Torque adjusting method for parking motor of electric automobile

Abstract

The invention relates to the technical field of electric vehicles, in particular to an electric vehicle parking motor torque adjusting method. The problems that an existing electric automobile parking system is insufficient in parking force control precision, lagged in response and low in energy consumption efficiency under complex working conditions are solved. According to the method, a multi-dimensional vehicle running state, a driver instruction and environment sensing data are collected in real time, a vehicle-environment coupling dynamic model is established and updated in real time, then an adaptive model predictive control (AMPC) strategy is utilized, the future movement trend of a vehicle is predicted based on the dynamic model and a target parking demand, and the vehicle-environment coupling dynamic model is established and updated in real time. And the optimal torque instruction of the parking motor in the current control period is optimized and calculated, and the parking motor is driven to generate corresponding torque. High precision, high adaptability, high safety and high stability of parking torque adjustment can be achieved, the energy efficiency is effectively improved, the service life of parts is effectively prolonged, and the intelligent level is enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a method for adjusting the torque of a parking motor of an electric vehicle. Background Art

[0002] The electric vehicle parking motor torque adjustment method achieves stable parking and safe starting under various operating conditions by precisely controlling the output torque of the electric vehicle's parking motor. This method aims to address potential issues such as slippage, impact, insufficient or overloaded parking force, and proposes an optimized torque adjustment strategy. By designing a collaborative mechanism between the parking motor and the vehicle control system, the method enables flexible adjustment and application of parking torque based on road conditions, slope, load, and user needs. In this method, the parking motor receives data from the vehicle's status sensors, calculates the required parking force, and accurately outputs the corresponding torque to achieve reliable braking and securing of the vehicle. Each parking motor, depending on its type and characteristics, independently or collaboratively performs torque output tasks, thereby enhancing the safety and intelligence of the overall parking system. The method also optimizes parking brake comfort and responsiveness by adjusting the torque output curve and release timing.

[0003] Existing technologies mostly rely on preset parameters during parking torque control and lack the ability to adapt to the dynamic changes in actual vehicle load and road friction coefficient. This makes it difficult to effectively ensure parking safety on extreme slopes or slippery roads. For example, when a heavily loaded vehicle is parked on a steep slope, the preset torque may not be sufficient to completely overcome gravity, causing the vehicle to slide. However, due to the lack of a real-time load assessment mechanism, the risk of sliding cannot be warned. The torque output of the parking motor is mostly based on a fixed pulse width modulation (PWM) cycle rather than the actual temperature evolution of the motor. This makes it difficult to accurately reflect the thermal state and output limit of the motor, thus hindering the motor's life and continuous parking capability. The lack of a coordination mechanism between the parking command and the vehicle control module may lead to conflicts between the parking motor and the main drive motor in emergency parking or frequent start-stop scenarios, resulting in energy loss in high-concurrency scenarios. There is no comparison mechanism between the parking status log and the environmental parameter data, which makes it difficult to detect non-optimal parking strategies and results in unnecessary energy waste. In terms of maintaining parking force, the existing technology mainly relies on constant torque output, ignoring the subtle fluctuations of parking force in different environments and energy-saving optimization, and is difficult to adapt to the regulation needs under dynamic changes. The problem is particularly prominent in the multi-condition parking of electric vehicles, which directly affects the overall parking safety and user experience. Summary of the Invention

[0004] The present invention relates to the field of electric vehicle technology, and in particular discloses a method for adjusting the torque of an electric vehicle parking motor, which aims to solve the problems of insufficient parking force control accuracy, delayed response, and low energy consumption efficiency in existing electric vehicle parking systems under complex working conditions. Traditional electric vehicle parking systems, whether through mechanical handbrake linkage or simple electronic parking brake systems, often adopt a preset fixed torque or a simple feedback control strategy based on basic sensors. This method is difficult to achieve refined torque control of the parking motor in complex environments such as parking on a slope, changes in road adhesion coefficient, or dynamic changes in vehicle load. It may cause excessive parking force to cause energy waste and component wear, or insufficient parking force to cause unexpected vehicle slippage, thereby affecting parking safety and user experience. In addition, existing systems generally lack the ability to deeply perceive and predict the status of the vehicle and the environment, and are unable to dynamically adjust the torque output to adapt to the ever-changing actual operating conditions, making the parking process less smooth and efficient.

