Energy control method and system of hybrid vehicle

Abstract

The invention discloses an energy control method and system for a hybrid vehicle, and the method comprises the steps: obtaining a trained vehicle speed prediction model, a preset torque control rule, and the driving vehicle speed data and the remaining electric quantity data of a target vehicle; inputting the driving vehicle speed data into the trained vehicle speed prediction model for vehicle speed prediction to obtain predicted vehicle speed data; according to the residual electric quantity data and the driving demand torque of the driving speed data, a driving working mode of the target vehicle is obtained; and performing energy control on the target vehicle according to the running working mode. According to the method, computing resources required for energy control of the hybrid vehicle can be effectively reduced, and the efficiency and effect of energy control are improved. The invention relates to the technical field of vehicles.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to an energy control method and system for a hybrid vehicle. Background Art

[0002] A hybrid vehicle is a vehicle that can travel under the coordinated drive of an electric motor and an engine. Optimal energy control of hybrid vehicles under various driving conditions is one of the important issues that the industry is concerned about.

[0003] At present, the existing technology usually realizes the energy control of hybrid vehicles by means of model predictive control (MPC), which specifically realizes the energy control of hybrid vehicles by predicting the vehicle speed within a certain time range in the future through a model. This method requires more computing resources and a large computing load, and the efficiency and effect of energy control are not satisfactory.

[0004] Therefore, the problems existing in the prior art still need to be solved and optimized. Summary of the invention

[0005] The purpose of the present invention is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, an object of an embodiment of the present invention is to provide an energy control method and system for a hybrid vehicle, wherein the method can effectively reduce the computing resources required for energy control of the hybrid vehicle and improve the efficiency and effect of energy control.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present application include:

[0008] In a first aspect, an embodiment of the present application provides an energy control method for a hybrid vehicle, comprising:

[0009] Obtain the trained vehicle speed prediction model and preset torque control rules, as well as the target vehicle's driving speed data and remaining power data;

[0010] Inputting the driving speed data into the trained speed prediction model to perform speed prediction to obtain predicted speed data;

[0011] According to the remaining power data and the driving demand torque of the driving speed data, obtaining the driving working mode of the target vehicle, wherein the driving working mode includes a regular working mode and an irregular working mode;

[0012] If the driving working mode is the regular working mode, energy control is performed on the target vehicle according to the torque control rule; or, if the driving working mode is the irregular working mode, torque distribution is performed on the target vehicle speed demand torque according to the driving vehicle speed data and the remaining power data to obtain an engine torque control sequence and a motor torque control sequence, and energy joint control is performed on the target vehicle according to the engine torque control sequence and the motor torque control sequence.

[0013] In addition, the method according to the above embodiment of the present application may also have the following additional technical features:

[0014] Further, in one embodiment of the present application, the step of obtaining the driving working mode of the target vehicle according to the remaining power data and the driving demand torque of the driving speed data includes:

[0015] Obtain the minimum power threshold, maximum power threshold, starting torque threshold and optimal torque threshold;

[0016] According to the minimum power threshold and the maximum power threshold, the remaining power data is compared to obtain a power comparison result;

[0017] According to the starting torque threshold and the optimal torque threshold, performing a torque comparison on the driving demand torque to obtain a torque comparison result;

[0018] The driving operation mode of the target vehicle is obtained according to the power comparison result and the torque comparison result.

[0019] Further, in one embodiment of the present application, the target vehicle speed demand torque is distributed according to the driving vehicle speed data and the remaining power data to obtain an engine torque control sequence and a motor torque control sequence, including:

[0020] Acquire the vehicle engine torque and the vehicle motor torque of the target vehicle, as well as the input shaft speed corresponding to the driving speed data and the power equivalent factor corresponding to the remaining power data;

[0021] Constructing a torque constraint problem according to the electric quantity equivalent factor, the input shaft speed, the target vehicle speed required torque, the vehicle engine torque and the vehicle motor torque;

[0022] The torque constraint problem is constrained and solved to obtain the engine torque control sequence and the motor torque control sequence.

[0023] Further, in one embodiment of the present application, performing constraint solving on the torque constraint problem to obtain the engine torque control sequence and the motor torque control sequence includes:

[0024] Obtaining a target torque function and a torque constraint condition of the torque constraint problem;

[0025] Constructing a first quadratic programming subproblem corresponding to the torque constraint problem according to the target torque function and the torque constraint condition;

[0026] The first quadratic programming sub-problem is optimized to obtain the engine torque control sequence and the motor torque control sequence.

[0027] Further, in one embodiment of the present application, the sub-problem optimization of the first quadratic programming sub-problem to obtain the engine torque control sequence and the motor torque control sequence includes:

[0028] Obtain the preset optimization accuracy conditions;

[0029] Obtaining a first control sequence, a first optimization point, and a first quadratic programming subproblem corresponding to the first optimization point;

[0030] According to the first optimization point, performing a gradient analysis on the target torque function to obtain a target torque gradient, and according to the first optimization point, performing a constraint condition analysis on the torque constraint condition to obtain a torque constraint matrix;

[0031] Solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain a target optimal solution;

[0032] According to the target optimal solution, optimizing and updating the first optimization point to obtain a second optimization point;

[0033] Performing a first condition verification on the optimization accuracy condition according to the first optimization point and the second optimization point to obtain a first condition verification result;

[0034] If the result of the first condition verification is that the optimization accuracy condition is met, the first control sequence is updated according to the target optimal solution to obtain the second control sequence, and the engine torque control sequence and the motor torque control sequence are obtained according to the second control sequence; or, if the result of the first condition verification is that the optimization accuracy condition is not met, the first control sequence is updated according to the target optimal solution, and the first optimization point is updated according to the second optimization point, and then the step of obtaining the first control sequence, the first optimization point and the first quadratic programming subproblem corresponding to the first optimization point is returned.

