Commercial vehicle energy-saving control method based on expected vehicle speed prediction

Abstract

The invention discloses a commercial vehicle energy-saving control method based on expected vehicle speed prediction, aims to improve the energy-saving effect of a commercial vehicle at the expected vehicle speed of a driver, and specifically comprises the following steps: (1) carrying out vehicle longitudinal dynamics modeling; (2) establishing a commercial vehicle fuel consumption model; and (3) establishing an objective function which considers the ability of tracking the expected speed of the driver, the energy-saving effect of the vehicle and the torque change of the vehicle, solving a multi-objective optimization problem by using model predictive control, and designing constraint conditions. And finally, the economic level of the commercial vehicle is improved on the premise of reasonably tracking the expected vehicle speed of the driver.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle energy conservation, and specifically relates to an energy-saving control method for commercial vehicles based on expected vehicle speed prediction. Background Technique

[0002] Energy-saving technology is a key technology for commercial vehicles, and it is crucial for improving the energy-saving effect of commercial vehicles. Currently, commonly used energy-saving control algorithms mainly include PID control, sliding mode control (SMC), LQR control, and model predictive control (MPC), etc. Among them, MPC, as a type of controller that solves rolling optimization problems with constraints, has outstanding advantages in solving the energy-saving control problem of commercial vehicles at the expected vehicle speed. However, traditional MPC rarely considers energy-saving control research under the driver's expected vehicle speed. Establish an objective function considering the ability to track the driver's expected vehicle speed, the vehicle's energy-saving effect, and the vehicle's torque change, use model predictive control to solve the multi-objective optimization problem, and design constraint conditions. Finally, on the premise of reasonably tracking the driver's expected vehicle speed, the economic level of commercial vehicles is improved.

[0003] Some existing patents, such as the invention patent with the patent number CN118254828A, propose an energy-saving control method for driverless vehicles, and the invention patent with the patent number CN114312341B proposes an energy-saving control method for electric vehicles. The former generates constraint conditions based on the original driving time of a single run of the vehicle on the planned path, the safe following distance when the vehicle follows, and the road speed limit, and obtains the optimal solution of the convex function according to the constraint conditions to obtain the energy-saving control strategy during a single run of the vehicle on the planned path. The latter controls multiple performances of the electric vehicle according to different driving modes. Both consider improving the vehicle's energy-saving level on the premise of reasonably tracking the driver's expected vehicle speed. Summary of the Invention

[0004] The present invention aims to improve the energy-saving effect of commercial vehicles at the driver's expected vehicle speed, establish a vehicle longitudinal dynamics model and a commercial vehicle fuel consumption model, establish an objective function considering the ability to track the driver's expected vehicle speed, the vehicle's energy-saving effect, and the vehicle's torque change, use model predictive control to solve the multi-objective optimization problem, and design constraint conditions. To solve the above technical problems, the present invention is implemented by the following technical solutions:

[0005] An energy-saving control method for commercial vehicles based on expected vehicle speed prediction, comprising the following steps:

[0006] S1: Establish a vehicle longitudinal dynamics model based on the balance relationship between driving and resistance:

[0007] F t =F w +Ff +F i +F j (1)

[0008] Among them, the driving force rolling resistance F f = μMgcosα, air resistance grade resistance F i = Mgsinα, acceleration resistance F j = δMa, η is the mechanical efficiency of the driveline, T e is the engine torque, I f is the transmission ratio of the vehicle's main reducer, I g is the transmission ratio of the vehicle's gearbox, r is the wheel radius, μ is the friction coefficient of the road surface on which the vehicle travels, α is the road grade, g is the acceleration due to gravity, C d is the air resistance coefficient in the vehicle's driving environment, A f is the vehicle's frontal windward area, ρ is the density of air, M is the vehicle mass, δ is the mass conversion coefficient, a is the vehicle acceleration;

[0009] Taking the vehicle speed and engine torque as vehicle states and discretizing them in the time domain gives:

[0010]

[0011] Among them, T s is the sampling time, v(k) is the vehicle speed at time k, v(k + 1) is the vehicle speed at time k + 1, T e (k + 1) is the engine torque at time k + 1, a(k) is the vehicle acceleration at time k; is the engine torque change rate at time k;

