Layered intelligent network connection heavy vehicle auxiliary fuel saving method and system

Abstract

The invention discloses a layered intelligent network connection heavy vehicle auxiliary fuel saving method and system. The method comprises the steps that traffic environment information and vehicle parameter information are obtained in real time through the V2X technology; establishing an optimization problem model, constructing a layered decoupling framework, and decomposing the established optimization problem model to obtain two optimization sub-problems; solving an optimal vehicle speed spectrum and a driving force / braking force spectrum of the heavy vehicle by utilizing a dynamic programming algorithm; and a dynamic programming algorithm is used for optimizing a heavy vehicle gear shifting strategy, and vehicle auxiliary fuel saving is completed. The method can effectively solve the problems that an existing related algorithm is low in solving efficiency and large in calculation load, the algorithm operation time is greatly saved while the oil saving effect is guaranteed, and the method has positive significance on oil saving technology research of heavy vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of economical driving of intelligent networked vehicles, and in particular to a layered intelligent networked heavy-duty vehicle auxiliary fuel saving method and system. Background Art

[0002] As one of the main options for road freight transportation, heavy trucks play an important role in the road traffic system. However, the high fuel consumption and carbon dioxide emissions of heavy vehicles have also attracted widespread attention. Data shows that fuel costs account for the highest proportion of heavy vehicles in their entire life cycle, reaching 35%. How to effectively reduce fuel consumption has become a problem that needs to be solved urgently.

[0003] At present, the research on vehicle auxiliary fuel saving devices mainly focuses on the power system itself, lacking comprehensive consideration of road traffic environment factors. However, with the increasing development of vehicle networking technology, vehicles can obtain local traffic environment through advanced communication technology. Summary of the invention

[0004] The purpose of the present invention is to provide a hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving method and system, and to construct a hierarchical decoupling framework that considers vehicle speed and gear position; the upper layer optimizes the driving speed of heavy-duty vehicles with the goal of minimizing driving force; the lower layer optimizes the gear position of heavy-duty vehicles with the goal of optimizing energy consumption, and combines the optimization results of the upper layer to tap the fuel saving potential of heavy-duty vehicles and improve fuel economy.

[0005] Technical solution of the present invention: The present invention provides a hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving method, comprising the following steps:

[0006] S1. Obtain traffic environment information and vehicle parameter information.

[0007] S2. Combine the road speed limit, the vehicle's allowed gears and dynamic constraints to establish an optimization problem model in the spatial domain.

[0008] S3. Construct a hierarchical decoupling framework to decompose the optimization problem model established in step S2 to obtain two optimization sub-problems.

[0009] S4. Considering the kinematic characteristics of the vehicle and taking the minimum driving force as the goal, the dynamic programming algorithm is used to solve the optimal (i.e. final) speed spectrum v of the heavy vehicle. opt and driving force / braking force spectrum

[0010] S5. Combining the optimal vehicle speed spectrum and driving force / braking force spectrum of step S4, with the goal of minimizing fuel consumption, a dynamic programming algorithm is used to optimize the heavy vehicle shifting strategy to achieve vehicle auxiliary fuel saving.

[0011] Furthermore, in step S1, the traffic environment information includes the slope of the road ahead and the road speed limit, and the vehicle parameter information includes body parameters and engine parameters; the slope of the road ahead is obtained using the on-board GPS, the road speed limit is uploaded to the on-board terminal using V2I technology, and the vehicle speed is directly transmitted to the on-board terminal using the CAN bus.

[0012] Furthermore, in step S2, the optimization problem model includes the following contents:

[0013] With the goal of minimizing fuel consumption during the entire driving process, and combined with the road speed limit, the vehicle's allowed gear position and dynamic constraints, an optimization problem model is established in the spatial domain. The specific expression is:

[0014]

[0015] The following conditions must be met:

[0016]

[0017] γ min ≤γ≤γ max

[0018] v min ≤v≤v max

[0019]

[0020] in, represents fuel consumption rate, γ represents gear position, v represents vehicle speed, S represents total mileage, represents the fuel consumption rate when the mileage is s, v(s) represents the vehicle speed when the mileage is s, m represents the vehicle mass, and F w () represents the vehicle driving / braking force, J() represents the total fuel consumption of the whole journey, ρ a represents the air density, A f represents the frontal area of ​​the vehicle, C D represents the air damping coefficient, g represents the acceleration of gravity, f represents the rolling resistance coefficient, θ(s) represents the road slope when the mileage is s, Indicates the maximum fuel consumption rate, n e represents the engine speed, γ min Indicates the minimum gear value allowed, γ max Indicates the maximum gear position allowed, v min Indicates the minimum allowed vehicle speed, v max represents the maximum allowed speed, T represents the total driving time, represents the expected average vehicle speed, T max Indicates the maximum allowed travel time.

