Intelligent networked mine vehicle fleet fuel consumption and emission coordination control method and system

Abstract

The application provides a kind of intelligent network connection mine truck team oil consumption and emission coordination control method and system, comprising: obtaining the data of the ith vehicle in mine truck team, environmental data;Establish mine truck optimization control model, the establishment process of mine truck optimization control model includes with vehicle speed as state variable, the longitudinal dynamics system equation of the ith vehicle is constructed by combining the data of the ith vehicle and environmental data;Fuel consumption and nitrogen oxide emissions (NOx) are used as optimization target to establish optimization model, obtain the mathematical expression of optimization control problem of mine truck team, get the reference speed of the ith vehicle and optimize;Vehicle operation can be controlled under the condition that acceleration changes little, reduce the increase of fuel consumption and NOx emissions caused by rapid acceleration, solve the problem of how to coordinate and optimize oil consumption and nitrogen oxide, reduce the energy loss of mine truck under long distance transportation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent transportation, and particularly relates to a kind of intelligent network connection mine vehicle team oil consumption and emission coordination control method and system. BACKGROUND

[0002] The development of intelligent transportation system provides a more flexible solution for the oil consumption and emission control of mine vehicles. One of the methods is to form a vehicle queue to travel with a small inter-vehicle distance. Studies have shown that when vehicles travel in a queue with a very small distance, the air resistance of the following vehicles can be effectively reduced, thereby reducing oil consumption and greenhouse gas emissions. Experimental evaluation of the vehicle queue shows that as the distance between the controlled vehicle and other vehicles decreases, fuel savings can save 5-20%.

[0003] The main pollutant emissions of mine vehicles are particulate matter (PM) and nitrogen oxides (NOx), which need to be reduced by control. With the increase of fuel prices, the demand for vehicle fuel consumption optimization is also increasingly urgent. The principle of reducing PM is consistent with that of reducing oil consumption, and the additional benefits brought by reducing PM are reduced; the mechanism of reducing nitrogen oxides is in conflict with the mechanism of reducing oil consumption (this consumes the other), how to coordinate and optimize oil consumption and nitrogen oxides to reduce the energy loss of mine vehicles in long-distance transportation is a difficult problem to be solved. SUMMARY

[0004] In order to solve the above problems, according to some embodiments, the application adopts the following technical scheme:

[0005] In a first aspect, the application provides a kind of intelligent network connection mine vehicle team oil consumption and emission coordination control method, comprising:

[0006] Obtain the data of the i-th vehicle in the mine vehicle team and the environmental data;

[0007] Establish a mine vehicle optimization control model, the establishment process of the mine vehicle optimization control model includes taking the vehicle speed as the state variable, combining the i-th vehicle data and the environmental data to construct the longitudinal dynamics system equation of the i-th vehicle; Establish an instantaneous fuel consumption model and a nitrogen oxide emission model.

[0008] Further, the data of the i-th vehicle in the mine vehicle team includes vehicle mass, wind area, inter-vehicle distance between the i-th vehicle and the (i+1)-th vehicle; The environmental data includes air density, air resistance coefficient, rolling resistance coefficient and road slope.

[0009] Further, the longitudinal dynamics equation of the i-th vehicle is that the instantaneous speed of the vehicle is equal to the acceleration provided by the diesel engine of the vehicle minus the deceleration caused by the wind resistance, minus the deceleration caused by the rolling resistance, and minus the deceleration caused by the slope resistance.

[0010] Further, the deceleration caused by the wind resistance is the product of the air density, the air resistance coefficient, and the windward area, divided by twice the mass of the vehicle, and the obtained value is multiplied by the square of the speed of the vehicle.

[0011] The intelligent connected mine vehicle fleet fuel consumption and emission coordination control method further comprises obtaining a road speed limit and a safety distance, inputting i-th vehicle data and environment data of a controlled vehicle in a mine vehicle fleet into a mine vehicle optimization control model by using the road speed limit and the safety distance, establishing a vehicle maximum speed limit expression, introducing a task demand coefficient, and obtaining a reference speed expression within an allowed range of the i-th vehicle.