[0012] Vehicle state space equation:

[0013]

[0014] Among them, the system state variables control variable y(k) is the system output,

[0015] B d = I 2×2 ,C c = I 2×2 ,

[0016] disturbance term

[0017] S2: Establishment of commercial vehicle fuel consumption model:

[0018] The fitting form of the engine fuel consumption rate model is as follows:

[0019]

[0020] Among them, L i,j is the fitting coefficient, and n e (k) is the engine speed at time k;

[0021] The conversion relationship between vehicle speed and rotational speed for the transmission ratio:

[0022]

[0023] Among them, I g (k) is the transmission ratio of the vehicle gearbox at time k;

[0024] Target fuel consumption model:

[0025]

[0026] Final target equation:

[0027]

[0028] S3: Construct the objective function for multi-objective optimization, solve the multi-objective optimization problem using model predictive control, and design the constraint conditions;

[0029] S31: Combine this non-linear multi-objective optimization problem to obtain the following comprehensive solution problem:

[0030]

[0031] Among them, N is the prediction horizon length, w r is the weight coefficient for vehicle speed tracking, v(k) is the vehicle speed state at time k in the prediction horizon, v r (k) is the desired vehicle speed predicted for the driver at time k, Δt is the time interval between every two time steps, w f is the weight for the fuel consumption target, w u is the weight for the torque change rate target;

[0032] S32: Prediction equation:

[0033] Y p (k + 1|k) = S x x(k) + S u U(k) + S d D(k) (9)

[0034] Among them,

[0035]

[0036] Constraint conditions:

[0037]

[0038] Among them, T e,min is the lower limit of the engine torque, and T e,max is the upper limit of the engine torque. is the lower limit of the engine torque change rate, is the upper limit of the engine torque change rate.

[0039] Compared with the prior art, the advantages of the present invention are as follows:

[0040] The commercial vehicle energy-saving control method based on expected vehicle speed prediction described in the present invention improves the energy-saving effect of commercial vehicles at the expected vehicle speed, and utilizes the model predictive control which is good at solving optimization problems with constraints, and has the feedforward-feedback characteristics of prediction function, rolling optimization and feedback correction. The controller has stronger adaptability, and ensures the improvement of the economic level of commercial vehicles on the premise of reasonably tracking the driver's expected vehicle speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the drawings and embodiments:

[0042] Figure 1 is the flow chart of a commercial vehicle energy-saving control method based on expected vehicle speed prediction described in the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the specific embodiments and with reference to the drawings. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0044] The present invention will be further described below in conjunction with the drawings.

[0045] Referring to Figure 1 , the present invention provides a commercial vehicle energy-saving control method based on expected vehicle speed prediction, which specifically includes the following steps:

[0046] S1: The vehicle longitudinal dynamics model is established based on the driving and resistance balance relationship:

[0047] F t = F w + F f + F i + F j (1)

[0048] Among them, the driving force The rolling resistance Ff =μMgcosα, air resistance Slope resistance F i =Mgsinα, acceleration resistance F j =δMa, η is the mechanical efficiency of the transmission system, T e is the engine torque, I f is the vehicle's final reducer transmission ratio, I g is the transmission ratio of the vehicle gearbox, r is the wheel radius, μ is the friction coefficient of the road surface on which the vehicle is traveling, α is the road slope, g is the acceleration of gravity, and C d is the air resistance coefficient in the vehicle driving environment, A f is the frontal area of the vehicle, ρ is the density of air, M is the mass of the vehicle, δ is the mass conversion factor, and a is the acceleration of the vehicle;

[0049] The vehicle speed and engine torque are used as vehicle states and discretized in the time domain to obtain:

[0050]

[0051] Among them, T s is the sampling time, v(k) is the vehicle speed at time k, v(k+1) is the vehicle speed at time k+1, T e (k+1) is the engine torque at time k+1, and a(k) is the vehicle acceleration at time k; is the engine torque change rate at time k;

[0052] Vehicle state space equations:

[0053]

[0054] Among them, the system state quantity Control volume y(k) is the system output,

[0055] B d =I 2×2 , C c =I 2×2 ,

[0056] Distractors

[0057] S2: Commercial vehicle fuel consumption model establishment:

