Predictive energy management control method based on road surface recognition

Abstract

The invention provides a predicted energy management control method based on road surface recognition, which comprises the following steps of: firstly, calculating a road surface attachment grade, and estimating motor driving power after a period of time; and according to the identified road adhesion grade, the predicted driving power and the actual driving power weight are adjusted to obtain new driving power, and the driving torque is adjusted by comparing the driving power with the maximum allowable driving power, so that the energy management of the whole vehicle is actively and reliably carried out in advance, and the energy utilization rate of the whole vehicle is improved. It is guaranteed that the actual discharging power of the battery does not exceed the maximum allowable discharging power of the battery, the vehicle safety of an electric vehicle or a hybrid vehicle is improved, and meanwhile the vehicle drivability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management of new energy vehicles, and in particular to a predictive energy management control method based on road surface recognition. Background Art

[0002] The energy of pure electric vehicles comes from the power battery pack. When accelerating on low-adhesion roads, the vehicle is prone to slipping, causing the motor speed to increase rapidly and the driving power to increase rapidly, thereby exceeding the available driving power of the battery pack, posing a threat to vehicle safety.

[0003] In order to solve this problem, the current energy management of new energy vehicles is mainly based on the difference between the battery's allowable discharge power and the actual discharge power for protection. When the actual discharge power exceeds the battery's allowable discharge power, the power over-limit protection is activated, and the drive motor torque is reduced according to a certain gradient, thereby reducing the drive motor power. In this method, when the tire slips, the motor speed will rise rapidly, and the motor power will rise rapidly, and the motor power cannot be well controlled, so that the actual discharge power of the battery is still easy to exceed the battery's allowable discharge power. The current solution is to reserve a certain amount of power. When the actual discharge power exceeds the allowable power minus the reserved power, the drive motor power is reduced. However, this will make it impossible to use more power for driving when the discharge allowable power is low, resulting in power waste. At the same time, this method cannot effectively reduce the drive power in a timely manner to protect the battery pack when the motor speed rises rapidly on extremely low-adhesion roads. And when this method is implemented, when the power limit is executed, the drive torque is quickly reduced, which will cause obvious setbacks to the vehicle and reduce the user's driving experience. Summary of the invention

[0004] In view of the defects in the prior art, the purpose of the present invention is to provide a predictive energy management control method based on road surface recognition that can effectively control the driving power when the vehicle slips, ensure that the actual discharge power of the battery does not exceed the maximum allowable discharge power of the battery, thereby improving the vehicle safety of electric vehicles or hybrid vehicles, and at the same time improving the drivability of the vehicle.

[0005] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0006] A predictive energy management control method based on road surface recognition comprises the following steps:

[0007] Step S1, the vehicle controller VCU collects the speed, longitudinal acceleration, four-wheel speed, accelerator pedal opening, slope, maximum allowable discharge power of the battery pack, actual motor driving torque, motor speed, and actual motor power of the target vehicle;

[0008] Step S2: The VCU obtains the maximum allowable driving power based on the maximum allowable discharge power of the battery minus the power reserved for DCDC and air conditioning;

[0009] Step S3: The VCU calculates the driver's required torque T according to the vehicle speed, accelerator pedal opening and the preset calibrated MAP table. k ;

[0010] Step S4, calculating the road adhesion coefficient and road adhesion grade of the four tires according to the vehicle speed, longitudinal acceleration, four-wheel speed, slope, and actual motor driving torque; the road adhesion coefficient takes the minimum value among the adhesion coefficients of the four tires; and calculating the road adhesion coefficient of each tire according to the following formula:

[0011] u=F x / F z

[0012] Among them, F x Represents the ground force per tire, F z Indicates the vertical load of each tire;

[0013] Step S5: Calculate the predicted driving power P according to the motor speed and the driver's required torque MN ;

[0014] Step S6: adjust the weight coefficient of the predicted driving power and the actual driving power according to the road adhesion level, and obtain the first driving power P according to the following formula: 1 ;

[0015] P 1 =K*P MN +(1-K)P Real

[0016] Where P 1 is the first driving power, P MN To predict the driving power, P Real is the actual motor power obtained by VCU, and K is the weight coefficient;

[0017] Step S7: performing energy management control according to the first driving power and the maximum allowable driving power;

[0018] When the maximum allowable driving power minus the first driving power P 1 When the difference is less than or equal to the preset threshold, the preset rate of change F k Reduce the driver's demand torque T k , the second driver demand torque T is calculated according to the following formula J :

[0019] T J =T k +∫Fk *dt

[0020] Among them, T J is the second driver demand torque, and t is time.

