Dynamic compensation control method and system for hill start distance of electric vehicle

Abstract

The application provides a slope vehicle distance dynamic compensation control method and system of an electric vehicle, and relates to the technical field of electric vehicle control. The method steps include: acquiring the vehicle longitudinal speed, vehicle longitudinal acceleration, pedal opening, slope angle, relative distance between the vehicle and the front vehicle, and relative speed between the vehicle and the front vehicle in real time; calculating the feedforward torque based on the slope angle, and calculating the PI feedback torque based on the error between the vehicle longitudinal speed and the preset reference value for proportional integral adjustment; obtaining the actual distance between the vehicle and the front vehicle through geometric compensation, dynamic correction, extended Kalman filtering algorithm, and recursive least squares method, and then calculating the distance compensation torque; dynamically switching between the maintaining mode, the accelerating mode, the transition mode, or the front vehicle sensing mode, and outputting the total control torque to each wheel hub motor of the vehicle to perform slope auxiliary control by comprehensively considering the feedforward torque, the PI feedback torque, and the distance compensation torque.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle control technology, and in particular to a method and system for dynamic compensation control of vehicle distance on slopes for electric vehicles. Background Technology

[0002] With the development of new energy vehicle technology, distributed hub drive architecture has gradually become an important development direction for intelligent electric passenger vehicles due to its advantages such as independent control, fast response and energy recovery.

[0003] During hill starts, the presence of gravity can easily cause vehicles to roll backward, experience start-up jerking, or lack sufficient driving force. Traditional hill-start assist control methods are mostly based on proportional-integral (PI) control or combined with feedforward compensation to achieve smooth start-up. However, these methods primarily focus on the vehicle's own dynamic response and lack perception and compensation for the surrounding environment, especially the state of the vehicle in front, resulting in the following shortcomings:

[0004] Firstly, it is not well adapted to slope conditions: traditional methods only rely on vehicle speed and torque feedback adjustment, without considering the impact of slope gradient changes on vehicle distance perception, which can easily lead to a deviation between the actual distance and the distance detected by the sensor.

[0005] Secondly, there are safety concerns: in scenarios where following another vehicle on a slope, if the vehicle in front rolls backward, the driver's control strategy may not react in time, which could easily lead to a rear-end collision and pose a safety hazard.

[0006] Third, the control mode is limited: existing technologies mostly adopt three modes: hold, accelerate, and transition, and have failed to introduce modes related to the dynamic interaction with the vehicle in front, which limits the level of intelligence of the system. Summary of the Invention

[0007] The purpose of this invention is to provide a dynamic compensation control method and system for the distance between electric vehicles on slopes, so as to solve the problem mentioned in the background art that traditional slope-assisted control methods lack perception and compensation of the surrounding environment, especially the state of the vehicle in front.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a dynamic compensation control method for vehicle distance on slopes for electric vehicles, comprising the following steps: real-time acquisition of vehicle longitudinal speed, vehicle longitudinal acceleration, pedal opening, slope angle, relative distance between the vehicle and the preceding vehicle, and relative speed between the vehicle and the preceding vehicle; calculation of feedforward torque based on the slope angle to compensate for slope resistance, and proportional-integral adjustment based on the error between the vehicle longitudinal speed and a preset reference value to calculate PI feedback torque; geometric compensation of the relative distance between the vehicle and the preceding vehicle based on the slope angle; dynamic correction of the geometrically compensated vehicle distance based on the vehicle longitudinal acceleration and the system perception and execution delay time; and dynamic correction of the distance using an extended Kalman filter algorithm. The corrected vehicle distance, the relative speed between the vehicle and the preceding vehicle, the slope angle, and the sensor offset are jointly estimated to dynamically correct the slope angle. The estimated residuals are learned and corrected online using the recursive least squares method to converge the vehicle distance error on the slope, obtaining the actual vehicle distance between the vehicle and the preceding vehicle, and then calculating the vehicle distance compensation torque. Based on the vehicle's longitudinal speed, the actual vehicle distance, the relative speed between the vehicle and the preceding vehicle, or the pedal opening, and compared with the corresponding preset thresholds, the vehicle dynamically switches between hold mode, acceleration mode, transition mode, or preceding vehicle sensing mode. The total control torque is output by combining the feedforward torque, the PI feedback torque, and the vehicle distance compensation torque and is distributed to each wheel hub motor of the vehicle for slope assist control.

[0009] Optionally, the step of calculating the feedforward torque based on the slope angle specifically includes: calculating the vertical loads of the front and rear axles of the vehicle according to the vehicle mass, center of gravity position and slope angle, and normalizing them into load ratio coefficients for each wheel, and distributing the vehicle holding torque to each wheel hub motor according to the load ratios for each wheel, so as to realize wheel-end level feedforward compensation in uphill, mid-slope and downhill conditions.

[0010] Optionally, the step of dynamically correcting the geometrically compensated vehicle distance based on the vehicle's longitudinal acceleration and the system's perception and execution delay time specifically includes: based on the vehicle's longitudinal acceleration, introducing the perception and execution delay time to obtain a dynamic correction amount, and then dynamically correcting the geometrically compensated vehicle distance to offset the ranging deviation caused by inertia and delay. The calculation formula is as follows: ; In the formula: This is a dynamic correction amount. For the longitudinal acceleration of the vehicle, To sense and execute delay time, This is the dynamically corrected vehicle distance. The distance between vehicles is the distance after geometric compensation.

[0011] Optionally, the step of jointly estimating the dynamically corrected vehicle distance, the relative speed between the vehicle and the preceding vehicle, the slope angle, and the sensor bias using the extended Kalman filter algorithm specifically includes: the state vector of the extended Kalman filter includes the dynamically corrected vehicle distance, the relative speed between the vehicle and the preceding vehicle, the slope angle, and the sensor bias; the slope angle and the relative speed between the vehicle and the preceding vehicle are used as observation values ​​to perform state prediction and update, so as to jointly estimate the dynamically corrected vehicle distance.

[0012] Optionally, the step of learning and correcting the estimated residuals online using the recursive least squares method specifically includes: fitting a linear residual model online using the recursive least squares algorithm based on the jointly estimated vehicle distance; recursively updating the least squares algorithm parameters each time new observation data is received, wherein the recursive update includes gain calculation, parameter update, and covariance update; adaptively adjusting the forgetting factor in the least squares algorithm according to the dynamic characteristics of the system; and compensating the vehicle distance estimate based on the updated residual model to obtain the actual vehicle distance between the vehicle and the vehicle in front.

