A driving control method for a dual-motor coupled driving vehicle system

Abstract

The present invention discloses a driving control method for a dual-motor coupled drive vehicle system, which can effectively improve the power, stability and smoothness of the vehicle, while maintaining good energy efficiency performance under variable driving conditions. A switching affine model of a dual-motor coupled drive system constructs the driving dynamics under four different modes of SM1, SM2, TC and SC into a unified mixed integer dynamic model, and uses binary variable decisions to represent the selection of different driving modes. A collaborative optimization method for a dual-motor coupled drive system based on mixed integer programming can collaboratively perform longitudinal speed control, optimal mode decision and optimal energy distribution within a time domain, while avoiding frequent mode switching as much as possible to reduce longitudinal impact. An adaptive parameter adjustment mechanism can flexibly adjust the weight coefficients of speed efficiency, power, and power loss according to different driving conditions to adapt to diverse driving conditions.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicle drive control, and in particular relates to a drive control method and system for a dual-motor coupled drive vehicle system. Background Art

[0002] Single-motor drive is the earliest pure electric vehicle drive configuration scheme. The technology is relatively mature and has been mass-produced, but it has disadvantages such as relatively low energy efficiency. Compared with the traditional single-motor drive scheme, the dual-motor coupled drive scheme can optimize the dual-motor working point and improve the drive system efficiency under different driving conditions by reasonably allocating torque or speed.

[0003] The dual-motor coupled drive system has a variety of different working modes, which can usually be divided into single-motor drive mode, torque-coupled drive mode and speed-coupled drive mode. How to make drive mode decisions and optimize energy distribution strategies is the key to improving energy efficiency under different driving conditions. At present, there have been relevant studies in this field, which can be summarized into 1) methods based on instantaneous optimality and 2) methods based on global optimality according to different optimization strategies.

[0004] About the technical solution of prior art 1:

[0005] Based on the instantaneous optimal method, the control strategy based on instantaneous optimality generally adopts a hierarchical architecture design. The upper layer is the driver operation module or intelligent driving controller, which outputs real-time torque and speed requirements; the middle layer is the mode selection and energy optimization module, which reasonably allocates the speed or torque of the dual motors only based on the optimality principle under the current instantaneous state; the lower layer is the motor control layer, which enables the motor to track the desired torque or speed.

[0006] For example, the article "Driving Mode Division and Optimization of Electric Vehicles with Dual-motor Coupling Drive" written by Lin Xinyou, Wu Jiajun, and Wei Shenshen was published in the 42nd volume, No. 4, of "Automotive Engineering" in 2020, with a page number range of 425-430. Based on the principle of instantaneous minimum power, the particle swarm optimization method was used to obtain the optimal driving mode and torque distribution method;

[0007] For example, the article "Efficiency study of a dual-motorcoupling EV powertrain" written by Hu M, Zeng J, Xu S and other authors was published in the 2014 "IEEE Transactions on Vehicular Technology" journal, Volume 64, Issue 6, with page numbers ranging from 2252 to 2260. Based on the principle of instantaneous optimal system efficiency, the article formulated the optimal mode conversion strategy and power allocation strategy.

[0008] The disadvantages of the above prior art 1:

[0009] The control strategy based on instantaneous optimality has the following limitations:

[0010] 1) The upper layer fails to consider the mode selection and energy optimization of the lower layer when calculating the expected torque and speed, and the lower layer only considers the instantaneous optimal power or efficiency at the current moment. Based on the principle of instantaneous optimality, certain torque-speed intervals are critical areas for selecting three or two modes. Frequent changes in vehicle speed or acceleration in this area will cause frequent switching of driving modes, resulting in greater longitudinal impact.

[0011] 2) The current optimal strategies can be summarized as those based on minimum power, minimum power loss, and optimal system efficiency. Using different optimization strategies will produce different energy allocation solutions, and only following a single strategy may not be able to adapt to diverse driving conditions.

[0012] Technical solution of the second prior art:

[0013] The control strategy based on global optimization is usually guided by the accumulated power (loss) or efficiency in a fixed time domain to optimize the energy of a specific driving mode.

[0014] For example, the article "Research on Real-time Adaptive Energy Management Strategy of Dual-motor Driven Electric Tractor" written by Li Tonghui, Xie Bin, Wang Dongqing and other authors was published in Volume 51 of the Journal of Agricultural Machinery in 2020, with pages 530-543. It adopts a stochastic dynamic programming control strategy based on optimal system efficiency to solve the finite-time power allocation and mode decision optimization problems of the dual-motor coupled drive system, and realizes self-optimization of the drive system efficiency and drive mode while meeting the required power.

