Vehicle multi-power coordination control method and system, terminal and medium

Abstract

The invention provides a vehicle multi-power-source coordination control method and system, a terminal and a medium, and belongs to the technical field of vehicle multi-power-source coordination control. The method comprises the steps of obtaining current driving state information of a vehicle; obtaining a preset model prediction controller; the current driving state information is input into a preset model prediction controller for prediction processing, and current state information of the vehicle and prediction state information of the next moment are obtained; and controlling the vehicle according to the current state information and the control information corresponding to the predicted state information at the next moment. The technical problem that how to effectively integrate and optimize power output from different power sources in vehicle multi-power coordination control in the prior art so as to ensure that the vehicle can achieve the optimal performance under various driving conditions is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control of multiple power sources of a vehicle, and in particular to a method, system, terminal and medium for coordinated control of multiple power sources of a vehicle. Background Art

[0002] With the development of the automobile industry, the complexity of vehicle power systems has increased day by day, especially with the rise of hybrid vehicles and electric vehicles, which has expanded the vehicle power source from a single traditional internal combustion engine to a combination of multiple power sources such as internal combustion engines and electric motors. Although this multi-power source configuration has brought better fuel economy, lower emissions and stronger power performance, it has also brought challenges to power coordination control.

[0003] Traditional vehicle power control methods are often based on simple logical judgments and fixed parameter settings, which cannot fully adapt to the dynamic changes of the vehicle under different driving conditions, resulting in unsmooth power output, poor fuel economy, and even in some extreme cases, driving safety may be affected. In addition, the response speed and characteristic differences between different power sources also make power coordination control more complicated.

[0004] In order to solve these problems, researchers began to explore more advanced control strategies, among which model predictive control (MPC) has attracted much attention because of its ability to predict future behavior based on the system model and optimize the current control input accordingly. However, there are still many difficulties in directly applying MPC to the coordinated control of multiple power sources in vehicles, such as how to accurately build a model of the vehicle power system, how to reasonably set the objective function and control constraints of MPC, and how to effectively handle the coordination relationship between multiple power sources.

[0005] Therefore, the present invention proposes a vehicle multi-power coordinated control method, which can fully consider the dynamic changes of the vehicle under different driving conditions, combine the characteristics of multiple power sources, and realize the optimal distribution of power output through advanced algorithms such as model predictive control, thereby improving the vehicle's fuel economy, power performance and driving safety. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a vehicle multi-power coordinated control method to solve the technical problem of how the vehicle multi-power coordinated control in the prior art should effectively integrate and optimize the power output from different power sources (such as internal combustion engines, electric motors, etc.) to ensure that the vehicle can achieve the best performance under various driving conditions, including fuel economy, power response speed, driving stability and overall driving safety.

[0007] The technical solution adopted by the present invention is:

[0008] A first aspect of an embodiment of the present application provides a vehicle multi-power coordinated control method, the method comprising:

[0009] Get the current driving status information of the vehicle;

[0010] Get a preset model predictive controller;

[0011] Inputting the current driving state information into a preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment;

[0012] The vehicle is controlled according to the control information corresponding to the current state information and the predicted state information at the next moment.

[0013] Furthermore, before obtaining the preset model predictive controller, the method further includes:

[0014] Build a preset model predictive controller;

[0015] The method of constructing a preset model predictive controller comprises:

[0016] obtaining an initial model controller, and obtaining a torque demand estimator;

[0017] The initial model controller and the torque demand estimator are fused to obtain a preset model predictive controller.

[0018] Furthermore, before obtaining the initial model controller, the method further includes:

[0019] Build the initial model controller;

[0020] The constructing of the initial model controller comprises:

[0021] Get the original model controller;

[0022] The objective function, control boundary conditions and state quantity boundary conditions of the original model controller are set to obtain an initial model controller.

[0023] Furthermore, the current driving state information includes the current driving speed, motor torque, throttle opening and engine speed.

[0024] The current driving state information is input into a preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment, including:

[0025] The current driving speed, the motor torque, the throttle opening and the engine speed are input into the torque demand estimator for calculation to obtain the torque demand information;

[0026] The current driving speed, motor torque, throttle opening, engine speed and torque demand information are input into the preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment.

