A commercial vehicle energy-saving control method based on expected vehicle speed prediction

Abstract

The application discloses a kind of commercial vehicle energy-saving control methods based on expected speed prediction, to improve the energy-saving effect of commercial vehicle at the expected speed of driver, specifically includes the following steps: (1) vehicle longitudinal dynamics modeling is carried out;(2) commercial vehicle fuel consumption model is established;(3) the objective function considering the tracking ability to the expected speed of driver, vehicle energy-saving effect and the change of vehicle torque is established, and the multi-objective optimization problem is solved using model predictive control, and constraint condition is designed.Finally, the premise that the expected speed of driver is reasonably tracked is realized to improve the economy level of commercial vehicle.

Description

Technical Field

[0001] This invention belongs to the field of vehicle energy-saving technology, specifically a commercial vehicle energy-saving control method based on expected vehicle speed prediction. Background Technology

[0002] Energy-saving technology is a key technology for commercial vehicles, and it is crucial for improving the energy efficiency of commercial vehicles. Currently, commonly used energy-saving control algorithms mainly include PID control, sliding mode control (SMC), low-level quality control (LQR), and model predictive control (MPC). Among them, MPC, as a type of controller that solves constrained optimization problems by rolling the algorithm, has significant advantages in solving the energy-saving control problem of commercial vehicles at desired speeds. However, traditional MPC rarely considers energy-saving control research under the driver's desired speed. This paper establishes an objective function that considers the ability to track the driver's desired speed, the vehicle's energy-saving effect, and changes in vehicle torque. Model predictive control is used to solve the multi-objective optimization problem, and constraints are designed to ultimately improve the economic level of commercial vehicles while reasonably tracking the driver's desired speed.

[0003] Existing patents, such as CN118254828A (invention patent), propose an energy-saving control method for autonomous vehicles, and CN114312341B (invention patent), propose an energy-saving control method for electric vehicles. The former generates constraints based on the vehicle's planned travel time on a single route, the safe following distance, and road speed limits, and then calculates the optimal solution of the convex function based on these constraints to obtain the energy-saving control strategy for the vehicle's single-trip on the planned route. The latter controls multiple performance aspects of the electric vehicle according to different driving modes. Both methods aim to improve vehicle energy efficiency while reasonably tracking the driver's desired speed. Summary of the Invention

[0004] This invention aims to improve the energy efficiency of commercial vehicles at the driver's desired speed. It establishes a longitudinal dynamics model and a fuel consumption model for the commercial vehicle, considering the ability to track the driver's desired speed, the vehicle's energy-saving effect, and the objective function of torque variation. Model predictive control is used to solve the multi-objective optimization problem, and constraints are designed. To solve the above technical problems, this invention adopts the following technical solution:

[0005] A commercial vehicle energy-saving control method based on expected vehicle speed prediction includes the following steps:

[0006] S1: The vehicle longitudinal dynamics model is established based on the balance between driving and resistance:

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

[0008] Among them, driving force Rolling resistance F f =μMgcosα, air resistance Slope resistance F i =Mgsinα, acceleration resistance F j =δMa, where η is the mechanical efficiency of the transmission system, T e For engine torque, I f I is the gear ratio of the vehicle's main reducer. g Where is the vehicle's gearbox ratio, r is the wheel radius, μ is the coefficient of friction of the road surface, α is the road gradient, g is the acceleration due to gravity, and C is the acceleration due to gravity. d A is the air resistance coefficient in the vehicle's driving environment. f ρ is the frontal area of ​​the vehicle, M is the density of the air, δ is the mass conversion factor, and a is the acceleration of the vehicle.

[0009] Vehicle speed and engine torque are taken as vehicle states and discretized in the time domain to obtain:

[0010]

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

[0012] Vehicle state-space equations:

[0013]

[0014] Among them, system state variables Control quantity y(k) is the system output.

[0015] B d =I 2×2 C c =I 2×2 ,

[0016] Interference items

[0017] S2: Establishment of a fuel consumption model for commercial vehicles:

[0018] The engine fuel consumption rate model fits the following form:

[0019]

[0020] Among them, L i,j n represents the fitting coefficients. e (k) represents the engine speed at time k;

[0021] The transmission ratio conversion relationship between vehicle speed and engine speed:

[0022]

[0023] Among them, I g (k) represents the vehicle's gearbox ratio at time k;

[0024] Target fuel consumption model:

[0025]

[0026] Final objective equation:

[0027]

[0028] S3: Construct the objective function for multi-objective optimization, use model predictive control to solve the multi-objective optimization problem, and design constraints;

[0029] S31: Combining this nonlinear multi-objective optimization problem yields the following comprehensive solution problem:

[0030]

[0031] Where, N is the prediction time domain length, w r Here, v(k) represents the weighting coefficient of the vehicle speed tracking term, and v(k) represents the vehicle speed state at time k in the prediction time domain. r (k) represents the predicted expected vehicle speed of the driver at time k, Δt is the time interval between two time steps, and w f As the weight for fuel consumption targets, w u The target weight for the rate of change of torque;

[0032] S32: Prediction Equation:

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

[0034] in,

[0035]

[0036] Constraints:

[0037]

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

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

[0040] The energy-saving control method for commercial vehicles based on expected vehicle speed prediction described in this invention improves the energy-saving effect of commercial vehicles at the expected vehicle speed. It utilizes model predictive control, which is good at solving constrained optimization problems, and has feedforward-feedback characteristics with predictive function, rolling optimization and feedback correction. The controller has stronger adaptive capability, ensuring that the economic level of commercial vehicles is improved under the premise of reasonable tracking of the driver's expected vehicle speed. Attached Figure Description

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0042] Figure 1 This is a flowchart of a commercial vehicle energy-saving control method based on expected vehicle speed prediction, as described in this invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0044] The invention will now be further described with reference to the accompanying drawings.