[0035] Further, in one embodiment of the present application, solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain the target optimal solution includes:

[0036] Conditionally solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain an intermediate optimal solution;

[0037] Performing optimal solution correction on the intermediate optimal solution to obtain the target optimal solution.

[0038] Further, in one embodiment of the present application, conditionally solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain an intermediate optimal solution includes:

[0039] According to the target torque gradient and the torque constraint matrix, a problem solution verification is performed on the first quadratic programming subproblem to obtain a problem solution verification result, wherein the problem solution verification result is used to indicate whether a problem solution exists for the first quadratic programming subproblem;

[0040] If the problem solution verification result is that the first quadratic programming subproblem has a solution, the first quadratic programming subproblem is originally solved according to the target torque gradient and the torque constraint matrix to obtain the intermediate optimal solution; or, if the problem solution verification result is that the first quadratic programming subproblem has a solution, a second quadratic programming subproblem corresponding to the first quadratic programming subproblem is obtained, and the second quadratic programming subproblem is replaced and solved according to the target torque gradient and the torque constraint matrix to obtain the intermediate optimal solution.

[0041] Further, in one embodiment of the present application, performing optimal solution correction on the intermediate optimal solution to obtain the target optimal solution includes:

[0042] Get the preset effect conditions;

[0043] According to the intermediate optimal solution, performing a second condition verification on the effect condition to obtain a second condition verification result;

[0044] If the second condition verification result is that the effect condition is met, the search direction of the intermediate optimal solution is corrected to obtain the target optimal solution; or, if the second condition verification result is that the effect condition is not met, the intermediate optimal solution is determined as the target optimal solution.

[0045] Further, in one embodiment of the present application, optimizing and updating the first optimization point according to the target optimal solution to obtain a second optimization point includes:

[0046] Obtaining a penalty factor corresponding to the target optimal solution and a benefit function corresponding to the target optimal solution;

[0047] Solving the benefit function according to the penalty factor to obtain a benefit step corresponding to the target optimal solution;

[0048] According to the benefit step and the target optimal solution, the first optimization point is updated to obtain the second optimization point.

[0049] In a second aspect, an embodiment of the present application provides an energy control system for a hybrid vehicle, comprising:

[0050] The first processing unit is used to obtain the trained vehicle speed prediction model and the preset torque control rule, as well as the driving speed data and the remaining power data of the target vehicle;

[0051] A second processing unit is used to input the driving speed data into the trained speed prediction model to perform speed prediction and obtain predicted speed data;

[0052] a third processing unit, configured to obtain a driving working mode of the target vehicle according to the remaining power data and the driving demand torque of the driving speed data, wherein the driving working mode includes a regular working mode and an irregular working mode;

[0053] The fourth processing unit is used to perform energy control on the target vehicle according to the torque control rule if the driving working mode is the regular working mode; or, if the driving working mode is the irregular working mode, perform torque distribution on the target vehicle speed demand torque according to the driving speed data and the remaining power data to obtain the engine torque control sequence and the motor torque control sequence, and perform energy joint control on the target vehicle according to the engine torque control sequence and the motor torque control sequence.

[0054] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0055] at least one processor;

[0056] at least one memory for storing at least one program;

[0057] When the at least one program is executed by the at least one processor, the at least one processor implements the method of the first aspect described above.

[0058] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor, and the program executable by the processor is used to implement the method of the first aspect when executed by the processor.

[0059] The advantages and benefits of the present application will be partially given in the following description, and partially become apparent from the following description, or be understood through the practice of the present application:

[0060] An energy control method and system for a hybrid vehicle disclosed in an embodiment of the present application, wherein the method obtains a trained vehicle speed prediction model and a preset torque control rule, as well as driving speed data and remaining power data of a target vehicle; inputs the driving speed data into the trained vehicle speed prediction model to perform vehicle speed prediction to obtain predicted vehicle speed data; obtains a driving operating mode of the target vehicle based on the remaining power data and the driving demand torque of the driving speed data, wherein the driving operating mode includes a regular operating mode and an irregular operating mode; if the driving operating mode is the regular operating mode, energy control is performed on the target vehicle according to the torque control rule; or, if the driving operating mode is the irregular operating mode, torque distribution is performed on the target speed demand torque based on the driving speed data and the remaining power data to obtain an engine torque control sequence and a motor torque control sequence, and energy joint control is performed on the target vehicle according to the engine torque control sequence and the motor torque control sequence. The method determines the target vehicle speed demand torque by predicting the target vehicle's future predicted vehicle speed data based on a vehicle speed prediction model, and then determines the target vehicle's driving operating mode based on the remaining power data and the target vehicle speed demand torque, and performs energy control based on the driving operating mode. This method can effectively reduce the computing resources required for energy control of hybrid vehicles and improve the efficiency and effectiveness of energy control. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the embodiments of the present application or the drawings of the related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0062] Figure 1 A schematic flow chart of an energy control method for a hybrid vehicle provided in an embodiment of the present application;

[0063] Figure 2 A schematic diagram of a framework of an energy control system for a hybrid vehicle provided in an embodiment of the present application;

[0064] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limitations on the present application. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0067] At present, the existing technology usually realizes the energy control of hybrid vehicles by means of model predictive control (MPC), which specifically realizes the energy control of hybrid vehicles by predicting the vehicle speed within a certain time range in the future through a model. However, since this method usually performs energy control on all driving conditions of the target vehicle, and the control performance of MPC is more dependent on the solution efficiency of the solver, this method requires more computing resources and a larger computing load, and the timeliness of energy control is not good, and the efficiency is not satisfactory. In addition, the control performance of MPC also depends on the predicted vehicle speed provided by the model. When the target vehicle is in a complex driving condition, the accuracy of the predicted vehicle speed provided by this method is not good, and the effect of energy control is not satisfactory.