[0021] Step S8: Set the second driver demand torque T J Pass it to MCU for execution to complete energy management control.

[0022] Preferably, in step S4, the vertical load of the left / right front wheel is equal to 1 / 2 of the vertical load of the front axle, and the vertical load of the left / right rear wheel is equal to 1 / 2 of the vertical load of the rear axle; the vertical load of the front axle and the vertical load of the rear axle are calculated by the following formula:

[0023]

[0024] Among them, F zf Indicates the vertical load on the front axle, F zr represents the vertical load on the rear axle, m represents the vehicle mass, l f is the distance from the front axle to the center of mass, l r is the distance from the rear axle to the center of mass, g is the acceleration of gravity, β is the road slope angle, a x is the vehicle longitudinal acceleration, h g is the height of the vehicle's center of mass from the ground;

[0025] Preferably, in step S4, the tire ground force is calculated by the following formula:

[0026]

[0027] Among them, F x Represents tire ground force, T tw represents the driving torque on the tire, J represents the tire moment of inertia, a represents the tire angular acceleration, and R represents the tire radius; when calculating the left front wheel / right front wheel tire ground force, T tw Equal to T FR / 2,T FR Refers to the actual output torque of the front axle motor; when calculating the ground force of the left / right rear wheel tire, T tw Equal to T RR / 2,T RR Refers to the actual output torque of the rear axle motor; when the vehicle is front-wheel drive, only the front axle motor has torque; when the vehicle is rear-wheel drive, only the rear axle motor has torque; when the vehicle is four-wheel drive, both the front and rear axle motors have torque;

[0028] The angular acceleration of the tire is calculated from the change in the tire angular velocity per unit time as a=dω / dt; the tire angular velocity is calculated based on the wheel speed as ω=V / R, where V represents the actual wheel speed of the tire and R represents the tire radius.

[0029] Preferably, in step S4, after calculating the adhesion coefficients of the four tires, the minimum value is taken as the road surface adhesion coefficient.

[0030] Preferably, in step S4, if the calculated road surface adhesion coefficient satisfies 0 < u < 0.2, its ground adhesion level is extremely low; if the road surface adhesion coefficient satisfies 0.3 < u < 0.4, its ground adhesion level is low; if the road surface adhesion coefficient satisfies 0.5 < u < 0.6, its ground adhesion level is medium; if the road surface adhesion coefficient satisfies 0.65 < u < 0.75, its ground adhesion level is high; if the road surface adhesion coefficient satisfies 0.8 < u < 1, its ground adhesion level is extremely high.

[0031] Preferably, step S5 specifically includes the following steps:

[0032] First, take the derivative of the motor speed to obtain the motor speed acceleration a n , where n k is the motor speed at the current moment, n k-1 is the motor speed at the previous moment, and t is the time interval between the two moments:

[0033]

[0034] According to the motor speed acceleration, calculate the predicted motor speed n after a period of time k+N :

[0035] n k+N = n k + a n * t N

[0036] where t N is a period of time (this value is a calibrated quantity and can be set according to different situations);

[0037] According to the predicted motor speed and the driver's required torque, calculate the predicted drive power P after a period of time MN :

[0038]

[0039] where, T k is the driver's required torque.

[0040] Preferably, in step S6, the weight coefficient is determined by the road surface adhesion level. When the road surface adhesion level is extremely low, the weight coefficient is 0.8; when the road surface adhesion level is low, the weight coefficient is 0.7; when the road surface adhesion level is medium, the weight coefficient is 0.6; when the road surface adhesion level is high, the weight coefficient is 0.4; when the road surface adhesion level is extremely high, the weight coefficient is 0.3.

