A predictive energy management control method based on road surface recognition

Abstract

The present invention provides a predictive energy management control method based on road surface recognition. The method first calculates the road adhesion level and estimates the motor drive power after a period of time. Based on the recognized road adhesion level, the weights of the predicted drive power and the actual drive power are adjusted to obtain a new drive power. This drive power is then compared with the maximum allowable drive power to adjust the drive torque, thereby achieving active, proactive, and reliable energy management of the entire vehicle, ensuring that the actual battery discharge power does not exceed the maximum allowable battery discharge power, thereby improving the vehicle safety of electric vehicles or hybrid vehicles and enhancing the vehicle's drivability.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management for 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 and drive power to increase rapidly, thereby exceeding the available drive power of the battery pack and posing a threat to vehicle safety.

[0003] To address this issue, current energy management systems for new energy vehicles primarily provide protection based on the difference between the battery's allowable discharge power and its actual discharge power. When the actual discharge power exceeds the battery's allowable discharge power, power overlimit protection is activated, reducing the drive motor torque and, in turn, the drive motor power. This approach fails to effectively control motor power when the tires slip, causing the motor speed and power to rise rapidly. This results in the battery's actual discharge power still easily exceeding the allowable discharge power. The current solution is to reserve a certain amount of power and reduce the drive motor power when the actual discharge power exceeds the allowable power minus the reserved power. However, this approach prevents more power from being used for driving when the allowable discharge power is low, resulting in power waste. Furthermore, this approach cannot effectively reduce the drive power to protect the battery pack when the motor speed rises rapidly on extremely low-adhesion roads. Furthermore, the rapid reduction in drive torque during power limiting can cause noticeable vehicle jerkiness, degrading the user's driving experience. Summary of the Invention

[0004] In response to the deficiencies 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, ensuring 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, while improving the vehicle's drivability.

[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 includes the following steps:

[0007] Step S1, 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 driving torque, motor speed, and actual motor power;

[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 DC-DC 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 calibration MAP table. k ;

[0010] Step S4: Calculate the road adhesion coefficients and road adhesion levels of the four tires based on the vehicle speed, longitudinal acceleration, four-wheel speed, slope, and actual motor drive torque; the road adhesion coefficient is the minimum of the adhesion coefficients of the four tires; and calculate 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: adjusting the weight coefficients of the predicted driving power and the actual driving power according to the road adhesion level, and obtaining the first driving power P1 according to the following formula;

[0015] P1=K*P MN +(1-K)P Real

[0016] Where P1 is the first driving power, P MN To predict the driving power, P Real is the actual motor power obtained by VCU, 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 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 , calculate the second driver demand torque T according to the following formula J :

[0019] T J =T k +∫F k *dt

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

[0021] Step S8: 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 using the following formula:

[0026]

[0027] Among them, F x Indicates 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 from the wheel speed as ω = V / R, where V represents the actual wheel speed 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 is 0 < u < 0.2, its ground adhesion level is extremely low; if the road surface adhesion coefficient is 0.3 < u < 0.4, its ground adhesion level is low; if the road surface adhesion coefficient is 0.5 < u < 0.6, its ground adhesion level is medium; if the road surface adhesion coefficient is 0.65 < u < 0.75, its ground adhesion level is high; if the road surface adhesion coefficient is 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 allowable 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 effectively control the 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 electric vehicles or hybrid vehicles 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 upon reading the detailed description of non-limiting embodiments 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 an embodiment;

[0046] Figure 2 4 is a control block diagram of the performance energy management method based on road surface recognition described in an embodiment. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein 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 protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application 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 acceleration and transmits them to the vehicle control unit (VCU). The VCU collects the target vehicle's speed, longitudinal acceleration, four-wheel speed, accelerator pedal position, slope, maximum allowable battery pack discharge power, actual motor torque, motor speed, and actual motor power. The VCU (Vehicle Control Unit) is responsible for collecting vehicle information and implementing predictive energy management based on road surface recognition. The maximum allowable battery pack discharge power is transmitted to the VCU by the battery management system (BMS).