[0012] Further, a speed tracking optimization problem is established according to the reference speed of the i-th vehicle, with fuel consumption and nitrogen oxide emission as optimization objectives, a mathematical expression of the optimization control problem of the mine vehicle fleet is obtained in combination with the longitudinal dynamics equation of the i-th vehicle, and the optimized reference speed is used to control the driving speed of the vehicle of the mine vehicle fleet.

[0013] Further, the mathematical expression of the optimization control problem of the mine vehicle fleet is:

[0014]

[0015] s.t.

[0016] a i (t)∈[a i,min (t),a i,max (t)].

[0017] wherein: J i is a control objective function, t0 is an initial time, N p is a prediction time domain, v i,ref (t) is a reference vehicle speed at time t, v i,ref (N p ) is a reference vehicle speed at time N p . ψ(v i (N p )-v i，ref (N p )) 2 is the tracking performance of the vehicle to the reference speed at the prediction terminal time of the control system; and V i =(v i (t)-v i，ref (t))2 is the speed tracking term in the control process, and the smaller this term is, the tighter the control system controls the vehicle to track the reference vehicle speed; ω f is the fuel consumption weight that needs to be adjusted, ω e is the emission weight that needs to be adjusted, ω a is the comfort weight, ω v is the speed tracking weight, and ψ is the terminal speed constraint weight in the prediction horizon, is an approximate description of the wind resistance deceleration, is the derivative of the vehicle speed v i (t), a i (t) is the acceleration provided by the diesel engine of the vehicle, a i，max (t) is the maximum braking acceleration, a i，min (t) is the maximum braking deceleration, F i (t) is the instantaneous fuel consumption, N i (t) is the nitrogen oxide emission, g is the gravitational acceleration, C r，i is the rolling resistance coefficient, θ is the road slope, and t is the time.

[0018] Further, the solving steps of the mathematical expression of the optimization control problem of the mine vehicle fleet include: constructing a Hamilton function by using an extremum principle, converting the original optimization problem into a first value problem of solving the Hamilton function covariant, solving the mathematical expression of the optimization control problem of the mine vehicle fleet, solving the mathematical expression of the optimization control problem of the mine vehicle fleet, obtaining an explicit expression of the optimal control input by using a recursive relationship and a bisection method, and applying the optimal control input to the vehicle system to control the i-th vehicle to track the reference speed.

[0019] In a second aspect, the present application provides a smart connected mine vehicle fleet fuel consumption and emission coordinated control system, comprising:

[0020] A data acquisition module configured to obtain i-th vehicle data and environment data of the mine vehicle fleet.

[0021] A mine vehicle optimization control model establishment module, and the establishment process of the mine vehicle optimization control model includes taking the vehicle speed as the state variable, combining the i-th vehicle data and the environment data to construct the longitudinal dynamics system equation of the i-th vehicle; and establishing an instantaneous fuel consumption model and a nitrogen oxide emission model.

[0022] A vehicle speed control module configured to input the i-th vehicle data and the environment data of the controlled vehicle in the mine vehicle fleet into the mine vehicle optimization control model to obtain the reference speed of the i-th vehicle.

[0023] A speed tracking optimization problem is established by taking fuel consumption and nitrogen oxide emission as optimization targets according to the reference speed of the i-th vehicle, and a mathematical expression of the optimization control problem of the mine vehicle fleet is obtained by combining the longitudinal dynamics system equation of the i-th vehicle.

[0024] Compared with the prior art, the present application has the following advantages:

[0025] 1. The intelligent networked mine vehicle fleet fuel consumption and emission coordination control method adopted by the present application comprises the following steps: obtaining i-th vehicle data and environment data in a mine vehicle fleet; establishing a mine vehicle optimization control model; obtaining a mathematical expression of the optimization control problem of the mine vehicle fleet based on the longitudinal dynamics system equation of the i-th vehicle, the instantaneous fuel consumption model and the nitrogen oxide emission model; inputting the i-th vehicle data and environment data of the controlled vehicle in the mine vehicle fleet into the mine vehicle optimization control model to obtain the reference speed of the i-th vehicle; and controlling the driving speed of the vehicle in the mine vehicle fleet according to the reference speed of the i-th vehicle. The control system proposed in the present application optimizes the fuel consumption and NOx emission of the vehicle fleet during the running of the vehicle fleet, can control the vehicle running under the condition that the acceleration changes little, reduces the increase of fuel consumption and NOx emission caused by rapid acceleration, and solves the problem of how to coordinate and optimize the fuel consumption and nitrogen oxide and reduce the energy loss of the mine vehicle during long-distance transportation.