[0013] Optionally, the holding mode is triggered when the vehicle rolls back relative to the slope, outputting holding torque; the acceleration mode is triggered when the accelerator pedal opening on the slope is greater than the vehicle's starting throttle threshold, outputting acceleration torque; the transition mode is triggered when switching between the holding mode and the acceleration mode, employing a weighted gradual release based on both time and vehicle speed factors to smoothly switch between the holding mode and the acceleration mode. In the formula: As a transition weight, it is 1 at the start of release and decreases to 0 with time t and vehicle speed v, achieving a smooth release of holding torque and seamless takeover of driving torque. (Parameter) The release rate constant can be dynamically adjusted based on the slope angle and longitudinal acceleration. To maintain torque, The accelerator pedal sensor calculates the required drive acceleration torque via the VCU; the preceding vehicle sensing mode is triggered when the relative speed or relative distance between the vehicle and the preceding vehicle is lower than a preset safety value, initiating the combined control of preceding vehicle sensing and slope compensation. In the formula: For feedforward torque, For PI feedback torque, To compensate for the torque, where the compensation torque is the braking torque, the direction is opposite to other torques, and the value is negative, it is used to prevent forward movement or rear-end collisions.

[0014] Optionally, the compensation torque is dynamically adjusted based on different slope angles and adhesion coefficients by introducing environmental impact factors, and its calculation formula is as follows: ; In the formula: This is the gain compensation coefficient, which is negative in value. To compensate for the actual distance between vehicles, For a safe distance, To adjust the nonlinear response, As environmental impact factors, The slope angle; The road surface adhesion coefficient, The weights for slope and adhesion effects are respectively.

[0015] On the other hand, the present invention also provides a dynamic compensation control system for ramp distance of electric vehicles, comprising: an acquisition module for real-time acquisition of vehicle longitudinal speed, vehicle longitudinal acceleration, pedal opening, ramp angle, relative distance between the vehicle and the preceding vehicle, and relative speed between the vehicle and the preceding vehicle; a feedforward-feedback torque calculation module for calculating feedforward torque based on the ramp angle to compensate for ramp resistance, and performing proportional-integral adjustment based on the error between the vehicle longitudinal speed and a preset reference value to calculate PI feedback torque; and a distance compensation torque calculation module for geometrically compensating the relative distance between the vehicle and the preceding vehicle based on the ramp angle, and dynamically correcting the geometrically compensated distance based on the vehicle longitudinal acceleration and the system perception and execution delay time, using an extended Kalman spectral density control system. The filtering algorithm jointly estimates the dynamically corrected vehicle distance, the relative speed between the vehicle and the preceding vehicle, the slope angle, and the sensor bias to dynamically correct the slope angle. It learns online and corrects the estimation residuals using the recursive least squares method to converge the vehicle distance error on the slope, obtaining the actual vehicle distance between the vehicle and the preceding vehicle, and then calculates the vehicle distance compensation torque. The mode switching output module is used to dynamically switch between hold mode, acceleration mode, transition mode, or preceding vehicle sensing mode based on the vehicle's longitudinal speed, the actual vehicle distance, the relative speed between the vehicle and the preceding vehicle, and the pedal opening, after comparing them with the corresponding preset thresholds. It also integrates the feedforward torque, the PI feedback torque, and the vehicle distance compensation torque to output the total control torque and distribute it to each wheel hub motor of the vehicle for slope assist control.

[0016] On the other hand, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described dynamic compensation control method for the distance between vehicles on ramps of electric vehicles.

[0017] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described dynamic compensation control method for the distance between vehicles on ramps of electric vehicles.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] This application uses a vehicle control unit and multiple sensors to collect real-time data on vehicle longitudinal speed, acceleration, pedal opening, slope angle, relative distance, and relative speed. Based on the slope angle, it calculates feedforward torque to compensate for slope resistance and combines it with vehicle longitudinal speed error for PI feedback adjustment. Feedforward control offsets the gravitational component by preloading torque, significantly reducing the risk of slippage. PI feedback eliminates speed error and enhances the system's robustness to load changes or disturbances. This composite control strategy is particularly suitable for distributed drive vehicles, as it can utilize the independent adjustment capability of the wheel ends to distribute torque, avoiding start-up jitter or insufficient driving force, thereby improving start-up smoothness and safety.

[0020] This application employs a combination of geometric compensation, dynamic correction, extended Kalman filter algorithm, and online learning via recursive least squares to obtain the actual vehicle distance and calculate the compensation torque. This solves the distance measurement error problem caused by slopes and achieves high-precision vehicle distance estimation. Geometric compensation corrects the sensor-sensed distance based on the slope angle and introduces a dynamic correction term to offset inertial delay. The extended Kalman filter algorithm integrates multi-sensor data to combat noise, while the recursive least squares method optimizes the residuals through online learning, making the vehicle distance estimation closer to the actual physical distance. This compensation mechanism effectively prevents rear-end collisions or vehicle rollback caused by slope misjudgment. Especially in transitional conditions between uphill and downhill slopes, it can dynamically adjust the safety threshold, greatly improving safety and environmental adaptability.

[0021] This application dynamically switches between hold, acceleration, transition, and forward-sensing modes based on actual distance to the vehicle, relative speed, and pedal signals. It integrates feedforward, PI, and compensated torque output to the wheel hub motors, achieving smooth control and situational adaptability. The mode-switching logic ensures the vehicle automatically adjusts torque on inclines based on real-time risks such as forward vehicle slippage, avoiding jerking caused by sudden torque changes. The forward-sensing mode proactively increases braking force when the distance to the vehicle falls below a safe threshold, preventing rear-end collisions. It fully leverages the advantages of distributed drive, improving vehicle safety and comfort in complex traffic scenarios through coordinated torque across all wheels. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0023] Figure 2 This is a block diagram of the preload torque adjustment control of the present invention.

[0024] Figure 3 This is a schematic diagram illustrating the vehicle's operation on slopes and in the middle of a slope, as described in this invention. Figure 3 Figure (a) in the diagram is a schematic diagram of the uphill working condition. Figure 3 Figure (b) is a schematic diagram of the working conditions in the middle of the slope. Figure 3 Figure (c) in the diagram is a schematic diagram of the downhill working condition.

[0025] Figure 4 This is a block diagram illustrating the principle of the slope compensation algorithm of this invention.

[0026] Figure 5 This is a flowchart of the control mode switching process of the present invention.

[0027] Figure 6 This is a schematic diagram of the system structure of the present invention.