[0051] 2. The VCU calculates the maximum allowable drive power by subtracting the power reserved for the DC / DC converter (DC / DC converter) and air conditioning from the battery's maximum allowable discharge power. If the battery's maximum allowable discharge power is 300 kW, 4 kW of power is reserved for the DC / DC converter (DC / DC converter) when the vehicle enters high-voltage mode, and 5 kW is reserved for the air conditioning when the user activates the air conditioning function, the maximum allowable drive power is 291 kW. Both the DC / DC and air conditioning reserve powers are calibrated values ​​and can be modified.

[0052] 3. The VCU calculates the driver's requested torque based on the vehicle speed and accelerator pedal opening using a pre-set calibrated MAP table. The calibrated MAP table has vehicle speed on the horizontal axis and accelerator pedal opening on the vertical axis, with the driver's requested torque being the table content.

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

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

[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 forces on each of the four tires using the following formula:

[0059]

[0060] Among them F x Indicates 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 tire's angular acceleration is calculated from the change in tire angular velocity per unit time: a = dω / dt. The tire's angular velocity is calculated from the wheel speed: ω = V / R, where V represents the actual tire speed 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 Indicates 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] Calculate the road adhesion grade according to Table 1 below and the road adhesion coefficient:

[0067] Entry conditions Road adhesion grade 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] Because the motor speed and vehicle speed are rigidly linked to the wheels via the speed reducer and differential, motor speed increases rapidly when the wheels slip, leading to a rapid increase in motor power. The wheels and motor experience inertia during rotation. Therefore, we first use the motor speed trend to predict the motor speed over a period of time. We then use this predicted speed and the driver's required torque to calculate the predicted drive power.

[0071] First, take the derivative of the motor speed to get the motor speed acceleration a n Where n k is the current motor speed, 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] According to 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] P1=K*P MN +(1-K)P Real

[0081] 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.

[0082] The relationship between the road adhesion grade and the weight coefficient is determined by the preset one-dimensional MAP 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's required torque, T J is the second driver demand torque, and t is time.

[0089] 8. The second driver's requested torque is transmitted to the MCU for execution, completing energy management control. The MCU (Motor Control Unit) is responsible for controlling the drive motor, transmitting the actual drive power, motor speed, and motor torque to the VCU, and executing the requested 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 scope of the technical concept of this 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 calibration 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 levels 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 level, 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 the VCU, and K is the weight coefficient; the weight coefficient is determined by the road adhesion level. When the road adhesion level is very low, the weight coefficient is 0.8; when the road adhesion level is low, the weight coefficient is 0.7; when the road adhesion level is medium, the weight coefficient is 0.6; when the road adhesion level is high, the weight coefficient is 0.4; when the road adhesion level is very high, the weight coefficient is 0.3; 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 , calculate the second driver demand torque T according to the following formula J : T J =T k +∫F k *dt Among them, T J is the second driver's required torque, t is the time; Step S8: The second driver demand torque T J Pass it to the MCU for execution to complete energy management control.

2. The predictive energy management control method based on road surface recognition according to claim 1, 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 Indicates 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 a = dω / dt; the tire angular velocity is calculated from the wheel speed 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 level is extremely low; if the road surface adhesion coefficient is 0.3 < u < 0.4, its ground adhesion level is low; if the road surface adhesion coefficient is 0.5 < u < 0.6, its ground adhesion level is medium; if the road surface adhesion coefficient is 0.65 < u < 0.75, its ground adhesion level is high; if the road surface adhesion coefficient is 0.8 < u < 1, its ground adhesion level 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 current motor speed, 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 For a period of time, the value is a calibration value and is set according to different situations; According to 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, 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, reduce the driver demand torque at a preset change rate of -2500 Nm / s.

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