[0021] Furthermore, in step S3, the hierarchical decoupling framework decomposes the optimization problem model established in step S2 into optimization sub-problems that are logically related to each other. The first optimization sub-problem is to minimize the mechanical kinetic energy required for the entire driving process according to the constraints and obtain the optimal vehicle speed curve; the second optimization sub-problem is to obtain the shifting strategy that minimizes fuel consumption according to the optimal vehicle speed curve. The hierarchical decoupling framework can greatly improve the computational efficiency and meet the real-time requirements while ensuring the quality of solving the optimization problem.

[0022] Furthermore, in step S4, solving the optimal vehicle speed spectrum and driving force / braking force spectrum of the heavy vehicle includes the following contents:

[0023] The particle model is used to describe the vehicle longitudinal dynamics model. The specific expression is:

[0024]

[0025] Where x1(k+1) represents the vehicle state at the k+1th step in the first optimization subproblem; x1(k) represents the vehicle state at the kth step in the first optimization subproblem, x1(k)=v 2 (k), v 2 (k) represents the square of the vehicle speed at the kth step of iteration; Δs represents the fixed discrete interval of the total mileage; F w (k) represents the vehicle driving / braking force at the k-th step of iteration; θ(k) represents the road slope at the k-th step of iteration.

[0026] The first optimization subproblem is as follows:

[0027]

[0028] The constraints for the first optimization subproblem are:

[0029]

[0030] v(k)∈[v min ,v max ]

[0031] u1(k)∈[F b (k),F d (k)]

[0032] Where J1 represents the objective function considering the vehicle kinetic energy and the time required for driving, N represents the total number of iterations, L1[x1(k),u1(k),Δs] represents the cost function in the first optimization subproblem, u1(k) represents the control amount when iterating to the kth step in the first optimization subproblem, β represents the penalty coefficient, and v(k) represents the vehicle speed when iterating to the kth step. represents the square of the initial vehicle speed, represents the square of terminal speed, F b (k) represents the maximum braking force at the kth step of iteration, F d (k) represents the maximum driving force when iterating to the kth step, and x1(N) represents the vehicle state when iterating to the Nth step, that is, the terminal state.

[0033] The cost function of the terminal of the dynamic programming algorithm is defined as:

[0034]

[0035] Where u1(N) represents the control amount when iterating to the Nth step in the first optimization subproblem, that is, the terminal control amount; F w (N) represents the vehicle driving / braking force when iterating to the Nth step; argmin represents the control quantity and state quantity function when taking the minimum value.

[0036] The dynamic programming reverse iteration cost function is defined as:

[0037]

[0038] Among them, u1(k+1) represents the control amount when iterating to the k+1th step in the first optimization subproblem.

[0039] By solving the above equation, the optimal speed spectrum v of heavy vehicles that meets the objective function requirements can be obtained. opt and optimal driving / braking force spectrum And lay the foundation for solving the second sub-problem.

[0040] Furthermore, the optimization of the heavy vehicle shifting strategy in step S5 includes the following contents:

[0041] The second optimization sub-problem is as follows:

[0042]

[0043] The corresponding state transfer equation is:

[0044] x2(k+1)=x2(k)+u2(k).

[0045] The constraints of the second optimization subproblem are:

[0046]

[0047] x2(0)=τ0

[0048] x2(N)=τ p

[0049] x2(k)∈[τ min ,τmax ]

[0050] u2(k)∈[-2;-1;0;+1;+2]

[0051] Wherein, J2 represents the objective function that comprehensively considers driving fuel consumption and shifting strategy; u2(k) is the control quantity when iterating to the kth step in the second optimization subproblem, and [±1; ±2; 0] is used to represent the upshift, downshift and maintaining the current gear action; x2(k) represents the vehicle state quantity when iterating to the kth step in the second optimization subproblem, x2(k) = τ(k), τ(k) represents the vehicle gear value when iterating to the kth step; Indicates that the engine is at speed n e and torque M e The fuel consumption rate under the working condition; β1, β2 and β3 are penalty coefficients; L2[x2(k),u2(k),Δs] represents the cost function in the second optimization subproblem; x2(k+1) represents the vehicle state at the k+1th step; v opt (k) represents the optimal vehicle speed value when iterating to the kth step; τ0 represents the initial gear value; v p Indicates the terminal gear value; τ min Indicates the lowest gear of the engine; τ max Indicates the highest engine gear.