[0068] In view of this, an embodiment of the present invention provides an energy control method and system for a hybrid vehicle, wherein the method determines a target speed demand torque based on the predicted speed data of the target vehicle in the future predicted by a speed prediction model, and then determines the driving working mode of the target vehicle based on the remaining power data and the target speed demand torque, and performs energy control based on the driving working mode, specifically using torque control rules to perform energy control on the target vehicle in a regular working mode, and using an engine torque control sequence and a motor torque control sequence to perform energy control on the target vehicle in an irregular working mode, which can effectively reduce the computing resources required for energy control of the target vehicle, reduce computing compliance, improve the timeliness of energy control, and thereby improve energy control efficiency; in addition, this method distributes torque to the target speed demand torque based on the driving speed data and the remaining power data, specifically by solving the torque constraint problem corresponding to the target speed demand torque, which can reduce the degree of dependence on the predicted speed provided by the model while ensuring energy control of the target vehicle, and improve the energy control effect of the target vehicle under complex driving conditions.

[0069] Reference Figure 1 In an embodiment of the present application, a method for controlling energy of a hybrid vehicle includes:

[0070] Step 110: obtaining a trained vehicle speed prediction model and a preset torque control rule, as well as the driving speed data and remaining power data of the target vehicle;

[0071] In an embodiment of the present application, the target vehicle may be a hybrid vehicle; the speed prediction model may be a long short-term memory neural network (LSTM) model, and the trained speed prediction model may be specifically obtained by collecting a historical speed data set of the target vehicle; then, the speed data of the target vehicle over a period of history is extracted and a sliding window is formed, and the sliding window and the predicted speed are input into the model for training, thereby obtaining a trained speed prediction model.

[0072] It can be understood that the torque control rule is used to achieve energy control of the target vehicle in the rule working mode, and the torque control includes no torque output, only motor output positive torque, only engine output positive torque, only motor output negative torque, etc. The target vehicle's driving speed data can be the target vehicle's speed data at the current moment; the remaining power data can be the current SOC (State of Charge) value of the target vehicle's power battery.

[0073] Step 120: input the driving speed data into the trained speed prediction model to perform speed prediction and obtain predicted speed data;

[0074] In an embodiment of the present application, the driving vehicle data of the target vehicle can be input into a trained speed prediction model, and the driving speed data can be predicted by the trained speed prediction model to obtain the future predicted speed data of the target vehicle. The predicted speed data can be the speed data of the target vehicle at a certain moment in the future, or it can be the speed data of the target vehicle within a certain period of time in the future.

[0075] It can be understood that after obtaining the predicted vehicle speed data of the target vehicle in the future, the corresponding target vehicle speed demand torque can be calculated based on the predicted vehicle speed data, and the target vehicle speed demand torque can be expressed as:

[0076] T dem =r·(mgfcosα+0.5A f C d ρ air v 2 +ma+mg sinα) / i t

[0077] Among them, T dem is the target speed torque; r is the tire radius of the target vehicle; m is the mass of the target vehicle; g is the acceleration of gravity; f is the rolling resistance coefficient; α is the road slope angle; A f is the frontal area of ​​the target vehicle; C d is the air resistance coefficient; ρ air is the air density; ν is the vehicle speed; a is the longitudinal acceleration of the target vehicle; i t is the transmission ratio of the target vehicle.

[0078] Step 130: acquiring a driving working mode of the target vehicle according to the remaining power data and the driving demand torque of the driving speed data, wherein the driving working mode includes a regular working mode and an irregular working mode;

[0079] In the embodiment of the present application, the driving demand torque is the current demand torque of the target vehicle. Step 130 can obtain the driving working mode of the target vehicle based on the remaining power data of the target vehicle and the driving demand torque corresponding to the driving speed data. The driving working mode includes a regular working mode and an irregular working mode. Among them, the regular working mode includes an idle parking mode, a pure motor drive mode, a pure engine drive mode, and a regenerative braking mode; the irregular working mode includes a driving charging mode and a hybrid drive mode.

[0080] In some embodiments, the step 130 of acquiring the driving working mode of the target vehicle according to the remaining power data and the driving demand torque of the driving speed data includes:

[0081] A1. Obtain the minimum power threshold, the maximum power threshold, the starting torque threshold and the optimal torque threshold;

[0082] A2. Comparing the remaining power data according to the minimum power threshold and the maximum power threshold to obtain a power comparison result;

[0083] A3. performing a torque comparison on the driving demand torque according to the starting torque threshold and the optimal torque threshold to obtain a torque comparison result;

[0084] A4. Obtaining the driving working mode of the target vehicle according to the power comparison result and the torque comparison result.