[0041] Preferably, in step S7, when the difference between the maximum permissible driving power and the first driving power is less than or equal to a threshold value of 5 kW, the driver demand torque is reduced at a preset rate of change of -2500 Nm / s.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The predictive energy management control method based on road surface recognition provided by the present invention can well realize the control of driving power when the vehicle slips. By identifying the road surface and predicting the driving power, the weights of the predicted driving power and the actual driving power are adjusted to ensure that the actual discharge power of the battery does not exceed the maximum allowable discharge power of the battery, thereby improving the vehicle safety of the electric vehicle or hybrid vehicle and improving the drivability of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0045] Figure 1 is a control flow chart of the performance energy management method based on road surface recognition described in the embodiment;

[0046] Figure 2 It is a control block diagram of the performance energy management method based on road surface recognition described in the embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0048] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0049] like Figure 1 , 2 As shown, this embodiment provides a predictive energy management control method based on road surface recognition, which specifically includes the following steps:

[0050] 1. The anti-lock braking system ABS calculates the four-wheel speed, vehicle speed and vehicle acceleration and transmits them to the vehicle controller VCU. The vehicle controller VCU collects the target vehicle's speed, longitudinal acceleration, four-wheel speed, accelerator pedal opening, slope, maximum allowable discharge power of the battery pack, actual motor drive torque, motor speed, and actual motor power; VCU (Vehicle Control Unit) is the vehicle controller responsible for collecting vehicle information and performing predictive energy management control based on road surface recognition. The maximum allowable discharge power of the battery pack is transmitted to the VCU by the battery management system BMS.

[0051] 2. VCU obtains the maximum allowable driving power after subtracting the power reserved for DCDC (DC / DC converter) and air conditioning from the maximum allowable discharge power of the battery. When the maximum allowable discharge power of the battery is 300KW, the power reserved for DCDC after the vehicle enters the high-voltage state is 4KW, and the power reserved for air conditioning after the user activates the air conditioning function is 5KW, then the maximum allowable driving power is 291KW. The reserved power of DCDC and air conditioning are both calibrated values ​​and can be modified.

[0052] 3. The VCU calculates the driver's required torque based on the vehicle speed and the accelerator pedal opening according to a preset calibrated MAP table. In the calibrated MAP table, the horizontal axis is the vehicle speed, the vertical axis is the accelerator pedal opening, and the table content is the driver's required torque.

[0053] 4. Calculate the road adhesion coefficient and road adhesion grade based on vehicle speed, longitudinal acceleration, four-wheel speed, slope, and actual motor driving torque.

[0054] To calculate the road adhesion coefficient, it is necessary to first calculate the dynamic load on the front and rear axles of the vehicle and the tire ground force.

[0055] The dynamic vertical loads on the front and rear axles are calculated according to the following formula:

[0056]

[0057] In the above formula, F zf Indicates the dynamic vertical load on the front axle, F zr represents the dynamic vertical load on the rear axle, m represents the vehicle mass, l f is the distance from the front axle to the center of mass, l r is the distance from the rear axle to the center of mass, g is the acceleration of gravity, β is the road slope angle, a x is the vehicle longitudinal acceleration, h g is the height of the vehicle's center of mass above the ground.

[0058] Calculate the ground force of the four tires according to the following formula:

[0059]

[0060] Among them, F x Represents tire ground force, T tw represents the driving torque on the tire, J represents the tire moment of inertia, a represents the tire angular acceleration, and R represents the tire radius. When calculating the left front wheel / right front wheel tire ground force, T tw Equal to T FR / 2,T FR The actual output torque of the front axle motor. When calculating the ground force of the left / right rear wheel tire, T tw Equal to T RR / 2,T RR The actual output torque of the rear axle motor. When the vehicle is front-wheel drive, only the front axle motor has torque. When the vehicle is rear-wheel drive, only the rear axle motor has torque. When the vehicle is four-wheel drive, both the front and rear axle motors have torque.

[0061] The angular acceleration of the tire is calculated from the change in the tire angular velocity per unit time as a=dω / dt. The tire angular velocity is calculated from the wheel speed as ω=V / R, where V represents the actual wheel speed of the tire and R represents the tire radius.

[0062] The road adhesion coefficient is calculated according to the following formula:

[0063] u=F x / F z

[0064] Among them, F x Represents the ground force per tire, F z It represents the vertical load of each tire. The vertical load of the left / right front wheel is equal to 1 / 2 of the vertical load of the front axle, and the vertical load of the left / right rear wheel is equal to 1 / 2 of the vertical load of the rear axle.

[0065] After calculating the adhesion coefficients of the four wheels, take the minimum value as the road adhesion coefficient.