[0028] In the diagram: 10 - Acquisition module, 20 - Feedforward torque calculation module, 30 - Distance compensation torque calculation module, 40 - Mode switching output module. Detailed Implementation

[0029] The present invention will now be clearly and completely described in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Those skilled in the art will understand that, unless explicitly stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in the specification of this application means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0032] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0033] It should be understood that the sequence number and size of each step in this embodiment do not imply the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0034] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] Please refer to Figures 1-5 This invention discloses a dynamic compensation control method for the distance between an electric vehicle and a vehicle on a slope. The method includes the following steps: real-time acquisition of the vehicle's longitudinal speed, longitudinal acceleration, pedal opening, slope angle, relative distance between the vehicle and the vehicle in front, and relative speed between the vehicle and the vehicle in front; calculation of feedforward torque based on the slope angle to compensate for slope resistance, and proportional-integral adjustment based on the error between the vehicle's longitudinal speed and a preset reference value to calculate the PI feedback torque; geometric compensation of the relative distance between the vehicle and the vehicle in front based on the slope angle; dynamic correction of the geometrically compensated distance based on the vehicle's longitudinal acceleration and the system's perception and execution delay time; and application of an extended Kalman filter algorithm to adjust the dynamically corrected distance. The relative speed between the vehicle and the vehicle in front, the slope angle, and the sensor offset are jointly estimated to dynamically correct the slope angle. The estimated residuals are learned and corrected online using the recursive least squares method to converge the distance error in the slope, thus obtaining the actual distance between the vehicle and the vehicle in front, and then the distance compensation torque is calculated. Based on the longitudinal speed of the vehicle, the actual distance between the vehicle and the vehicle in front, the relative speed between the vehicle and the vehicle in front, or the pedal opening, and compared with the corresponding preset thresholds, the vehicle dynamically switches between hold mode, acceleration mode, transition mode, or vehicle-in-front sensing mode. The total control torque is output by combining the feedforward torque, the PI feedback torque, and the distance compensation torque and is distributed to the wheel hub motors of the vehicle for slope assist control.

[0036] Specifically, the vehicle control unit (VCU) and multiple sensors collect the following information in real time: the vehicle's longitudinal speed is measured by GPS; the acceleration is measured by an acceleration sensor; the slope angle is measured by a slope angle sensor; the driver's accelerator pedal opening and brake pedal opening are measured by the driver; the relative distance and relative speed of the vehicle in front are measured by radar; and the speed and output torque of each wheel motor are measured.

[0037] These signals are transmitted to the vehicle control unit (VCU). The VCU analyzes, processes, and judges the signals through a pre-set program to generate instructions for the motor controller, which then controls the motor torque.

[0038] Pre-loading appropriate drive torque when a vehicle is about to roll backwards is an effective way to prevent it from rolling backwards. However, due to limitations in slope measurement accuracy and the complex operating conditions of vehicles, accurate calculation of slope resistance is very difficult. Therefore, it is necessary to introduce closed-loop control of vehicle speed to dynamically suppress rolling backwards. To fully utilize the reference slope information output by the slope sensor, this application proposes a pre-loaded torque control method with a "feedforward + feedback" structure. In the distributed architecture, the feedforward control quantity is calculated by the vehicle's central controller based on the overall slope, while the feedback PI adjustment is executed independently by the controllers of each wheel motor.

[0039] Dynamic torque coordination is achieved by uploading feedback signals from each wheel, including wheel speed, torque, and current, to the central control unit via a real-time communication network. The system can automatically adjust the weights according to the differences in the response of each wheel, realizing coordinated anti-slip and differentiated feedforward correction between the front and rear axles, thus ensuring the longitudinal stability of the entire vehicle.

[0040] Based on the vehicle dynamics equations, calculate the theoretically required ramp feedforward torque:

[0041] ;

[0042] In the formula: This is the feedforward torque, which is calculated in advance by sensing the slope through the slope sensor. As a feedforward amount, it is used to quickly counteract the gravity component of the slope and provide the basic holding torque. For the overall vehicle quality, Let θ be the acceleration due to gravity and θ be the slope angle. The radius is the wheel radius.

[0043] When the vehicle is stationary, the slope sensor can measure the slope, and the hill-start assist algorithm balances the slope resistance by applying driving torque. This is under the assumption of a small angle. The feedforward control law for the control system is designed as follows:

[0044] ;

[0045] In the formula: This is the feedforward torque; θ is the feedforward proportional coefficient, which represents the compensation ratio for the estimated slope resistance, and θ is the slope angle.

[0046] Using vehicle speed as the feedback variable and employing proportional-integral (PI) control as the feedback loop, the calculation formula is as follows:

[0047] ;

[0048] In the formula: For feedback torque; This is the proportional control coefficient; These are integral control coefficients; For reference input; This refers to the longitudinal speed of the vehicle.

[0049] Without incorporating the perception of the preceding vehicle, the total driving torque is the sum of the torque calculations from the feedforward loop and the feedback loop, i.e.:

[0050] ;

[0051] In the formula: This refers to the total drive torque without incorporating the perception of the vehicle ahead; This is the feedforward torque; This is for feedback torque.

[0052] Based on practical applications, the following are some typical parameter setting reference values, such as... Figure 2 As shown:

[0053] Feedforward scaling factor : During starting or hill start conditions, It can be set to 0.7 to 0.9 to ensure the dominant role of the feedforward. On a flat road, It can be lowered to 0.5.

[0054] PI control parameters: proportional gain Adjusted according to the system's dynamic response, typically set between 0.5 and 1.5; Integral gain Set it to 0.1 to 0.3 to avoid overcompensation.

[0055] Based on the vehicle's longitudinal dynamics model, a Laplace transform is performed on the above formula. The control block diagram of the entire control system is shown in the figure. For the Laplace operator.

[0056] This application uses a vehicle control unit and multiple sensors to collect real-time data on vehicle longitudinal speed, acceleration, pedal opening, slope angle, relative distance, and relative speed. Based on the slope angle, it calculates feedforward torque to compensate for slope resistance and combines it with vehicle longitudinal speed error for PI feedback adjustment. Feedforward control offsets the gravitational component by preloading torque, significantly reducing the risk of slippage. PI feedback eliminates speed error and enhances the system's robustness to load changes or disturbances. This composite control strategy is particularly suitable for distributed drive vehicles, as it can utilize the independent adjustment capability of the wheel ends to distribute torque, avoiding start-up jitter or insufficient driving force, thereby improving start-up smoothness and safety.

[0057] This application employs a combination of geometric compensation, dynamic correction, extended Kalman filter algorithm, and online learning via recursive least squares to obtain the actual vehicle distance and calculate the compensation torque. This solves the distance measurement error problem caused by slopes and achieves high-precision vehicle distance estimation. Geometric compensation corrects the sensor-sensed distance based on the slope angle and introduces a dynamic correction term to offset inertial delay. The extended Kalman filter algorithm integrates multi-sensor data to combat noise, while the recursive least squares method optimizes the residuals through online learning, making the vehicle distance estimation closer to the actual physical distance. This compensation mechanism effectively prevents rear-end collisions or vehicle rollback caused by slope misjudgment. Especially in transitional conditions between uphill and downhill slopes, it can dynamically adjust the safety threshold, greatly improving safety and environmental adaptability.

