consumption model and NO are related_xR of emission model characterization correlation²All are larger than 0.7, which indicates that the model can better describe the vehicle dynamics of the line pipe and can be used as a vehicle control-oriented model for the next optimization planning.

The method comprises the steps of solving to obtain a vehicle speed which enables oil consumption and emission to be comprehensively optimal and a corresponding vehicle input control force, wherein when the input control force is a negative value, the input control force is indicated to be a braking force, a brake of a vehicle works, the braking force can be directly provided for the brake of a vehicle model, when the input control force obtained through solving is a positive value, the input control force is indicated to be a driving force, the driving force is converted into a target effective torque of an engine controller through the speed ratio of a transmission system, the engine controller controls an engine to provide the required torque, and the vehicle is driven to advance through the conversion of the transmission system.

The final solution result is compared with the existing queue merging method based on average speed optimization to verify the effectiveness of the method provided by the patent. The experimental graphs are shown in the attached figures, and the experimental data and simple analysis are shown in the attached tables.

As can be seen from the first two diagrams in fig. 4, the method proposed by the present patent can effectively form a formation of vehicles, and as can be seen from the last two diagrams in fig. 4, the wind resistance of the formation of vehicles is actually reduced after the formation of vehicles is driven, so the fuel consumption caused by the wind resistance is also reduced. As can be seen from fig. 4, 5, 7 and table 3, compared with the vehicle queue merging method optimized for average speed, the vehicle queue merging method optimized for instantaneous vehicle speed provided by the present patent can dynamically adjust the speed of each vehicle participating in formation, so that the front vehicle can decelerate to wait for the rear vehicle, and the rear vehicle can accelerate to catch up with the front vehicle, so that although the fuel consumption of the vehicles participating in formation is higher or lower, the effect of lowest fuel consumption of the whole queue can be achieved. Also, as can be seen from fig. 4, 6, 8 and table 4, compared with the vehicle queue merging method optimized for average speed, the vehicle queue merging method optimized for instantaneous vehicle speed proposed in this patent can dynamically adjust the speed of each vehicle participating in formation, where the front vehicle decelerates to wait for the rear vehicle, and the rear vehicle accelerates to catch up with the front vehicle, so that although the emission of the vehicles participating in formation is high or low, the effect of lowest fuel consumption of the whole queue is achieved. The percentage of fuel consumption and emission change for the individual vehicle and the percentage of overall fuel consumption and emission change for the fleet are summarized in tables 3 and 4. It should be particularly explained that the fuel consumption and the emission of the vehicles 2 and 3 are the lowest because the vehicles 2 and 3 are in the deceleration state and the engine is maintained at the idle speed during the instant speed optimization.

TABLE 1 model parameters

According to the assumption that the value of the ith vehicle is i equal to 1,2, …, N, which is the value of each vehicle participating in formation.

In the present application, 5 vehicles were selected as the study target, so N is 5

TABLE 2 initial states of participating formation vehicles

Vehicle serial number	Initial velocity (m/s)	Initial position (m)
			Vehicle 1(i ═ 1)	28	250
Vehicle 2(i ═ 2)	26	210
			Vehicle 3(i ═ 3)	24	190
Vehicle 4(i ═4)	22	160
			Vehicle 5(i ═ 5)	20	130

TABLE 3 oil consumption test results

In the data analysis, the percentage is the calculation result of the instantaneous speed optimization relative to the average speed optimization, namely (instantaneous speed optimization oil consumption result-average speed optimization oil consumption result)/average speed optimization oil consumption result

TABLE 4NO_xResults of emission experiments

The percentages in the "data analysis" in the table are calculated as instantaneous speed optimization versus average speed optimization, i.e. (instantaneous speed optimization NO)_xResults-average velocity optimization of NO_xResult)/average velocity optimized NO_xResults