[0026] 2. The establishment process of the mine vehicle optimization control model adopted in the present application comprises the following steps: taking the vehicle speed as a state variable, combining the i-th vehicle data and environment data to construct the longitudinal dynamics system equation of the i-th vehicle, and controlling the steady operation of the vehicle speed when the speed of the leading vehicle changes and the controlled vehicle is disturbed. The control system is directed to the intelligent networked mine vehicle, coordinates and controls the fuel consumption and emission of the mine vehicle during the formation of the mine vehicle fleet, and the simulation verifies that the method can achieve the expected optimization effect on fuel consumption and emission.

[0027] The advantages of the additional aspects of the present application will be partially given in the following description, partially become obvious from the following description, or be learned by the practice of the present application.

[0028] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are used for detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0029] The drawings accompanying the specification of the present application serve to provide a further understanding of the present application, and the schematic embodiments of the present application and the description thereof serve to explain the present application, and do not constitute an improper limitation on the present application.

[0030] Figure 1 Block diagram of the mine vehicle platoon control system of the present application;

[0031] Figure 2 Graph of the wind resistance deceleration fitting model of the present application;

[0032] Figure 3 Schematic diagram of the explicit solution iteration of the present application;

[0033] Figure 4 Graph of the lead vehicle speed and road slope of the present application;

[0034] Figure 5 Comparison graph of the 2nd, 3rd, 4th and 5th vehicles subjected to external disturbance of the present application;

[0035] Figure 6 Comparison graph of the in-platoon vehicle running trajectory of the present application;

[0036] Figure 7 Comparison graph of the in-platoon vehicle speed of the present application;

[0037] Figure 8 Comparison graph of the in-platoon vehicle acceleration of the present application;

[0038] Figure 9 Statistical analysis graph of the in-platoon vehicle total fuel consumption and NOx of the present application;

[0039] Figure 10 Flow chart of the intelligent networked mine vehicle platoon fuel consumption and emission coordination control method of the present application. DETAILED DESCRIPTION

[0040] The present application will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed description is illustrative only and is intended to provide further description of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application pertains.

[0042] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0043] Example 1

[0044] As Figure 1As shown, the embodiment provides a smart connected mine vehicle fleet fuel consumption and emission coordination control method, comprising:

[0045] Obtain the i-th vehicle data and environment data in the mine vehicle fleet;

[0046] Establish a mine vehicle optimization control model, select vehicle speed as a state variable, use Newton's second law to establish the longitudinal dynamics system equation of the i-th vehicle, and obtain the expression of the wind resistance deceleration;

[0047] Reference speed acquisition tracking subsystem, acquire the speed information of the front vehicle, obtain the safety speed constraint combined with its own state, use the maximum speed limit of road regulations and the maximum speed limit of safety distance, establish the expression of the maximum speed limit of the vehicle, and obtain the maximum deceleration of the i-th vehicle when braking, introduce the task demand coefficient, and obtain the reference speed within the allowed speed range.

[0048] Reference speed optimization tracking subsystem, establish a channel from the vehicle state to the fuel consumption and NO x emission of the power system, establish a model of the instantaneous fuel consumption of the vehicle, according to the working mechanism of the diesel engine, the temperature and the residual oxygen are related to the kinetic energy of the vehicle, and establish a NO x emission model

[0049] Include fuel consumption (7) and nitrogen oxide emission (8) in the optimization objective, and establish the mathematical expression form of the optimization control problem of the mine vehicle fleet.

[0050] Use the extremum principle to construct the Hamilton function, convert the original optimization problem into a first value problem of solving the Hamilton function covariant, solve the mathematical expression form of the optimization control problem of the mine vehicle fleet, use the recursive relationship and the bisection method to obtain the explicit expression of the optimal control input, and apply the optimal control input to the vehicle system to control the i-th vehicle to track the reference speed.