[0058] This application dynamically switches between hold, acceleration, transition, and forward-sensing modes based on actual distance to the vehicle, relative speed, and pedal signals. It integrates feedforward, PI, and compensated torque output to the wheel hub motors, achieving smooth control and situational adaptability. The mode-switching logic ensures the vehicle automatically adjusts torque on inclines based on real-time risks such as forward vehicle slippage, avoiding jerking caused by sudden torque changes. The forward-sensing mode proactively increases braking force when the distance to the vehicle falls below a safe threshold, preventing rear-end collisions. It fully leverages the advantages of distributed drive, improving vehicle safety and comfort in complex traffic scenarios through coordinated torque across all wheels.

[0059] In some embodiments, the step of calculating the feedforward torque based on the slope angle specifically includes: calculating the vertical loads of the front and rear axles of the vehicle according to the vehicle mass, center of gravity position and slope angle, normalizing them to the load ratio coefficients of each wheel, and distributing the vehicle holding torque to each hub motor according to the load ratio of each wheel, so as to realize wheel-end level feedforward compensation in uphill, mid-slope and downhill conditions.

[0060] Specifically, the process of a vehicle driving uphill and leaving the slope can be divided into three stages: entering the slope, driving on the slope, and leaving the slope. The condition where the preceding vehicle is driving on the slope while the main vehicle is driving on a straight road is defined as the uphill condition. The condition where both the preceding vehicle and the main vehicle are driving on the slope is defined as the mid-slope condition. When the preceding vehicle leaves the slope and is driving on a straight road, while the main vehicle is still driving on the slope, the condition is defined as the downhill condition.

[0061] When vehicles travel on slopes, the actual geometric distance between the main vehicle and the vehicle in front will be affected by the slope if the vehicle or the target vehicle is traveling on a slope and a straight road, respectively. This will cause a certain deviation between the actual geometric distance and the distance obtained by the perception layer. Therefore, a compensation algorithm needs to be designed to optimize the actual vehicle distance.

[0062] On a sloped road surface, the vehicle's forward distance is the perceived distance obtained through radar, lidar, ultrasonic, or visual sensors. The distance is usually measured along the direction the sensor is installed, while the actual horizontal distance between the vehicle and the vehicle in front is affected by the slope angle. The impact of slope. Therefore, this invention first performs preliminary compensation on the sensing distance based on a geometric correction model to obtain the geometric distance after slope compensation:

[0063] ;

[0064] When the slope angle is small, an approximation can be taken. At this point, the compensation calculation is simple and has high real-time performance.

[0065] PI feedback regulation is used to eliminate steady-state errors and enhance system robustness. In a distributed hub drive system, the calculation of feedforward torque can be further decomposed to each wheel end. By estimating the vertical load distribution of each wheel, the vehicle holding torque is distributed according to the load ratio of the front and rear axles and the left and right wheels, so that the feedforward torque output by each motor matches the slope resistance it bears. Assuming the vehicle mass is... The acceleration due to gravity is The front and rear wheelbase is The distance from the center of gravity to the front axle is The slope angle is Then the vertical loads on the front and rear axles , It can be represented as: ; ;

[0066] In the formula: For the front axle vertical load, For the vertical load on the rear axle, For the overall vehicle quality, It is the acceleration due to gravity. This refers to the front and rear wheelbase. The distance from the center of gravity to the front axle is, The slope angle, The height of the vehicle's center of gravity. This is the distance from the center of gravity to the rear axle.

[0067] The vehicle control unit (VCU) normalizes this load distribution result into a load proportion coefficient for each wheel. This is used to distribute the holding torque to each wheel. This method enables wheel-end level feedforward compensation, improving vehicle stability and anti-skid performance on asymmetrical slopes.

[0068] This application calculates the vertical load ratio of each wheel based on the center of gravity position and slope angle, and proportionally distributes the vehicle's holding torque to the wheel hub motor, achieving wheel-end level feedforward compensation. This not only counteracts slope resistance but also avoids single-wheel overload or slippage, making it particularly suitable for transitional conditions between uphill and downhill slopes, enhancing the vehicle's anti-skid performance and dynamic response on complex road surfaces.

[0069] In some embodiments, the step of dynamically correcting the geometrically compensated vehicle distance based on the vehicle's longitudinal acceleration and the system's perception and execution delay time specifically includes: based on the vehicle's longitudinal acceleration, introducing the perception and execution delay time to obtain a dynamic correction amount, and then dynamically correcting the geometrically compensated vehicle distance to offset the ranging deviation caused by inertia and delay. The calculation formula is as follows: ; In the formula: This is a dynamic correction amount. For the longitudinal acceleration of the vehicle, To sense and execute delay time, This is the dynamically corrected vehicle distance. The distance between vehicles is the distance after geometric compensation.

[0070] Specifically, during slope changes or vehicle dynamics, simple geometric compensation is insufficient to reflect the true changes in distance. Due to the presence of vehicle longitudinal acceleration and perception delay, there is a lag error in the perceived distance. Therefore, a dynamic correction term is designed, and its calculation formula is as follows:

[0071] ;

[0072] In the formula: This is a dynamic correction amount; For the longitudinal acceleration of the vehicle, This is to account for the sensing and execution delay time. This correction effectively offsets ranging errors caused by inertia and delay.

[0073] In addition to geometric compensation, a dynamic correction is added:

[0074] ;

[0075] In the formula: This is the dynamically corrected vehicle distance. The distance between vehicles is the distance after geometric compensation.

[0076] If the vehicle accelerates, the correction term becomes positive, and the compensation distance increases; if the vehicle decelerates, the correction term becomes negative, and the compensation distance decreases; thus, the dynamically corrected distance is adjusted. It is closer to the actual physical distance between the driver and the vehicle in front at this moment.

[0077] This application introduces a dynamic correction term to compensate for inertial delay in vehicle distance. By combining longitudinal acceleration and system delay time, the geometrically compensated vehicle distance is dynamically corrected, effectively offsetting the distance measurement deviation caused by vehicle acceleration or deceleration, reducing the response lag of the control system, and making the vehicle distance estimation closer to the actual physical distance. This avoids misjudgment in slope following scenarios, improves the ability to track the dynamic changes of the vehicle in front, and provides a more reliable input basis for safe distance control.

