A control method and device for electric vehicle energy management

Abstract

The disclosed embodiment provides a control method, device, storage medium and electronic device for energy management of an electric vehicle, wherein the electric vehicle comprises a first motor, a second motor and a battery, wherein the first motor is connected to the front axle of the electric vehicle via a dog clutch, and the second motor is connected to the rear axle of the electric vehicle, and the control method comprises: constructing a prediction model based on the power system model and working mode of the electric vehicle; predicting the speed value of the electric vehicle based on the prediction model; establishing a cost function based on the predicted speed value to obtain an optimal control sequence for energy management. The disclosed embodiment establishes a vehicle power system model, establishes a vehicle speed prediction model, and forms a state space equation based on model predictive control, thereby relying on the optimal control method to assign different weights to power, economy and comfort, and finally obtains the optimal control sequence for energy management.

Description

Technical Field

[0001] The present disclosure relates to the field of electric vehicle energy control, and in particular to a control method, device, storage medium and electronic device for electric vehicle energy management. Background Art

[0002] Most of the existing energy management algorithms are aimed at hybrid electric vehicles, which belong to rule-based control strategies and optimization theory-based control strategies. They generally seek energy management control strategies for hybrid electric vehicles through optimization control. However, there is currently a lack of relevant energy management control strategies for electric vehicles, especially electric vehicles with dog clutches. Summary of the invention

[0003] The purpose of the embodiments of the present disclosure is to provide a control method, device, storage medium and electronic device for electric vehicle energy management to solve the problems existing in the prior art.

[0004] In order to solve the above technical problems, the embodiments of the present disclosure adopt the following technical solutions:

[0005] The embodiment of the present disclosure provides a control method for energy management of an electric vehicle, wherein the electric vehicle comprises a first motor, a second motor and a battery, wherein the first motor is connected to a front axle of the electric vehicle via a dog clutch, and the second motor is connected to a rear axle of the electric vehicle, and the control method comprises:

[0006] Building a prediction model based on the power system model and working mode of the electric vehicle;

[0007] Predicting a speed value of the electric vehicle based on the prediction model;

[0008] A cost function is established based on the predicted speed value to obtain an optimal control sequence for energy management.

[0009] In some embodiments, the power system model includes at least a motor power model, a battery model, and a vehicle dynamics model.

[0010] In some embodiments, the motor power model is established based on the relationship between the motor speed and torque of each motor, and is expressed by the following formula:

[0011] In drive mode: P m =Tm*n / 9550η;

[0012] In energy recovery mode: P m =Tm*n*η / 9550;

[0013] Where P mis the motor power, Tm is the motor torque, n is the motor speed, η is the motor efficiency; and / or

[0014] The battery model is expressed by the following formula:

[0015]

[0016] Wherein, P is the motor power, t is the time, V is the open circuit voltage of the battery, R is the internal resistance of the battery, and Q is the maximum capacity of the battery; and / or

[0017] The vehicle dynamics model is expressed by the following formula:

[0018]

[0019] Where M T,D is the motor torque, θ is the road slope, C D is the air resistance coefficient, A is the frontal area, ρ is the air density, v is the vehicle speed, m is the vehicle curb mass, g is the gravity coefficient, and f r is the rolling resistance coefficient and r is the wheel radius.

[0020] In some embodiments, the working mode includes a driving mode and an energy recovery mode, the driving mode includes a two-wheel drive mode and a four-wheel drive mode, and the energy recovery mode includes a rear axle energy recovery mode and a front and rear axle energy recovery mode.

[0021] In some embodiments, predicting the speed value of the electric vehicle based on the prediction model includes:

[0022] Acquire a speed value data set and an acceleration value data set of the electric vehicle based on a predetermined operating condition;

[0023] Determining the acceleration state transition probability in each speed state interval based on the speed value data set and the acceleration data set;

[0024] The speed value of the electric vehicle at the next moment is predicted based on the acceleration state transition probability.

[0025] In some embodiments, determining the acceleration state transition probability in each speed state interval based on the speed value data set and the acceleration data set includes:

[0026] Obtain the number of transitions from the first acceleration state interval to the second acceleration state interval in each speed state interval;

[0027] An acceleration state transfer number matrix is ​​obtained based on the transfer number.