[0027] Furthermore, the current driving state information is input into the torque demand estimator for calculation to obtain torque demand information, including:

[0028] The torque demand information is obtained by performing calculations in the torque demand estimator using the method shown in the following formula:

[0029]

[0030] Among them, C 1 and C 2 is the parameter related to friction; r W is the radius of the wheel; m is the mass of the vehicle; g is the acceleration due to gravity, T d The torque indicated by the torque demand information, C D Drag coefficient, v Vehicle speed, Av(t) 2 It is the square of the vehicle's speed multiplied by the cross-sectional area, and represents the air resistance encountered by the vehicle when it is moving.

[0031] Furthermore, the control information includes:

[0032] Slow control information, general control information and emergency control information;

[0033] The slow control information is expressed as:

[0034]

[0035] Among them, is ω e Engine speed; b is fuel consumption rate; ρ is fuel density; S is driving distance; X is control quantity, Ω is control domain;

[0036] The general control information is expressed as:

[0037]

[0038] Among them, T ωmax is the maximum torque that the motor can output at the current speed, T ref is the output torque reference value;

[0039] The emergency control information is expressed as:

[0040]

[0041] A second aspect of an embodiment of the present application provides a vehicle multi-power coordinated control system, the system comprising:

[0042] Driving state perception module, to obtain the current driving state information of the vehicle;

[0043] A model predictive controller loading module is used to obtain a preset model predictive controller;

[0044] A prediction processing module, inputting the current driving state information into a preset model prediction controller for prediction processing, and obtaining the current state information of the vehicle and the predicted state information at the next moment;

[0045] The control execution module controls the vehicle according to the control information corresponding to the current state information and the predicted state information at the next moment.

[0046] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method described in the first aspect.

[0047] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method described in the first aspect.

[0048] It can be seen from the above technical solution that the beneficial technical effects of the present invention are as follows:

[0049] 1. The present invention fully considers the dynamic changes of the vehicle under different driving conditions, combines the characteristics of multiple power sources, and realizes the optimal distribution of power output through advanced algorithms such as model predictive control (MPC), thereby significantly improving the vehicle's fuel economy, power performance and driving safety. At the same time, through real-time monitoring and feedback mechanisms, the system can dynamically adjust the control strategy to adapt to changing driving conditions.

[0050] 2. The present invention inputs the current driving state information into the preset model prediction controller for accurate prediction processing, so that the system can comprehensively consider the current and future operation requirements of the vehicle (such as road conditions and load conditions) and optimize the power distribution and torque output between multiple power sources (such as engines and motors). This coordinated control method effectively reduces the redundancy and conflict of the power system, improves the overall power performance and response speed, and ensures the safety and efficiency of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the prior art description. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0052] Figure 1 This is a flow chart of an embodiment of the present invention;

[0053] Figure 2 This is a flow chart of a control system based on MPC according to an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of the system dynamics of an embodiment of the present invention;

[0055] Figure 4 1 is a block diagram of an MPC controller in Matlab according to an embodiment of the present invention;

[0056] Figure 5 This is a battery SOC variation diagram of an embodiment of the present invention;

[0057] Figure 6 A vehicle speed following diagram according to an embodiment of the present invention;

[0058] Figure 7 This is a speed following diagram of the engine and the motor according to an embodiment of the present invention;

[0059] Figure 8 This is a power variation diagram of each component in the embodiment of the present invention;

[0060] Fig. 9 The embodiment of the present invention is a bus voltage variation curve;

[0061] Fig.10 This is a system framework diagram of an embodiment of the present invention;

[0062] Fig.11 This is a terminal framework diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0064] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0065] Example

[0066] This embodiment provides a vehicle multi-power coordinated control method, such as Figure 1 As shown, the following steps are included:

[0067] S01: Obtaining the current driving status information of the vehicle;

[0068] S02: Build and obtain a preset model predictive controller;

[0069] Specifically, the steps to build a preset model predictive controller include:

[0070] Get the initial model controller and build the torque demand estimator.

[0071] The initial model controller is fused with the torque demand estimator to form a preset model predictive controller.

[0072] Before getting the initial model controller, the following steps need to be performed:

[0073] Constructing an initial model controller specifically includes obtaining the original model controller, and setting the objective function, control boundary conditions and state quantity boundary conditions, so as to obtain the initial model controller.

[0074] S03: Input the current driving state information into a preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment.

[0075] Specifically, the current driving state information includes: current driving speed, motor torque, throttle opening and engine speed. This process can be achieved in the following ways:

[0076] The current driving speed, motor torque, throttle opening and engine speed are input into the torque demand estimator for calculation to obtain torque demand information.

