Electric vehicle power source dynamic cooperative control method based on road condition prediction

Abstract

The invention discloses an electric vehicle power source dynamic cooperative control method based on road condition prediction, and the method comprises the steps: firstly building a three-degree-of-freedom vehicle dynamics model, and constructing an extended state observer of a yaw velocity, so as to accurately estimate a vehicle motion state and system disturbance; then designing a whole vehicle attitude compensator comprising an instruction processing link, a feedforward input link and an attitude correction link, generating an anti-interference correction control instruction, and finally driving an EPS motor to output a compensation torque through a steering system controller so as to realize closed-loop adjustment of a vehicle yaw attitude; according to the method, the model predictive control and the disturbance observation technology are combined, vehicle deviation can be restrained in real time under complex internal and external interference, the problems of error accumulation, response lag and sudden change of the driving hand feeling existing in a traditional compensation method are effectively solved, and the linear driving stability and the track control precision of the vehicle are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle posture adjustment technology, and in particular to a dynamic collaborative control method for electric vehicle power sources based on road condition prediction. Background Technology

[0002] With the rapid iteration of the new energy vehicle industry and the widespread adoption of intelligent driving technology, electric power steering (EPS) has become a core component of the automotive chassis. As a crucial hub connecting the driver's steering wheel operation with the vehicle's driving trajectory, EPS not only needs to provide precise steering assistance under different speeds and operating conditions, ensuring steering ease and consistent feel, but also undertakes core tasks such as vehicle attitude control and path tracking correction. It is a core guarantee for achieving vehicle driving stability and improving driving comfort. Especially in L2 and above intelligent driving scenarios, the precise control of the vehicle's driving trajectory requires millimeter-level precision. The dynamic response speed and control accuracy of EPS directly determine the reliability and safety of the intelligent driving system, thus the demand for its trajectory control capabilities is becoming increasingly urgent.

[0003] However, vehicle drift during driving remains a key technical bottleneck restricting EPS control performance. The causes of drift are complex and diverse. They stem from inherent issues such as uneven tire wear, tire pressure imbalance, suspension bushing aging, chassis parameter drift, and accumulated steering system clearance. These deviations lead to an imbalance of forces in the vehicle's steering system, resulting in a persistent drift tendency. External factors such as crosswinds (especially sudden or continuous crosswinds at high speeds), road slope, bumps and impacts from uneven roads, and uneven road friction coefficients can directly disrupt the vehicle's force balance, causing momentary or continuous trajectory deviation.

[0004] The aforementioned deviation directly causes the vehicle to deviate from its intended trajectory while driving in a straight line, resulting in abnormal changes in body posture such as tilting and drifting. To maintain the normal driving path, the driver needs to continuously apply additional steering correction torque, which can easily lead to muscle fatigue over time and severely reduce driving comfort. More importantly, unexpected deviations in body posture significantly increase safety risks—in complex scenarios such as high-speed driving, emergency lane changes, and inclement weather, even slight deviations can be amplified into loss of trajectory control, or even cause a collision. Furthermore, the deviation problem creates a significant conflict with the precise trajectory control requirements of intelligent driving systems, leading to increased path tracking errors and impacting the user experience and safety of autonomous driving functions.

[0005] Currently, while mainstream deviation compensation strategies are widely used in EPS systems, a series of problems still exist in actual vehicles. First, the compensation torque suffers from sluggishness, insufficient real-time performance, and deficiencies in accuracy and response speed. Furthermore, the compensation current is not adjustable and relies on complex calibration parameters, making it difficult to accurately adapt to different vehicle types. Second, excessive compensation torque in some scenarios may affect driving safety, but simply limiting the output torque weakens the deviation suppression effect. Simultaneously, the driver's feel changes significantly when the compensation function intervenes and de-intervenes, and in non-straight-line driving conditions, residual compensation torque can interfere with driving feel. In addition, the compensation current is reset to zero after power-off, requiring re-establishment upon the next startup, leading to untimely compensation. Storing deviation torque in each ignition cycle generates cumulative errors, causing the compensation to deviate from actual needs and failing to effectively suppress vehicle deviation.

[0006] Therefore, designing a vehicle body deviation correction strategy that can adapt to multiple scenarios and causes, dynamically adjusting the EPS control logic, and effectively suppressing lane departure during straight-line driving, thereby improving vehicle straight-line stability and trajectory control accuracy, not only solves the problem of vehicle drift in traditional driving but also meets the high-precision requirements of intelligent driving for steering systems. This is of great significance for promoting the further development of new energy vehicles and intelligent driving technologies. Therefore, in response to the problems mentioned above, this invention proposes a dynamic collaborative control method for electric vehicle power sources based on road condition prediction. Summary of the Invention

[0007] To overcome the problem of vehicle drift in traditional driving and meet the high precision requirements of intelligent driving for steering systems, this invention proposes a dynamic cooperative control method for electric vehicle power sources based on road condition prediction.