[0052] It should be noted that the engine operating point, that is, the speed n e With torque M e , are the optimal vehicle speed spectrum v obtained by solving the first optimization subproblem opt and optimal driving / braking force spectrum Provide, specifically:

[0053]

[0054] Among them, i(τ(k)) represents the transmission ratio value corresponding to the gear position of the heavy vehicle when the dynamic programming algorithm iterates to the kth step, r w Indicates the wheel radius, n e (k) represents the speed at the kth step of iteration, M e (k) represents the torque at the kth step of iteration.

[0055] The cost function of the terminal of the dynamic programming algorithm is defined as:

[0056]

[0057] Among them, u2(N) represents the terminal control quantity in the second optimization subproblem, and argmin represents the control quantity and state quantity function when taking the minimum value.

[0058] The cost function of the reverse iteration of dynamic programming is defined as:

[0059]

[0060] Among them, u2(k+1) represents the control amount when iterating to the k+1th step in the second optimization subproblem.

[0061] By solving the above equation, the optimal shift strategy τ for heavy vehicles that meets the objective function requirements can be obtained: opt .

[0062] Therefore, by combining all the above characteristics, the optimal speed curve v that meets the fuel saving needs of heavy-duty vehicles in actual driving scenarios can be obtained. opt and the corresponding shift strategy τ opt .

[0063] Furthermore, the present invention also proposes a hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving system, comprising:

[0064] The information acquisition module is used to obtain traffic environment information and vehicle parameter information.

[0065] Model building module, used to build optimization problem models by combining road speed limits, vehicle gears, and dynamic constraints.

[0066] The problem optimization module is used to build a hierarchical decoupling framework to decompose the optimization problem model into two optimization sub-problems.

[0067] The vehicle fuel saving module is used to combine the vehicle kinematic characteristics and use the dynamic programming algorithm to solve the final vehicle speed spectrum and driving force / braking force spectrum of the heavy vehicle; combined with the final vehicle speed spectrum and driving force / braking force spectrum, with the goal of minimizing fuel consumption, the dynamic programming algorithm is used to optimize the heavy vehicle shifting strategy to complete vehicle auxiliary fuel saving.

[0068] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the aforementioned layered intelligent networked heavy-duty vehicle auxiliary fuel saving method when executing the computer program.

[0069] Furthermore, the present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is run by a processor, it executes the layered intelligent networked heavy-duty vehicle auxiliary fuel saving method described above.

[0070] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0071] 1. The present invention takes heavy vehicles as the research object, and can effectively reduce the fuel consumption of vehicles during the cargo transportation process while ensuring the timeliness of cargo transportation.

[0072] 2. The hierarchical decoupling framework constructed by the present invention can reduce the computational load, improve the solution efficiency, and increase the real-time performance of the algorithm without destroying the optimality of the results.

[0073] 3. The present invention adopts the most advanced vehicle networking technology (V2I, V2V), and has superior performance in information sharing and environmental perception. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is an overall implementation flow chart of the present invention.

[0075] Figure 2 Schematic diagram of economical driving of a heavy-duty vehicle in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0077] To achieve the above objectives, the present invention proposes a hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving method, which combines the traffic environment information of the cargo transportation route with the heavy-duty vehicle's own parameter information, such as Figure 1 , 2 As shown, the specific steps are as follows:

[0078] S1. Obtain traffic environment information and vehicle parameter information, the specific contents are as follows:

[0079] Traffic environment information includes the slope of the road ahead and the road speed limit, and vehicle parameter information includes body parameters and engine parameters. The slope of the road ahead is obtained using the on-board GPS, the road speed limit is uploaded to the on-board terminal using V2I technology, and the vehicle speed is directly transmitted to the on-board terminal using the CAN bus. The vehicle parameters (vehicle unloaded mass, wheel radius, front windward area, etc.) and engine parameters are known when the vehicle leaves the factory.