[0015] Disadvantages of the second prior art:

[0016] The above control strategy based on global optimization can realize the real-time adaptive energy management strategy of the dual-motor coupled drive system to a certain extent. However, considering the driver's required power as a random disturbance does not conform to the actual driving conditions, and its adaptability to complex dynamic traffic scenes is limited. With the development of autonomous driving technology, on-board intelligent driving controllers often have the ability to plan vehicle speed in complex dynamic environments. However, the above method is difficult to coordinate with the speed planning module of the intelligent driving controller, and cannot accurately and explicitly consider the impact of the expected speed sequence on the drive system mode decision and energy allocation strategy. 。

[0017] In summary, the current driving mode decision-making and energy allocation strategies have limitations to varying degrees. The control strategy based on instantaneous optimality may have frequent mode switching and is not adaptable to different working conditions. The control strategy based on global optimality cannot coordinate with the speed planning layer to enhance the adaptability to complex dynamic working conditions. Summary of the invention

[0018] The purpose of the present invention is to address the deficiencies in the prior art and to provide a drive control method for a dual-motor coupled drive vehicle system that can collaboratively achieve vehicle longitudinal speed control, optimal drive mode decision and optimal energy distribution.

[0019] In order to solve the above technical problems, the technical method adopted by the present invention is as follows: the present invention discloses a driving control method of a dual-motor coupled driving vehicle system, comprising the following steps:

[0020] S1. Determine the working mode of the dual-motor coupling drive system, including motor 1 single drive mode SM1, motor 2 single drive mode SM2, dual-motor torque coupling drive mode TC and dual-motor speed coupling drive mode SC;

[0021] S2. The vehicle control unit VCU is responsible for obtaining the desired vehicle speed, motor speed, vehicle acceleration and other information, and outputs the desired motor torque, motor speed and drive mode to the motor control unit MCU;

[0022] According to the instructions of VCU, MCU controls the torque and speed of the motor, and controls the status of brakes B1, B2 and clutches C1, C2 to achieve drive mode switching;

[0023] S3. Establish vehicle dynamics and drive system models, including vehicle longitudinal dynamics model, tire model and dynamics model of drive mode;

[0024] S4. Control strategy design and optimization, including drive system efficiency and power model, mixed integer programming optimization, design cost function and drive mode switching cost;

[0025] The drive system efficiency and power model establishes a drive power and efficiency model according to the working state in each mode; the power, efficiency and motor drive efficiency in different modes are calculated and applied to the optimization process;

[0026] The mixed integer programming optimization constructs a mixed integer optimization problem based on a rolling horizon and selects the optimal driving mode by optimizing the cost function;

[0027] The design cost function, the cost items include longitudinal speed control accuracy, vehicle stability, and drive system efficiency;

[0028] During the optimization process, consider reducing the number of driving mode switching to avoid longitudinal impact caused by frequent mode switching;

[0029] S5. Adaptive parameter adjustment mechanism, including battery state of charge (SOC) monitoring and adaptive adjustment;

[0030] The battery state of charge (SOC) monitoring adjusts the weight coefficient in the cost function according to the real-time state of charge of the battery;

[0031] The adaptive adjustment includes increasing the weight of efficiency and power loss when the battery is charging; increasing the weight of power cost and reducing the weight of efficiency and power loss cost when the battery is low;

[0032] S6. Driving mode decision and switching, including selecting the optimal driving mode based on real-time optimization results and control strategies; switching driving modes through MCU control of brakes and clutches.

[0033] Furthermore, the vehicle longitudinal dynamics model:

[0034]

[0035] Where k∈{0,1,…,N-1} is a discrete time point, m is the total mass of the vehicle, v represents the longitudinal velocity of the vehicle, ΔT represents the discrete time step, and F x represents the ground tangential reaction force on the tire, ρ is the air density, C d is the air resistance coefficient, A is the frontal area, g is the acceleration of gravity, θ is the road slope, I is the wheel moment of inertia, ω is the wheel angular velocity, T d is the expected total torque, R is the effective radius of the wheel;

[0036] The tire model calculates the ground tangential reaction force F on the tire. x :

[0037]

[0038] In the formula, C x is the longitudinal stiffness of the wheel, is the nonlinear characteristic boundary value of the tire longitudinal force, κ is the speed influence factor, and s is the driving slip rate, which is calculated as follows:

[0039]

[0040] The dynamic model of the driving mode:

[0041]

[0042] Wherein, T1 and T2 are the torques of driving motors 1 and 2 respectively, ω1 and ω2 are the speeds of driving motors 1 and 2 respectively, and i1 and i2 are transmission ratio parameters in different modes.