[0008] The technical solution of this invention is: a dynamic cooperative control method for electric vehicle power sources based on road condition prediction, comprising the following steps: S1. Establish a three-degree-of-freedom vehicle dynamics model. This model includes longitudinal motion equations, lateral motion equations, and yaw motion equations. The yaw motion equations are constructed based on vehicle parameters and tire forces. The vehicle parameters include mass m, yaw moment of inertia Iz, longitudinal distances from the center of mass to the front and rear wheels, wheelbases lf, lr, and lw on the left and right sides, and lateral tire forces at the left front, right front, left rear, and right rear. Longitudinal forces of the front left, front right, rear left, and rear right tires ; S2, based on a three-degree-of-freedom vehicle dynamics model, constructs an extended state observer for the vehicle's yaw rate. The extended state observer is used to estimate the vehicle's lateral velocity, the sum of the yaw rate and the unmodeled dynamics of the system and external disturbances, based on the actual yaw rate collected by the sensors. Its state equations include differential equations for lateral velocity estimation, yaw rate estimation, and disturbance estimation. Lateral velocity estimation error gain g1, yaw rate estimation error gain g2, and system disturbance estimation error gain g3 are introduced for correction. S3, a vehicle attitude compensator is designed based on the vehicle yaw motion equation and reference model. The vehicle attitude compensator receives the yaw rate, the desired yaw rate and the vehicle state parameters estimated by the extended state observer and outputs the front wheel steering angle target command for vehicle attitude adjustment. The control law includes three parts: command processing, feedforward input and attitude correction. The attitude correction is used to estimate and compensate for the internal parameter mismatch and external disturbance of the system. S4 converts the front wheel angle target command output by the vehicle attitude compensator into a steering wheel angle target command and inputs it to the steering system controller. The steering system controller calculates and outputs the adjustment torque to the electric power steering system motor based on the difference between the actual steering wheel angle and the steering wheel angle target command, thereby driving the steering system to move and realize closed-loop adjustment and correction of the vehicle's yaw attitude.

[0009] Preferably, in step S1, the three-degree-of-freedom vehicle dynamics model is specifically a three-degree-of-freedom four-wheel model, and its equations of motion are as follows: ; in, Front axle steering angle, These are longitudinal and lateral accelerations, respectively. These are the longitudinal and lateral velocities, respectively. The yaw rate is angular velocity. This is the yaw acceleration.

[0010] Preferably, in step S1, the three-degree-of-freedom four-wheel model is further simplified to a three-degree-of-freedom two-wheel model, wherein the lateral stiffness of the left and right wheels of the front axle is set to be the same. The left and right rear axle wheels have the same lateral stiffness. The front axle speed angle is the same. The rear axle velocity angle is the same. And the steering wheel angle With front axle angle Satisfy the transmission ratio relationship The simplified equations for lateral and yaw motion are: ; Among them, velocity angle and Vehicle longitudinal speed lateral velocity With yaw rate The calculation yielded: .

[0011] Preferably, the extended state observer of the vehicle yaw rate constructed in step S2 takes the following form: ; in, For the yaw rate estimation error, For the estimated lateral velocity, To estimate the yaw rate, This is the sum of the unmodeled dynamics and external disturbances of the estimated system.

[0012] Preferably, the reference model mentioned in step S3 is: ; in, As a reference model, yaw rate For the desired yaw rate, It is a constant greater than zero.

[0013] Preferably, the control law of the vehicle attitude compensator in step S3 is: ; Among them, the first term on the right side of the equation The second item constitutes the instruction processing stage. The third item constitutes the feedforward input circuit. The attitude correction stage is defined by L⁻¹{·}, where L⁻¹ represents the inverse Laplace transform, β is the filter coefficient, and s is the Laplace operator.

[0014] Preferably, in the posture correction process... It passes through a first-order low-pass filter. It is then smoothed before being used for compensation.

[0015] The beneficial effects of this invention are: 1. This invention, by constructing a yaw rate estimator with an integrated extended state observer and a vehicle attitude compensator with an active disturbance rejection structure, achieves for the first time real-time accurate estimation and dynamic feedforward compensation of complex internal and external disturbances at the EPS control level. This solves the problems of error accumulation, response lag, and poor scene adaptability caused by excessive reliance on fixed calibration parameters in traditional deviation compensation methods.