[0028] In some embodiments, the system cost function is expressed as:

[0029]

[0030] Where Q, R, and F are weight coefficients, which respectively represent the change weights of the speed value, SOC value, and system control quantity of the electric vehicle at each moment. i represents the speed value of the electric car at each moment, v ref Indicates the predicted speed value; SOC i Indicates the SOC value of the electric vehicle at each moment, SOC ref Indicates the reference SOC value; u=[u t1 u t2 u e ] T is the system control quantity, u t1 u t2 are the required torques of the first motor and the second motor respectively; e Used for the current operating state of the first motor, wherein its value is represented by 0 or 1; Δu represents the change of the system control quantity at each calculation step.

[0031] The embodiment of the present disclosure also provides a control device for energy management of an electric vehicle, wherein the electric vehicle comprises a first motor, a second motor and a battery, wherein the first motor is connected to a front axle of the electric vehicle via a dog clutch, and the second motor is connected to a rear axle of the electric vehicle, and the control device comprises:

[0032] A building module, used to build a prediction model based on a power system model and an operating mode of the electric vehicle;

[0033] A prediction module, used for predicting a speed value of the electric vehicle based on the prediction model;

[0034] The acquisition module is used to establish a cost function based on the predicted speed value to obtain an optimal control sequence for energy management.

[0035] The present disclosure also provides a storage medium storing a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0036] The present disclosure also provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program on the memory.

[0037] Compared with the prior art, the disclosed embodiment realizes the power transmission and termination of the front motor through the engagement and disengagement of the dog clutch, so that the engagement / disengagement state of the dog clutch can be controlled according to the driver's intention, and the required torque is dynamically distributed to the front and rear motors, so as to realize the optimal control of the energy management of the whole vehicle. The disclosed embodiment establishes a vehicle power system model, a vehicle speed prediction model, and forms a state space equation based on model predictive control, thereby relying on the optimal control method to assign different weights to power, economy, and comfort, and finally obtains the optimal control sequence for energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0039] Figure 1 It is a structural schematic diagram of an electric vehicle in an embodiment of the present disclosure;

[0040] Figure 2 A schematic diagram of the steps of a control method for energy management of an electric vehicle in an embodiment of the present disclosure;

[0041] Figure 3 It is a schematic diagram of the principle of the control method of electric vehicle energy management in the embodiment of the present disclosure;

[0042] Figure 4 Schematic diagram of the steps of the control method for energy management of an electric vehicle in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] Various aspects and features of the present disclosure are described herein with reference to the drawings.

[0044] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0045] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0046] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0047] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0048] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0049] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0050] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0051] The first embodiment of the present disclosure relates to an energy management control method for an electric vehicle, such as Figure 1 As shown, the electric vehicle here includes a first motor 10, a second motor 20 and a battery, the first motor 10 is connected to the front axle 50 of the electric vehicle through a dog clutch 40, the second motor 20 is connected to the rear axle 60 of the electric vehicle, and the battery is connected to the first motor 10 and the second motor 20.

[0052] Thus, the electric vehicle layout targeted by the embodiment of the present disclosure is characterized in that motors are respectively provided at the front axle 50 and the rear axle 60 to realize driving, and a dog clutch 40 is provided between the motor 10 located at the front and the drive shaft, wherein the dog clutch 40 is used for outputting and cutting off the power of the front axle 50. Compared with general four-wheel drive vehicles, the electric vehicle targeted by the embodiment of the present disclosure can switch the engagement / disengagement state of the dog clutch 40 in a timely manner according to the power, economy and comfort requirements of the whole vehicle, and will meet the power, economy and comfort requirements of the whole vehicle to the greatest extent.

[0053] Furthermore, the electric vehicle involved in the embodiment of the present disclosure also includes a vehicle controller, a first motor controller, a second motor controller, an accelerator pedal position sensor module, a gear sensor module, an electronic parking sensor module, a brake master cylinder pressure sensor module, a longitudinal acceleration sensor module, a hard wire module, a CAN communication module, etc., and all of the above devices are arranged on the pure electric vehicle according to existing methods.