[0043] Furthermore, the efficiency and power models in each mode are:

[0044]

[0045] Where P SM1 , P SM2 , P TC and P SC Represent the driving power in SM1, SM2, TC and SC modes respectively, η SM1 , η SM2 , η TC and η SC Represents the drive system efficiency in four modes, η M1 , η M2 are the driving efficiencies of motor 1 and motor 2 respectively.

[0046] Furthermore, the motor efficiency is considered as a function of the motor torque and speed, that is,

[0047]

[0048] Furthermore, define the Boolean variable α SM1 , α SM2 , α TC and α SC Represents four different modes: SM1, SM2, TC and SC, and satisfies the following relationship:

[0049] α SM1 (k)+α SM2 (k)+α TC (k)+α SC (k) = 1 (7)

[0050] In the formula, at any k-th moment, the dual-motor coupled drive system can only operate in one driving mode;

[0051] Combined with the dynamic model of the driving mode, there is the following logical relationship:

[0052]

[0053] Where χ is a lumped variable, the subscripts SM1, SM2, TC, and SC represent four different modes, and the lumped variables, torque, and speed after adding the subscripts are all intermediate variables;

[0054] Such as T 1,SM1 The meaning is the torque of motor 1 assuming that the current working mode is SM1;

[0055] The relationship between the actual torque and speed variables of the motor of the drive system and the intermediate variables is as follows:

[0056]

[0057] Furthermore, the logic implied in the dynamic model of the driving mode is constructed into the following mixed integer inequality:

[0058] In the formula, m j and M j are the upper and lower bounds of the variable, j∈{SM1,SM2,TC,SC};

[0059] m SM1 =[T 1,min ,0,ω 1,min ,0] T ,M SM1 =[T 1,max ,0,ω 1,max ,0] T ,

[0060] m SM2 =[0,T 2,min ,0,ω 2,min ] T ,M SM2 =[0,T 2,max ,0,ω 2,max ] T ,

[0061] m TC =[T 1,min +T 2,min ,ω 1,min ,ω 2,min ] T ,M TC =[T 1,max +T 2,max ,ω 1,max ,ω 2,max ] T ,

[0062] m SC =[T 1,min ,T 2,min ,ω 1,min +ω 2,min ] T ,M SC =[T 1,max ,T 2,max ,ω 1,max +ω 2,max ] T ,

[0063] Wherein, the subscripts min and max represent the minimum and maximum values ​​of the torque and speed.

[0064] Furthermore, the total efficiency η of the drive system is as follows:

[0065] η(k)=α SM1 (k)η SM1 (k)+α SM2 (k)η SM2 (k)

[0066] +α TC (k)η TC (k)+α SC (k)η SC (k)

[0067] The total power P of the drive system is as follows:

[0068] P(k)=α SM1 (k)P SM1 (k)+α SM2 (k)P SM2 (k)

[0069] +α TC (k)P TC (k)+α SC (k)P SC (k)

[0070] The power loss ΔP of the drive system is as follows:

[0071]

[0072] Furthermore, the number of mode switching times n of the drive system within the prediction time domain N is sw Can be represented by a pattern variable:

[0073]

[0074] Where j∈{SM1,SM2,TC,SC}.

[0075] Furthermore, the optimal driving mode is: In the formula, [α * ,T1 * ,T2 * ,n1 * ,n2 * ] 0:N-1 It represents the optimal driving mode decision, motor torque and speed sequence in the prediction time domain 0 to N-1;

[0076] The cost function J is as follows:

[0077]

[0078] In the formula, the cost function consists of 10 cost terms, among which λ i ,i∈{1,2,…,10} is the weight of each cost item;

[0079] The first term considers the accuracy of longitudinal velocity tracking control, v ref is the reference longitudinal velocity;

[0080] The second term considers the rate of change of longitudinal velocity;

[0081] The third item considers vehicle driving stability and hopes to minimize the deviation of the slip rate from the optimal slip rate;

[0082] The fourth, fifth, and sixth items consider the drive system efficiency, power, and power loss, respectively;

[0083] Items 7 and 8 penalize the rate of change of output torque of a single motor;

[0084] The ninth item penalizes the rate of change of the output torque of the dual-motor coupled drive system;

[0085] The tenth item penalizes the number of drive mode switches.