[0016] 2. The composite control architecture based on model and disturbance observation of the present invention does not rely on a large number of additional sensor threshold judgments or complex operating condition calibrations. It can achieve high-precision attitude correction using only the vehicle's normal state signals. This not only reduces the system's dependence on specific sensors, but also enables the EPS system to output smooth and timely auxiliary torque in multi-source disturbance scenarios, including sudden crosswinds, changes in road inclination angles, and gradual changes in tire parameters.

[0017] 3. This invention couples the vehicle's yaw dynamics with EPS execution control, enabling the vehicle's yaw rate to be stably controlled within a very small range near zero during straight-line driving. This directly translates into higher trajectory-keeping accuracy and better straight-line driving stability, significantly reducing the driver's operational burden and providing a highly reliable underlying trajectory tracking execution guarantee for L2 and higher-level intelligent driving systems. This meets the stringent requirements of advanced autonomous driving systems for steering system response accuracy and stability. Attached Figure Description

[0018] Figure 1 The diagram shown is a schematic representation of a three-degree-of-freedom four-wheeled vehicle model of the present invention. Figure 2 The diagram shown is a schematic representation of a three-degree-of-freedom two-wheeled vehicle model of the present invention. Figure 3 The diagram shown is a block diagram of the vehicle attitude control system of the present invention. Figure 4 The diagram shows the tracking effect of the yaw rate estimator of the present invention. Figure 5 The diagram shows a comparison of the effects of the vehicle attitude correction strategy of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention provides an embodiment of a dynamic cooperative control method for electric vehicle power sources based on road condition prediction: The control system architecture of the method described in this invention mainly consists of three core parts: a yaw rate estimator, a vehicle attitude compensator, and a steering system controller. This method achieves dynamic correction and stabilization of the vehicle's straight-line driving attitude through closed-loop control.

[0021] The workflow is as follows: (1) The system collects the vehicle's steering wheel angle, vehicle speed and actual yaw rate directly measured by the on-board sensors in real time.

[0022] (2) The collected steering wheel angle, vehicle speed and actual yaw rate are input into the yaw rate estimator. Based on the constructed vehicle dynamics model and extended state observer algorithm, the estimator filters and optimizes the actual sensor signals affected by noise, outputs a more accurate and smooth estimated yaw rate, and simultaneously estimates the overall disturbance of the system.

[0023] (3) The estimated yaw rate and the desired yaw rate are input together into the vehicle attitude compensator. The compensator calculates the target front wheel steering angle command required to maintain or restore the vehicle's straight-line driving attitude according to the predetermined control law.

[0024] (4) The target front wheel steering angle command is converted into the corresponding steering wheel steering angle command and transmitted to the motor controller of the steering EPS system. The controller compares the target steering wheel steering angle with the actual steering wheel steering angle, calculates the compensation torque that the EPS motor needs to output through its internal control algorithm, and the EPS motor executes the torque to drive the steering system to produce a small steering correction, thereby actively adjusting the yaw attitude of the whole vehicle and suppressing the deviation.

[0025] (5) The action of the EPS actuator will change the real-time motion state of the vehicle. The new yaw rate and other signals are collected by the sensor again and fed back to step 1 to form closed-loop control, so as to realize continuous and dynamic adjustment of the vehicle attitude.

[0026] Please see Figure 1 The ideal three-degree-of-freedom four-wheel vehicle dynamics model is as follows: (1) Wherein, the front axle rotation angle is ? , These represent the longitudinal forces of the front left, front right, rear left, and rear right tires, respectively. These represent the lateral forces of the tires at the front left, front right, rear left, and rear right, respectively. and These are the longitudinal distances from the center of gravity to the front and rear wheels, and the track width between the left and right wheels, respectively. Expressed as longitudinal acceleration and lateral acceleration of the vehicle. Expressed as the vehicle's longitudinal and lateral speeds. Expressed as yaw rate, It is expressed as yaw acceleration.

[0027] Ideally, the lateral force of a vehicle is proportional to the slip angle, and the proportionality coefficient is the slip stiffness.

