A method for optimizing energy consumption in a distributed drive vehicle powertrain system

Abstract

This invention discloses an energy consumption optimization method for a distributed drive vehicle powertrain system, comprising: Step 1, obtaining the desired yaw rate and desired vehicle speed of the vehicle; Step 2, obtaining the desired additional yaw torque based on the desired yaw rate and the longitudinal demand force based on the desired vehicle speed; Step 3, obtaining the weight coefficients of the motor loss objective function, tire utilization rate, longitudinal demand force, and desired additional yaw torque weight coefficient through fuzzy control based on the desired additional yaw torque and the longitudinal demand force; Step 4, establishing an efficiency-adhesion-power optimization function and solving the function using a quadratic programming optimization algorithm to obtain the desired longitudinal force of the four wheels; Step 5, obtaining the desired torque of the four hub motors after energy consumption optimization based on the desired longitudinal force of the four wheels. This invention features reduced energy consumption and improved energy utilization.

Description

Technical Field

[0001] This invention relates to the field of four-wheel drive torque distribution technology for new energy vehicles, and more specifically, to a method for optimizing energy consumption in a distributed drive vehicle power system. Background Technology

[0002] With dwindling global energy reserves and escalating environmental pollution, new energy vehicles have become a crucial research direction for my country's automotive industry. As technology matures, the number of new energy vehicles continues to grow steadily and rapidly. Distributed drive vehicles, also known as hub motor drive vehicles, feature a four-wheel distributed drive structure, resulting in a more streamlined chassis compared to traditional centralized motor drive vehicles. The hub motors, wheels, and braking systems are integrated into automatic wheels, making the vehicle structure more compact and saving considerable space. This allows for more flexible vehicle layout, and its linear control capabilities make the chassis lighter, transmission more efficient, and power output more efficient and stable. Due to its unique distributed drive, during vehicle operation, the hub motor torque can be rationally distributed based on longitudinal demand and desired yaw torque. This is achieved through motor efficiency and tire utilization, minimizing the sum of the power of the four hub motors, maximizing tire utilization, and minimizing battery energy consumption while maintaining vehicle handling stability, thus improving energy efficiency and driving safety.

[0003] Currently, research on energy consumption optimization for distributed vehicles tends to focus on longitudinal driving conditions while neglecting torque distribution during steering. Torque distribution is mostly based on front-to-rear axle coefficients rather than distribution among the four wheels. How to use the weighting coefficients of the objective function to distribute torque when there are both longitudinal demand forces and expected additional yaw moments is an important research direction.

[0004] Therefore, there is an urgent need for an energy consumption optimization control method that, based on longitudinal demand force and additional yaw moment, can rationally prioritize four demand objectives during vehicle operation, and distribute the torque of the hub motors in a distributed drive vehicle according to the principles of motor efficiency, tire utilization, and meeting the vehicle's power demand. Summary of the Invention

[0005] The purpose of this invention is to design and develop an energy consumption optimization method for a distributed drive vehicle power system. By minimizing the sum of power loss of the hub motors and maximizing the sum of tire utilization rates, the torque distribution of the four hub motors is carried out while satisfying the power requirements. This ensures handling stability, improves energy utilization, and reduces the operating energy consumption of the hub motors.

[0006] The technical solution provided by this invention is as follows:

[0007] A method for optimizing energy consumption in a distributed drive vehicle powertrain system includes the following steps:

[0008] Step 1: Obtain the desired yaw rate of the vehicle based on the vehicle speed and wheel rotation angle, and obtain the desired vehicle speed based on the accelerator pedal opening and maximum vehicle speed.

[0009] Step 2: Obtain the desired additional yaw moment based on the desired yaw rate of the vehicle, and obtain the longitudinal force required by the vehicle based on the desired vehicle speed.

[0010] Step 3: Based on the expected additional yaw moment and the vehicle's longitudinal demand force, obtain the weight coefficients of the motor loss objective function, the tire utilization rate, the longitudinal demand force, and the expected additional yaw moment through fuzzy control.