[0086] Furthermore, when the battery is fully charged, the SOC is 1, and when it is fully discharged, the SOC is 0, and SOC∈[0,1] is set;

[0087] When the battery power decreases, the weights λ4 and λ6 of the system efficiency and the power loss cost are reduced, and the weight λ5 of the power cost is increased; the adaptive adjustment is:

[0088] λ′4=λ4×SOC,

[0089] λ′5=λ5×(1-SOC),

[0090] λ′6=λ6×SOC.

[0091] Where λ4', λ5' and λ6' are the adjusted system efficiency, power and power loss cost weight coefficients 。

[0092] Beneficial effects:

[0093] 1) Different from the prior art 1, the present invention abandons the traditional hierarchical architecture, and collaboratively considers the vehicle motion control level and the drive system control level, while realizing speed control, mode decision and energy distribution, avoiding frequent mode switching to reduce longitudinal impact.

[0094] 2) Different from the second prior art, the present invention is based on a mixed integer optimization strategy in the rolling time domain, which can realize the optimal driving mode decision at each moment in the time domain and has a stronger adaptability to time-varying working conditions.

[0095] 3) Different from the prior arts 1 and 2, the present invention has an adaptive parameter adjustment mechanism, which can flexibly adjust the weights of efficiency, power, and power loss according to different driving conditions to adapt to various driving conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 Schematic diagram of a typical dual-motor coupling drive system configuration applicable to the present invention;

[0097] Figure 2 This is an architecture diagram of a dual-motor coupling drive control system applicable to the present invention. DETAILED DESCRIPTION

[0098] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0099] The present invention designs a drive controller for a typical dual-motor coupling drive system. The drive system configuration is as follows: Figure 1 As shown. The system configuration includes two drive motors M1 and M2, two brakes B1 and B2, and two clutches C2 and C2. When the vehicle is running, different drive mode switching is achieved by controlling the locking or disconnection of the brakes and the engagement or separation of the clutches.

[0100] 1) SM1 mode: When brake B1 is disconnected, B2 is locked, and clutch C1 and C2 are disengaged, motor M1 is driven alone, and it is in SM1 mode.

[0101] 2) SM2 mode: When brake B1 is locked, B2 is disconnected, clutch C1 is disengaged, C2 is engaged, and motor M2 is driven alone, it is in SM2 mode.

[0102] 3) TC mode: When brake B1 is disconnected, B2 is locked, and clutch C1 is engaged, C2 is disengaged, motors M1 and M2 are driven at the same time, and their torques are coupled together through C1. At this time, it is in TC mode.

[0103] 4) SC mode: When brakes B1 and B2 are disconnected, and clutches C1 and C2 are disengaged, motors M1 and M2 are driven simultaneously, and their speeds are coupled together through the planetary carrier. This is the SC mode.

[0104] The present invention adopts Figure 2The control architecture shown, in which VCU is the vehicle controller, integrates the collaborative control algorithm proposed in the present invention, and can simultaneously perform vehicle longitudinal speed control, optimal driving mode decision and optimal energy distribution.

[0105] The VCU obtains information such as the desired vehicle speed, motor speed, vehicle acceleration, and vehicle speed, and outputs the desired motor torque, motor speed, and drive mode to the motor controller MCU.

[0106] The MCU controls the torque and speed of the drive motor, and also controls the brakes B1, B2 and the clutches C1, C2 to switch the drive mode.

[0107] Detailed steps of dual-motor coupled drive vehicle system and drive control method thereof

[0108] Step 1: Determine the operating mode of the dual-motor coupled drive system

[0109] The dual-motor coupling drive system of the present invention has the following four working modes, specifically:

[0110] 1. Motor 1 single drive mode (SM1):

[0111] Brake B1 is disengaged and B2 is locked;

[0112] Clutch C1 is disengaged, C2 is disengaged;

[0113] Motor M1 is driven alone and the system is in SM1 mode.

[0114] 2. Motor 2 single drive mode (SM2):

[0115] Brake B1 is locked and B2 is disconnected;

[0116] Clutch C1 is disengaged and C2 is engaged;

[0117] Motor M2 is driven alone and the system is in SM2 mode.

[0118] 3. Dual motor torque coupling drive mode (TC):

[0119] Brake B1 is disengaged and B2 is locked;

[0120] Clutch C1 is engaged and C2 is disengaged;

[0121] Motors M1 and M2 are driven simultaneously, their torques are coupled through C1, and the system is in TC mode.

[0122] 4. Dual motor speed coupling drive mode (SC):

[0123] Brake B1 is disconnected, and B2 is disconnected;

[0124] Clutch C1 is disengaged and C2 is engaged;

[0125] Motors M1 and M2 are driven simultaneously, their speeds are coupled through the planetary carrier, and the system is in SC mode.