[0066] According to the following table 1 and the road adhesion coefficient, calculate the road adhesion grade:

[0067] Entry conditions Road adhesion level 0<u<0.2 Very low 0.3<u<0.4 Low 0.5<u<0.6 middle 0.65<u<0.75 high 0.8<u<1 Very high

[0068] Table 1

[0069] 5. Calculate the predicted drive power based on the motor speed and the driver's required torque.

[0070] Since the motor speed and vehicle speed are hard-wired to the wheels through the reducer, differential, and wheels, the motor speed will also increase rapidly when the wheels slip, causing the motor power to increase rapidly. The wheels and motors have inertia during rotation. Therefore, the motor speed change trend is first used to predict the motor speed after a period of time, and then the predicted motor speed and the driver's required torque are used to calculate the predicted driving power.

[0071] First, take the derivative of the motor speed to get the motor speed acceleration a n Where n k is the motor speed at the current moment, n k-1 is the motor speed at the previous moment, and t is the interval between two moments:

[0072]

[0073] According to the motor speed acceleration, calculate the predicted motor speed n after a period of time k+N :

[0074] n k+N =n k +a n *t N

[0075] where t N A period of time (this value is a calibration value and can be set according to different situations).

[0076] Based on the predicted motor speed and the driver's required torque, the predicted driving power P after a period of time is calculated. MN :

[0077]

[0078] Where T k Torque for the driver's needs.

[0079] 6. Adjust the weight coefficient of the predicted driving power and the actual driving power according to the road adhesion level to obtain the first driving power.

[0080] P 1 =K*P MN +(1-K)P Real

[0081] Where P 1 is the first driving power, P MN To predict the driving power, P Real is the actual motor power obtained by VCU, and K is the weight coefficient.

[0082] The relationship between the road adhesion grade and the weight coefficient is determined by a preset one-dimensional MAP as shown in Table 2 below. The table values ​​can be adjusted and modified according to actual conditions.

[0083] Very low Low middle high Very high Weight coefficient K 0.8 0.7 0.6 0.4 0.3

[0084] Table 2

[0085] 7. Perform energy management control according to the first driving power and the maximum allowable driving power.

[0086] When the difference between the maximum allowable driving power and the first driving power is less than or equal to the threshold value (5kw), the preset change rate F k (-2500Nm / s) Reduce the driver's required torque:

[0087] T J =T k +∫F k *dt

[0088] Where T k is the driver demand torque, T J is the second driver demand torque, and t is time.

[0089] 8. The second driver's required torque is transmitted to the MCU for execution to complete energy management control. The MCU (Motor Control Unit) is the motor control unit responsible for controlling the drive motor, transmitting the actual drive power, motor speed, and motor torque to the VCU, and executing the required torque transmitted by the VCU.

[0090] The above describes the specific embodiments of the present invention. Based on the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention.

Claims

1. A predictive energy management control method based on road surface recognition, characterized in that: It includes the following steps: Step S1: The vehicle control unit (VCU) collects the vehicle speed, longitudinal acceleration, four-wheel speeds, accelerator pedal opening, slope, maximum allowable discharge power of the battery pack, actual driving torque of the motor, motor speed, and actual power of the motor of the target vehicle; Step S2: The VCU subtracts the power reserved for the DCDC and air conditioner from the maximum allowable discharge power of the battery to obtain the maximum allowable driving power; Step S3: The VCU calculates the driver's required torque T according to the vehicle speed, accelerator pedal opening and the preset calibrated MAP table. k ; Step S4: According to the vehicle speed, longitudinal acceleration, four-wheel speeds, slope, and actual driving torque of the motor, calculate the road surface adhesion coefficients and road surface adhesion grades of the four tires; the road surface adhesion coefficient takes the minimum value among the adhesion coefficients of the four tires; calculate the road surface adhesion coefficient of each tire according to the following formula: u=F x / F z Among them, F x Represents the ground force per tire, F z Indicates the vertical load of each tire; Step S5: Calculate the predicted driving power P according to the motor speed and the driver's required torque MN ; Step S6: Adjust the weight coefficients of the driving predicted power and the actual driving power according to the road surface adhesion grade, and obtain the first driving power P1 according to the following formula: P1=K*P MN +(1-K)P Real Where P1 is the first driving power, P MN To predict the driving power, P Real is the actual motor power obtained by VCU, and K is the weight coefficient; Step S7: Perform energy management control according to the first driving power and the maximum allowable driving power; When the difference between the maximum allowable driving power and the first driving power P1 is less than or equal to the preset threshold, the preset change rate F k Reduce the driver's demand torque T k , the second driver demand torque T is calculated according to the following formula J : T J =T k +∫F k *dt Among them, T J is the second driver demand torque, and t is time. Step S8: Set the second driver demand torque T J Pass it to MCU for execution to complete energy management control.