[0011] Step 4: Establish the efficiency-adhesion-dynamics optimization function;

[0012] minJ = w p J p +w λ J λ +w x J x +w z J z ;

[0013]

[0014] In the formula, J is the overall objective function, and w p w represents the weighting coefficients of the objective function for motor losses. λ w is the weighting factor for tire utilization. x w is the weighting coefficient for vertical demand forces. z To add a weighting factor to the desired yaw moment, J p Let J be the objective function for the losses of the hub motor. λ Let J be the objective function for tire utilization. x To satisfy the objective function of longitudinal demand force, J z To satisfy the objective function of the additional yaw moment, T i Let T be the torque of the i-th hub motor. imax F is the maximum torque of the i-th hub motor. xi Let μ be the longitudinal force of the i-th wheel. i Let F be the tire adhesion ratio of the i-th wheel. zi Let be the tire adhesion force of the i-th wheel, and i = 1, 2, 3, 4. When i = 1, the wheel is the left front wheel; when i = 2, the wheel is the right front tire; when i = 3, the wheel is the left rear tire; and when i = 4, the wheel is the right rear tire.

[0015] Step 5: Obtain the desired torque of the four hub motors after energy consumption optimization based on the longitudinal expected force of the four wheels.

[0016] Preferably, the desired yaw rate satisfies:

[0017]

[0018] In the formula, ω rd Let k1 be the desired yaw rate of the vehicle, k2 be the stiffness of the front axle, k2 be the stiffness of the rear axle, β be the sideslip angle of the vehicle's center of gravity, u be the longitudinal speed, a be the distance from the vehicle's center of gravity to the front axle, b be the distance from the vehicle's center of gravity to the rear axle, δ be the standard wheel steering angle, and I be the yaw rate. z The moment of inertia of the vehicle;

[0019] The desired speed of the vehicle satisfies:

[0020] v req =θ·v max ;

[0021] In the formula, v req Let v be the desired vehicle speed, θ be the accelerator pedal opening, and v be the throttle position. max This is the maximum speed.

[0022] Preferably, the longitudinal force requirement of the vehicle satisfies:

[0023]

[0024] In the formula, F(t) represents the longitudinal demand force of the vehicle, e1(t) represents the vehicle speed error at time t, and K... p K is the proportionality coefficient. i K is the integral coefficient. d is the differential coefficient.

[0025] Preferably, the desired additional yaw moment satisfies:

[0026]

[0027] In the formula, M zD To add a yaw moment as desired, Let ε be the derivative of the desired yaw rate, τ be the boundary layer thickness, τ be the approach rate exponent, sat(S) be the saturation function, S be the slid integral function, λ be the slid parameters, e² be the yaw rate error, and ω be the yaw rate error. r This represents the vehicle's actual yaw rate.

[0028] Preferably, the synovial integral function is:

[0029]

[0030] The saturation function is:

[0031]

[0032] In the formula, α is the reciprocal of the boundary layer thickness, and Δ is the boundary layer thickness.

[0033] Preferably, the fuzzy control in step three specifically includes:

[0034] The inputs to the fuzzy control model are the absolute values ​​of the desired additional yaw moment and the longitudinal demand force;

[0035] The output of fuzzy control is the weighting coefficient of the motor loss objective function, the weighting coefficient of the tire utilization objective function, the weighting coefficient of the longitudinal demand force objective function, and the weighting coefficient of the expected additional yaw moment objective function.

[0036] The range of variation of the desired additional yaw moment is:

[0037] The range of variation of the absolute value of the vertical demand force is:

[0038] The weighting coefficients of the objective function for motor loss vary within the range of [0,1].

[0039] The weighting coefficients of the objective function for tire utilization rate vary within the range of [0, 10].

[0040] The weighting coefficients of the vertical demand objective function vary within the range of [0, 100].

[0041] The range of the weighting coefficients of the objective function for the desired additional yaw moment is [0, 100].