[0078] In some embodiments, the step of jointly estimating the dynamically corrected vehicle distance, the relative speed between the vehicle and the preceding vehicle, the slope angle, and the sensor bias using the extended Kalman filter algorithm specifically includes: the state vector of the extended Kalman filter includes the dynamically corrected vehicle distance, the relative speed between the vehicle and the preceding vehicle, the slope angle, and the sensor bias; the state prediction and update are performed using the slope angle and the relative speed between the vehicle and the preceding vehicle as observation values ​​to jointly estimate the dynamically corrected vehicle distance.

[0079] Specifically, this application utilizes multi-sensor information from the vehicle, including IMU pitch angle, wheel speed, radar relative speed, and GPS attitude, to achieve joint estimation of distance, relative speed, and slope angle by using an extended Kalman filter (EKF) to correct the motion state from multiple sensors. The state vector of this filter is defined as:

[0080] ;

[0081] In the formula: This is the actual distance between vehicles. The relative speed between the main vehicle and the vehicle in front. The slope angle, This is the offset for sensor ranging.

[0082] The system state equation is:

[0083] ;

[0084] Observation model from sensors:

[0085] ;

[0086] In the formula: , The noise term is assumed to be Gaussian. The posterior estimate is obtained by using EKF for prediction and updating. Covariance . The perceived distance between the vehicle and the vehicle in front is measured by distance sensors such as radar, lidar, or ultrasound. The slope angle observation value is obtained by the inertial measurement unit (IMU). The relative speed between the vehicle and the vehicle in front is measured by radar speed measurement or wheel speed sensor RS.

[0087] Final output of the Extended Kalman Filter (EKF) algorithm:

[0088] ;

[0089] In the formula: The distance output is the distance estimated by EKF. This provides variance estimation for EKF distance. EKF can fuse IMU slope information, radar line-of-sight, Doppler speed, wheel speed, etc., to achieve noise immunity and delay compensation. It can also estimate sensor bias. This improves long-term stability.

[0090] This application uses an extended Kalman filter (EKF) to jointly estimate vehicle distance, relative speed, and slope angle. The EKF algorithm integrates information from sensors such as radar and IMU, effectively suppressing noise and measurement delay, improving the robustness of vehicle distance and slope estimation. It not only corrects sensor bias but also enhances the prediction accuracy of the system under dynamic conditions, providing a consistent and reliable state input for subsequent control decisions and reducing the control risk caused by the failure of a single sensor.

[0091] In some embodiments, the step of learning and correcting the estimated residuals online using the recursive least squares method specifically includes: fitting a linear residual model online using the recursive least squares algorithm based on the jointly estimated vehicle distance; recursively updating the least squares algorithm parameters each time new observation data is received, wherein the recursive update includes gain calculation, parameter update, and covariance update; adaptively adjusting the forgetting factor in the least squares algorithm according to the dynamic characteristics of the system; and compensating the vehicle distance estimate based on the updated residual model to obtain the actual vehicle distance between the vehicle and the vehicle in front.

[0092] Specifically, this invention introduces adaptive residual learning based on the output of the Extended Kalman Filter (EKF) algorithm. It utilizes historical high-confidence data to learn and correct systematic errors online, thereby obtaining the final compensated actual vehicle distance. .

[0093] Recursive least squares (RLS) is used for residual modeling, and a linear, real-time online corrected error residual model is fitted online.

[0094] ;

[0095] ;

[0096] Each time a new observation is received Where y(t) = r(t), it is updated recursively according to the following steps:

[0097] Gain calculation:

[0098] ;

[0099] Parameter update:

[0100] ;

[0101] Covariance update:

[0102] ;

[0103] In the formula: For discrete time steps, For at any time The regression vector is composed of the system's features, including the slope estimate from the EKF output. Relative velocity Longitudinal acceleration and their nonlinear terms, for example , ; For the parameter vector that needs to be estimated online; The observed residual is usually taken as the measured vehicle distance or the EKF estimated vehicle distance, or some transformation of the difference between the two; It is the parametric covariance matrix or uncertainty matrix, which is positive definite and symmetric. Forgetting factor; This is the RLS gain vector, which is equivalent to the Kalman gain. This represents the observation noise variance when used in the Joseph form.

[0104] Initialization method:

[0105] Initial value of parameter vector :

[0106] If there is no prior knowledge, take If offline training or engineering estimation is used, this prior value can be used to accelerate convergence; initial value of the covariance matrix. Scalar magnification identity matrix: .

[0107] Selection of forgetting factors:

[0108] Values ​​close to 1, such as 0.995–0.9999, are suitable for slowly changing systems and noisy scenarios; they are smooth but slow to respond to abrupt changes.

[0109] Smaller values, such as 0.95–0.99: Improve the ability to track dynamic changes, suitable for sudden events requiring rapid response, such as when the vehicle in front suddenly rolls backward.

[0110] in: , For a relatively large constant, the example in this embodiment takes... , Forgetting factor In this embodiment, a value of 0.995 is used to balance tracking performance and robustness. When a sudden event is detected, such as the vehicle ahead rapidly slipping, the value can be temporarily reduced to 0.98 to accelerate tracking. To ensure numerical stability, [the following is omitted as the text is incomplete and requires further context]. Upper and lower limits are imposed on the diagonal elements, for example in this implementation. and and the regression vector Implement normalization processing.

[0111] The learning objectives are:

[0112] ;

[0113] Estimating systematic residuals by updating parameters online. The final output is obtained as follows:

[0114] ;

[0115] In the formula: This is the confidence index output by EKF, used to assess the strength of dynamic weighted residual correction.

[0116] This application employs Recursive Least Squares (RLS) for online residual learning. By fitting the residual model in real time and dynamically updating parameters, the RLS module can compensate for systematic errors, such as deviations caused by sensor aging or environmental changes. This enables the distance compensation algorithm to self-optimize over time, maintaining high accuracy without relying on offline calibration. It is particularly suitable for sudden scenarios such as rapid rollback of the vehicle in front, enhancing the intelligence and reliability of control while reducing maintenance requirements.

[0117] In some embodiments, the comprehensive control output torque is calculated as follows:

[0118] ;in: This is the feedforward torque, used to compensate for slope resistance; For PI feedback torque; To maintain torque; To maintain the acceleration torque when torque lock-up is engaged, it is equivalent to adding an extra acceleration torque to keep the car stationary; For the total acceleration torque, To compensate for torque.

[0119] When a vehicle is on a slope, maintaining torque prevents it from rolling backward. At this point, the car is stationary or moving at a constant speed on the slope, and the acceleration torque is 0. .

[0120] When the vehicle accelerates on a slope, the total acceleration torque Including the holding torque required to overcome the current slope And the acceleration torque that provides acceleration to the vehicle. This is equivalent to adding an extra acceleration torque to keep the car stationary, that is... .