[0085] In the embodiment of the present application, the starting torque threshold may be the engine starting torque of the target vehicle's engine at a specific engine speed, and the optimal torque threshold may be the engine optimal torque of the target vehicle's engine at a specific engine speed, wherein the starting torque threshold and the optimal torque threshold change with the engine speed, and the optimal torque threshold is greater than the starting torque threshold. Specifically, for a certain engine speed, the minimum power threshold and the remaining power data may be compared first to obtain a power comparison result; then, the peak torque threshold and the driving demand torque are compared, as well as the optimal torque threshold and the driving demand torque, to obtain a torque comparison result.

[0086] It can be understood that if the power comparison result is that the remaining power data is greater than or equal to the minimum power threshold and the torque comparison result is that the driving demand torque is greater than or equal to the optimal torque threshold, then the driving working mode of the target vehicle is the hybrid drive mode in the irregular working mode; or, if the power comparison result is that the remaining power data is less than the minimum power threshold and the torque comparison result is that the driving demand torque is greater than or equal to the optimal torque threshold, then the driving working mode of the target vehicle is the pure engine drive mode in the regular working mode.

[0087] If the power comparison result is that the remaining power data is less than the maximum power threshold, the torque comparison result is that the driving demand torque is greater than or equal to the starting torque threshold, and the driving demand torque is less than the optimal torque threshold, then the driving working mode of the target vehicle is the driving charging mode in the irregular working mode; or, if the power comparison result is that the remaining power data is greater than or equal to the maximum power threshold, the torque comparison result is that the driving demand torque is greater than or equal to the starting torque threshold, and the driving demand torque is less than the optimal torque threshold, then the driving working mode of the target vehicle is the pure motor drive mode in the regular working mode.

[0088] If the power comparison result is that the remaining power data is greater than or equal to the minimum power threshold, and the torque comparison result is that the driving demand torque is less than the starting torque threshold, then the driving working mode of the target vehicle is the pure motor drive mode in the regular working mode; or, if the power comparison result is that the remaining power data is less than the minimum power threshold, and the torque comparison result is that the driving demand torque is less than the starting torque threshold, then the driving working mode of the target vehicle is the pure engine drive mode in the regular working mode.

[0089] Step 140: If the driving working mode is the regular working mode, energy control is performed on the target vehicle according to the torque control rule;

[0090] In the embodiment of the present application, step 140 may be to perform energy control on the target vehicle based on the torque control rule corresponding to the regular working mode. Specifically, if the regular working mode is the idle parking mode, the energy control of the target vehicle may be that both the engine and the motor have no torque output; or, if the regular working mode is the pure motor drive mode, the energy control of the target vehicle may be based on the motor outputting positive torque only; or, if the regular working mode is the pure engine drive mode, the energy control of the target vehicle may be based on the engine outputting positive torque only; or, if the regular working mode is the regenerative braking mode, the energy control of the target vehicle may be based on the motor outputting negative torque only.

[0091] Alternatively, in step 150, if the driving operating mode is the irregular operating mode, the target vehicle speed demand torque is distributed according to the driving vehicle speed data and the remaining power data to obtain an engine torque control sequence and a motor torque control sequence, and energy joint control is performed on the target vehicle according to the engine torque control sequence and the motor torque control sequence.

[0092] In an embodiment of the present application, if the driving working mode is an irregular working mode, the target vehicle speed demand torque can be distributed based on the principle of minimum equivalent fuel consumption and according to the driving vehicle speed data and the remaining power data, so as to obtain an engine torque control sequence corresponding to the engine and a motor torque control sequence corresponding to the motor, so that the engine can realize energy joint control of the target vehicle based on the engine torque control sequence, and the motor can realize energy joint control of the target vehicle based on the motor torque control sequence.

[0093] In some embodiments, the step 150 of performing torque distribution on the target vehicle speed demand torque according to the vehicle speed data and the remaining power data to obtain an engine torque control sequence and a motor torque control sequence includes:

[0094] B1. Acquire the vehicle engine torque and the vehicle motor torque of the target vehicle, as well as the input shaft speed corresponding to the driving speed data and the power equivalent factor corresponding to the remaining power data;

[0095] B2. constructing a torque constraint problem according to the electric quantity equivalent factor, the input shaft speed, the target vehicle speed required torque, the vehicle engine torque and the vehicle motor torque;

[0096] In the embodiment of the present application, the vehicle engine torque may be the current engine torque of the target vehicle, the vehicle motor torque may be the current motor torque of the target vehicle; the input shaft speed may be the speed of the gearbox input shaft converted from the target vehicle's driving speed data; the power equivalent factor may be a dimensionless value corresponding to the remaining power data, and the function form of the power equivalent factor may be expressed as:

[0097]

[0098] Among them, s(SOC) is the function form of the power equivalence factor; SOC is the remaining power data.

[0099] It is understandable that step B2 can be based on the principle of minimum equivalent fuel consumption, and construct a corresponding torque constraint problem according to the power equivalent factor, input shaft speed, target vehicle speed demand data, vehicle engine torque and vehicle motor torque.

[0100] B3. Perform constraint solving on the torque constraint problem to obtain the engine torque control sequence and the motor torque control sequence.