[0051] The embodiment adopts a hierarchical framework. First, according to the constraints imposed by the front vehicle and the traffic flow, the reference speed of the controlled vehicle in the mine vehicle fleet is obtained. Then, the speed tracking control of the vehicle is carried out. When planning the speed of the leading vehicle, it is not necessary to consider the safety constraints brought by the following vehicles, so the leading vehicle only needs to set the speed within the road speed limit range according to the time requirement of the transportation task and the task demand. Since the speed of the leading vehicle is easy to obtain, it will not be discussed further. In this embodiment, how to obtain the reference speed of each independent vehicle in the queue after obtaining the speed of the leading vehicle through intelligent connection will be studied.

[0052] Specifically, the optimization control model of the mine truck is established, including: controlling N mine trucks in a truck fleet, selecting vehicle speed as a state variable, using Newton's second law to establish the longitudinal dynamics system equation of the i-th vehicle, the instantaneous speed of the vehicle is equal to the acceleration (a i (t)) provided by the diesel engine of the vehicle minus the deceleration (a a，i (t)) caused by wind resistance, minus the deceleration (a r，i (t)) caused by rolling resistance, minus the deceleration (a g，i (t)) caused by slope resistance, t is time, which can be omitted in subsequent expressions.

[0053]

[0054] wherein is the derivative of the vehicle speed v i (t), that is, the acceleration embodied by the vehicle, the deceleration caused by wind resistance is related to the mass of the mine truck and the windward area, and the specific form is:

[0055] wherein p is the air density, C d，i (t) is the air resistance coefficient, A f，i is the windward area, and M i is the mass of the vehicle; the deceleration caused by rolling resistance is related to the road slope and the rolling resistance coefficient of the tire, and the specific form is:

[0056] a r，i (t) = gC r，i cos(0), wherein g is the acceleration of gravity, C r，i is the rolling resistance coefficient, and 0 is the road slope; the deceleration caused by slope resistance has the specific form a g，i (t) = g sin(0).

[0057] When the vehicles form a stable queue, the influence of the disturbance change of the relative distance of the vehicles on the wind resistance can be ignored. In order to reduce the complexity of solving the controller to facilitate obtaining a global optimal solution, an approximate description of the wind resistance deceleration is given as follows:

[0058]

[0059] wherein l1 = 0.002261, l2 = -1.446, and l3 = 0.05999 are fitting coefficients, and d i，i+1 (t) is the distance between the i-th vehicle and the (i+1)-th vehicle, Figure 2 shows the effect of the approximate expression of the wind resistance deceleration. It can be seen that the determination coefficient R 2It is close to 1, which proves that the accuracy of the approximate expression of windage deceleration is sufficient for the optimization of vehicles in the mining fleet.

[0060] Referring to the vehicle speed acquisition subsystem, to ensure the safe operation of the convoy, speed limits and intra-convoy distance constraints must be considered. Since the method proposed in this embodiment can optimize the instantaneous behavior of vehicles, the safe distance constraint can be equivalent to the safe speed constraint, thus providing a real-time, variable vehicle safe speed constraint. Through intelligent network information, the vehicle can obtain the speed information of the vehicle ahead and, based on its own state, obtain the safe speed constraint. The maximum vehicle speed limit is as follows:

[0061] v i，max (t) = min{v i，max，1 (t), v i，max，2 (t), v i，max，3 (t)}, (3)

[0062] where v i，max，1 (t) is the maximum speed limit stipulated by road regulations, v i，max，2 (t) is the maximum speed limit required to maintain a safe distance from the vehicle ahead, without considering the effect of vehicle distance on fuel consumption and emissions, and is in the following form:

[0063]

[0064] where s i+1 (t) is the displacement of the (i+1)th vehicle, s i (t) is the displacement of the i-th vehicle, τ is the sampling time, T react，i is the emergency response time of the vehicle; v i，max，3 (t) is the maximum safe speed limit of the vehicle during emergency braking, in the form of:

[0065]

[0066] where a b，max，i is the maximum deceleration of the i-th vehicle when braking, a b，max，i+1 is the maximum deceleration of the (i+1)th vehicle when braking, which can be obtained through intelligent networking. Define c(t) = s i+1 (t)-s i (t-1)-v i (t-1)τMinimum vehicle speed constraint v i，min Can be set according to road regulations.