[0080] S2. Combined with the road speed limit, the vehicle's allowed gear position and dynamic constraints, an optimization problem model is established in the spatial domain. The specific contents are as follows:

[0081] With the goal of minimizing fuel consumption during the entire driving process, and combined with the road speed limit, the vehicle's allowed gear position and dynamic constraints, an optimization problem model is established in the spatial domain. The specific expression is:

[0082]

[0083] The following conditions must be met:

[0084]

[0085] γ min ≤γ≤γ max

[0086] v min ≤v≤v max

[0087]

[0088] in, represents fuel consumption rate, γ represents gear position, v represents vehicle speed, S represents total mileage, represents the fuel consumption rate when the mileage is s, v(s) represents the vehicle speed when the mileage is s, m represents the vehicle mass, and F w () represents the vehicle driving / braking force, J() represents the total fuel consumption of the whole journey, ρ a represents the air density, A f represents the frontal area of ​​the vehicle, C D represents the air damping coefficient, g represents the acceleration of gravity, f represents the rolling resistance coefficient, θ(s) represents the road slope when the mileage is s, Indicates the maximum fuel consumption rate, n e represents the engine speed, γ min Indicates the minimum gear value allowed, γ max Indicates the maximum gear position allowed, v min Indicates the minimum allowed vehicle speed, v max represents the maximum allowed speed, T represents the total driving time, represents the expected average vehicle speed, T max Indicates the maximum allowed travel time.

[0089] S3, build a hierarchical decoupling framework, decompose the optimization problem model established in step S2, and obtain two optimization sub-problems. The specific contents are:

[0090] The hierarchical decoupling framework decomposes the optimization problem model established in step S2 into optimization sub-problems that are logically related to each other. The first optimization sub-problem is to minimize the mechanical kinetic energy required for the entire driving process according to the constraints and obtain the optimal vehicle speed curve; the second optimization sub-problem is to obtain the shifting strategy that minimizes fuel consumption according to the optimal vehicle speed curve. The hierarchical decoupling framework can greatly improve the computational efficiency and meet the real-time requirements while ensuring the quality of solving the optimization problem.

[0091] S4. Considering the kinematic characteristics of the vehicle and taking the minimum driving force as the goal, the dynamic programming algorithm is used to solve the optimal speed spectrum v of the heavy vehicle optand driving force / braking force spectrum The specific contents are:

[0092] The particle model is used to describe the vehicle longitudinal dynamics model. The specific expression is:

[0093]

[0094] Where x1(k+1) represents the vehicle state at the k+1th step in the first optimization subproblem; x1(k) represents the vehicle state at the kth step in the first optimization subproblem, x1(k)=v 2 (k), v 2 (k) represents the square of the vehicle speed at the kth step of iteration; Δs represents the fixed discrete interval of the total mileage; F w (k) represents the vehicle driving / braking force at the k-th step of iteration; θ(k) represents the road slope at the k-th step of iteration.

[0095] The first optimization subproblem is as follows:

[0096]

[0097] The following conditions must be met:

[0098]

[0099] v(k)∈[v min ,v max ]

[0100] u1(k)∈[F b (k),F d (k)]

[0101] Where J1 represents the objective function considering the vehicle kinetic energy and the time required for driving, N represents the total number of iterations, L1[x1(k),u1(k),Δs] represents the cost function in the first optimization subproblem, u1(k) represents the control amount when iterating to the kth step in the first optimization subproblem, β represents the penalty coefficient, and v(k) represents the vehicle speed when iterating to the kth step. represents the square of the initial vehicle speed, represents the square of terminal speed, F b (k) represents the maximum braking force at the kth step of iteration, F d (k) represents the maximum driving force when iterating to the kth step, and x1(N) represents the vehicle state when iterating to the Nth step in the first optimization subproblem, that is, the terminal state.

[0102] It should be noted that the first square term F in the cost function L1[x1(k),u1(k),Δs] w (k)2 The first one is to eliminate the impact of the reduction of the cost function due to vehicle braking, and the second one is to ensure that while satisfying the maximum transportation time constraint, it avoids introducing additional time state quantities that will increase the program calculation time and memory usage.