[0077] Next, the current driving speed, motor torque, throttle opening, engine speed and torque demand information are input into the preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment.

[0078] The torque demand information is calculated as follows:

[0079]

[0080] Among them, C 1 and C 2 is the parameter related to friction; r W is the radius of the wheel; m is the mass of the vehicle; g is the acceleration due to gravity, T d The torque indicated by the torque demand information, C D Drag coefficient, v Vehicle speed, Av(t) 2It is the square of the vehicle's speed multiplied by the cross-sectional area, and represents the air resistance encountered by the vehicle when it is moving.

[0081] S04: Control the vehicle according to control information corresponding to the current state information and the predicted state information at the next moment.

[0082] Specifically, control information is divided into slow control information, general control information and emergency control information:

[0083] Slow control information, expressed as:

[0084]

[0085] Among them, is ω e Engine speed; b is fuel consumption rate; ρ is fuel density; S is driving distance; X is control quantity, Ω is control domain;

[0086] General control information, expressed as:

[0087]

[0088] Among them, T ωmax is the maximum torque that the motor can output at the current speed, T ref is the output torque reference value;

[0089] Emergency control information, expressed as:

[0090]

[0091] The working principle of the embodiment is described in detail below:

[0092] At present, the research and development of multi-power coordinated control and electromechanical composite transmission systems in China is relatively lagging, mainly due to the limitations of electrical control technology and mechanical manufacturing technology. At present, the solutions on the market are mainly based on series and parallel structures. Relatively speaking, developed countries such as Europe and the United States have generally adopted electromechanical composite transmission systems in product development, using one or more planetary transmission mechanisms to achieve coordinated control between different power sources.

[0093] In 2005, a US automobile company jointly developed a dual-mode electromechanical hybrid transmission system with DaimlerChrysler and BMW. This system has two sets of motors, which can realize two energy modes. When the vehicle is running in the first mode, the engine and the motor can be driven independently or together. Usually, when the vehicle is running, one set of motors acts as an engine, and the other set of motors acts as an electric motor, which can provide main power or auxiliary power when driving the car.

[0094] When the automobile engine is working in the second mode, the system is always in a high-speed no-load operation state. At this time, the two generators are in a normal working state. By steplessly changing the speed direction of the two drive motors, a two-way stepless change from the automobile engine speed direction to the motor output motor speed direction is completed.

[0095] In the current research on multi-power coordinated control of hybrid vehicles, heuristic methods are the most common choice, with high potential for real-time implementation, but they are still lacking in terms of optimality, with the main disadvantage being that the system cannot adapt well to changing traffic conditions. On the other hand, depending on how they are formulated, methods based on optimization to derive global optimal solutions can be complex and can only be solved numerically at the cost of high computational load, which greatly reduces the possibility of implementation.

[0096] There are still the following deficiencies in the current research on multi-power coordinated control of hybrid vehicles:

[0097] Torque output has a delay. Most of the current coordinated control strategies are based on the assumption of ideal engine and clutch torque output, and the torque response delay is ignored, that is, the torque of each part of the system can immediately achieve the target state after the system receives the command signal. However, in some practical applications, the torque transmission process still has a certain lag.

[0098] Structural parameters are variable and external interference is random. In the current research on HEV coordinated control strategies, most of the assumptions are that there is no external interference and the changes in structural parameters are not considered. This will lead to a certain gap between the conclusions drawn from the research and the actual application, and it is impossible to adapt well to the situation with external interference and make the vehicle show ideal characteristics.

[0099] The applicability of coordinated control systems needs to be improved. The progress of control theory provides a solid theoretical basis for the research of coordinated control systems. The optimization of the system is achieved by building a model to ensure that the control system exhibits ideal excellent characteristics under normal working conditions. The model established based on control theory will inevitably reduce the practicality of the controller while improving its accuracy due to its high-order and low robustness.

[0100] The flexibility of the coordinated control system needs to be improved. The existing systems cannot adapt well to changing traffic conditions. Therefore, the model used to estimate the instantaneous power does not represent the vehicle behavior accurately enough, the optimality of the solution will be difficult to show and the percentage of improvement may be low, and a new meta-model must be recalculated for each new driving profile.