[0067] In order to optimize the problem solving and hardware application, this patent simplifies the processing of state constraints and introduces the task requirement coefficient Within the allowable speed range, the reference speed is obtained by using this coefficient as follows:

[0068]

[0069] When the vehicle reference speed approaches v i，max , corresponding to faster driving, at this time The value of tends to 1; when the vehicle reference speed tends to v i，min For a gentler ride, The value tends to 0.

[0070] With reference to the vehicle speed optimization tracking subsystem, in order to add the characteristics of the mining vehicle's underlying diesel engine system to the vehicle level optimization, a system from vehicle status to power system fuel consumption and NO x Emissions are expressed through a channel. According to thermal and mechanical principles, the energy generated by fuel combustion in the engine drives the vehicle through the transmission system. Therefore, the vehicle's kinetic energy is directly proportional to the engine's fuel consumption. Since the vehicle's kinetic energy in the time domain is its driving force, the vehicle's instantaneous fuel consumption can be modeled as follows:

[0071]

[0072] Where p1 is the regression parameter. The vehicle speed and corresponding fuel consumption data collected by the simulation model can be used to obtain the specific values ​​shown in Table 1 through data regression.

[0073] As long as the engine is running, NOx emissions are generated, especially in high-temperature, oxygen-rich environments, where production accelerates. Based on the operating mechanism of diesel engines, temperature and residual oxygen are related to the vehicle's kinetic energy. Therefore, the following NOx emission model is established:

[0074] N i (t) = q 0，0 +q 1，0 a i (t)+q 0，1 v i (i)+q 2，0 a i (t) 2 +q 0，2 v i (t) 2 (8)

[0075] where q 0，0 ,q 0，1 ,q 1，0 ,q 2，0 and q 0，2 is a regression parameter. Based on the speed and corresponding NOx emission data collected by the simulation model, the specific values ​​shown in Table 1 can be obtained through data regression.

[0076] Table 1 Fuel consumption and NOx model parameters

[0077] Table 1 Fuel consumption and NOx model parameters

[0078]

[0079]

[0080] In order to achieve the fuel consumption and NO x Comprehensive optimization of emissions, including fuel consumption (7) and NO x Emissions(8):

[0081]

[0082] where N p For the prediction time domain, J i is the control objective function, t0 is the initial time, N p is the prediction time domain, v i,ref (t) is the reference vehicle speed at time t, v i,ref (N p ) is N p Reference vehicle speed at the moment. ψ(v i (N p )-v i，ref (N P )) 2 is the vehicle's tracking performance of the reference vehicle's speed when the control system predicts the terminal time;

[0083] V i =(v i (t)-v i，ref (t)) 2 is the speed tracking term during the control process. The smaller this term is, the closer the control system controls the vehicle to track the reference speed. f is the fuel consumption weight that needs to be adjusted, ω e is the emission weight that needs to be adjusted, ω a is the comfort weight, ω v is the speed tracking weight, ψ is the terminal speed constraint weight in the prediction time domain, and adjustment of these parameters can be used to optimize different performances.

[0084] Since the vehicle has a performance range, the vehicle's driving force has a maximum driving force limit (F t，max (t)) and the maximum braking limit (F b，max (t)), so the acceleration that the vehicle system can provide itself has a certain range.

[0085] According to Newton's second law, the maximum acceleration that the vehicle can provide is a i，max (t) = Ft，max (t) / M i , the maximum braking deceleration is a i，min (t) = F b，min (t) / M i .

[0086] Therefore, the mathematical expression of the optimization control problem of the mine truck team is:

[0087]

[0088] In order to improve the application speed in the actual vehicle system and facilitate practical application, the above mine truck optimization problem (10) can be solved by using the extremum principle combined with the bisection method. The following will introduce how to solve it specifically.