[0042] Among them, T imax Let R be the maximum torque that the i-th hub motor can provide, R be the rolling radius of the tire, and M be the maximum torque that the hub motor can provide. imax This represents the maximum yaw torque that the i-th hub motor can provide.

[0043] Preferably, the efficiency-adhesion-power optimization function needs to minimize the objective function of hub motor loss and the objective function of tire utilization, and also needs to satisfy vehicle dynamics.

[0044] The objective function for minimizing the losses of the hub motor is:

[0045]

[0046] In the formula, J p Let P be the objective function for the losses of the hub motor. loss-i Let be the power loss of the i-th hub motor;

[0047] The power loss of the i-th motor satisfies:

[0048]

[0049] In the formula, P motor-i Let η be the output power of the i-th hub motor. i Let T be the efficiency of the i-th hub motor. i Let n be the torque of the i-th hub motor. i Let be the rotational speed of the i-th wheel;

[0050] The torque of the i-th hub motor satisfies:

[0051] T i =F xi R;

[0052] In the formula, F xi Let be the longitudinal force of the i-th wheel.

[0053] Preferably, the objective function for tire utilization is minimized to:

[0054]

[0055] In the formula, J λ Let η be the objective function for tire utilization. zi For tire utilization rate;

[0056] Tire utilization rate meets:

[0057]

[0058] In the formula, F xi Let F be the longitudinal force of the i-th wheel. zi Let μ be the tire adhesion force of the i-th wheel. i Let be the tire adhesion rate of the i-th wheel.

[0059] Preferably, the objective function for satisfying vehicle dynamics is:

[0060]

[0061]

[0062] In the formula, F xD For vertical demand forces, M i M is the yaw moment generated by the i-th wheel. zD To add a yaw moment as desired.

[0063] Preferably, in step four, the efficiency-adhesion-dynamics optimization function is solved using a quadratic programming optimization algorithm;

[0064] The quadratic programming optimization algorithm is as follows:

[0065]

[0066] In the formula, H is a positive definite matrix, A·x≤b is an inequality constraint, A is the inequality constraint matrix of the corresponding dimension, b is the column vector of the inequality constraint conditions, Aeq·x=beq is an equality constraint, Aeq is the equality constraint matrix of the corresponding dimension, beq is the column vector of the equality constraint conditions, ub is the upper bound of the solution value, and lb is the lower bound of the solution value.

[0067] x = [F xL1 F xR1 F xL2 F xR2 ] T ;

[0068] H = w p H p +w λ H λ +w x H x +w z H z ;

[0069]

[0070]

[0071] When the total longitudinal desired force or the desired additional yaw moment is greater than the maximum value that the four hub motors can provide:

[0072]

[0073] When the total longitudinal desired force and the desired additional yaw moment both do not exceed the maximum value that the four hub motors can provide:

[0074]

[0075] In the formula, d is the wheel track.

[0076] The beneficial effects of this invention are as follows:

[0077] This invention presents a distributed drive vehicle power system energy consumption optimization method. By minimizing the sum of power loss of the hub motors and the sum of tire utilization, the torque distribution of the four hub motors is carried out while satisfying power requirements. This improves the energy utilization of electric vehicles, significantly reduces the working energy consumption of the hub motors, and makes full use of the maximum adhesion of the tires, ensuring the vehicle's handling stability and greatly improving the driving range of electric vehicles. Attached Figure Description

[0078] Figure 1 This is a flowchart illustrating the energy consumption optimization method for distributed drive vehicle power systems described in this invention.

[0079] Figure 2 This is a schematic diagram of the membership function of the longitudinal demand force in the fuzzy control model described in this invention.

[0080] Figure 3 This is a schematic diagram of the membership function for the desired additional yaw moment in the fuzzy control model described in this invention.

[0081] Figure 4 This is a schematic diagram of the membership function of the weight coefficients of the objective function of motor loss in the fuzzy control model described in this invention.

[0082] Figure 5 This is a schematic diagram of the membership function of the weight coefficients of the objective function of tire utilization rate in the fuzzy control model described in this invention.