[0028] ; (2) in, For the left front wheel lateral stiffness, For the right front wheel lateral stiffness, For the left rear wheel lateral stiffness, For the right rear wheel lateral stiffness, The speed angle of the left front wheel. The speed angle of the right front wheel. The speed angle of the left rear wheel. This refers to the speed angle of the right rear wheel. Generally, the stiffness of the two front wheels and the two rear wheels is the same, and their speeds are also the same. Simultaneously, the front axle steering angle is related to the steering wheel angle. The values ​​between these are the transmission ratio coefficients. Therefore, we believe that: ; (3) ; (4) ; (5) Meanwhile, the formula for the velocity angle is as follows: ; (6) in, For the front wheel lateral stiffness, For rear wheel lateral stiffness, The front axle velocity angle, The rear axle velocity angle is K, and the transmission proportional coefficient is K. It is expressed as the vehicle's longitudinal speed and lateral speed.

[0029] Please see Figure 2 Furthermore, we can simplify the three-degree-of-freedom four-wheeled vehicle model into a three-degree-of-freedom two-wheeled vehicle model. Therefore, equation (2) can be further simplified to the following equation: ; (7) By combining equations (2) to (7), we obtain a two-wheeled vehicle model with three degrees of freedom: ; (8) Based on the above formula, construct an extended state observer for the vehicle's yaw rate: ; (9) in, To estimate the error gain for lateral velocity, To estimate the error gain for yaw rate, The error gain is estimated for the system position disturbance. Therefore, the estimated yaw rate is obtained through the estimator.

[0030] The equation for the yaw motion of the entire vehicle is: ; (10) in, Let be the sideslip angle of the centroid. Considering parameter mismatch and treating the sideslip angle as an interference term, the above equation can be written as: ; (11) Therefore, the initial vehicle attitude control rate is: ;(12) Establish a reference model: ; (13) Substituting equation (13) into equation (10), we get: ;(14) Substituting equation (13) into equation (11) yields: ; (15) Subtracting equation (13) from equation (15) yields: ; (16) because According to the Lyapunov stability criterion, equation (11) satisfies the stability condition. Furthermore, the internal and external disturbances of the system are estimated, and the estimated system disturbance is: ; (17) Therefore, the final control rate can be written as: ; (18) As can be seen from the above formula, the vehicle attitude compensator consists of a command processing stage, a feedforward input stage, and an attitude correction stage.

[0031] Please see Figure 3 By constructing the yaw rate extended state observer of formula (9) and the vehicle attitude compensator of formula (18), the vehicle attitude controller is finally obtained. The controller outputs the steering wheel angle, vehicle speed and yaw rate from the vehicle output of link 1 to link 2 yaw rate estimator, and further adjusts it with the desired yaw rate through the vehicle attitude compensator of link 3 in a closed loop. The output is the desired steering wheel angle, which is then adjusted with the actual steering wheel angle through the steering system controller of link 4 to output the system torque, thereby realizing the attitude control of the whole vehicle.

[0032] This invention provides an embodiment: This example demonstrates, through experimentation, the tracking and estimation of the vehicle's yaw rate based on the yaw rate estimator.

[0033] In this example, the vehicle is driven and the original yaw rate sensor signal is collected in a real vehicle or high-fidelity simulation environment, while the yaw rate estimator algorithm of this invention is run.

[0034] Please see Figure 4 The results show that the yaw rate curve output by the estimator smoothly tracks the changing trend of the actual signal, while effectively suppressing high-frequency noise. This demonstrates that the estimator has good estimation accuracy and filtering effect as a state observer, providing high-quality state feedback for the upper-level controller.

[0035] This invention provides an embodiment: This example uses experiments to verify the vehicle yaw rate under this control structure.

[0036] In this example, the same experimental scenario was set up to conduct two sets of comparative tests: one set turned off the vehicle posture correction function of the present invention and relied only on the driver or basic EPS; the other set turned on the complete control strategy of the present invention.

[0037] Please see Figure 5 The horizontal axis represents time, and the vertical axis represents the vehicle's yaw rate.

[0038] When the correction strategy is not enabled, the vehicle's yaw rate will deviate significantly from zero under the influence of disturbance, producing positive or negative fluctuations, which directly corresponds to the vehicle veering to the left or right.

[0039] After activating the correction strategy of this invention, the vehicle's yaw rate is effectively and stably controlled within a very small range near zero. This indicates that the dynamic output compensation torque of the control system of this invention successfully counteracts the effects of crosswinds, road surface disturbances, etc., enabling the vehicle to maintain a good straight-line driving posture and significantly suppressing deviation.