[0089] Solving the control system, using the extremum principle to construct the Hamilton function, converting the original optimization problem into solving the initial value problem of the Hamilton function covariant, and the Hamilton function corresponding to the optimization problem (10) is as follows:

[0090]

[0091] Where λ i (t) is the covariant variable to be solved. According to the maximum value principle, the necessary condition for obtaining the optimal H(v i (t), a i (t)) is as follows:

[0092]

[0093] The corresponding stopping condition is:

[0094] λ i (t+N p ) = 2ψ(v i (t+N p )-v i,ref (t+N p )) (13)

[0095] The Hamilton function is reconstructed as a quadratic function of the control input as follows:

[0096] H(v i (t), a i (t)) = h1(t)a i (t) 2 +h2(t)a i (t)+h3(t) (14)

[0097] Where:

[0098]

[0099] Thus, the explicit expression of the optimal control input is

[0100]

[0101] According to the equations (1), (2), (12), (14), (15) and (16), the recursive relationship between the initial value λ i (t0) and the terminal value λ i (N p ) can be obtained, and the termination condition (13) can be satisfied. It should be noted that t is omitted in λ i (N p ) because the time t is taken as the reference to N p , so the reference is omitted to simplify the expression. The iterative relationship between the vehicle state v i (t) and λ i (t) is shown in Fig. 3. For the same vehicle in the prediction time domain, at time t, λ i (t) and v i (t) are used to obtain and at time t, λ i (t), v i (t) and are used to obtain λ i (t+1) and v i (t+1), and so on.

[0102]

[0103]

[0104] The original optimal control problem (10) is converted into a new simplified control problem: finding a proper initial value of the covariant λ i (t0), proceeding along the equations (1), (2), (12), (14), (15) and (16), and making the terminal λ i (N p ) satisfy the termination condition (13), then the form of the new optimization problem is:

[0105]

[0106] wherein, represents the real number set, and J λ (λ i (t0)) is the control objective function related to λ i (t0). The above optimization problem (17) can be solved by using the most widely used dichotomy method.

[0107] Simulation verification of the optimized control system for mining vehicles. In order to verify the effectiveness of the proposed control system and reduce the testing cost and risk, simulation tests are required. The control system designed by this patent is compared with the widely used PID control method to verify the effects on fuel consumption and NO. x Emission benefits and potential costs. Assume that the speed information of the leader vehicle and the change of road slope are known. Figure 4 As shown in Figure 1, the main vehicle speeds include stable, accelerating, and decelerating, and the road slopes include flat, uphill, and downhill. When external information changes, this change verifies that the method has a certain anti-interference ability.

[0108] Furthermore, to verify whether the controller can ensure stable operation of the platoon when vehicles are subjected to different disturbances, different forms of disturbances were added to the vehicles. The specific forms of disturbances are shown in Figure 5. The second vehicle is subjected to a variable constant disturbance, the third vehicle is not disturbed, the fourth vehicle is subjected to a constant variable disturbance, and the fifth vehicle is subjected to a random disturbance.

[0109] from Figure 6 It can be seen that both the proposed control system and the PID method can maintain stable queue operation. However, the proposed control system is slower than the PID method in forming a fixed queue because the optimization of fuel consumption and NO x Emissions will reduce acceleration changes, which can be seen in subsequent analysis. The stable operation of the queue is to analyze fuel consumption and NO x The main basis of emissions.

[0110] from Figure 7 It can be seen from the speed curve in that as the speed of the leader vehicle changes, the control system can control the steady-state operation of the vehicle speed when the speed of the leader vehicle changes and the controlled vehicle itself is disturbed.

[0111] From the vehicle acceleration curve Figure 8 It can be seen that compared with the PID method, when the speed of the leading vehicle changes (around 10 seconds and 40 seconds), the control system can control the vehicle operation with a smaller acceleration change, which can reduce the fuel consumption and NO caused by rapid acceleration. x Increase in emissions.

[0112] Figure 9 The instantaneous fuel consumption and NO of the 2nd to 5th vehicles x The emission is analyzed, where the horizontal axis number 1 is the control system proposed in this patent, and the horizontal axis number 2 is the commonly used PID control system. Figure 9 Fuel consumption and NO x It can be seen from the total emission that this control system can make mining vehicles with low fuel consumption and NO xThe cost is that the speed of the vehicle platoon is adjusted to a steady form slower than the PID method (as shown in Figure 6 , Figure 7 This is acceptable for mining vehicles that are biased towards transporting weight rather than speed.