[0103] The cost function of the terminal of the dynamic programming algorithm is defined as:

[0104]

[0105] Among them, u1(N) represents the terminal control quantity in the first optimization subproblem, F w (N) represents the vehicle driving / braking force at the Nth iteration, and argmin represents the control quantity and state quantity function when the minimum value is taken.

[0106] The cost function of the reverse iteration of dynamic programming is defined as:

[0107]

[0108] Among them, u1(k+1) represents the control amount when iterating to the k+1th step.

[0109] By solving the above equation, the optimal speed spectrum v of heavy vehicles that meets the objective function requirements can be obtained. opt and optimal driving / braking force spectrum And lay the foundation for solving the second sub-problem.

[0110] S5. Combining the optimal vehicle speed spectrum and driving force / braking force spectrum of step S4, with the goal of minimizing fuel consumption, the heavy vehicle shifting strategy is optimized using a dynamic programming algorithm to complete vehicle auxiliary fuel saving. The specific contents are as follows:

[0111] The second optimization sub-problem is as follows:

[0112]

[0113] The corresponding state transfer equation is:

[0114] x2(k+1)=x2(k)+u2(k).

[0115] satisfy:

[0116]

[0117] x2(0)=τ0

[0118] x2(N)=τ p

[0119] x2(k)∈[τ min ,τ max ]

[0120] u2(k)∈[-2;-1;0;+1;+2]

[0121] Wherein, J2 represents the objective function that comprehensively considers driving fuel consumption and shifting strategy; u2(k) is the control quantity when iterating to the kth step in the second optimization subproblem, and [±1; ±2; 0] is used to represent the upshift, downshift and maintaining the current gear action; x2(k) represents the vehicle state quantity when iterating to the kth step in the second optimization subproblem, x2(k) = τ(k), τ(k) represents the vehicle gear value when iterating to the kth step; Indicates that the engine is at speed n e and torque M e The fuel consumption rate under the working condition; β1, β2 and β3 are penalty coefficients; L2[x2(k),u2(k),Δs] represents the cost function in the second optimization subproblem; x2(k+1) represents the vehicle state at the k+1th step; v opt (k) represents the optimal vehicle speed value when iterating to the kth step; τ0 represents the initial gear value; τ p Indicates the terminal gear value; τ min Indicates the lowest gear of the engine; τ max Indicates the highest engine gear.

[0122] The first term in the cost function L2[x2(k),u2(k),Δs] represents the fuel consumption, the second and third terms are to punish frequent and unreasonable gear shifting operations, and the fourth term is to avoid the engine operating at high speed.

[0123] It should be noted that the engine operating point, that is, the speed n e With torque M e , are the optimal vehicle speed spectrum v obtained by solving the first optimization subproblem opt and optimal driving / braking force spectrum Provide, specifically:

[0124]

[0125] Among them, i(τ(k)) represents the transmission ratio value corresponding to the gear position of the heavy vehicle when the dynamic programming algorithm iterates to the kth step, r w Indicates the wheel radius, n e (k) represents the speed at the kth step of iteration, M e (k) represents the torque at the kth step of iteration.

[0126] The cost function of the terminal of the dynamic programming algorithm is defined as:

[0127]

[0128] Among them, u2(N) represents the terminal control quantity in the second optimization subproblem, and argmin represents the control quantity and state quantity function when taking the minimum value.

[0129] The cost function of the reverse iteration of dynamic programming is defined as:

[0130]

[0131] Among them, u2(k+1) represents the control amount when iterating to the k+1th step in the second optimization subproblem.

[0132] By solving the above equation, the optimal shift strategy τ for heavy vehicles that meets the objective function requirements can be obtained: opt .

[0133] Therefore, by combining all the above characteristics, the optimal speed curve v that meets the fuel saving needs of heavy-duty vehicles in actual driving scenarios can be obtained. opt and the corresponding shift strategy τ opt .