[0126] Step 2: Design a dual-motor coupled drive control system

[0127] 1. Control architecture design:

[0128] The VCU (Vehicle Control Unit) is responsible for obtaining the desired vehicle speed, motor speed, vehicle acceleration and other information, and outputs the desired motor torque, motor speed and drive mode to the MCU (Motor Control Unit);

[0129] According to the instructions of VCU, MCU controls the torque and speed of the motor, and controls the status of brakes B1, B2 and clutches C1, C2 to achieve drive mode switching.

[0130] Step 3: Build vehicle dynamics and drive system model

[0131] 1. Vehicle longitudinal dynamics model:

[0132] Considering factors such as the total mass of the vehicle, the longitudinal speed of the vehicle, the reaction force between the tire and the ground, air resistance, road slope, wheel rotation inertia, etc., the longitudinal dynamic model of the vehicle is established at discrete time points, as shown in formula (1):

[0133]

[0134] Where k∈{0,1,…,N-1} is a discrete time point, m is the total mass of the vehicle, v represents the longitudinal velocity of the vehicle, ΔT represents the discrete time step, and F x represents the ground tangential reaction force on the tire, ρ is the air density, C d is the air resistance coefficient, A is the frontal area, g is the acceleration of gravity, θ is the road slope, I is the wheel moment of inertia, ω is the wheel angular velocity, T d is the expected total torque, and R is the effective radius of the wheel.

[0135] 2. Tire model:

[0136] The Dugoff tire model is used to calculate the ground tangential reaction force on the tire, as shown in formula (2):

[0137] The Dugoff tire model is used to calculate the ground tangential reaction force F on the tire. x :

[0138]

[0139] Among them, Cx is the longitudinal stiffness of the wheel, is the nonlinear characteristic boundary value of the tire longitudinal force, κ is the speed influence factor, and s is the driving slip rate, which is calculated as follows:

[0140]

[0141] 3. Dynamic model of driving mode:

[0142] Dynamic modeling for each drive mode (SM1, SM2, TC, SC)

[0143]

[0144] Wherein, T1 and T2 are the torques of driving motors 1 and 2 respectively, ω1 and ω2 are the speeds of driving motors 1 and 2 respectively, and i1 and i2 are the transmission ratio parameters in different modes.

[0145] Step 4: Control strategy design and optimization

[0146] 1. Drive system efficiency and power model:

[0147] According to the working status in each mode, the driving power and efficiency model is established, as shown in formula (5).

[0148]

[0149] Among them, P SM1 , P SM2 , P TC and P SC Represent the driving power in SM1, SM2, TC and SC modes respectively, η SM1 , η SM2 , η TC and η SC Represents the drive system efficiency in four modes, η M1 , η M2 are the driving efficiencies of motor 1 and motor 2 respectively.

[0150] 2. Motor efficiency calculation:

[0151] The motor efficiency is considered as a function of motor torque and speed, that is,

[0152]

[0153] This function can be calibrated through motor bench tests.

[0154] 3. Drive mode selection and switching logic:

[0155] The Boolean variable formula (7) is defined to indicate that the system can only be in one driving mode at any time.

[0156] Define a Boolean variable α SM1 , α SM2 , α TC and α SC Represents four different modes: SM1, SM2, TC and SC, and satisfies the following relationship:

[0157] α SM1 (k)+α SM2 (k)+α TC (k)+α SC (k)=1. (7)

[0158] This relationship shows that at any k-th moment, the dual-motor coupled drive system can only operate in one driving mode.

[0159] The switching between different modes is controlled by the logical relationship formula (8), and then the motor torque and speed are adjusted by formula (9)

[0160]

[0161] in, Among them, χ is a lumped variable, and the subscripts SM1, SM2, TC, and SC represent four different modes, respectively. The lumped variables, torque, and speed after adding the subscripts are all intermediate variables (or auxiliary variables). 1,SM1 The meaning is the torque of motor 1 when the current working mode is SM1. The relationship between the actual torque, speed variable and intermediate variable of the drive system is as follows:

[0162]

[0163] 4. Drive system to switch affine model:

[0164] The big M method is used to transform the driving mode switching logic formula (10) into a mixed integer inequality:

[0165]

[0166] Among them, m j and M j Are the upper and lower bounds of the variable, j∈{SM1,SM2,TC,SC}.

[0167] Specifically,

[0168]

[0169] The subscripts min and max represent the minimum and maximum values ​​of the torque and speed.