[0113] Table 2 Total fuel consumption statistics of vehicles in the platoon

[0114]

[0115] Table 3 Total NOx statistics of vehicles in the platoon

[0116]

[0117] The embodiment is directed to a smart mining vehicle platoon control system for mining vehicles. The control system is oriented towards intelligent networked mining vehicles, and coordinates control of fuel consumption and emissions when the mining vehicles are platooning. The method is verified by simulation to achieve the expected optimization effect on fuel consumption and emissions.

[0118] Embodiment Two:

[0119] The embodiment provides a smart networked mining vehicle platoon fuel consumption and emission coordination control system, comprising:

[0120] The data acquisition module is configured to obtain the i-th vehicle data and environmental data in the mining vehicle platoon;

[0121] The mining vehicle optimization control model establishment module includes taking the vehicle speed as the state variable, combining the i-th vehicle data and environmental data to construct the longitudinal dynamics system equation of the i-th vehicle; and establishing an instantaneous fuel consumption model and a nitrogen oxide emission model.

[0122] The vehicle speed control module is configured to input the i-th vehicle data and environmental data of the controlled vehicle in the mining vehicle platoon into the mining vehicle optimization control model to obtain the reference speed of the i-th vehicle.

[0123] According to the reference speed of the i-th vehicle, a speed tracking optimization problem is established with fuel consumption and nitrogen oxide emissions as optimization objectives, a mathematical expression of the optimization control problem of the mining vehicle platoon is obtained in combination with the longitudinal dynamics system equation of the i-th vehicle, the optimized reference speed is solved, and the optimized reference speed is used to control the driving speed of the vehicle in the mining vehicle platoon.

[0124] The data acquisition module, the mining vehicle optimization control model establishment module, and the vehicle speed control module in the embodiment are configured with specific content corresponding to the technical content described in Embodiment One.

[0125] The above describes the specific embodiments of the present application in conjunction with the drawings, but is not a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications or variations can be made on the basis of the technical solutions of the present application without creative labor, and still fall within the scope of protection of the present application.

Claims

1. A smart connected mine vehicle fleet fuel consumption and emission coordination control method, characterized in that, The method comprises the following steps: obtaining the data of the i-th vehicle in the mine truck fleet and environmental data; establishing a mine truck optimization control model, the establishment process of the mine truck optimization control model comprising taking the vehicle speed as a state variable, combining the data of the i-th vehicle and the environmental data to construct a longitudinal dynamics system equation of the i-th vehicle; establishing a transient fuel consumption model and a nitrogen oxide emission model; inputting the data of the i-th vehicle in the mine truck fleet and the environmental data of the controlled vehicle into the mine truck optimization control model to obtain the reference speed of the i-th vehicle; establishing a speed tracking optimization problem with fuel consumption and nitrogen oxide emission as the optimization objectives according to the reference speed of the i-th vehicle, combining the longitudinal dynamics system equation of the i-th vehicle to obtain a mathematical expression of the optimization control problem of the mine truck fleet, solving the mathematical expression to obtain an optimized reference speed, and controlling the driving speed of the vehicle in the mine truck fleet by using the optimized reference speed; the mathematical expression of the optimization control problem of the mine truck fleet is as follows: in: To control the objective function, is the initial moment, For the prediction time domain, is the reference speed at time t, for Reference vehicle speed at the moment; is the vehicle's tracking performance of the reference vehicle's speed when the control system predicts the terminal time; It is the speed tracking item in the control process; is the fuel consumption weight that needs to be adjusted, is the emission weight that needs to be adjusted, is the comfort weight, is the velocity tracking weight, is the terminal velocity constraint weight in the prediction time domain, is an approximate description of windage deceleration, Vehicle speed The derivative of The acceleration provided by the vehicle's diesel engine, is the maximum braking acceleration, is the maximum braking deceleration, is the instantaneous fuel consumption, P1 is the regression parameter, M i (t) is the vehicle mass, is the nitrogen oxide emission, q 0,0 ,q 0,1 ,q 1,0 ,q 2,0 and q 0,2 is the regression parameter, is the acceleration due to gravity, is the rolling resistance coefficient, is the road slope, and t is the time. 2.The intelligent network connected mine truck fleet fuel consumption and emission coordination control method of claim 1, wherein, The intelligent networked mine truck fleet fuel consumption and emission coordination control method further comprises obtaining a road speed limit and a safety distance, establishing a vehicle maximum speed limit expression by using the road speed limit and the safety distance, introducing a task demand coefficient, and obtaining a reference speed expression within the allowable range of the i-th vehicle. 3.The intelligent network connected mine truck fleet fuel consumption and emission coordination control method of claim 1, wherein, The data of the i-th vehicle in the mine truck fleet comprises the vehicle mass, the windward area, and the inter-vehicle distance between the i-th vehicle and the (i+1)-th vehicle; and the environmental data comprises the air density, the air resistance coefficient, the rolling resistance coefficient, and the road slope. 4.The intelligent network connected mine truck fleet fuel consumption and emission coordination control method of claim 1, wherein, The longitudinal dynamics system equation of the i-th vehicle is that the instantaneous speed of the vehicle is equal to the acceleration provided by the diesel engine of the vehicle minus the deceleration caused by the wind resistance, minus the deceleration caused by the rolling resistance, and minus the deceleration caused by the slope resistance. 5.The intelligent network connected mine truck fleet fuel consumption and emission coordination control method of claim 4, wherein, The deceleration caused by the wind resistance is the product of the air density, the air resistance coefficient, and the windward area, divided by twice the vehicle mass, multiplied by the square of the vehicle speed.