[0101] Furthermore, the step B3, performing constraint solving on the torque constraint problem to obtain the engine torque control sequence and the motor torque control sequence, includes:

[0102] B31. Obtaining the target torque function and torque constraint condition of the torque constraint problem;

[0103] B32. Constructing a first quadratic programming subproblem corresponding to the torque constraint problem according to the target torque function and the torque constraint condition;

[0104] In the embodiment of the present application, if the driving working mode is the driving charging mode in the irregular working mode, the target torque function can be expressed as:

[0105]

[0106] Where f(x) is the target torque function; min() is the minimum value function; f fuek(x1,ω) is the function of fuel consumption with respect to engine torque x1 and input shaft speed x1; s is the power equivalent factor; x1 is the engine torque; ω is the input shaft speed; x2 is the motor torque; Q L is the lower calorific value of diesel; f eff (x2,ω) is the function of motor efficiency with respect to motor torque x2 and input shaft speed ω.

[0107] The corresponding torque constraint can be expressed as:

[0108]

[0109] Among them, T e_min (ω) is the minimum torque value of the engine at the input shaft speed ω; T e_max (ω) is the maximum torque value of the engine at the input shaft speed ω; T m_max (ω) is the maximum torque value of the motor at the input shaft speed ω; T req (ω) is the target vehicle speed required torque.

[0110] It can be understood that if the driving working mode is a hybrid driving mode in the irregular working mode, the corresponding target torque function and torque constraint condition can be expressed as:

[0111]

[0112] It should be noted that step B32 can be based on the SQP (Sequential Quadratic Programming) algorithm, and the torque constraint problem of the nonlinear programming is approximated by quadratic programming at a series of optimization points to obtain the first quadratic programming sub-problem. Specifically, for the kth optimization point, the corresponding first quadratic programming sub-problem can be expressed as:

[0113]

[0114] in, x k is the kth optimization point, x k+1 is the k+1th optimization point; B k is the Quasi-Newton matrix of the kth optimization point; δ x The variable change from the kth optimization point to the k+1th optimization point; is δ x The transposed vector of g(x k ) is the target torque gradient of the kth optimization point; C E (x k ) is the equality constraint function of the kth optimization point; J E (x k) is the equality constraint function C E (x k )’s Jacobian matrix; C I (x k ) is the inequality constraint function of the kth optimization point; J I (x k ) is the inequality constraint function C I (x k ) is the Jacobian matrix of .

[0115] B33. Optimize the first quadratic programming sub-problem to obtain the engine torque control sequence and the motor torque control sequence.

[0116] Furthermore, the step B33, performing sub-problem optimization on the first quadratic programming sub-problem to obtain the engine torque control sequence and the motor torque control sequence, includes:

[0117] B331. Obtain the preset optimization accuracy conditions;

[0118] B332. Obtain a first control sequence, a first optimization point, and a first quadratic programming subproblem corresponding to the first optimization point;

[0119] B333. According to the first optimization point, performing a gradient analysis on the target torque function to obtain a target torque gradient, and according to the first optimization point, performing a constraint condition analysis on the torque constraint condition to obtain a torque constraint matrix;

[0120] In the embodiment of the present application, the target optimal solution of the first quadratic programming subproblem at several optimization points can be obtained by loop iteration, and then the engine torque control sequence and the motor torque control sequence are determined based on all the obtained target optimal solutions. In addition, the optimization accuracy condition can be a preset accuracy requirement, and its specific value can be set according to the actual situation; the first control sequence is a sequence set of target optimal solutions corresponding to the previous optimization point; the first optimization point can be the optimization point of the first quadratic programming subproblem in the current loop iteration round.

[0121] It is understandable that the gradient analysis in step B333 can be to calculate the gradient of the target torque function to obtain the target torque gradient. There are many specific ways to calculate the gradient, which will not be repeated in this application. In addition, step B333 can first obtain the inequality constraint function and the equality constraint function corresponding to the torque constraint condition at the first optimization point; then calculate the Jacobian matrix corresponding to the inequality constraint function of the first optimization point and the Jacobian matrix corresponding to the equality constraint function of the first optimization point respectively, and determine the obtained two Jacobian matrices as the torque constraint matrix.

[0122] B334. Solve the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain a target optimal solution;

[0123] Further, the step B334, solving the first quadratic programming sub-problem according to the target torque gradient and the torque constraint matrix to obtain the target optimal solution, includes:

[0124] B3341. Conditionally solve the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain an intermediate optimal solution;

[0125] Further, the step B3341, conditionally solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain an intermediate optimal solution, includes:

[0126] B33411. According to the target torque gradient and the torque constraint matrix, verify the solution of the first quadratic programming subproblem to obtain a solution verification result, wherein the solution verification result is used to indicate whether the first quadratic programming subproblem has a solution;

[0127] B33412. If the problem solution verification result is that the first quadratic programming subproblem has a problem solution, performing an original solution on the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain the intermediate optimal solution;

[0128] Alternatively, B33413, if the problem solution verification result is that a solution to the first quadratic programming subproblem exists, then obtain a second quadratic programming subproblem corresponding to the first quadratic programming subproblem, and perform a replacement solution on the second quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain the intermediate optimal solution.

[0129] In an embodiment of the present application, for the first quadratic programming subproblem corresponding to the first optimization point, the first quadratic programming subproblem corresponding to the first optimization point can first be attempted to be solved, so as to determine whether the first quadratic programming subproblem has a solution to the problem, and obtain a problem solution verification result. Specifically, if the problem solution verification result is that the first quadratic programming subproblem of the first optimization point has a solution to the problem, then the intermediate optimal solution corresponding to the first optimization point can be directly determined based on the target torque gradient and the torque constraint matrix, and the intermediate optimal solution is used to characterize the initial update direction of the first optimization point; or, if the problem solution verification result is that the first quadratic programming subproblem of the first optimization point does not have a solution to the problem, then the second quadratic programming subproblem corresponding to the first quadratic programming subproblem can be obtained, and the second quadratic programming subproblem can be solved by the target torque gradient and the torque constraint matrix to obtain an intermediate optimal solution.