2. The predictive energy management control method based on road surface recognition according to claim 1 is characterized in that: In step S4, the vertical load of the left / right front wheel is equal to 1 / 2 of the vertical load of the front axle, and the vertical load of the left / right rear wheel is equal to 1 / 2 of the vertical load of the rear axle; the vertical loads of the front axle and the rear axle are calculated by the following formula: Among them, F zf Indicates the vertical load on the front axle, F zr represents the vertical load on the rear axle, m represents the vehicle mass, l f is the distance from the front axle to the center of mass, l r is the distance from the rear axle to the center of mass, g is the acceleration of gravity, β is the road slope angle, a x is the vehicle longitudinal acceleration, h g is the height of the vehicle's center of mass above the ground.

3. The predictive energy management control method based on road surface recognition according to claim 1, characterized in that: In step S4, the tire ground force is calculated by the following formula: Among them, F x Represents tire ground force, T tw represents the driving torque on the tire, J represents the tire moment of inertia, a represents the tire angular acceleration, and R represents the tire radius; when calculating the left front wheel / right front wheel tire ground force, T tw Equal to T FR / 2,T FR Refers to the actual output torque of the front axle motor; when calculating the ground force of the left / right rear wheel tire, T tw Equal to T RR / 2,T RR Refers to the actual output torque of the rear axle motor; when the vehicle is front-wheel drive, only the front axle motor has torque; when the vehicle is rear-wheel drive, only the rear axle motor has torque; when the vehicle is four-wheel drive, both the front and rear axle motors have torque; The angular acceleration of the tire is calculated from the change in the tire angular velocity per unit time as a = dω / dt; the tire angular velocity is calculated from the wheel speed as ω = V / R, where V represents the actual wheel speed of the tire and R represents the tire radius.

4. The predictive energy management control method based on road surface recognition according to claim 1, characterized in that: In step S4, after calculating the adhesion coefficients of the four tires, take the minimum value as the road surface adhesion coefficient.

5. The predictive energy management control method based on road surface recognition according to claim 1, characterized in that: In step S4, if the calculated road surface adhesion coefficient is 0 < u < 0.2, its ground adhesion grade is extremely low; if the road surface adhesion coefficient is 0.3 < u < 0.4, its ground adhesion grade is low; if the road surface adhesion coefficient is 0.5 < u < 0.6, its ground adhesion grade is medium; if the road surface adhesion coefficient is 0.65 < u < 0.75, its ground adhesion grade is high; if the road surface adhesion coefficient is 0.8 < u < 1, its ground adhesion grade is extremely high.

6. The predictive energy management control method based on road surface recognition according to claim 1, characterized in that: Step S5 specifically includes the following steps: First, take the derivative of the motor speed to get the motor speed acceleration a n , where n k is the motor speed at the current moment, n k-1 is the motor speed at the previous moment, and t is the interval between two moments: According to the motor speed acceleration, calculate the predicted motor speed n after a period of time k+N : n k+N =n k +a n *t N where t N A period of time (this value is a calibration value and can be set according to different situations); Based on the predicted motor speed and the driver's required torque, the predicted driving power P after a period of time is calculated. MN : Among them, T k Torque for the driver's needs.

7. The predictive energy management control method based on road surface recognition according to claim 1 is characterized in that: In step S6, the weight coefficient is determined by the road surface adhesion grade. When the road surface adhesion grade is extremely low, the weight coefficient is 0.8; when the road surface adhesion grade is low, the weight coefficient is 0.7; when the road surface adhesion grade is medium, the weight coefficient is 0.6; when the road surface adhesion grade is high, the weight coefficient is 0.4; when the road surface adhesion grade is extremely high, the weight coefficient is 0.

3.

8. The predictive energy management control method based on road surface recognition according to claim 1, characterized in that: In step S7, when the difference between the maximum allowable driving power and the first driving power is less than or equal to the threshold value of 5 kW, the driver demand torque is reduced at a preset change rate of -2500 Nm / s.

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