[0054] Among them, the accelerator pedal position sensor module, gear sensor module, electronic parking sensor module, brake master cylinder pressure sensor module, and longitudinal acceleration sensor module are electrically connected to the input end of the vehicle controller. The accelerator pedal position sensor module here is used to detect the accelerator pedal signal of the electric vehicle, and send the signal to the vehicle controller through the hard wire module; the gear sensor module is used to detect the gear signal of the electric vehicle, and send the signal to the vehicle controller through CAN communication; the electronic parking sensor module is used to detect the parking signal of the electric vehicle, and send the signal to the vehicle controller through CAN communication; the brake master cylinder pressure sensor module is used to detect the brake master cylinder pressure signal of the electric vehicle, and send the signal to the vehicle controller through CAN communication; the longitudinal acceleration sensor module is used to detect the longitudinal acceleration signal of the electric vehicle, and send the signal to the vehicle controller through CAN communication.

[0055] The output end of the vehicle controller is electrically connected to the input end of the first motor controller and the second motor controller, and the output ends of the first motor controller and the second motor controller are electrically connected to the corresponding drive motors respectively, so as to control the operation of the first motor 10 and the second motor 20 respectively.

[0056] Specifically, since a motor is provided on each of the front axle 50 and the rear axle 60 of the electric vehicle to realize driving, the dog clutch 40 provided between the first motor 10 and the front axle 50 can be engaged and disengaged according to the driver's intention, so as to realize the power intervention and withdrawal of the first motor 10. Here, the operation of the first motor 10 and the second motor 20 in the electric vehicle and the engagement and disengagement of the dog clutch 40 are controlled by the vehicle controller. Specifically, the actual axial force of the dog clutch 40 is controlled by the electronic control unit to perform axial movement, so as to realize the engagement and disengagement of the dog clutch 40, wherein the dog clutch 40 can be engaged and disengaged in real time according to the torque required by the driver, and the power of the vehicle can be guaranteed when engaged, but the energy consumption of driving two motors at the same time is significantly higher than that of driving a single motor. When disconnected, the single motor drive can meet the economy of the vehicle, but the power of the vehicle is slightly worse than that of driving the front and rear motors at the same time.

[0057] Based on the engagement and disengagement of the dog clutch 40, the electric vehicle using the embodiment of the present disclosure can be operated in two driving modes, namely a two-wheel drive mode and a four-wheel drive mode, wherein in the two-wheel drive mode, when the driver's required torque is relatively small, the dog clutch 40 located at the front end is disengaged, and the entire vehicle is driven solely by the second motor 20 located at the rear axle 60; in the four-wheel drive mode, when the driver's demand is relatively large, the dog clutch 40 located at the front end is engaged, and the entire vehicle is driven jointly by the first motor 10 located at the front axle 50 and the second motor 20 located at the rear axle 60.

[0058] The disclosed embodiments specifically relate to an energy management control method for an electric vehicle having a dog clutch, such as Figure 2 As shown, it includes the following steps:

[0059] S101, constructing a prediction model based on the power system model and working mode of the electric vehicle.

[0060] In this step, a nonlinear prediction model is constructed based on the power system model of the electric vehicle. Specifically, in this step, it is necessary to first establish the power system model of the electric vehicle, where the power system model is used to describe the operation of the electric vehicle and its main components, and based on the state of the dog clutch 40, different working modes are given to achieve the establishment of the power system model, where the power system model at least includes a motor power model, a battery model, and a vehicle dynamics model; the power system model is also established based on the working mode, where the working mode of the electric vehicle includes a driving mode and an energy recovery mode, wherein the driving mode includes a two-wheel drive mode and a four-wheel drive mode, and the energy recovery mode includes a rear axle energy recovery mode and a front and rear axle energy recovery mode.

[0061] In this way, the motor power model is established based on the relationship between the motor speed and torque of each motor, which is expressed by the following formula (1) and formula (2):

[0062] In drive mode: P m =Tm*n / 9550η (1)

[0063] In energy recovery mode: P m =Tm*n*η / 9550 (2)

[0064] Where P m is the motor power, Tm is the motor torque, n is the motor speed, and η is the motor efficiency;

[0065] Furthermore, the battery model is established by the following formula (3):

[0066]

[0067] Wherein, P is the motor power, t is the time, V is the open circuit voltage of the battery, R is the internal resistance of the battery, and Q is the maximum capacity of the battery.