[0083] Figure 6 This is a schematic diagram of the membership function of the weight coefficients of the longitudinal demand force objective function in the fuzzy control model described in this invention.

[0084] Figure 7 This is a schematic diagram of the membership function of the weight coefficients of the objective function of the additional yaw moment in the fuzzy control model described in this invention.

[0085] Figure 8 This is a schematic diagram of the double-line-shifting operation in the simulation experiment described in this invention.

[0086] Figure 9 This is a schematic diagram of the sliding film control for yaw rate tracking in the dual-line-shifting condition simulation experiment described in this invention.

[0087] Figure 10 This is a schematic diagram of the torque distribution of the hub motor under the dual lane-shifting condition in the simulation experiment described in this invention.

[0088] Figure 11 This is a schematic diagram comparing energy consumption under the dual-line-shifting condition in the simulation experiment described in this invention. Detailed Implementation

[0089] The present invention will now be described in further detail so that those skilled in the art can implement it based on the description.

[0090] like Figure 1 As shown, the present invention provides an energy consumption optimization method for a distributed drive vehicle powertrain system, which specifically includes the following steps:

[0091] Step 1: Calculate the desired yaw rate of the vehicle using an ideal two-degree-of-freedom model based on the vehicle speed and wheel rotation angle, and obtain the desired vehicle speed based on the accelerator pedal opening and vehicle speed.

[0092] The ideal two-degree-of-freedom model takes the following form:

[0093]

[0094] In the formula, k1 is the stiffness of the front axle, k2 is the stiffness of the rear axle, β is the sideslip angle of the vehicle's center of gravity, u is the longitudinal speed, a is the distance from the vehicle's center of gravity to the front axle, b is the distance from the vehicle's center of gravity to the rear axle, and ω... r δ represents the vehicle's actual yaw rate, m represents the standard wheel rotation angle, v represents the vehicle's mass, and v represents the lateral speed. For the vehicle's acceleration, I z Let be the vehicle's moment of inertia. This is the derivative of the vehicle's actual yaw rate;

[0095] The standard wheel angle is the average wheel angle of the two front wheels of the vehicle, which is obtained by sensors.

[0096] Therefore, the desired yaw rate output by the ideal two-degree-of-freedom model can be expressed as:

[0097]

[0098] In the formula, ω rd Let be the vehicle's desired yaw rate.

[0099] Step 2: Based on the difference between the vehicle's expected speed and actual speed, calculate the vehicle's longitudinal demand force using the driver model; based on the difference between the vehicle's expected yaw rate and actual yaw rate, calculate the expected additional yaw moment using the slick membrane control model.

[0100] The error between the desired speed and the actual speed of the vehicle is:

[0101] e1(t)=v req (t)-v real (t);

[0102] In the formula, e1(t) is the vehicle speed error at time t, and v req (t) represents the expected vehicle speed at time t, v real (t) represents the actual vehicle speed at time t;

[0103] The longitudinal force requirement of the vehicle is satisfied as follows:

[0104]

[0105] In the formula, F xD (t) represents the longitudinal demand force of the vehicle, K p K is the proportionality coefficient. i K is the integral coefficient. d These are the differential coefficients;

[0106] The error between the vehicle's expected yaw rate and its actual yaw rate is:

[0107] e2=ω r -ω rd ;

[0108] In the formula, e2 is the yaw rate error;

[0109] The sliding integral function of the sliding control model is as follows:

[0110]

[0111]

[0112] In the formula, S is the sliding membrane integral function. Let λ be the synovial index convergence rate, λ be the synovial parameter, and λ = 0.8;

[0113] The synovial index convergence rate satisfies:

[0114]

[0115] In the formula, ε is the boundary layer thickness, which takes a value of 0.01, τ is the approach rate exponent, which takes a value of 500, and sat(S) is the saturation function, and both the boundary layer thickness and the approach rate exponent are positive numbers;

[0116] The saturation function satisfies:

[0117]

[0118] In the formula, α is the reciprocal of the boundary layer thickness, and α = 20; Δ is the boundary layer thickness, and Δ = 0.05.