[0170] Drive system overall efficiency and power:

[0171] Calculate the total efficiency of the drive system using formula (12), total power using formula (13) and power loss using formula (14).

[0172] The overall efficiency η of the drive system is given by:

[0173]

[0174] The total power P of the drive system is as follows:

[0175]

[0176] The power loss ΔP of the drive system is as follows:

[0177]

[0178] 5. Calculation of mode switching times:

[0179] The number of mode switching in the prediction time domain is expressed by the mode variable formula (15), which is used to evaluate the possible impact of frequent switching.

[0180] The number of mode switching times n of the drive system within the prediction time domain N sw Can be represented by a pattern variable:

[0181]

[0182] Where j∈{SM1,SM2,TC,SC}.

[0183] Step 5: Cost function design and optimization

[0184] 1. Cost function design:

[0185] A cost function formula (16) is designed to comprehensively consider multiple factors such as longitudinal velocity tracking control accuracy, longitudinal velocity change rate, vehicle driving stability, drive system efficiency, power and power loss.

[0186] The design cost function J is as follows:

[0187]

[0188] The cost function consists of 10 cost terms, among which λ i , i∈{1,2,…,10} is the weight of each cost term. The first term considers the accuracy of longitudinal velocity tracking control, v ref is the reference longitudinal velocity;

[0189] The second term considers the rate of change of longitudinal velocity;

[0190] The third item considers vehicle driving stability and hopes to minimize the deviation of the slip rate from the optimal slip rate;

[0191] The fourth, fifth, and sixth items consider the drive system efficiency, power, and power loss, respectively;

[0192] Items 7 and 8 penalize the rate of change of output torque of a single motor;

[0193] The ninth item penalizes the rate of change of the output torque of the dual-motor coupled drive system;

[0194] The tenth item penalizes the number of drive mode switches.

[0195] 2. Optimization process:

[0196] Based on the mixed integer optimization method formula (17) in the rolling horizon, a cost function is used to select the optimal driving mode, motor torque and speed sequence.

[0197] The mixed integer optimization problem based on the rolling horizon is constructed as follows:

[0198]

[0199] Among them, [α * ,T1 * ,T2 * ,n1 * ,n2 * ] 0:N-1 It represents the optimal driving mode decision, motor torque and speed sequence in the prediction time domain 0 to N-1.

[0200] Step 6: Adaptive parameter adjustment mechanism

[0201] 1. Battery State of Charge (SOC) monitoring:

[0202] The weight coefficient of economy in the cost function formula (16) is adjusted in real time according to the battery state of charge. Specifically, when the battery power is sufficient, system efficiency and power loss are given priority, while when the battery power is low, power consumption is given priority.

[0203] 2. Adaptive adjustment mechanism:

[0204] The weight coefficient formula (18) is dynamically adjusted according to the SOC change, as follows:

[0205] When the battery is fully charged, the SOC is 1, and when it is fully discharged, the SOC is 0, so SOC∈[0,1]. When the battery power decreases, the weights λ4 and λ6 of the system efficiency and power loss cost can be reduced, while the weight λ5 of the power cost can be increased. This shows that at low SOC, it is hoped that the drive system consumes as little battery energy as possible. Based on this adjustment strategy, the following weight parameter adaptive adjustment mechanism can be adopted:

[0206]

[0207] Among them, λ4', λ5' and λ6' are the adjusted system efficiency, power and loss power cost weight coefficients.

[0208] Step 7: Drive Mode Decision and Switching

[0209] 1. Mode selection:

[0210] In each control cycle, the most appropriate driving mode is selected through the optimal mode decision algorithm formula (17) to optimize energy distribution and improve system efficiency.

[0211] 2. Mode switching:

[0212] According to the instructions of VCU, MCU controls the brake and clutch to switch the driving mode. When switching, consider minimizing the number of mode switching to avoid frequent longitudinal impact.

[0213] Step 8: System Verification and Optimization

[0214] 1. Performance evaluation:

[0215] Multiple rounds of tests were conducted on the dual-motor coupled drive system to verify its performance under various working conditions, including economy, power, stability and smoothness.

[0216] 2. Optimization and adjustment:

[0217] According to actual driving conditions and test results, the control strategies and parameters are optimized to meet the needs of different driving environments.

[0218] Through these detailed steps, the dual-motor coupling drive control method of the present invention can effectively improve the power, stability and smoothness of the vehicle, while maintaining good energy efficiency performance under variable driving conditions.

[0219] 1) A switching affine model of a dual-motor coupled drive system is proposed. The driving dynamics in four different modes, SM1, SM2, TC and SC, are constructed into a unified mixed integer dynamic model, and the choice of different driving modes is represented by binary variable decision.