[0134] The embodiment of the present invention also proposes a hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving system, including an information acquisition module, a model building module, a problem optimization module, a vehicle fuel saving module and a computer program that can be run on a processor. It should be noted that each module in the above system corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0135] The embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, the specific steps of the method provided in the embodiment of the present invention are corresponding to the specific steps of the method provided in the embodiment of the present invention, and the processor has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0136] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. It should be noted that when the computer program is executed by the processor, it corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0137] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving method, characterized in that: include: S1. Obtaining traffic environment information and vehicle parameter information; S2. Establish an optimization problem model by combining road speed limit, vehicle gear and dynamic constraints; S3, construct a hierarchical decoupling framework, decompose the optimization problem model established in step S2, and obtain two optimization sub-problems; S4. Combined with the vehicle kinematic characteristics, based on the optimization sub-problem, the dynamic programming algorithm is used to solve the final speed spectrum v of the heavy vehicle opt Power spectrum S5. Combining the final vehicle speed spectrum and power spectrum of step S4, with the goal of minimizing fuel consumption, the heavy vehicle shifting strategy is optimized using a dynamic programming algorithm to achieve vehicle auxiliary fuel saving.

2. The layered intelligent networked heavy-duty vehicle auxiliary fuel saving method according to claim 1 is characterized in that: In step S1, the traffic environment information includes the slope of the road ahead and the road speed limit, and the vehicle parameter information includes body parameters and engine parameters; the slope of the road ahead is obtained using the vehicle-mounted GPS, and the road speed limit is uploaded to the vehicle-mounted terminal using V2I technology.

3. The hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving method according to claim 1, characterized in that: In step S2, establishing the optimization problem model includes the following contents: With the goal of minimizing fuel consumption during the entire driving process, and combined with the road speed limit, the vehicle's allowed gear position and dynamic constraints, an optimization problem model is established in the spatial domain. The specific expression is: The following conditions must be met: c min ≤γ≤γ max in min ≤v≤v max in, represents fuel consumption rate, γ represents gear position, v represents vehicle speed, S represents total mileage, represents the fuel consumption rate when the mileage is s, v(s) represents the vehicle speed when the mileage is s, m represents the vehicle mass, and F w () represents the vehicle driving / braking force, J() represents the total fuel consumption of the whole journey, ρ a represents the air density, A f represents the frontal area of ​​the vehicle, C D represents the air damping coefficient, g represents the acceleration of gravity, f represents the rolling resistance coefficient, θ(s) represents the road slope when the mileage is s, Indicates the maximum fuel consumption rate, n e represents the engine speed, γ min Indicates the minimum gear value allowed, γ max Indicates the maximum gear position allowed, v min Indicates the minimum allowed vehicle speed, v max represents the maximum allowed speed, T represents the total driving time, represents the expected average vehicle speed, T max Indicates the maximum allowed travel time.

4. The hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving method according to claim 1, characterized in that: In step S3, the hierarchical decoupling framework decomposes the optimization problem model established in step S2 into optimization sub-problems. The first optimization sub-problem is to reduce the mechanical kinetic energy required for the entire driving process according to the constraints and obtain the vehicle speed curve. The specific expression is: Where J1 represents the objective function of vehicle kinetic energy and driving time, N represents the total number of iterations, L1,x1(k),u1(k),Δs- represents the cost function in the first optimization subproblem, and u1(k) represents the control amount when iterating to the kth step in the first optimization subproblem; The second optimization sub-problem is to obtain the shift strategy of the final fuel consumption according to the vehicle speed curve. The specific expression is: Among them, J2 represents the objective function of driving fuel consumption and gear shifting strategy; u2(k) represents the control quantity when iterating to the k-th step in the second optimization subproblem; x2(k) represents the vehicle state quantity when iterating to the k-th step in the second optimization subproblem, x2(k) = τ(k), τ(k) represents the vehicle gear value when iterating to the k-th step; Δs represents the fixed discrete interval of total mileage.