6. The intelligent network connected mine truck fleet fuel consumption and emission coordinated control method of claim 1, wherein, The solving steps of the mathematical expression of the optimization control problem of the mine truck fleet comprise: constructing a Hamilton function by using the extremum principle to convert the original optimization problem into a first value problem of solving the Hamilton function covariant, solving the mathematical expression of the optimization control problem of the mine truck fleet, solving the explicit expression of the optimal control input by using the recursive relationship and the bisection method, and controlling the i-th vehicle to track the reference speed by using the optimal control input on the vehicle system.

7. An intelligent networked mine vehicle fleet fuel consumption and emission coordination control system, characterized in that, The method comprises the following steps: a data acquisition module configured to obtain the data of the i-th vehicle in the mine truck fleet and environmental data; a mine truck optimization control model establishment module, the establishment process of the mine truck optimization control model comprising taking the vehicle speed as a state variable, combining the data of the i-th vehicle and the environmental data to construct a longitudinal dynamics system equation of the i-th vehicle; establishing a transient fuel consumption model and a nitrogen oxide emission model; a vehicle driving speed control module configured to input the data of the i-th vehicle in the mine truck fleet and the environmental data of the controlled vehicle into the mine truck optimization control model to obtain the reference speed of the i-th vehicle; According to the reference speed of the i-th vehicle, a speed tracking optimization problem is established with fuel consumption and nitrogen oxide emission as optimization objectives, a mathematical expression of the optimization control problem of the mine vehicle fleet is obtained in combination with a longitudinal dynamics system equation of the i-th vehicle, an optimized reference speed is solved, and the driving speed of the mine vehicle fleet is controlled by using the optimized reference speed; The mathematical expression form of the optimization control problem of the mine vehicle fleet is: in: To control the objective function, is the initial moment, For the prediction time domain, is the reference speed at time t, for Reference vehicle speed at the moment; is the vehicle's tracking performance of the reference vehicle's speed when the control system predicts the terminal time; It is the speed tracking item in the control process; is the fuel consumption weight that needs to be adjusted, is the emission weight that needs to be adjusted, is the comfort weight, is the velocity tracking weight, is the terminal velocity constraint weight in the prediction time domain, is an approximate description of windage deceleration, Vehicle speed The derivative of The acceleration provided by the vehicle's diesel engine, is the maximum braking acceleration, is the maximum braking deceleration, is the instantaneous fuel consumption, P1 is the regression parameter, M i (t) is the vehicle mass, is the nitrogen oxide emission, q 0,0 ,q 0,1 ,q 1,0 ,q 2,0 and q 0,2 is the regression parameter, is the acceleration due to gravity, is the rolling resistance coefficient, is the road slope, and t is the time.

Images