[0101] In one embodiment, in order to maximize the fuel economy and power of a hybrid electric vehicle, the present invention adopts a multi-power control strategy based on model predictive control to improve various vehicle performances according to the driver's driving characteristics.

[0102] Establish model predictive control systems;

[0103] The application of linear model predictive control (MPC) and dynamic matrix control (DMC) in the industry is becoming more and more widespread. Because model predictive control can solve constraint problems and has the advantages of feedforward-feedback, it can predict the changes of system parameters, etc. The control system based on MPC can be achieved through Figure 2 The process shown is implemented as follows, Figure 2 Where R is the vector of the reference output, U is the vector of the controlled quantity of the controlled object, and Y is the vector of the output of the controlled object.

[0104] In order to predict the output of the model in the future, the output of the model is considered as a function of the control input, the measured disturbance, the unmeasured disturbance and the two dynamic models, as shown in the following formula:

[0105]

[0106] Where u(k) is the control input; d(k) is the measured disturbance; w y (k) is the unmeasured interference; P u is the system dynamics of the control input; P d To measure the system dynamics of the disturbance, Figure 3 shown.

[0107] Building state-space expressions

[0108] The state space expression is used to estimate the future state x(k+1) and state disturbance w of the model. x (k+1), output disturbance w y (k+1) and the model’s future output y(y+1).

[0109] The state space expression is shown below:

[0110] x(k+1)=Ax(k)+B u u(k)+B w w x (k)+B d d(k)

[0111] y(k)=Cx(k)+w y (k)

[0112] Where A is the state transfer matrix, which describes the evolution law of the system state in one time step; B uis the input matrix, which indicates how the input of the system affects the evolution of the system state; B w is the process noise gain matrix, which describes the response of the system to the process noise; w x (k) is the process noise vector at time step (k), which represents the random disturbance in the system caused by unmodeled external factors or uncertainties; B d is the external disturbance gain matrix, which describes the response of the system to external disturbances. C is the bookcase matrix, which describes the relationship between the system state and output. x(k) is the state vector of the system at time step (k), which describes the state of the system at the current time step.

[0113] In order to be closer to the actual situation, white noise ε is added to the input and output disturbances 1 (k) and white noise ε 2 (k) The following formula is obtained:

[0114] w x (k+1)=w x (k)+ε 1 (k)

[0115] w y (k+1)=w y (k)+ε 2 (k)

[0116] Adding white noise and input and output interference into the state space expression yields the following equation:

[0117]

[0118] (k) = Cx(k) + w y (k)

[0119] in:

[0120]

[0121] Among them, I is the unit matrix, which is a square matrix with all elements on the diagonal being 1 and all other elements being 0. It represents a special linear transformation that keeps a vector unchanged. Y is the output vector of the system, which contains the observed value or measurement result; c is the output matrix, which describes the relationship between the system state and the output; x is the state vector of the system, which contains the state variables of the system; D is the control input matrix, which describes the influence of the external control input on the evolution of the system state; u is the control input vector, which contains the external control input of the system; SOC is the remaining power of the battery; v is the voltage of the battery; ω e is the speed of the motor; T e is the torque of the motor; T A is the torque of mechanical system A; T Bis the torque of mechanical system B; δSOC is the rate of change of the remaining battery capacity; δv is the rate of change of the battery voltage; δω e is the rate of change of motor speed; v c is the control input; δT e is the rate of change of the motor torque; δT A is the rate of change of torque of mechanical system A; δT B The rate of change of torque of mechanical system B.

[0122] Establishment of objective function

[0123] Quadratic Programming can solve general linear constrained optimization problems and is therefore widely used. The objective function of QP is a quadratic function, and the constraint function of the boundary condition is a linear function. For minimization problems, the objective function is a convex function. By solving the solution of the QP problem sequence, any local minimum of the problem will be the global minimum.

[0124] The general objective function can be described as follows:

[0125]

[0126] stX is ≥ 0

[0127] In the formula, c 1 …c n is the coefficient vector in the objective function; d 11 …d nn is the quadratic term coefficient matrix, which describes the coefficients of the quadratic terms in the objective function; a 11 …a mn is the element of the matrix on the left side of the constraint condition, describing the linear relationship in the constraint condition; b 1 …b m is the right-hand side vector of the constraints, which contains the upper bound of the constraints.