[0119] Based on the nonlinear two-degree-of-freedom equation, the formula for the derivative of the yaw rate can be obtained:

[0120]

[0121] In the formula, M zD To add a desired yaw moment;

[0122] Calculate the expected additional yaw moment using the formula for the synovial index approximation rate:

[0123]

[0124] Step 3: Based on the desired additional yaw moment and the vehicle's longitudinal force requirement, output the weight coefficients w of the motor loss objective function using fuzzy control. p The weighting coefficient w for tire utilization rate λ Vertical demand force weighting coefficient w x And the expected additional yaw moment weighting coefficient w z Specifically:

[0125] This fuzzy controller employs Mamdani fuzzy control. It takes two variables as input and outputs four variables based on a fuzzy rule table. By inputting the desired additional yaw moment and longitudinal demand force into the fuzzy control model, w is obtained. p w λ w x and w z The range of variation for the two input variables is set as follows: and T imax Let R be the maximum torque that the i-th hub motor can provide, R be the rolling radius of the tire, and M be the maximum torque that the hub motor can provide. imax The four output variables w represent the maximum yaw torque that the i-th hub motor can provide. p w λ w x and w z The ranges of variation are set to [0,1], [0,10], [0,100], [0,100] respectively.

[0126] Since the longitudinal demand force and expected additional yaw moment are different for different operating conditions (such as when the vehicle is going straight or turning), four weight coefficients are output for different operating conditions through the fuzzy control model.

[0127] The fuzzy control rules are shown in Tables 1 to 4, and the membership functions of the fuzzy control input and output variables are as follows: Figure 2-7 As shown in the table: In the range of input variables, ZE is small, PS is relatively small, PM is relatively large, and PB is large; in the range of output variables, S is small, M is medium, and B is large.

[0128] Table 1. Weighting coefficients of the objective function for motor losses.

[0129]

[0130] Table 2. Weighting coefficients for tire utilization rate

[0131]

[0132] Table 3 Values ​​of Vertical Demand Force Weighting Coefficients

[0133]

[0134] Table 4 Values ​​of the Weighting Coefficient for Expected Additional Yaw Moment

[0135]

[0136]

[0137] Step 4: Based on the conditions of minimizing the sum of power loss of the hub motor and the sum of tire utilization, and simultaneously satisfying the vehicle's driving dynamics, establish an "efficiency-adhesion-power" optimization function.

[0138] Establish an objective function to minimize hub motor losses based on motor power loss:

[0139]

[0140] In the formula, J p Let P be the objective function for the losses of the hub motor. loss-i Let be the power loss of the i-th hub motor, and i = 1, 2, 3, 4. When i = 1, the wheel is the left front wheel; when i = 2, the wheel is the right front tire; when i = 3, the wheel is the left rear tire; and when i = 4, the wheel is the right rear tire.

[0141] The power loss of the i-th motor satisfies:

[0142]

[0143] In the formula, P motor-i Let η be the output power of the i-th hub motor. i Let T be the efficiency of the i-th hub motor. i Let n be the torque of the i-th hub motor. i Let be the rotational speed of the i-th wheel;

[0144] The torque of the i-th hub motor satisfies:

[0145] T i =F xi R;

[0146] In the formula, F xi R is the longitudinal force of the i-th wheel, and R is the tire rolling radius;

[0147] The objective function is established based on minimizing tire utilization:

[0148]

[0149] In the formula, J λ Let η be the objective function for tire utilization. zi For tire utilization rate;

[0150] The tire utilization rate satisfies:

[0151]

[0152] In the formula, F zi Let μ be the tire adhesion force of the i-th wheel. i Let be the tire adhesion rate of the i-th wheel;

[0153] Establish the objective function based on satisfying vehicle dynamics requirements:

[0154]

[0155]

[0156] In the formula, J x To satisfy the objective function of longitudinal demand force, J z To satisfy the objective function of the additional yaw moment, F xD For vertical demand forces, M i M is the yaw moment generated by the i-th wheel. zD To add a desired yaw moment;

[0157] Integrating four objective functions:

[0158] minJ = w p J p +w λ J λ +w x J x +w z J z ;

[0159] In the formula, J is the overall objective function, and w p w represents the weighting coefficients of the objective function for motor losses. λ w is the weighting factor for tire utilization. x w is the weighting coefficient for vertical demand forces. z Add a weighting factor to the desired yaw moment;

[0160] Its constraints are:

[0161]

[0162] In the formula, T imax This represents the maximum torque that the i-th hub motor can provide.