[0040] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A dynamic cooperative control method for electric vehicle power sources based on road condition prediction, characterized in that, It includes the following steps: S1. Establish a three-degree-of-freedom vehicle dynamics model, which includes longitudinal motion equations, lateral motion equations, and yaw motion equations. The yaw motion equations are constructed based on vehicle parameters and tire forces. S2, based on a three-degree-of-freedom vehicle dynamics model, constructs an extended state observer for the vehicle's yaw rate. The extended state observer is used to estimate the vehicle's lateral velocity, the sum of the yaw rate and the unmodeled dynamics of the system and external disturbances based on the actual yaw rate collected by the sensors. Its state equations include differential equations for lateral velocity estimation, yaw rate estimation, and disturbance estimation. S3, a vehicle attitude compensator is designed based on the vehicle yaw motion equation and reference model. The vehicle attitude compensator receives the yaw rate, the desired yaw rate and the vehicle state parameters estimated by the extended state observer and outputs the front wheel steering angle target command for vehicle attitude adjustment. S4 converts the front wheel angle target command output by the vehicle attitude compensator into the steering wheel angle target command and inputs it to the steering system controller. The steering system controller calculates and outputs the adjustment torque to the electric power steering system motor based on the difference between the actual steering wheel angle and the steering wheel angle target command, thereby driving the steering system to move and realize closed-loop adjustment and correction of the vehicle's yaw attitude.

2. The method for dynamic cooperative control of electric vehicle power source based on road condition prediction according to claim 1, characterized in that: The vehicle parameters in step S1 include mass m, yaw moment of inertia Iz, longitudinal distance from the center of mass to the front and rear wheels, wheelbases lf, lr, and lw on the left and right sides, and lateral forces of the left front, right front, left rear, and right rear tires. Longitudinal forces of the front left, front right, rear left, and rear right tires .

3. The method for dynamic cooperative control of electric vehicle power sources based on road condition prediction according to claim 2, characterized in that: The control law in step S3 includes three parts: instruction processing, feedforward input, and attitude correction. The attitude correction is used to estimate and compensate for the mismatch of internal parameters and external disturbances of the system.

4. The method for dynamic cooperative control of electric vehicle power sources based on road condition prediction according to claim 3, characterized in that, In step S1, the three-degree-of-freedom vehicle dynamics model is specifically a three-degree-of-freedom four-wheel model, and its equations of motion are as follows: ； in, Front axle steering angle, These are longitudinal and lateral accelerations, respectively. These are the longitudinal and lateral velocities, respectively. The yaw rate is angular velocity. This is the yaw acceleration.

5. The method for dynamic cooperative control of electric vehicle power sources based on road condition prediction according to claim 4, characterized in that, In step S1, the three-degree-of-freedom four-wheel model is further simplified into a three-degree-of-freedom two-wheel model, wherein the lateral stiffness of the left and right wheels of the front axle is set to be the same. The lateral stiffness of the left and right rear axle wheels is the same. The front axle speed angle is the same. The rear axle velocity angle is the same. And the steering wheel angle With front axle angle Satisfy the transmission ratio relationship The simplified equations for lateral and yaw motion are: ； Among them, velocity angle and Vehicle longitudinal speed lateral velocity With yaw rate The calculation yielded: 。 6. The method for dynamic cooperative control of electric vehicle power sources based on road condition prediction according to claim 5, characterized in that, The extended state observer of the vehicle yaw rate constructed in step S2 takes the following specific form: ； in, For the yaw rate estimation error, For the estimated lateral velocity, To estimate the yaw rate, This is the sum of the unmodeled dynamics and external disturbances of the estimated system.

7. The method for dynamic cooperative control of electric vehicle power sources based on road condition prediction according to claim 6, characterized in that, The reference model mentioned in step S3 is: ； in, As a reference model, yaw rate For the desired yaw rate, It is a constant greater than zero.

8. The method for dynamic cooperative control of electric vehicle power sources based on road condition prediction according to claim 7, characterized in that, The control law for the vehicle attitude compensator in step S3 is: ； Among them, the first term on the right side of the equation The second item constitutes the instruction processing stage. The third item constitutes the feedforward input circuit. The attitude correction stage is defined by L⁻¹{·}, where L⁻¹ represents the inverse Laplace transform, β is the filter coefficient, and s is the Laplace operator.

9. The method for dynamic cooperative control of electric vehicle power source based on road condition prediction according to claim 8, characterized in that: In the posture correction process It passes through a first-order low-pass filter. It is then smoothed before being used for compensation.

10. The method for dynamic cooperative control of electric vehicle power source based on road condition prediction according to claim 9, characterized in that: In step S2, the lateral velocity estimation error gain g1, the yaw rate estimation error gain g2, and the system disturbance estimation error gain g3 are also introduced for correction.

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