[0128] For the (dynamic matrix control) DMC algorithm, the following quadratic objective function is established:

[0129]

[0130] In the formula, is a sequence of control input increments, which are control inputs predicted from the current time (k) to the next (m) time points; is the error weight matrix, which is used to adjust the degree of penalty for the output error at the future (p) moments; p is the moment; y(k+l|k) is the output value at the moment (k+l) predicted at the moment (k); r(k+l) is the reference value or expected output value at the moment (k+l); m is the number of control quantities; is the control quantity weight matrix, which is used to adjust the penalty degree of the control increment; Δ(k+l-1)] is the (l)th increment value in the control increment sequence.

[0131] Establish control quantity boundary conditions and state quantity boundary conditions;

[0132] (1) Establishment of boundary conditions of control quantity

[0133] The constraint conditions satisfied by the feasible domain of engine speed control are analyzed as follows:

[0134] Assume the driver's required power is P req , considering the battery state of charge, satisfying the constraints:

[0135]

[0136] Where P ch / disch The charging or discharging power of the battery, depending on whether the battery is in the charging or discharging state; The status of the battery, usually refers to the remaining capacity of the battery.

[0137] After adding the engine speed constraint, the engine control torque decision domain is reduced to:

[0138] Ω e ={T e |T g (ω e )-σ e T emin (ω e )≤T e ≤T gmax (ω e )

[0139] Where, T e is the output torque of the generator, which is the variable that needs to be controlled; T g (ω e ) is the mechanical torque-speed characteristic curve of the generator, which describes the mechanical output torque of the generator at a given generator speed; σ e The electrical angular velocity error usually indicates the difference between the actual electrical angular velocity of the motor or generator and the expected electrical angular velocity; ω e is the electrical angular velocity of the motor or generator.

[0140] In one embodiment, the constraints satisfied by the feasible domain of the speed control of the two motors of the research object are analyzed as follows:

[0141] Control domain Ω of motor A and motor B A , control domain Ω B .

[0142] ΩA ={T A |-T Amax ≤T A ≤-T Amax}

[0143] Ω B ={T B |-T Bmax ≤T B ≤-T Bmax}

[0144] Where, T A is the torque of the first component or part; T B is the torque of the second component or part; T Amax Indicates the maximum torque value that motor A can provide; T Bmax Indicates the maximum torque value that motor B can provide.

[0145] The control torque should satisfy the following formula after being discretized:

[0146]

[0147] in, and Respectively represent the torque associated with the motor and other components; and represents the torque values ​​of motor B and motor A at the end of the next control cycle, and these values ​​must remain within the maximum torque limit of the motor; T Amin Indicates the minimum torque value that motor A can provide; T Bmin Indicates the minimum torque value that motor B can provide.

[0148] (2) Establishment of state boundary conditions

[0149] Determine the internal state variables of the system, that is, determine x = [δSOC, δv, δω e ,SOC,v,ω e ] T The value range of .

[0150]

[0151] stΩ SOC =SOC|0.45 <SOC<0.75

[0152] Where V oc (t) is the open circuit voltage of the battery, that is, the voltage of the battery when there is no load; R batt (t) is the internal resistance of the battery, reflecting the internal loss and impedance of the battery; f batt (T A (t),TB (t) is a function of the battery, which is a specific function related to temperature and is used to describe the performance of the battery at different temperatures; C batt is the capacity of the battery, that is, the amount of charge the battery can store.

[0153] During the δt time, the engine speed should also satisfy:

[0154]

[0155] In the formula, J e is the change in motor speed; k 1 k 2 i f η f It is the comprehensive influence of motor characteristics, including the product of factors such as torque constant, field current and efficiency; and Represents the torque associated with the motor and other components, respectively.

[0156] During vehicle driving, the relationship between the change in vehicle speed and the control amount should also be subject to the following restrictions:

[0157]

[0158] Where, T o (t) is the time-dependent external torque, affecting the change in speed; r w is the radius of the wheel; C D is the aerodynamic drag coefficient of the vehicle; Av(t) 2 It is the square of the vehicle speed multiplied by the cross-sectional area, representing the air resistance encountered by the vehicle when it moves; C g is the center of gravity of the vehicle or other parameters related to gravity; G is the acceleration of gravity; δm is the mass change rate of the vehicle, that is, the rate of change of the vehicle's mass over time.

[0159] A model predictive controller is established for the vehicle control system based on the above MPC;

[0160] In order to evaluate the model predictive MPC controller and implement the vehicle control system based on MPC, this embodiment designs a model predictive controller. The goal of the controller is to enable the vehicle to track the input driving cycle and improve the driving performance.