[0130] Exemplarily, in the embodiment of the present application, the first optimization point is taken as the kth optimization point as an example, and the second quadratic programming subproblem can be expressed as:

[0131]

[0132] in, is the equality constraint penalty factor; is the inequality constraint penalty factor; m1 is the total number of equality constraints; m2 is the total number of inequality constraints; ξ, ζ and β are artificial variables; ξ i is the i-th artificial variable ξ; i is the i-th artificial variable ζ; is the i-m1th artificial variable β.

[0133] B3342. Perform optimal solution correction on the intermediate optimal solution to obtain the target optimal solution.

[0134] Furthermore, the step B3342, performing optimal solution correction on the intermediate optimal solution to obtain the target optimal solution, includes:

[0135] B33421, obtain the preset effect conditions;

[0136] B33422. According to the intermediate optimal solution, perform a second condition verification on the effect condition to obtain a second condition verification result;

[0137] B33423. If the second condition verification result shows that the effect condition is satisfied, the search direction of the intermediate optimal solution is corrected to obtain the target optimal solution;

[0138] Alternatively, B33424, if the result of verifying the second condition is that the effect condition is not met, the intermediate optimal solution is determined as the target optimal solution.

[0139] In an embodiment of the present application, the effect condition may be a verification condition for the Maratos effect in the SQP algorithm; the second condition verification may first be to obtain the optimal solution update vector corresponding to the intermediate optimal solution, and then determine whether the optimal solution update vector satisfies the effect condition. If the optimal solution update vector satisfies the effect condition, a second condition verification result representing that the effect condition is satisfied is obtained. At this time, a second-order correction may be performed on the intermediate optimal solution based on a second-order correction method to obtain a target optimal solution. Alternatively, if the optimal solution update vector does not satisfy the effect condition, a second condition verification result representing that the effect condition is not satisfied is obtained. At this time, the intermediate optimal solution may be directly determined as the target optimal solution.

[0140] Exemplarily, for the intermediate optimal solution corresponding to the kth optimization point, its optimal solution update vector can be expressed as:

[0141]

[0142] in, is the optimal solution update vector corresponding to the kth optimization point; c i (x k ) is the value of the i-th constraint function at the k-th optimization point, and the i-th constraint function is a sub-function of the equality constraint function or a sub-function of the inequality constraint function; d k is the intermediate optimal solution corresponding to the kth optimization point;

[0143] The effect condition can be expressed as:

[0144]

[0145] Among them, A k is the index of all constraint functions that satisfy constraint ε at the kth optimization point; c i (x k +d k ) is the i-th constraint function at the optimization point x k +d k The value at .

[0146] B335. Optimize and update the first optimization point according to the target optimal solution to obtain a second optimization point;

[0147] Furthermore, the step B335, optimizing and updating the first optimization point according to the target optimal solution to obtain a second optimization point, includes:

[0148] B3351. Obtain a penalty factor corresponding to the target optimal solution and a benefit function corresponding to the target optimal solution;

[0149] B3352. Solving the benefit function according to the penalty factor to obtain a benefit step corresponding to the target optimal solution;

[0150] B3353. According to the benefit step and the target optimal solution, the first optimization point is updated to obtain the second optimization point.

[0151] In the embodiment of the present application, the penalty factor includes an equality constraint penalty factor and an inequality constraint penalty factor. The embodiment of the present application takes the penalty factor corresponding to the k-th optimization point as an example. The equality constraint penalty factor of the k-th optimization point can be expressed as:

[0152]

[0153] in, is the equality constraint penalty factor of the kth optimization point; g k is g(x k ), which is the target torque gradient of the kth optimization point;

[0154] The inequality constraint penalty factor of the kth optimization point can be expressed as:

[0155]

[0156] in, is the inequality constraint penalty factor for the kth optimization point; [·] T is the transpose symbol.

[0157] The benefit function can be expressed as:

[0158]

[0159] in, is the effect function of the kth optimization point; is the equality constraint penalty factor of the kth optimization point; is the inequality constraint penalty factor of the kth optimization point; f(x k ) is the target torque function; c i (x k ) is the value of the i-th constraint function at the k-th optimization point.

[0160] It can be understood that, for a certain optimization point, step B3352 can be to input the equality constraint penalty factor and inequality constraint penalty factor of the optimization point into the benefit function, and obtain the step length of the benefit function at the optimization point along the direction indicated by the target optimal solution by minimizing the benefit function, so as to obtain the benefit step length; then, based on the benefit step length and the target optimal solution, the first optimization point is updated to obtain the second optimization point. Specifically, if the first optimization point is the kth optimization point, and the intermediate optimal solution of the first optimization point is the same as the target optimal solution, then the corresponding second optimization point can be expressed as:

[0161] x k+1 =x k +α k d k

[0162] Among them, x k+1 is the second optimization point; α k is the benefit step length of the kth optimization point; d k is the optimal solution for the kth optimization point.

[0163] B336. Perform a first condition verification on the optimization accuracy condition according to the first optimization point and the second optimization point to obtain a first condition verification result;

[0164] B337. If the first condition verification result is that the optimization accuracy condition is satisfied, the first control sequence is updated according to the target optimal solution to obtain a second control sequence, and the engine torque control sequence and the motor torque control sequence are obtained according to the second control sequence;

[0165] Alternatively, B338, if the result of the first condition verification is that the optimization accuracy condition is not met, the first control sequence is updated according to the target optimal solution, and the first optimization point is updated according to the second optimization point, and then the step of obtaining the first control sequence, the first optimization point and the first quadratic programming subproblem corresponding to the first optimization point is returned.