[0121] When the vehicle is moving on a slope and the vehicle in front is detected to be rolling backward, compensation torque is increased. Apply braking force by gradually outputting braking torque according to the load on each wheel to suppress the forward movement of the main vehicle and avoid a rear-end collision. The detection conditions for the vehicle ahead rolling back are that the relative speed between the two vehicles is less than 0, or the distance to the vehicle behind after compensation is less than the safe distance. At least one of the following: if the relative speed between the vehicle and the vehicle in front is greater than 0, or the distance to the vehicle behind after compensation is greater than the safe distance. When the system smoothly exits and enters acceleration mode, it smoothly reduces the output torque according to the load on each wheel. ,Right now .

[0122] In some embodiments, executed by the vehicle control unit (VCU), the holding mode is triggered when the vehicle rolls back relative to an incline, outputting holding torque; the acceleration mode is triggered when the accelerator pedal opening on the incline is greater than the vehicle's starting throttle threshold, outputting acceleration torque; the transition mode is triggered when switching between the holding mode and the acceleration mode, employing a weighted gradual release based on both time and vehicle speed factors to smoothly switch between the holding mode and the acceleration mode. In the formula: As a transition weight, it is 1 at the start of release and decreases to 0 with time t and vehicle speed v, achieving a smooth release of holding torque and seamless takeover of driving torque. (Parameter) The release rate constant can be dynamically adjusted based on the slope angle and longitudinal acceleration. To maintain torque, The accelerator pedal sensor calculates the required drive acceleration torque via the VCU; the preceding vehicle sensing mode is triggered when the relative speed or relative distance between the vehicle and the preceding vehicle is lower than a preset safety value, initiating the combined control of preceding vehicle sensing and slope compensation. In the formula: For feedforward torque, For PI feedback torque, To compensate for the torque, where the compensation torque is the braking torque, the direction is opposite to other torques, and the value is negative, it is used to prevent forward movement or rear-end collisions.

[0123] Specifically, the control modes include hold mode, acceleration mode, transition mode, and forward-sensing mode. Mode determination is achieved through threshold fusion, and mode switching is performed smoothly through a weighted function to achieve torque continuity.

[0124] Holding mode: When the vehicle rolls back on a slope, it outputs holding torque. To prevent the vehicle from rolling away.

[0125] Entry condition: Vehicle longitudinal speed Slipping on a relative slope; accelerator pedal opening The brake pedal is released; the slope angle θ > the threshold, which is set to 2° in this embodiment; the vehicle in front is stationary or at low speed.

[0126] Control logic: The controller determines the slope angle based on the slope angle. Calculate feedforward holding torque Combined with PI feedback torque Closed-loop adjustment is performed to maintain the speed error. This is to prevent the vehicle from rolling backward.

[0127] Exit conditions: The accelerator pedal signal is greater than the starting accelerator threshold; or the preceding vehicle is detected to begin accelerating; the system switches to acceleration or transition mode.

[0128] Acceleration Mode: When the driver presses the accelerator, the control output is... This ensures a smooth start.

[0129] Entry conditions: The driver presses the accelerator; the vehicle is in hold or transition mode; the vehicle in front accelerates or maintains a safe distance. .

[0130] Control logic: Total system output acceleration drive torque: ;in For feedforward compensation torque, For speed closed-loop torque regulation, To maintain acceleration torque during torque lock-up, the control objective is smooth, jerky acceleration. The vehicle uses distributed hub drive, with the controller distributing torque according to axle load. Independent torque control is achieved for each wheel motor.

[0131] Exit conditions: accelerator pedal released; the vehicle in front shows signs of rolling backward and the relative speed is less than 0; enter the vehicle in front sensing mode or transition mode.

[0132] Transition mode: Smoothly switches between hold and acceleration using a weighted function to avoid sudden torque changes.

[0133] Entry conditions: The system switches from hold mode to acceleration mode; or the system detects that the driver has lightly pressed the accelerator and the vehicle has not yet fully started moving.

[0134] Control logic: Output torque is smoothly transitioned by a weighted function.

[0135] ;

[0136] In the formula: As a transition weight, it is 1 at the start of release and decreases to 0 with time t and vehicle speed v, achieving a smooth release of holding torque and seamless takeover of driving torque. (Parameter) The release rate constant can be dynamically adjusted based on the slope angle and longitudinal acceleration. To maintain torque, The required drive acceleration torque is calculated by the VCU from the accelerator pedal sensor.

[0137] ;

[0138] When the vehicle's longitudinal speed reaches the threshold In this embodiment, at a speed of 2 km / h, the torque is fully released, and the system automatically switches to acceleration mode. This control method ensures continuous and smooth torque output during hill starts, avoiding vehicle vibration and jerking, while maintaining safe anti-skid operation.

[0139] Exit condition: vehicle speed This embodiment Set to 2km / h; weighting factor The system switches to acceleration mode.

[0140] Forward Vehicle Perception: The system utilizes multi-sensor fusion technology, such as LiDAR, visual sensors, IMU, GPS, and wheel speed sensors, to achieve high-precision perception of the vehicle ahead and the surrounding environment. After synchronous preprocessing of data from each sensor, the system employs an Extended Kalman Filter (EKF) combined with a Recursive Least Squares (RLS) model to jointly estimate the gradient, distance, and relative speed, thereby correcting the actual distance to the vehicle in real time. This method significantly improves the reliability and reaction speed of forward vehicle rollback detection, providing accurate input for subsequent adaptive torque adjustment. If the forward vehicle rolls back and the compensated actual distance is... Less than the safety threshold The system automatically increases braking torque to prevent rear-end collisions; if the vehicle in front begins to accelerate and Then, the acceleration torque is gradually released, smoothly switching to acceleration mode.

[0141] Entry condition: Relative speed of the vehicle in front The system detects that the vehicle in front is rolling backward; or it compensates for the distance to the vehicle behind. Below the safety threshold.

[0142] Control logic: The system initiates combined control of forward vehicle perception and gradient compensation.

[0143] ;

[0144] When a vehicle is moving on a slope, if the relative speed between the vehicle and the vehicle in front is less than 0, or the following distance is less than a safe distance, the vehicle is moving on a slope. At that time, by increasing the compensation torque Apply braking force, gradually outputting braking torque according to the load on each wheel to suppress the vehicle's forward movement and avoid a rear-end collision; if the relative speed between the vehicle and the vehicle in front is greater than 0, or the distance to the vehicle behind is greater than a safe distance. When the system smoothly exits and enters acceleration mode, it smoothly reduces the output torque according to the load on each wheel. .

[0145] Exit conditions: The vehicle in front accelerates and increases the distance; compensate for the distance to the vehicle behind. The driver pressed the accelerator again.