[0161] The torque demand estimator provides the torque demand to the MPC controller. Based on the torque demand, current driving speed, motor torque, throttle opening, and engine speed, the MPC controller predicts the future model output within the prediction horizon and predicts the changes in model input within the control horizon. The MPC controller provides the optimal device input for the vehicle devices.

[0162] The torque demand estimate is given by:

[0163]

[0164] Among them, C 1 and C 2 is the parameter related to friction; r W is the radius of the wheel; m is the mass of the vehicle; g is the acceleration due to gravity, T d The torque indicated by the torque demand information, C D Drag coefficient, v Vehicle speed, Av(t) 2 It is the square of the vehicle's speed multiplied by the cross-sectional area, and represents the air resistance encountered by the vehicle when it is moving.

[0165] The MPC-based vehicle control system was implemented and evaluated in the MATLAB / Simulink environment. After obtaining the torque demand estimate, the MPC controller was established in the MATLAB / Simulink environment:

[0166] The MATLAB Model Predictive Control Toolbox is used to easily build an MPC controller. The block diagram of the MPC controller in MATLAB is as follows: Figure 4 As shown, in Figure 4 The controller consists of a state observer, set points, cost functions and constraints, and a quadratic programming (QP) solver. The measured disturbances are independent inputs that affect the operation of the controlled object.

[0167] In model predictive control (MPC), first setpoints define the target state or output of the system. The state observer uses the current input and output data of the system to estimate its internal state. The model predictive controller receives these estimated states and setpoints, predicts future behavior, and uses a cost function to evaluate the effectiveness of different control strategies. The cost function usually includes factors such as tracking error and control energy consumption. The goal is to find the optimal control input that minimizes the cost function. The QP solver plays a key role in this process. It calculates the optimal control input based on the cost function and system constraints. The control input is applied to the controlled object, resulting in the actual system response. The output feedback then compares the measured actual output with the reference input, and any deviation is used to update the state observer and controller for adjustment in the next control cycle. This process is repeated at each time step, ensuring that the system can adapt to changing conditions and optimize its performance.

[0168] After establishing the MPC control framework, select the appropriate objective function

[0169] After establishing the MPC control framework, it is crucial to select a suitable objective function. The objective function defines the goal of the control strategy optimization, which usually includes indicators such as tracking error, control energy consumption, and system stability. Different driving intentions (such as slow, normal, and urgent) require different objective functions to adapt to changes in driving behavior and road conditions.

[0170] The selected objective function is directly linked to the performance and optimization goals of the control strategy. Different objective functions reflect different driving needs and priorities. For example, a slow driving intention may focus more on fuel efficiency, while an urgent driving intention requires fast power output. These objective functions ensure that the MPC controller can adjust its behavior according to driving conditions and intentions to achieve the best driving experience and system performance.

[0171] Different objective functions are selected based on different driving characteristics (including driving intention and driving style).

[0172] The slow objective function form is shown as follows:

[0173]

[0174] Among them, is ω e Engine speed; b is fuel consumption rate; ρ is fuel density; S is driving distance; X is control quantity, Ω is control domain;

[0175] The general objective function is as follows:

[0176]

[0177] Among them, T ωmax is the maximum torque that the motor can output at the current speed, T ref is the output torque reference value;

[0178] The emergent objective function is as follows:

[0179]

[0180] Different objective functions correspond to different driving intentions. When it is a slow intention, the objective function considers economic factors; when it is a general intention, the objective function considers the combination of vehicle and vehicle; when it is an emergency intention, the objective function considers dynamics.

[0181] Mode switching strategy based on Stateflow

[0182] Based on the mode switching strategy of Stateflow, the logical conditions for switching between modes make the conditions for switching between states clearer and the switching more stable;

[0183] Simulink / Stateflow can be used to efficiently develop mode switching strategies. Stateflow uses finite state machine theory and implements complex logic development through simple graphical interactions. Stateflow can also automatically compile complex graphical logic into C language. In this mode, each state module can correspond to a state module. The arrows between states are the transfer conditions between states, that is, the logical conditions for switching between modes, making the conditions clearer and the switching more stable when switching between states.