[0166] In an embodiment of the present application, step B336 may firstly obtain the optimization norm between the first optimization point and the second optimization point, specifically, perform a difference calculation on the first optimization point and the second optimization point, and then calculate the modulus of the difference vector to obtain the optimization norm; then, compare the size relationship between the optimization norm and the optimization precision condition. If the optimization norm is less than the optimization precision condition, a first condition verification result characterizing that the optimization precision condition is satisfied may be obtained, which indicates that the torque distribution has reached the exit condition of the loop iteration. At this time, the latest target optimal solution may be added to the first control sequence to obtain the second control sequence, and then the engine torque control sequence and the motor torque control sequence may be obtained based on the second control sequence; or, if the optimization norm is greater than or equal to the optimization precision condition, a first condition verification result indicating that the optimization precision condition is not satisfied may be obtained, which indicates that the torque distribution has not reached the exit condition of the loop iteration. At this time, the first control sequence may be added and updated according to the target optimal solution, and the updated first control sequence may be used as the first control sequence of the next loop iteration round, and the second optimization point may be determined as the first optimization point of the next loop iteration round, and then return to execute step B32.

[0167] It is understandable that the target optimal solution corresponding to each optimization point of the control sequence can be the energy control strategy corresponding to the engine or the energy control strategy corresponding to the motor, and the present application does not limit this. In addition, when the target vehicle is energy-jointly controlled using the engine torque control sequence and the motor torque control sequence, the target vehicle can be caused to execute each energy control strategy in the engine torque control sequence and each energy control strategy in the motor torque control sequence in turn, and when all energy control strategies in the control sequence have been executed, the step 110 is returned to; or, the step 110 is returned to after the target vehicle executes the first energy control strategy in the engine torque control sequence and the first energy control strategy in the motor torque control sequence, and the target vehicle is energy-jointly controlled by acquiring the latest engine torque control sequence and motor torque control sequence in real time, which can continuously correct the energy control deviation and is conducive to improving the effect of energy control.

[0168] An energy control system for a hybrid vehicle proposed according to an embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0169] Reference Figure 2 , an energy control system for a hybrid vehicle proposed in an embodiment of the present application includes:

[0170] The first processing unit 101 is used to obtain the trained vehicle speed prediction model and the preset torque control rule, as well as the driving speed data and the remaining power data of the target vehicle;

[0171] The second processing unit 102 is used to input the driving speed data into the trained speed prediction model to perform speed prediction and obtain predicted speed data;

[0172] The third processing unit 103 is used to obtain the driving working mode of the target vehicle according to the remaining power data and the driving demand torque of the driving speed data, wherein the driving working mode includes a regular working mode and an irregular working mode;

[0173] The fourth processing unit 104 is used to perform energy control on the target vehicle according to the torque control rule if the driving working mode is the regular working mode; or, if the driving working mode is the irregular working mode, perform torque distribution on the target vehicle speed demand torque according to the driving speed data and the remaining power data to obtain the engine torque control sequence and the motor torque control sequence, and perform energy joint control on the target vehicle according to the engine torque control sequence and the motor torque control sequence.

[0174] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0175] Reference Figure 3 , the embodiment of the present application further provides an electronic device, including:

[0176] at least one processor 201;

[0177] At least one memory 202, used to store at least one program;

[0178] When the at least one program is executed by the at least one processor 201, the at least one processor 201 implements the above method embodiment.

[0179] Similarly, it can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0180] The embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored. The program executable by the processor 201 is used to implement the above-mentioned method embodiment when executed by the processor 201.

[0181] Similarly, the contents of the above method embodiments are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0182] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the application is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part described as a larger operation is performed independently.

[0183] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present application. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional techniques of the engineer. Therefore, those skilled in the art can implement the present application set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the attached claims and their equivalents.

[0184] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method of the embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0185] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0186] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0187] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0188] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0189] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0190] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Technical personnel familiar with the field can make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A method for controlling energy of a hybrid vehicle, characterized in that: include: Obtain the trained vehicle speed prediction model and preset torque control rules, as well as the target vehicle's driving speed data and remaining power data; Inputting the driving speed data into the trained speed prediction model to perform speed prediction to obtain predicted speed data; According to the remaining power data and the driving demand torque of the driving speed data, obtaining the driving working mode of the target vehicle, wherein the driving working mode includes a regular working mode and an irregular working mode; If the driving working mode is the regular working mode, energy control is performed on the target vehicle according to the torque control rule; Alternatively, if the driving working mode is the irregular working mode, the target vehicle speed demand torque is distributed according to the driving vehicle speed data and the remaining power data to obtain an engine torque control sequence and a motor torque control sequence, and energy joint control is performed on the target vehicle according to the engine torque control sequence and the motor torque control sequence.

2. The method according to claim 1, characterized in that The step of obtaining the driving working mode of the target vehicle according to the driving demand torque of the remaining power data and the driving speed data includes: Obtain the minimum power threshold, maximum power threshold, starting torque threshold and optimal torque threshold; According to the minimum power threshold and the maximum power threshold, the remaining power data is compared to obtain a power comparison result; According to the starting torque threshold and the optimal torque threshold, performing a torque comparison on the driving demand torque to obtain a torque comparison result; The driving operation mode of the target vehicle is obtained according to the power comparison result and the torque comparison result.