[0146] This application defines a multi-mode dynamic switching logic. Through intelligent switching between four modes—hold, accelerate, transition, and forward-sensing—the system can adjust torque output in real time based on thresholds such as vehicle distance and pedal signal. The transition mode uses a weighted function to smoothly release the holding torque, avoiding sudden torque changes during mode switching, thereby improving vehicle start-up smoothness and ride comfort. The forward-sensing mode automatically intervenes when a risk is detected to prevent rear-end collisions, demonstrating the control system's rapid response capability to dynamic environments.

[0147] In some embodiments, the compensating torque is dynamically adjusted based on different slope angles and adhesion coefficients by introducing environmental impact factors, and its calculation formula is as follows: ; In the formula: This is the gain compensation coefficient, which is negative in value. To compensate for the actual distance between vehicles, For a safe distance, To adjust the nonlinear response, As environmental impact factors, The slope angle; The road surface adhesion coefficient, The weights for slope and adhesion effects are respectively.

[0148] Specifically, compensation torque As an additional control variable based on distance compensation, when the system detects that the vehicle ahead is slipping and the distance is below the safety threshold, it calculates the vertical load on each wheel based on the vehicle attitude and slope information estimated by the multi-sensor fusion module. With road adhesion coefficient This application introduces environmental impact factors that can dynamically adjust the compensation torque for different slopes based on different slope gradients and adhesion coefficients. Dynamically adjust additional compensation torque: ;

[0149] In the formula: This is the gain compensation coefficient. To compensate for the actual distance between vehicles, For a safe distance, To adjust the nonlinear response, a value of 1 to 2 is used to ensure that the smaller the difference, the stronger the response. These are environmental impact factors.

[0150] Define environmental impact factors:

[0151] ;

[0152] in: The slope angle; The road surface adhesion coefficient; The weights for slope and adhesion effects are respectively, with calibration ranges of 0.8–1.2 and 0.5–1.0. Indicating increased environmental resistance, such as slope or headwind, the system automatically increases compensating torque; when This indicates that the vehicle is under sufficient load, and the system smoothly decays to maintain torque. This mechanism enables the wheel-end torque to adaptively match the actual load and environmental disturbances, achieving longitudinal stability and rear-end collision prevention control.

[0153] When a car goes uphill, the holding torque ensures it doesn't roll back. At this point, the car is stationary or moving at a constant speed on the slope, and the acceleration torque is zero. When the car accelerates, it includes the feedforward and PI components of the current moment, i.e., the holding torque, which is the basic torque required to overcome the current gradient. Based on this latched torque, the driver's required torque is directly added, consisting of the acceleration torque. It provides the acceleration needed by the car. When the car is moving at a constant speed or accelerating on a slope, if the car in front rolls back less than the safe distance, the compensating torque will brake the car to a stop, and the holding torque will keep the car stopped on the road or slope.

[0154] when At that time, the output torque is reduced according to the load on each wheel, causing the vehicle to decelerate; when At that time, depending on the load on each wheel, the above-mentioned weighted gradual release method based on time and vehicle speed is used to release the output torque, and the vehicle accelerates slowly or at a constant speed.

[0155] This application introduces a dynamic adjustment compensation torque based on environmental impact factors. By combining the slope angle and road surface adhesion coefficient to calculate the environmental impact factors, the compensation torque can be non-linearly adjusted according to actual road conditions, ensuring increased braking intervention on steep slopes or low-adhesion surfaces, while smoothly attenuating on flat surfaces. This enables the system to not only cope with changes in slope but also adapt to disturbances such as headwinds or slippery road surfaces, improving the vehicle's longitudinal stability and safety redundancy in variable environments.

[0156] Please refer to Figure 6 On the other hand, the present invention also provides a dynamic compensation control system for ramp distance of electric vehicles, comprising: an acquisition module for real-time acquisition of vehicle longitudinal speed, vehicle longitudinal acceleration, pedal opening, ramp angle, relative distance between the vehicle and the preceding vehicle, and relative speed between the vehicle and the preceding vehicle; a feedforward-feedback torque calculation module for calculating feedforward torque based on the ramp angle to compensate for ramp resistance, and performing proportional-integral adjustment based on the error between the vehicle longitudinal speed and a preset reference value to calculate PI feedback torque; and a distance compensation torque calculation module for geometrically compensating the relative distance between the vehicle and the preceding vehicle based on the ramp angle, and dynamically correcting the geometrically compensated distance based on the vehicle longitudinal acceleration and the system perception and execution delay time, by extending the Cartesian... The Mann filter algorithm jointly estimates the dynamically corrected vehicle distance, the relative speed between the vehicle and the preceding vehicle, the slope angle, and the sensor bias to dynamically correct the slope angle. It uses a recursive least squares method to learn online and correct the estimation residuals to converge the vehicle distance error on the slope, obtaining the actual vehicle distance between the vehicle and the preceding vehicle, and then calculates the distance compensation torque. The mode switching output module dynamically switches between hold mode, acceleration mode, transition mode, or preceding vehicle sensing mode based on the vehicle's longitudinal speed, the actual vehicle distance, the relative speed between the vehicle and the preceding vehicle, and the pedal opening, compared with corresponding preset thresholds. It also integrates the feedforward torque, the PI feedback torque, and the distance compensation torque to output a total control torque, which is then distributed to each wheel hub motor of the vehicle for slope assist control.

[0157] On the other hand, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described dynamic compensation control method for the distance between vehicles on ramps of electric vehicles.

[0158] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described dynamic compensation control method for the distance between vehicles on ramps of electric vehicles.

[0159] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0161] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention's specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for dynamic compensation control of vehicle distance on slopes for electric vehicles, characterized in that the steps include: include: Real-time acquisition of vehicle longitudinal velocity, vehicle longitudinal acceleration, pedal opening, slope angle, relative distance between the vehicle and the vehicle in front, and relative speed between the vehicle and the vehicle in front; The feedforward torque is calculated based on the slope angle to compensate for the slope resistance, and proportional-integral adjustment is performed based on the error between the vehicle's longitudinal speed and the preset reference value to calculate the PI feedback torque. Based on the slope angle, geometric compensation is performed on the relative distance between the vehicle and the vehicle in front. Based on the longitudinal acceleration of the vehicle and the system perception and execution delay time, the geometrically compensated vehicle distance is dynamically corrected. The dynamically corrected vehicle distance, the relative speed between the vehicle and the vehicle in front, the slope angle, and the sensor bias are jointly estimated by the extended Kalman filter algorithm to perform dynamic slope angle correction. The estimated residual is learned and corrected online by the recursive least squares method to converge the vehicle distance error in the slope, obtain the actual vehicle distance between the vehicle and the vehicle in front, and then calculate the vehicle distance compensation torque. Based on the vehicle's longitudinal speed, the actual distance between the vehicles, the relative speed between the vehicle and the vehicle in front, or the pedal opening, and after comparing with the corresponding preset thresholds, the system dynamically switches between hold mode, acceleration mode, transition mode, or vehicle-aware mode. It also integrates the feedforward torque, the PI feedback torque, and the distance compensation torque to output a total control torque and distribute it to each wheel hub motor of the vehicle for hill start assist control.