[0220] 2) A collaborative optimization method for a dual-motor coupled drive system based on mixed integer programming can collaboratively perform longitudinal speed control, optimal mode decision and optimal energy distribution within a time domain, while avoiding frequent mode switching as much as possible to reduce longitudinal impact.

[0221] 3) An adaptive parameter adjustment mechanism that can flexibly adjust the weight coefficients of speed efficiency, power, and power loss according to different driving conditions to adapt to diverse driving conditions.

[0222] The present invention is directed to a typical dual-motor coupling drive configuration (such as Figure 1 The controller design is performed based on the proposed control method (as shown in the figure), but the proposed control method is also applicable to other dual-motor coupled drive systems. It only requires modifying the drive system dynamics model (4), efficiency and power model (5).

[0223] The adaptive adjustment method (18) of the present invention can be replaced by other weighting methods for adjusting system efficiency, power and power loss in real time based on SOC changes, such as fuzzy reasoning, fitting function and the like, but the core idea remains the same.

[0224] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A driving control method for a dual-motor coupled driving vehicle system, characterized in that: The steps include: S1. Determine the working mode of the dual-motor coupling drive system, including motor 1 single drive mode SM1, motor 2 single drive mode SM2, dual-motor torque coupling drive mode TC and dual-motor speed coupling drive mode SC; S2. The vehicle control unit VCU is responsible for obtaining the desired vehicle speed, motor speed, and vehicle acceleration information, and outputs the desired motor torque, motor speed, and drive mode to the motor control unit MCU; According to the instructions of VCU, MCU controls the torque and speed of the motor, and controls the status of brakes B1, B2 and clutches C1, C2 to achieve drive mode switching; S3. Establish vehicle dynamics and drive system models, including vehicle longitudinal dynamics model, tire model and dynamics model of drive mode; S4. Control strategy design and optimization, including drive system efficiency and power model, mixed integer programming optimization, design cost function and drive mode switching cost; The drive system efficiency and power model establishes a drive power and efficiency model according to the working state in each mode; the power, efficiency and motor drive efficiency in different modes are calculated and applied to the optimization process; The mixed integer programming optimization constructs a mixed integer optimization problem based on a rolling horizon and selects the optimal driving mode by optimizing the cost function; The design cost function, the cost items include longitudinal speed control accuracy, vehicle stability, drive system efficiency and the number of switching times of drive modes; During the optimization process, consider reducing the number of driving mode switching to avoid longitudinal impact caused by frequent mode switching; S5. Adaptive parameter adjustment mechanism, including battery state of charge (SOC) monitoring and adaptive adjustment; The battery state of charge (SOC) monitoring adjusts the weight coefficient in the cost function according to the real-time state of charge of the battery; The adaptive adjustment includes increasing the weight of efficiency and power loss when the battery is charging; increasing the weight of power cost and reducing the weight of efficiency and power loss cost when the battery is low; S6. Driving mode decision and switching, including selecting the optimal driving mode based on real-time optimization results and control strategies; switching driving modes through MCU control of brakes and clutches.

2. The driving control method of the dual-motor coupled driving vehicle system according to claim 1, characterized in that: The vehicle longitudinal dynamics model: Where k∈{0,1,…,N-1} is a discrete time point, m is the total mass of the vehicle, v represents the longitudinal velocity of the vehicle, ΔT represents the discrete time step, and F x represents the ground tangential reaction force on the tire, ρ is the air density, C d is the air resistance coefficient, A is the frontal area, g is the acceleration of gravity, θ is the road slope, I is the wheel moment of inertia, ω is the wheel angular velocity, T d is the expected total torque, R is the effective radius of the wheel; The tire model calculates the ground tangential reaction force F on the tire. x : In the formula, C x is the longitudinal stiffness of the wheel, is the nonlinear characteristic boundary value of the tire longitudinal force, κ is the speed influence factor, and s is the driving slip rate, which is calculated as follows: The dynamic model of the driving mode: SM1 Mode SM2 Mode TC Mode SC mode Wherein, T1 and T2 are the torques of driving motors 1 and 2 respectively, ω1 and ω2 are the speeds of driving motors 1 and 2 respectively, and i1 and i2 are the transmission ratio parameters in different modes.

3. The driving control method of the dual-motor coupled driving vehicle system according to claim 2, characterized in that: Efficiency and power model in each mode: Where P SM1 , P SM2 , P TC and P SC Represent the driving power in SM1, SM2, TC and SC modes respectively, η SM1 , η SM2 , η TC and η SC Represents the drive system efficiency in four modes, η M1 , η M2 are the driving efficiencies of motor 1 and motor 2 respectively.