[0068] The vehicle dynamics model is established by the following formula (4):

[0069]

[0070] Where M T,D is the motor torque, θ is the road slope, C D is the air resistance coefficient, A is the frontal area, ρ is the air density, v is the vehicle speed, m is the curb weight of the vehicle, g is the gravity coefficient, and f r is the rolling resistance coefficient and r is the wheel radius.

[0071] Further, the conversion is performed through the above formula:

[0072]

[0073]

[0074] According to the above formulas (1)-(4), the nonlinear prediction model of the electric vehicle is constructed as follows:

[0075]

[0076] y=Cx (5)

[0077] Where x = [v SOC] T represents the system state, t is time, u=[u t1 u t2 u e ] T is the system control quantity, u t1 u t2 are the required torques of the first motor 10 and the second motor 20, u e Indicates the operating state of the first motor 10, 0 represents off, and 1 represents on.

[0078] S102: predicting a speed value of the electric vehicle based on the prediction model.

[0079] After the prediction model is constructed based on the power system model and working mode of the electric vehicle in step S101, in this step, the speed of the electric vehicle is predicted based on the prediction model. Specifically, Figure 3As shown, the speed value of the electric vehicle can be predicted by a Markov prediction model based on standard working conditions. The Markov prediction model here can achieve a more accurate modeling of the real working conditions of the electric vehicle, and can be used as a basis for predicting the speed value of the electric vehicle, and can also be used as a basis for energy management. Specifically, Figure 4 As shown, including:

[0080] S201, acquiring a speed value data set and an acceleration value data set of the electric vehicle based on a predetermined operating condition.

[0081] In this step, a speed value data set and an acceleration value data set of the electric vehicle are obtained based on a predetermined working condition. Specifically, a certain predetermined working condition is selected in advance, such as building a traffic road network, and the speed value and acceleration value of the electric vehicle in the predetermined working condition are statistically counted by a statistical method, for example, recorded in the following format: v = [v1, v2, ... vn] and a = [a1, a2, ... an].

[0082] S202: Determine the acceleration state transition probability in each speed state interval based on the speed value data set and the acceleration data set.

[0083] After obtaining the speed value data set and the acceleration value data set of the electric vehicle based on the predetermined working condition in the above step S201, in this step, based on the speed value data set and the acceleration data set, the acceleration state transition probability in each speed state interval is determined. Specifically, in the statistical speed value data set and the acceleration data set, any time is recorded as the current time corresponding to the speed value v t , acceleration value a t And the acceleration value at the next moment.

[0084] Based on the change law of the acceleration values ​​at all times in the acceleration value data set, each speed state interval v is obtained. n The first acceleration state interval a under (n=1, 2, ..., x) i (i=1, 2, ..., y) transfer to the second acceleration state interval a j The number of transitions N of (j=1, 2, ..., y) n , where n, i, and j are state values.

[0085] Further, based on the number of transfers N n Get the acceleration state transition times matrix M n , which is represented by:

[0086]

[0087] Furthermore, the acceleration state transition probability in each speed state interval is calculated by the following formula:

[0088]

[0089] Finally, the acceleration state transition probability is converted into the acceleration state transition probability matrix,

[0090]

[0091] S203: predicting a speed value of the electric vehicle at a next moment based on the acceleration state transition probability.

[0092] After determining the acceleration state transition probability in each speed state interval based on the speed value data set and the acceleration data set in the above step S202, in this step, the speed value of the electric vehicle at the next moment is predicted based on the acceleration state transition probability. Specifically, the core of this step is to use the acceleration state with the largest transition probability to predict the speed value of the electric vehicle at the next moment. The predicted speed value here can be used as a basis for the power change of the whole vehicle, and at the same time as a basis for the reference SOC change of the whole vehicle.

[0093] S103: establishing a cost function based on the predicted speed value to obtain an optimal control sequence for energy management.

[0094] After predicting the speed value of the electric vehicle based on the prediction model in step S102, in this step, a cost function is established based on the predicted speed value to obtain the optimal control sequence of the motor. Specifically, on the basis of obtaining the predicted speed value, a system cost function based on factors including the reference SOC and the start / stop state of the first motor 10 is established to obtain the optimal control sequence of energy management, thereby improving the economy and adaptability of the algorithm to working conditions.