3. The method according to claim 1, characterized in that The method of performing torque distribution on the target vehicle speed demand torque according to the vehicle speed data and the remaining power data to obtain an engine torque control sequence and a motor torque control sequence includes: Acquire the vehicle engine torque and the vehicle motor torque of the target vehicle, as well as the input shaft speed corresponding to the driving speed data and the power equivalent factor corresponding to the remaining power data; Constructing a torque constraint problem according to the electric quantity equivalent factor, the input shaft speed, the target vehicle speed required torque, the vehicle engine torque and the vehicle motor torque; The torque constraint problem is constrained and solved to obtain the engine torque control sequence and the motor torque control sequence.

4. The method according to claim 3, characterized in that The constrained solution of the torque constraint problem to obtain the engine torque control sequence and the motor torque control sequence includes: Obtaining a target torque function and a torque constraint condition of the torque constraint problem; Constructing a first quadratic programming subproblem corresponding to the torque constraint problem according to the target torque function and the torque constraint condition; The first quadratic programming sub-problem is optimized to obtain the engine torque control sequence and the motor torque control sequence.

5. The method according to claim 4, characterized in that The sub-problem optimization of the first quadratic programming sub-problem to obtain the engine torque control sequence and the motor torque control sequence includes: Obtain the preset optimization accuracy conditions; Obtaining a first control sequence, a first optimization point, and a first quadratic programming subproblem corresponding to the first optimization point; According to the first optimization point, performing a gradient analysis on the target torque function to obtain a target torque gradient, and according to the first optimization point, performing a constraint condition analysis on the torque constraint condition to obtain a torque constraint matrix; Solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain a target optimal solution; According to the target optimal solution, optimizing and updating the first optimization point to obtain a second optimization point; Performing a first condition verification on the optimization accuracy condition according to the first optimization point and the second optimization point to obtain a first condition verification result; If the result of the first condition verification is that the optimization accuracy condition is met, the first control sequence is updated according to the target optimal solution to obtain the second control sequence, and the engine torque control sequence and the motor torque control sequence are obtained according to the second control sequence; or, if the result of the first condition verification is that the optimization accuracy condition is not met, the first control sequence is updated according to the target optimal solution, and the first optimization point is updated according to the second optimization point, and then the step of obtaining the first control sequence, the first optimization point and the first quadratic programming subproblem corresponding to the first optimization point is returned.

6. The method according to claim 5, characterized in that The step of solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain a target optimal solution includes: Conditionally solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain an intermediate optimal solution; Performing optimal solution correction on the intermediate optimal solution to obtain the target optimal solution.

7. The method according to claim 6, characterized in that The conditionally solving the first quadratic programming subproblem according to the target torque gradient and the torque constraint matrix to obtain an intermediate optimal solution includes: According to the target torque gradient and the torque constraint matrix, a problem solution verification is performed on the first quadratic programming subproblem to obtain a problem solution verification result, wherein the problem solution verification result is used to indicate whether a problem solution exists for the first quadratic programming subproblem; If the problem solution verification result is that the first quadratic programming subproblem has a solution, the first quadratic programming subproblem is originally solved according to the target torque gradient and the torque constraint matrix to obtain the intermediate optimal solution; or, if the problem solution verification result is that the first quadratic programming subproblem has a solution, a second quadratic programming subproblem corresponding to the first quadratic programming subproblem is obtained, and the second quadratic programming subproblem is replaced and solved according to the target torque gradient and the torque constraint matrix to obtain the intermediate optimal solution.

8. The method according to claim 6, characterized in that The performing optimal solution correction on the intermediate optimal solution to obtain the target optimal solution includes: Get the preset effect conditions; According to the intermediate optimal solution, performing a second condition verification on the effect condition to obtain a second condition verification result; If the second condition verification result is that the effect condition is met, the search direction of the intermediate optimal solution is corrected to obtain the target optimal solution; or, if the second condition verification result is that the effect condition is not met, the intermediate optimal solution is determined as the target optimal solution.

9. The method according to claim 5, characterized in that The step of optimizing and updating the first optimization point according to the target optimal solution to obtain a second optimization point includes: Obtaining a penalty factor corresponding to the target optimal solution and a benefit function corresponding to the target optimal solution; Solving the benefit function according to the penalty factor to obtain a benefit step corresponding to the target optimal solution; According to the benefit step and the target optimal solution, the first optimization point is updated to obtain the second optimization point.

10. An energy control system for a hybrid vehicle, characterized in that: include: The first processing unit is used to obtain the trained vehicle speed prediction model and the preset torque control rule, as well as the driving speed data and the remaining power data of the target vehicle; A second processing unit is used to input the driving speed data into the trained speed prediction model to perform speed prediction and obtain predicted speed data; a third processing unit, configured to obtain a driving working mode of the target vehicle according to the remaining power data and the driving demand torque of the driving speed data, wherein the driving working mode includes a regular working mode and an irregular working mode; a fourth processing unit, configured to perform energy control on the target vehicle according to the torque control rule if the driving working mode is the regular working mode; Alternatively, if the driving working mode is the irregular working mode, the target vehicle speed demand torque is distributed according to the driving vehicle speed data and the remaining power data to obtain an engine torque control sequence and a motor torque control sequence, and energy joint control is performed on the target vehicle according to the engine torque control sequence and the motor torque control sequence.

Images