5. The hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving method according to claim 4 is characterized in that: In step S4, solving the final speed spectrum and power spectrum of the heavy vehicle includes the following contents: The particle model is used to describe the vehicle longitudinal dynamics model. The specific expression is: Where x1(k+1) represents the vehicle state at the k+1th step in the first optimization subproblem; x1(k) represents the vehicle state at the kth step in the first optimization subproblem, x1(k)=v 2 (k), v 2 (k) represents the square of the vehicle speed at the kth step of iteration; Δs represents the fixed discrete interval of the total mileage; F w (k) represents the vehicle driving / braking force at the k-th step of iteration; θ(k) represents the road slope at the k-th step of iteration; The constraints for the first optimization subproblem are: v(k)∈,v min ,v max - u1(k)∈,F b (k),F d (k)- Among them, β represents the penalty coefficient, represents the expected average speed, v(k) represents the speed at the kth step, represents the square of the initial vehicle speed, represents the square of terminal speed, F b (k) represents the maximum braking force at the kth step of iteration, F d (k) represents the maximum driving force when iterating to the kth step, and x1(N) represents the vehicle state when iterating to the Nth step in the first optimization subproblem; Based on the first optimization subproblem and its constraints, the cost function of the dynamic programming algorithm at step N is obtained, and the optimal vehicle speed spectrum and optimal power spectrum are obtained here. The specific expression is: Among them, u1(N) represents the control amount when iterating to the Nth step in the first optimization subproblem, and F w (N) represents the vehicle driving / braking force at the Nth iteration, and argmin represents the minimum function; According to the optimal vehicle speed spectrum and optimal power spectrum at the Nth step, the cost function of the reverse iteration of the dynamic programming algorithm is obtained. The specific expression is: Among them, u1(k+1) represents the control amount when iterating to the k+1th step in the first optimization subproblem; Therefore, the final speed spectrum v of the heavy vehicle is obtained opt And the final power spectrum 6. The hierarchical intelligent networked heavy-duty vehicle auxiliary fuel saving method according to claim 4, characterized in that: In step S5, optimizing the heavy vehicle shifting strategy includes the following contents: The state transition equation for the second optimization subproblem is: x2(k+1)=x2(k)+u2(k) Among them, x2(k+1) represents the vehicle state at the k+1th step of iteration; The constraints of the second optimization subproblem are: x2(0)=τ0 x2(N)=τ p x2(k)∈,τ min ,t max - u2(k)∈,-2;-1;0;+1;+2- in, Indicates that the engine is at speed n e and torque M e Fuel consumption rate under working conditions; β1, β2 and β3 are penalty coefficients; L2, x2(k), u2(k), Δs- represent the cost function in the second optimization sub-problem; v opt (k) represents the optimal vehicle speed value when iterating to the kth step; τ0 represents the initial gear value; τ p Indicates the terminal gear value; τ min Indicates the lowest gear of the engine; τ max represents the highest gear of the engine; x2(k) represents the vehicle state when iterating to the Nth step in the second optimization subproblem; Engine speed n e With torque M e The calculation formula is: Among them, i(τ(k)) represents the transmission ratio value corresponding to the gear position of the heavy vehicle when iterating to the kth step, r w Indicates the wheel radius, n e (k) represents the speed at the kth step of iteration, M e (k) represents the torque at the kth step of iteration, v opt (k) represents the final vehicle speed spectrum at the kth step of iteration, Represents the final dynamic spectrum when iterating to the kth step; Based on the two optimization sub-problems and their constraints, the cost function of the dynamic programming algorithm at step N is obtained, and the optimal shift strategy here is obtained. The specific expression is: Among them, u2(N) represents the terminal control quantity in the second optimization subproblem, and argmin represents the function of taking the minimum value; According to the optimal shift strategy at step N, the cost function of the reverse iteration of dynamic programming is obtained. The specific expression is: Among them, u2(k+1) represents the control amount when iterating to the k+1th step in the second optimization subproblem; Therefore, the final shift strategy τ for heavy vehicles is obtained opt .

7. A system for the layered intelligent networked heavy-duty vehicle auxiliary fuel saving method according to claim 1, characterized in that: include: An information acquisition module is used to obtain traffic environment information and vehicle parameter information; Model building module, which is used to build optimization problem models by combining road speed limits, vehicle gears and dynamic constraints; The problem optimization module is used to build a hierarchical decoupling framework to decompose the optimization problem model into two optimization sub-problems; The vehicle fuel saving module is used to combine the vehicle kinematic characteristics and use the dynamic programming algorithm to solve the final speed spectrum and power spectrum of the heavy-duty vehicle; combined with the final speed spectrum and power spectrum, with the goal of minimizing fuel consumption, the dynamic programming algorithm is used to optimize the heavy-duty vehicle's shifting strategy to complete vehicle auxiliary fuel saving.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of any one of the layered intelligent connected heavy-duty vehicle auxiliary fuel saving methods of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the layered intelligent connected heavy-duty vehicle auxiliary fuel saving method according to any one of claims 1 to 6 is executed.

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