4. The driving control method of the dual-motor coupled driving vehicle system according to claim 3, characterized in that: The motor efficiency is considered as a function of motor torque and speed, that is, 5. The driving control method of the dual-motor coupled driving vehicle system according to claim 4, characterized in that: Define a Boolean variable α SM1 , α SM2 , α TC and α SC Represents four different modes: SM1, SM2, TC and SC, and satisfies the following relationship: a SM1 (k)+a SM2 (k)+a TC (k)+a SC (k)=1(7) In the formula, at any k-th moment, the dual-motor coupled drive system can only operate in one driving mode; Combined with the dynamic model of the driving mode, there is the following logical relationship: Where χ is a lumped variable, and the subscripts SM1, SM2, TC, and SC represent four different modes, respectively. The lumped variables, torque, and speed after adding the subscripts are all intermediate variables. 1,SM1 The meaning is the torque of motor 1 assuming that the current working mode is SM1; The relationship between the actual torque and speed variables of the motor of the drive system and the intermediate variables is as follows:

6. The driving control method of the dual-motor coupled driving vehicle system according to claim 5, characterized in that: The logic implied in the dynamic model of the driving mode is formulated as the following mixed integer inequality: In the formula, m j and M j are the upper and lower bounds of the variable, j∈{SM1,SM2,TC,SC}; m SM1 =[T 1,min ,0,ω 1,min ,0] T ,M SM1 =[T 1,max ,0,ω 1,max ,0] T , m SM2 =[0,T 2,min ,0,ω 2,min ] T ,M SM2 =[0,T 2,max ,0,ω 2,max ] T , m TC =[T 1,min +T 2,min ,oh 1,min ,oh 2,min ] T ,M TC =[T 1,max +T 2,max ,oh 1,max ,oh 2,max ] T , m SC =[T 1,min ,T 2,min ,oh 1,min +oh 2,min ] T ,M SC =[T 1,max ,T 2,max ,oh 1,max +oh 2,max ] T , Wherein, the subscripts min and max represent the minimum and maximum values ​​of the torque and speed.

7. The driving control method of the dual-motor coupled driving vehicle system according to claim 6, characterized in that: The overall efficiency η of the drive system is given by: η(k)=α SM1 (k)h SM1 (k)+a SM2 (k)h SM2 (k) +a TC (k)h TC (k)+a SC (k)h SC (k) The total power P of the drive system is as follows: P(k)=α SM1 (k)P SM1 (k)+α SM2 (k)P SM2 (k) +α TC (k)P TC (k)+α SC (k)P SC (k) The power loss ΔP of the drive system is as follows:

8. The driving control method of the dual-motor coupled driving vehicle system according to claim 7, characterized in that: The number of mode switching times n of the drive system within the prediction time domain N sw Can be represented by a pattern variable: Where j∈{SM1,SM2,TC,SC}.

9. The driving control method of the dual-motor coupled driving vehicle system according to claim 8, characterized in that: The optimal The drive modes are: In the formula, It represents the optimal driving mode decision, motor torque and speed sequence in the prediction time domain 0 to N-1; The cost function J is as follows: In the formula, the cost function consists of 10 cost terms, among which λ i ,i∈{1,2,…,10} is the weight of each cost item; The first term considers the accuracy of longitudinal velocity tracking control, v ref is the reference longitudinal velocity; The second term considers the rate of change of longitudinal velocity; The third item considers vehicle driving stability and hopes to minimize the deviation of the slip rate from the optimal slip rate; The fourth, fifth, and sixth items consider the drive system efficiency, power, and power loss, respectively; Items 7 and 8 penalize the rate of change of output torque of a single motor; The ninth item penalizes the rate of change of the output torque of the dual-motor coupled drive system; The tenth item penalizes the number of drive mode switches.

10. The driving control method of the dual-motor coupled driving vehicle system according to claim 9, characterized in that: When the battery is fully charged, SOC is 1, and when it is fully discharged, SOC is 0. Set SOC∈[0,1]; When the battery power decreases, the weights λ4 and λ6 of the system efficiency and the power loss cost are reduced, and the weight λ5 of the power cost is increased; the adaptive adjustment is: λ′4=λ4×SOC, λ′5=λ5×(1-SOC), λ′6=λ6×SOC. Where λ′4, λ′5 and λ′6 are the adjusted system efficiency, power and power loss cost weight coefficients.

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