[0045] See Figure 1 This invention provides a commercial vehicle energy-saving control method based on expected vehicle speed prediction, specifically including the following steps:

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

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

[0048] Among them, driving force Rolling resistance Ff =μMgcosα, air resistance Slope resistance F i =Mgsinα, acceleration resistance F j =δMa, where η is the mechanical efficiency of the transmission system, T e For engine torque, I f I is the gear ratio of the vehicle's main reducer. g Where is the vehicle's gearbox ratio, r is the wheel radius, μ is the coefficient of friction of the road surface, α is the road gradient, g is the acceleration due to gravity, and C is the acceleration due to gravity. d A is the air resistance coefficient in the vehicle's driving environment. f ρ is the frontal area of ​​the vehicle, M is the density of the air, δ is the mass conversion factor, and a is the acceleration of the vehicle.

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

[0050]

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

[0052] Vehicle state-space equations:

[0053]

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

[0055] B d =I 2×2 C c =I 2×2 ,

[0056] Interference items

[0057] S2: Establishment of a fuel consumption model for commercial vehicles:

[0058] The engine fuel consumption rate model fits the following form:

[0059]

[0060] Among them, L i,j n represents the fitting coefficients.e (k) represents the engine speed at time k;

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

[0062]

[0063] Among them, I g (k) represents the vehicle's gearbox ratio at time k;

[0064] Target fuel consumption model:

[0065]

[0066] Final objective equation:

[0067]

[0068] S3: Construct the objective function for multi-objective optimization, use model predictive control to solve the multi-objective optimization problem, and design constraints;

[0069] S31: Combining this nonlinear multi-objective optimization problem yields the following comprehensive solution problem:

[0070]

[0071] Where, N is the prediction time domain length, w r Here, v(k) represents the weighting coefficient of the vehicle speed tracking term, and v(k) represents the vehicle speed state at time k in the prediction time domain. r (k) represents the predicted expected vehicle speed of the driver at time k, Δt is the time interval between two time steps, and w f As the weight for fuel consumption targets, w u The target weight for the rate of change of torque;

[0072] S32: Prediction Equation:

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

[0074] in,

[0075]

[0076] Constraints:

[0077]

[0078] Among them, T e,min T is the lower limit of engine torque.e,max This is the upper limit of engine torque. This represents the lower limit of the rate of change of engine torque. This represents the upper limit of the engine torque change rate.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for energy saving control of a commercial vehicle based on prediction of desired vehicle speed, characterized by, The method comprises the following steps: S1: Vehicle longitudinal dynamics model is established based on driving and resistance balance relationship: F t = F w + F f + F i + F j (1) wherein the driving force the rolling resistance F f = μMg cos α, the air resistance the gradient resistance F i = Mg sin α, the acceleration resistance F j = δMa, η is the mechanical efficiency of the drive train, T e is the engine torque, I f is the transmission ratio of the vehicle main differential, I g is the transmission ratio of the vehicle gearbox, r is the wheel radius, μ is the friction coefficient of the vehicle travel surface, α is the road gradient, g is the gravitational acceleration, C d is the air resistance coefficient in the vehicle travel 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, and a is the vehicle acceleration. Vehicle speed and engine torque are taken as vehicle state and discretized in time domain: wherein 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 rate of change at time k. Vehicle state space equation: where the system state quantity Control quantity y(k) is the system output, B d = I 2×2 , C c = I 2×2 , interference term S2: Commercial vehicle fuel consumption model is established: The fitting form of engine fuel consumption rate model is as follows: wherein L i,j is a fitting coefficient, n e (k) is the engine speed at time k. Transmission ratio conversion relationship of vehicle speed and rotating speed: where I g (k) is the transmission ratio of the vehicle gearbox at time k Target fuel consumption model: Final target equation: S3: The target function of multi-objective optimization is constructed, the multi-objective optimization problem is solved by using model predictive control, and constraint conditions are designed; S31: The nonlinear multi-objective optimization problem is combined to obtain the following comprehensive solving problem: where N is the prediction horizon length, w r is the weight coefficient of the vehicle speed tracking term, v r (k) is the predicted expected vehicle speed of the driver at time k, Δt is the time interval of each two time steps, w f is the weight coefficient of the fuel consumption target, w u is the weight coefficient of the torque rate target; S32: Prediction equation: Y p (k+1|k) = S x x(k) + S u U(k) + S d D(k) (9) wherein Constraint condition: wherein T e,min is a lower limit of engine torque, T e,max is an upper limit of engine torque, is a lower limit of engine torque change rate, is an upper limit of engine torque change rate.

Images