[0163] The longitudinal expected forces of the four wheels are calculated using a quadratic programming optimization algorithm, which specifically satisfies the following:

[0164]

[0165] In the formula, H is a positive definite matrix, A·x≤b is an inequality constraint, A is the inequality constraint matrix of the corresponding dimension, b is the column vector of the inequality constraint conditions, Aeq·x=beq is an equality constraint, Aeq is the equality constraint matrix of the corresponding dimension, beq is the column vector of the equality constraint conditions, ub is the upper bound of the solution value, and lb is the lower bound of the solution value.

[0166] x = [F xL1 F xR1 F xL2 F xR2 ] T ;

[0167] H = w p H p +w λ H λ +w x H x +w z H z ;

[0168]

[0169]

[0170] When the total longitudinal desired force or the desired additional yaw moment is greater than the maximum value that the four hub motors can provide:

[0171]

[0172] When the total longitudinal desired force and the desired additional yaw moment both do not exceed the maximum value that the four hub motors can provide:

[0173]

[0174] In the formula, d is the wheel track.

[0175] Step 5: Input the vehicle's longitudinal demand force, expected additional yaw moment, adhesion force and rotational speed of the four wheels detected by the sensors into the torque distribution algorithm model, and reasonably solve for the optimal energy consumption torque of the four wheels.

[0176] The longitudinal expected force of the four wheels and the torque formula of the hub motor, obtained by solving the quadratic programming optimization algorithm, can determine the expected torque of the four hub motors after energy consumption optimization.

[0177] The hub motor model uses a second-order transfer function, as shown in the following formula:

[0178]

[0179] In the formula, T m T is the mechanical time constant.e Let G(s) be the electrical time constant, and G(s) be the second-order response transfer function.

[0180] This invention uses Simulink 2020b / CarSim 2019 software to conduct a dual lane change simulation experiment, and compares the experimental results with the average torque distribution of four wheels:

[0181] This invention calculates the desired yaw rate of the vehicle based on an ideal two-degree-of-freedom model. Based on the difference between the desired and actual yaw rates, a sliding mode control model calculates the desired additional yaw torque. Based on the difference between the desired and actual vehicle speeds, a PID algorithm using a driver model calculates the vehicle's longitudinal demand force. Under the conditions of minimizing the sum of power losses from the in-wheel motors and the sum of tire utilization rates, while simultaneously satisfying vehicle driving dynamics, an "efficiency-adhesion-power" optimization function is established. Based on the desired additional yaw torque and longitudinal demand force, a fuzzy control model outputs four weighting coefficients. The desired additional yaw torque, longitudinal demand force, and the four weighting coefficients, along with the adhesion and rotational speeds of the four wheels detected by sensors, are input into a torque distribution algorithm model to rationally solve for the energy-optimal torque of the four wheels. This energy-optimal torque is then input into the in-wheel motor model, and after passing through a second-order response transfer function, the hysteresis torque is obtained and input into the distributed drive vehicle. The distributed drive vehicle outputs various required vehicle motion parameters based on onboard sensors.

[0182] Experimental scenario:

[0183] like Figure 8 As shown, the parameters for the double lane change condition are set as follows: vehicle speed is 50 km / h, adhesion rate μ is 0.85, and time is 15 seconds.