[0095] The system cost function here is expressed as follows:

[0096]

[0097] In the formula, Q, R, and F are weight coefficients, which respectively represent the speed value, SOC value, and change weight of the system control quantity of the electric vehicle at each moment. The three weight coefficients correspond to the power, economy, and comfort of the electric vehicle, respectively. In this way, the optimal control sequence of the motor is obtained according to the weights of the three. i represents the speed value of the electric car at each moment, v ref Indicates the predicted speed value; SOC i Indicates the SOC value of the electric vehicle at each moment, SOCref represents the reference SOC value; here u=[u t1 u t2 u e ] T is the system control quantity, u t1 u t2 are the required torques of the first motor 10 and the second motor 20 respectively; e It is used for the current operating state of the first motor 10, wherein its value is represented by 0 or 1, wherein 0 represents off and 1 represents on; Δu represents the change of the system control quantity at each calculation step.

[0098] The constraints for the above system cost function are as follows:

[0099] 0≤v≤v max

[0100] SOC min ≤SOC≤SOC max

[0101] U t1min ≤U t1 ≤U t1max

[0102] U t2min ≤U t2 ≤U t2max

[0103] v max Indicates the maximum speed that the electric vehicle can reach, SOC min Indicates the minimum limit of the battery's allowed discharge, SOC max Indicates the maximum limit of the battery allowed to discharge, U t1min , U t1max represent the minimum and maximum torque limits of the first motor 10, U t2min , U t2max represent the minimum and maximum torque limits of the second electric machine 20 , respectively.

[0104] In this way, the optimal control sequence of each motor is solved through the control system based on model prediction and quadratic programming equation established above.

[0105] The disclosed embodiment is used for an electric vehicle with a dog-type clutch, which realizes power transmission and termination of the front motor through the engagement and disengagement of the dog-type clutch, so that the engagement / disengagement state of the dog-type clutch can be controlled according to the driver's intention, and the required torque can be dynamically distributed to the front and rear motors, thereby realizing optimal control of the energy management of the entire vehicle.

[0106] The disclosed embodiment establishes a vehicle power system model and a vehicle speed prediction model to form a state space equation based on model predictive control, thereby relying on the optimal control method to assign different weights to power, economy, and comfort, and ultimately obtains the optimal control sequence for energy management.

[0107] A second embodiment of the present disclosure relates to a control device for energy management of an electric vehicle, comprising a mutually coupled building module, a prediction module, and an acquisition module, wherein the electric vehicle comprises a first motor, a second motor, and a battery, wherein the first motor is connected to a front axle of the electric vehicle via a dog clutch, and the second motor is connected to a rear axle of the electric vehicle, wherein:

[0108] The building module is used to build a prediction model based on the power system model and working mode of the electric vehicle;

[0109] The prediction module is used to predict the speed value of the electric vehicle based on the prediction model;

[0110] The acquisition module is used to establish a cost function based on the predicted speed value to obtain an optimal control sequence for energy management.

[0111] Furthermore, the power system model at least includes a motor power model, a battery model, and a vehicle dynamics model.

[0112] Furthermore, the motor power model is established based on the relationship between the motor speed and torque of each motor, and is expressed by the following formula:

[0113] In drive mode: P m =Tm*n / 9550η;

[0114] In energy recovery mode: P m =Tm*n*η / 9550;

[0115] Where P m is the motor power, Tm is the motor torque, n is the motor speed, η is the motor efficiency; and / or

[0116] The battery model is expressed by the following formula:

[0117]

[0118] Wherein, P is the motor power, t is the time, V is the open circuit voltage of the battery, R is the internal resistance of the battery, and Q is the maximum capacity of the battery; and / or

[0119] The vehicle dynamics model is expressed by the following formula:

[0120]

[0121] Where M T,D is the motor torque, θ is the road slope, C D is the air resistance coefficient, A is the frontal area, ρ is the air density, v is the vehicle speed, m is the vehicle curb mass, g is the gravity coefficient, and f r is the rolling resistance coefficient and r is the wheel radius.