[0184] Offline simulation results

[0185] Offline simulation results, such as Figures 5 to 9 As shown, the simulation results show that the battery SOC can be in the range of 0.6 to 0.45, and the battery SOC can be kept stable or even charged when the power demand begins to rise around 1150 seconds; the vehicle speed following diagram shows that the vehicle speed following error is always less than 1%, and the model can complete the vehicle speed following very well; the engine speed is between 3300 and 4000 rpm, corresponding to the high-efficiency area in the engine power diagram; the motor speed change is consistent with the vehicle speed change trend. Since the object of the present invention is a series hybrid vehicle, the vehicle speed change depends on the motor change; the bus voltage change range is between 840 volts and 940 volts, and the voltage is relatively stable.

[0186] like Fig.10 As shown, the embodiment of the present application also provides a vehicle multi-power coordinated control system, the system comprising:

[0187] The driving state perception module 501 obtains the current driving state information of the vehicle;

[0188] A model prediction controller loading module 502 obtains a preset model prediction controller;

[0189] The prediction processing module 503 inputs the current driving state information into a preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment;

[0190] The control execution module 504 controls the vehicle according to the control information corresponding to the current state information and the predicted state information at the next moment.

[0191] like Fig.11As shown, an embodiment of the present application also provides a terminal, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute part or all of the steps described in a vehicle multi-power coordinated control method in an embodiment of the present application.

[0192] An embodiment of the present application also provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes part or all of the steps described in a vehicle multi-power coordinated control method in an embodiment of the present application.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A vehicle multi-power coordinated control method, characterized in that: The method includes: Get the current driving status information of the vehicle; Get a preset model predictive controller; Inputting the current driving state information into a preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment; The vehicle is controlled according to the control information corresponding to the current state information and the predicted state information at the next moment.

2. A vehicle multi-power coordinated control method according to claim 1, characterized in that: Before obtaining the preset model predictive controller, the method further includes: Build a preset model predictive controller; The method of constructing a preset model predictive controller comprises: obtaining an initial model controller, and obtaining a torque demand estimator; The initial model controller and the torque demand estimator are fused to obtain a preset model predictive controller.

3. A vehicle multi-power coordinated control method according to claim 2, characterized in that: Before obtaining the initial model controller, the method further includes: Build the initial model controller; The constructing of the initial model controller comprises: Get the original model controller; The objective function, control boundary conditions and state quantity boundary conditions of the original model controller are set to obtain an initial model controller.

4. A vehicle multi-power coordinated control method according to claim 3, characterized in that: The current driving state information includes the current driving speed, motor torque, throttle opening and engine speed; The current driving state information is input into a preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment, including: The current driving speed, the motor torque, the throttle opening and the engine speed are input into the torque demand estimator for calculation to obtain the torque demand information; The current driving speed, motor torque, throttle opening, engine speed and torque demand information are input into the preset model prediction controller for prediction processing to obtain the current state information of the vehicle and the predicted state information at the next moment.

5. A vehicle multi-power coordinated control method according to claim 4, characterized in that: The current driving state information is input into the torque demand estimator for calculation to obtain torque demand information, including: The torque demand information is obtained by performing calculations in the torque demand estimator using the method shown in the following formula: Among them, C1 and C2 are parameters related to friction; r W is the radius of the wheel; m is the mass of the vehicle; g is the acceleration due to gravity, T d The torque indicated by the torque demand information, C D Drag coefficient, v Vehicle speed, Av(t) 2 It is the square of the vehicle's speed multiplied by the cross-sectional area, and represents the air resistance encountered by the vehicle when it is moving.

6. A vehicle multi-power coordinated control method according to claim 1, characterized in that: The control information includes: Slow control information, general control information and emergency control information; The slow control information is expressed as: Among them, is ω e Engine speed; b is fuel consumption rate; ρ is fuel density; S is driving distance; X is control quantity, Ω is control domain; The general control information is expressed as: Among them, T ωmax is the maximum torque that the motor can output at the current speed, T ref is the output torque reference value; The emergency control information is expressed as:

7. A vehicle multi-power coordinated control system, characterized in that: The system comprises: Driving state perception module, which obtains the current driving state information of the vehicle; A model predictive controller loading module is used to obtain a preset model predictive controller; A prediction processing module, inputting the current driving state information into a preset model prediction controller for prediction processing, and obtaining the current state information of the vehicle and the predicted state information at the next moment; The control execution module controls the vehicle according to the control information corresponding to the current state information and the predicted state information at the next moment.

8. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to claims 1 to 6.

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