[0184] Experimental results:

[0185] It can be done Figure 9 It can be seen that the yaw rate output by the sliding mode control model can follow the desired yaw rate output by the ideal two-degree-of-freedom model very well. For example... Figure 10 As shown, the torque output of the four hub motors through the torque distribution algorithm model is smooth and stable, as... Figure 11 As shown, under the dual lane-change condition, the total input power of the four hub motors is less than the energy consumed by the average distribution, reducing the energy consumption by about 30% and significantly optimizing the energy consumption of distributed electric vehicles.

[0186] This invention presents a distributed drive vehicle power system energy consumption optimization method. By minimizing the sum of power loss of the hub motors and maximizing the sum of tire utilization, the torque distribution of the four hub motors is carried out while satisfying power requirements. This improves the energy utilization of electric vehicles, significantly reduces the working energy consumption of the hub motors, and makes full use of the maximum adhesion of the tires, ensuring the vehicle's handling stability and greatly improving the driving range of electric vehicles.

[0187] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for optimizing energy consumption in a distributed drive vehicle powertrain, characterized in that, Includes the following steps: Step 1: Obtain the desired yaw rate of the vehicle based on the vehicle speed and wheel rotation angle, and obtain the desired vehicle speed based on the accelerator pedal opening and maximum vehicle speed. The desired yaw rate satisfies: In the formula, ω rd Let k1 be the desired yaw rate of the vehicle, k2 be the stiffness of the front axle, k2 be the stiffness of the rear axle, β be the sideslip angle of the vehicle's center of gravity, u be the longitudinal speed, a be the distance from the vehicle's center of gravity to the front axle, b be the distance from the vehicle's center of gravity to the rear axle, δ be the standard wheel steering angle, and I be the yaw rate. z The moment of inertia of the vehicle; The desired speed of the vehicle satisfies: v req =θ·v max ; In the formula, v req Let v be the desired vehicle speed, θ be the accelerator pedal opening, and v be the throttle position. max Maximum speed; Step 2: Obtain the desired additional yaw moment based on the desired yaw rate of the vehicle, and obtain the longitudinal force required by the vehicle based on the desired vehicle speed. The longitudinal force requirement of the vehicle is satisfied as follows: In the formula, F(t) represents the longitudinal demand force of the vehicle, e1(t) represents the vehicle speed error at time t, and K... p K is the proportionality coefficient. i K is the integral coefficient. d These are the differential coefficients; The desired additional yaw moment satisfies: In the formula, M zD To add a yaw moment as desired, Let ε be the derivative of the desired yaw rate, τ be the boundary layer thickness, τ be the approach rate exponent, sat(S) be the saturation function, S be the slid integral function, λ be the slid parameters, e² be the yaw rate error, and ω be the yaw rate error. r This refers to the vehicle's actual yaw rate. Step 3: Based on the expected additional yaw moment and the vehicle's longitudinal demand force, obtain the weight coefficients of the motor loss objective function, the tire utilization rate, the longitudinal demand force, and the expected additional yaw moment through fuzzy control. Step 4: Establish the efficiency-adhesion-dynamics optimization function; minJ=w p J p +w λ J λ +w x J x +w z J z ; In the formula, J is the overall objective function, and w p w represents the weighting coefficients of the objective function for motor losses. λ w is the weighting factor for tire utilization. x w is the weighting coefficient for vertical demand forces. z To add a weighting factor to the desired yaw moment, J p Let J be the objective function for the losses of the hub motor. λ Let J be the objective function for tire utilization. x To satisfy the objective function of longitudinal demand force, J z To satisfy the objective function of the additional yaw moment, T i Let T be the torque of the i-th hub motor. imax F is the maximum torque of the i-th hub motor. xi Let μ be the longitudinal force of the i-th wheel. i Let F be the tire adhesion ratio of the i-th wheel. zi Let be the tire adhesion force of the i-th wheel, and i = 1, 2, 3, 4. When i = 1, the wheel is the left front wheel; when i = 2, the wheel is the right front tire; when i = 3, the wheel is the left rear tire; and when i = 4, the wheel is the right rear tire. Step 5: Obtain the desired torque of the four hub motors after energy consumption optimization based on the longitudinal expected force of the four wheels.