[0122] Furthermore, the working mode includes a driving mode and an energy recovery mode, the driving mode includes a two-wheel drive mode and a four-wheel drive mode, and the energy recovery mode includes a rear axle energy recovery mode and a front and rear axle energy recovery mode.

[0123] The prediction module comprises:

[0124] An acquisition unit, configured to acquire a speed value data set and an acceleration value data set of the electric vehicle based on a predetermined operating condition;

[0125] A determination unit, configured to determine an acceleration state transition probability in each speed state interval based on the speed value data set and the acceleration data set;

[0126] A prediction unit is used to predict the speed value of the electric vehicle at a next moment based on the acceleration state transition probability.

[0127] The determining unit is specifically used for: obtaining the number of transitions from the first acceleration state interval to the second acceleration state interval in each speed state interval; and obtaining an acceleration state transition number matrix based on the number of transitions.

[0128] Furthermore, the system cost function is expressed as:

[0129]

[0130] Where Q, R, and F are weight coefficients, which respectively represent the change weights of the speed value, SOC value, and system control quantity of the electric vehicle at each moment. i represents the speed value of the electric car at each moment, v ref Indicates the predicted speed value; SOC i Indicates the SOC value of the electric vehicle at each moment, SOC ref Indicates the reference SOC value; u=[u t1 u t2 u e ] T is the system control quantity, u t1 u t2 are the required torques of the first motor and the second motor respectively; eUsed for the current operating state of the first motor, wherein its value is represented by 0 or 1; Δu represents the change of the system control quantity at each calculation step.

[0131] The disclosed embodiment is used for an electric vehicle with a dog-type clutch, which realizes power transmission and termination of the front motor through the engagement and disengagement of the dog-type clutch, so that the engagement / disengagement state of the dog-type clutch can be controlled according to the driver's intention, and the required torque can be dynamically distributed to the front and rear motors, thereby realizing optimal control of the energy management of the entire vehicle.

[0132] The disclosed embodiment establishes a vehicle power system model and a vehicle speed prediction model to form a state space equation based on model predictive control, thereby relying on the optimal control method to assign different weights to power, economy, and comfort, and ultimately obtains the optimal control sequence for energy management.

[0133] The third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the method provided by the first embodiment of the present disclosure is implemented, including the following steps S11 to S13:

[0134] S11, constructing a prediction model based on the power system model and working mode of the electric vehicle;

[0135] S12, predicting a speed value of the electric vehicle based on the prediction model;

[0136] S13, establishing a cost function based on the predicted speed value to obtain an optimal control sequence for energy management.

[0137] Furthermore, when the computer program is executed by the processor, other methods provided by the first embodiment of the present disclosure are implemented.

[0138] The disclosed embodiment establishes a vehicle power system model and a vehicle speed prediction model to form a state space equation based on model predictive control, thereby relying on the optimal control method to assign different weights to power, economy, and comfort, and ultimately obtains the optimal control sequence for energy management.

[0139] The fourth embodiment of the present disclosure provides an electronic device, which includes at least a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method provided in any embodiment of the present disclosure when executing the computer program in the memory. Exemplarily, the steps of the electronic device computer program are as follows S21 to S23:

[0140] S21, constructing a prediction model based on the power system model and working mode of the electric vehicle;

[0141] S22, predicting a speed value of the electric vehicle based on the prediction model;

[0142] S23, establishing a cost function based on the predicted speed value to obtain an optimal control sequence for energy management.

[0143] Furthermore, the processor also executes the computer program in the third embodiment

[0144] The disclosed embodiment establishes a vehicle power system model and a vehicle speed prediction model to form a state space equation based on model predictive control, thereby relying on the optimal control method to assign different weights to power, economy, and comfort, and ultimately obtains the optimal control sequence for energy management.