2. The method for dynamic compensation control of vehicle spacing on slopes for electric vehicles according to claim 1, characterized in that, The step of calculating the feedforward torque based on the slope angle specifically includes: Based on the vehicle's mass, center of gravity position, and slope angle, the vertical loads on the front and rear axles are calculated and normalized to the load ratio coefficients of each wheel. The overall vehicle holding torque is then distributed to each wheel hub motor according to the aforementioned load ratios to achieve wheel-end feedforward compensation in uphill, mid-slope, and downhill conditions.

3. The method for dynamic compensation control of vehicle spacing on slopes for electric vehicles according to claim 1, characterized in that, The step of dynamically correcting the geometrically compensated vehicle distance based on the vehicle's longitudinal acceleration and the system's perception and execution delay time specifically includes: Based on the vehicle's longitudinal acceleration, a dynamic correction amount is obtained by introducing the sensing and execution delay time. This amount is then used to dynamically correct the geometrically compensated vehicle distance to offset the ranging deviation caused by inertia and delay. The calculation formula is as follows: ； ； In the formula: This is a dynamic correction amount. For the longitudinal acceleration of the vehicle, To sense and execute delay time, This is the dynamically corrected vehicle distance. The distance between vehicles is the distance after geometric compensation.

4. The method for dynamic compensation control of vehicle spacing on slopes for electric vehicles according to claim 1, characterized in that, The step of jointly estimating the dynamically corrected vehicle distance, the relative speed between the vehicle and the vehicle in front, the slope angle, and the sensor offset using the extended Kalman filter algorithm specifically includes: The extended Kalman filter's state vector includes the dynamically corrected vehicle distance, the relative speed between the vehicle and the vehicle in front, the slope angle, and the sensor bias. The slope angle and the relative speed between the vehicle and the vehicle in front are used as observations to perform state prediction and updates, so as to jointly estimate the dynamically corrected vehicle distance.

5. The method for dynamic compensation control of vehicle spacing on slopes for electric vehicles according to claim 1, characterized in that, The steps of learning and correcting the estimated residuals online using the recursive least squares method specifically include: Based on the jointly estimated vehicle distance, the recursive least squares algorithm is used to fit the linear residual model online. Each time new observation data is received, the least squares algorithm parameters are recursively updated, wherein the recursive update includes gain calculation, parameter update and covariance update; Based on the dynamic characteristics of the system, the forgetting factor in the least squares algorithm is adaptively adjusted. The vehicle distance estimate is compensated based on the updated residual model to obtain the actual distance between the vehicle and the vehicle in front.

6. The method for dynamic compensation control of vehicle distance on slopes for electric vehicles according to claim 1, characterized in that, The holding mode is triggered when the vehicle rolls back relative to the ramp, outputting holding torque; The acceleration mode is triggered when the accelerator pedal opening is greater than the vehicle's starting throttle threshold on a slope, and outputs acceleration torque. The transition mode is triggered when switching between the hold mode and the acceleration mode, and uses a weighted gradual release based on both time and vehicle speed factors to smoothly switch between the hold mode and the acceleration mode: In the formula: As a transition weight, it is 1 at the start of release and decreases to 0 with time t and vehicle speed v, achieving a smooth release of holding torque and seamless takeover of driving torque. (Parameter) The release rate constant can be dynamically adjusted based on the slope angle and longitudinal acceleration. To maintain torque, The required drive acceleration torque is calculated by the VCU from the accelerator pedal sensor. The preceding vehicle sensing mode is triggered when the relative speed or relative distance between the vehicle and the preceding vehicle is lower than a preset safety value, initiating joint control of preceding vehicle sensing and slope compensation: In the formula: For feedforward torque, For PI feedback torque, To compensate for the torque, where the compensating torque is the braking torque, which is opposite in direction to other torques and is negative in value.

7. The method for dynamic compensation control of vehicle spacing on slopes for electric vehicles according to claim 6, characterized in that, The compensation torque is dynamically adjusted by incorporating environmental impact factors, based on different slope angles and adhesion coefficients. Its calculation formula is as follows: ； ； Where: Where: This is the gain compensation coefficient, which is negative in value. To compensate for the actual distance between vehicles, For a safe distance, To adjust the nonlinear response, As an environmental impact factor, The slope angle; The road surface adhesion coefficient, The weights for slope and adhesion effects are respectively.

8. A dynamic compensation control system for gradient vehicle distance in electric vehicles, characterized in that, include: The acquisition module is used to acquire vehicle longitudinal speed, vehicle longitudinal acceleration or pedal opening, slope angle, vehicle relative distance and vehicle relative speed to the vehicle in front in real time; The feedforward-feedback torque calculation module is used to calculate the feedforward torque based on the slope angle to compensate for the slope resistance, and to perform proportional-integral adjustment based on the error between the vehicle's longitudinal speed and a preset reference value to calculate the PI feedback torque. The distance compensation torque calculation module is used to perform geometric compensation on the relative distance between the vehicle and the vehicle in front based on the slope angle. Based on the longitudinal acceleration of the vehicle and the system perception and execution delay time, it dynamically corrects the geometrically compensated distance. It uses an extended Kalman filter algorithm to jointly estimate the dynamically corrected distance, the relative speed between the vehicle and the vehicle in front, the slope angle, and the sensor bias to perform dynamic slope angle correction. It uses recursive least squares method to learn online and correct the estimation residual to converge the distance error in the slope, obtain the actual distance between the vehicle and the vehicle in front, and then calculate the distance compensation torque. The mode switching output module is used to dynamically switch between hold mode, acceleration mode, transition mode, or sensing the preceding vehicle mode based on the vehicle's longitudinal speed, the actual distance between the vehicles, the relative speed between the vehicle and the preceding vehicle, or the pedal opening, and after comparing them with the corresponding preset thresholds. It also integrates the feedforward torque, the PI feedback torque, and the distance compensation torque to output a total control torque and distribute it to each wheel hub motor of the vehicle for hill start assist control.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the slope distance dynamic compensation control method for electric vehicles according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the slope distance dynamic compensation control method for electric vehicles according to any one of claims 1 to 7.

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