2. The energy consumption optimization method for a distributed drive vehicle powertrain system as described in claim 1, characterized in that, The sliding integral function is: The saturation function is: In the formula, α is the reciprocal of the boundary layer thickness, and Δ is the boundary layer thickness.

3. The energy consumption optimization method for a distributed drive vehicle powertrain system as described in claim 2, characterized in that, The fuzzy control in step three specifically includes: The inputs to the fuzzy control model are the absolute values ​​of the desired additional yaw moment and the longitudinal demand force; The output of fuzzy control is the weighting coefficient of the motor loss objective function, the weighting coefficient of the tire utilization objective function, the weighting coefficient of the longitudinal demand force objective function, and the weighting coefficient of the expected additional yaw moment objective function. The range of variation of the desired additional yaw moment is: The range of variation of the absolute value of the vertical demand force is: The weighting coefficients of the objective function for motor loss vary within the range of [0,1]. The weighting coefficients of the objective function for tire utilization rate vary within the range of [0, 10]. The weighting coefficients of the vertical demand objective function vary within the range of [0, 100]. The range of the weighting coefficients of the objective function for the desired additional yaw moment is [0, 100]. Among them, T imax Let R be the maximum torque that the i-th hub motor can provide, R be the rolling radius of the tire, and M be the maximum torque that the hub motor can provide. imax This represents the maximum yaw torque that the i-th hub motor can provide.

4. The energy consumption optimization method for a distributed drive vehicle powertrain system as described in claim 3, characterized in that, The efficiency-adhesion-power optimization function requires minimizing the objective functions of hub motor loss and tire utilization, and also needs to satisfy vehicle dynamics. The objective function for minimizing the losses of the hub motor is: In the formula, J p Let P be the objective function for the losses of the hub motor. loss-i Let be the power loss of the i-th hub motor; The power loss of the i-th motor satisfies: In the formula, P motor-i Let η be the output power of the i-th hub motor. i Let T be the efficiency of the i-th hub motor. i Let n be the torque of the i-th hub motor. i Let be the rotational speed of the i-th wheel; The torque of the i-th hub motor satisfies: T i =F xi R; In the formula, F xi Let be the longitudinal force of the i-th wheel.

5. The energy consumption optimization method for a distributed drive vehicle powertrain system as described in claim 4, characterized in that, The minimum objective function for tire utilization is: In the formula, J λ Let η be the objective function for tire utilization. zi For tire utilization rate; Tire utilization rate meets: In the formula, F xi Let F be the longitudinal force of the i-th wheel. zi Let μ be the tire adhesion force of the i-th wheel. i Let be the tire adhesion rate of the i-th wheel.

6. The energy consumption optimization method for a distributed drive vehicle powertrain system as described in claim 5, characterized in that, The objective function that satisfies the vehicle dynamics is: In the formula, F xD For vertical demand forces, M i M is the yaw moment generated by the i-th wheel. zD To add a yaw moment as desired.

7. The energy consumption optimization method for a distributed drive vehicle powertrain system as described in claim 6, characterized in that, In step four, the efficiency-adhesion-dynamics optimization function is solved using a quadratic programming optimization algorithm. The quadratic programming optimization algorithm is as follows: In the formula, H is a positive definite matrix, A·x≤b is an inequality constraint, A is the inequality constraint matrix of the corresponding dimension, b is the column vector of the inequality constraint conditions, Aeq·x=beq is an equality constraint, Aeq is the equality constraint matrix of the corresponding dimension, beq is the column vector of the equality constraint conditions, ub is the upper bound of the solution value, and lb is the lower bound of the solution value. x=[F xL1 F xR1 F xL2 F xR2 ] T ; H=w p H p +w λ H λ +w x H x +w z H z ; When the total longitudinal desired force or the desired additional yaw moment is greater than the maximum value that the four hub motors can provide: When the total longitudinal desired force and the desired additional yaw moment both do not exceed the maximum value that the four hub motors can provide: In the formula, d is the wheel track.

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