[0145] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0146] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0147] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0148] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a separate software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any storage medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0150] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0151] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0152] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0153] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0154] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0155] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0156] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0157] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A control method for energy management of an electric vehicle, wherein the electric vehicle comprises a first motor, a second motor and a battery, wherein the first motor is connected to a front axle of the electric vehicle via a dog clutch, and the second motor is connected to a rear axle of the electric vehicle, wherein: The control method comprises: Building a prediction model based on a power system model and an operating mode of the electric vehicle, wherein the power system model includes at least a motor power model, a battery model, and a vehicle dynamics model; Predicting the speed value of the electric vehicle based on the prediction model includes acquiring a speed value data set and an acceleration value data set of the electric vehicle based on a predetermined operating condition; determining an acceleration state transition probability in each speed state interval based on the speed value data set and the acceleration data set; and predicting the speed value of the electric vehicle at the next moment based on the acceleration state transition probability; A cost function is established based on the predicted speed value to obtain an optimal control sequence for energy management, wherein the cost function is expressed as: Where Q, R, and F are weight coefficients, which respectively represent the change weights of the speed value, SOC value, and system control quantity of the electric vehicle at each moment. i represents the speed value of the electric car at each moment, v ref Indicates the predicted speed value; SOC i Indicates the SOC value of the electric vehicle at each moment, SOC ref Indicates the reference SOC value; u=[u t1 u t2 u e ] T is the system control quantity, u t1 u t2 are the required torques of the first motor and the second motor respectively; e Used for the current operating state of the first motor, wherein its value is represented by 0 or 1; Δu represents the change of the system control quantity at each calculation step.

2. The control method according to claim 1, characterized in that: The motor power model is established based on the relationship between the motor speed and torque of each motor and is expressed by the following formula: In drive mode: P m =Tm*n / 9550η; In energy recovery mode: P m =Tm*n*η / 9550; Where P m is the motor power, Tm is the motor torque, n is the motor speed, η is the motor efficiency; and / or The battery model is expressed by the following formula: Wherein, P is the motor power, t is the time, V is the open circuit voltage of the battery, R is the internal resistance of the battery, and Q is the maximum capacity of the battery; and / or The vehicle dynamics model is expressed by the following formula: Where M T,D is the motor torque, θ is the road slope, C D is the air resistance coefficient, A is the frontal area, ρ is the air density, v is the vehicle speed, m is the vehicle curb mass, g is the gravity coefficient, and f r is the rolling resistance coefficient and r is the wheel radius.

3. The control method according to claim 1, characterized in that: The working modes include a driving mode and an energy recovery mode. The driving mode includes a two-wheel drive mode and a four-wheel drive mode. The energy recovery mode includes a rear axle energy recovery mode and a front and rear axle energy recovery mode.

4. The control method according to claim 1, characterized in that: The determining, based on the speed value data set and the acceleration data set, the acceleration state transition probability in each speed state interval comprises: Obtain the number of transitions from the first acceleration state interval to the second acceleration state interval in each speed state interval; An acceleration state transfer number matrix is ​​obtained based on the transfer number.

5. A control device for energy management of an electric vehicle, the electric vehicle comprising a first motor, a second motor and a battery, the first motor being connected to the front axle of the electric vehicle via a dog clutch, the second motor being connected to the rear axle of the electric vehicle, characterized in that: The control device comprises: A construction module, used to construct a prediction model based on a power system model and an operating mode of the electric vehicle, wherein the power system model at least includes a motor power model, a battery model, and a vehicle dynamics model; A prediction module, used to predict the speed value of the electric vehicle based on the prediction model; the prediction module includes: an acquisition unit, used to acquire a speed value data set and an acceleration value data set of the electric vehicle based on a predetermined working condition; a determination unit, used to determine the acceleration state transition probability in each speed state interval based on the speed value data set and the acceleration data set; a prediction unit, used to predict the speed value of the electric vehicle at the next moment based on the acceleration state transition probability; An acquisition module is used to establish a cost function based on the predicted speed value to obtain an optimal control sequence for energy management, wherein the cost function is expressed as: Where Q, R, and F are weight coefficients, which respectively represent the change weights of the speed value, SOC value, and system control quantity of the electric vehicle at each moment. i represents the speed value of the electric car at each moment, v ref Indicates the predicted speed value; SOC i Indicates the SOC value of the electric vehicle at each moment, SOC ref Indicates the reference SOC value; u=[u t1 u t2 u e ] T is the system control quantity, u t1 u t2 are the required torques of the first motor and the second motor respectively; e Used for the current operating state of the first motor, wherein its value is represented by 0 or 1; Δu represents the change of the system control quantity at each calculation step.

6. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

7. An electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, wherein: The processor implements the steps of the method of any one of claims 1 to 4 when executing the computer program on the memory.

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