[0058] The engine fuel consumption rate model fitting form is as follows:

[0059]

[0060] Among them, L i,j is the fitting coefficient, ne (k) is the engine speed at time k;

[0061] The conversion relationship between vehicle speed and transmission ratio:

[0062]

[0063] where I g (k) is the transmission ratio of the vehicle gearbox at time k;

[0064] Target fuel consumption model:

[0065]

[0066] Final target equation:

[0067]

[0068] S3: Construct the objective function for multi-objective optimization, solve the multi-objective optimization problem using model predictive control, and design the constraint conditions;

[0069] S31: Combine this non-linear multi-objective optimization problem to obtain the following comprehensive solution problem:

[0070]

[0071] where N is the prediction horizon length, w r is the weight coefficient of the vehicle speed tracking term, v(k) is the vehicle speed state at time k in the prediction horizon, v r (k) is the desired vehicle speed predicted for the driver at time k, Δt is the time interval between every two time steps, w f is the weight of the fuel consumption target, w u is the weight of the torque change rate target;

[0072] S32: Prediction equation:

[0073] Y p (k + 1|k) = S x x(k) + S u U(k) + S d D(k) (9)

[0074] where,

[0075]

[0076] Constraint conditions:

[0077]

[0078] where T e,min is the lower limit of the engine torque, Te,max is the upper limit of the engine torque, is the lower limit of the engine torque change rate, is the upper limit of the engine torque change rate.

[0079] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A commercial vehicle energy-saving control method based on expected vehicle speed prediction, characterized in that It includes the following steps: S1: The vehicle longitudinal dynamics model is established based on the balance relationship between driving force and resistance: F t = F w + F f + F i + F j (1) Among them, the driving force Rolling resistance F f = μMgcosα, air resistance Gradient resistance F i = Mg sinα, acceleration resistance F j = δMa, η is the mechanical efficiency of the driveline, T e is the engine torque, I f is the transmission ratio of the vehicle's main reducer, I g is the transmission ratio of the vehicle's gearbox, r is the wheel radius, μ is the friction coefficient of the road surface on which the vehicle travels, α is the road gradient, g is the acceleration due to gravity, C d is the air resistance coefficient in the vehicle's driving environment, A f is the vehicle's frontal area, ρ is the density of air, M is the vehicle mass, δ is the mass conversion coefficient, a is the vehicle acceleration; Taking vehicle speed and engine torque as vehicle states and discretizing them in the time domain, we get: Among them, T s is the sampling time, v(k) is the vehicle speed at time k, v(k + 1) is the vehicle speed at time k + 1, T e (k + 1) is the engine torque at time k + 1, a(k) is the vehicle acceleration at time k; is the engine torque change rate at time k; Vehicle state space equation: Among them, the system state variables Control variables y(k) is the system output, B d = I 2×2 ,C c = I 2×2 , Interference item S2: Establish the fuel consumption model for commercial vehicles: The fitting form of the engine fuel consumption rate model is as follows: where L i,j is the fitting coefficient, and n e (k) is the engine speed at time k; The conversion relationship between vehicle speed and rotational speed with the transmission ratio: Among them, I g (k) is the transmission ratio of the vehicle gearbox at time k; Target fuel consumption model: Final target equation: S3: Construct the objective function for multi-objective optimization, solve the multi-objective optimization problem using model predictive control, and design the constraint conditions; S31: Combine this non-linear multi-objective optimization problem to obtain the following comprehensive solution problem: where N is the prediction time domain length, w r is the weight coefficient of the vehicle speed tracking term, v(k) is the vehicle speed state at time step k in the prediction time domain, v r (k) is the expected vehicle speed of the driver predicted at time step k, Δt is the time interval between every two time steps, w f is the weight of the fuel consumption target, w u is the weight of the torque change rate target; S32: Prediction equation: Y p (k + 1|k) = S x x(k) + S u U(k) + S d D(k) (9) Among them, Constraint conditions: Among them, T e,min is the lower limit of the engine torque, and T e,max is the upper limit of the engine torque, is the lower limit of the engine torque change rate, is the upper limit of the engine torque change rate.

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