[0005] To address the aforementioned technical issues, the present invention proposes a method for adjusting the parking motor torque in electric vehicles based on multi-source information fusion and adaptive predictive control. This method constructs a dynamic vehicle-environment coupling model by collecting multi-dimensional vehicle state and environmental parameters in real time. Utilizing an adaptive model predictive control (AMPC) strategy, it accurately predicts the vehicle's future motion trends and optimizes the calculation of the optimal torque output sequence for the parking motor under specific parking requirements. This significantly improves the accuracy, response speed, and energy efficiency of parking torque adjustment while ensuring parking safety and stability. This method not only considers the vehicle's current state but also incorporates predictions of future operating conditions, achieving forward-looking parking torque management.

[0006] According to one aspect of the present invention, a method for adjusting the torque of a parking motor of an electric vehicle is provided, the method comprising: Real-time collection of vehicle operating status data, driver operating instructions and environmental perception data, the vehicle operating status data including but not limited to vehicle speed, acceleration, slope angle, battery power, and drive motor torque; the driver operating instructions including but not limited to parking request, parking release request, and parking mode selection; the environmental perception data including but not limited to road adhesion coefficient estimation.

[0007] Based on the real-time collected data, a current vehicle-environment coupled dynamic model is established and the parameters of the model are updated in real time. The vehicle-environment coupled dynamic model is used to describe the parking mechanical characteristics of the vehicle under different slopes, road adhesion coefficients, and load conditions.

[0008] A target parking force requirement is determined based on the driver's operation command and the vehicle-environment coupled dynamic model. The target parking force requirement reflects the parking force required to ensure that the vehicle is stationary or moves smoothly.

[0009] An adaptive model predictive control strategy, based on the vehicle-environment coupled dynamic model and the target parking force requirement, predicts the vehicle's future state within a preset prediction horizon and optimizes the calculation of the optimal torque command for the parking motor within the current control cycle. This optimal torque command is designed to bring the vehicle state as close as possible to the target parking state while minimizing energy consumption and torque fluctuation.

[0010] The optimal torque command is sent to the parking motor controller to drive the parking motor to generate corresponding torque to achieve vehicle parking or parking release.

[0011] Furthermore, during the real-time acquisition phase, the present invention uses internal vehicle sensors (such as an inertial measurement unit, wheel speed sensors, and brake pressure sensors) to acquire the vehicle's real-time posture and kinematic information. Simultaneously, combined with the vehicle's CAN bus data, it acquires battery state of charge (SOC) and energy consumption data provided by the battery management system, as well as drive torque information provided by the drive motor controller. For environmental perception data, the present invention utilizes sensor data such as on-board cameras and millimeter-wave radar, combined with high-precision map information, to accurately estimate the vehicle's slope angle using visual recognition algorithms or radar ranging and speed measurement algorithms. Estimation of the road adhesion coefficient can be achieved through real-time correlation analysis of tire slip and drive / braking torque, or through inverse estimation using a vehicle dynamics model.

[0012] During the phase of establishing a vehicle-environment coupled dynamic model and updating parameters in real time, the present invention constructs a comprehensive model capable of describing the vehicle's longitudinal motion, lateral slip tendency, and the transmission characteristics of the parking mechanism. This model not only takes into account fixed parameters such as vehicle mass, wheelbase, and center of gravity height, but more importantly, it dynamically incorporates and updates key parameters such as the actual vehicle mass (accounting for load variations), road rolling resistance coefficient, air resistance coefficient, and the actual transmission efficiency of the parking mechanism. Real-time parameter updates can be achieved using state estimation methods such as Kalman filtering, extended Kalman filtering, or unscented Kalman filtering. Iterative corrections are performed based on the discrepancy between the vehicle's actual motion response and the model's predicted output to ensure model accuracy and timeliness.

[0013] The target parking force requirement is determined based on a comprehensive assessment of the driver's intent and the current vehicle-environmental conditions. When the driver requests a parking request, the system calculates the minimum parking force required to overcome gravity and rolling resistance based on the vehicle's current slope and estimated road adhesion. To enhance safety, a safety margin is typically added to the minimum parking force. When the driver releases the brake, the target parking force gradually decreases to zero, or, under certain conditions, maintains a low level of auxiliary braking force to ensure a smooth and impact-free vehicle launch.

[0014] The adaptive model predictive control strategy is the core of this invention. Its basic idea is to use the updated vehicle-environment coupling dynamic model in each control cycle to predict the future motion state of the vehicle within a limited prediction time domain. On this basis, a quadratic programming problem is solved by an optimization algorithm to obtain the optimal torque sequence of the parking motor in the future control time domain. This optimization problem aims to minimize the deviation between the parking state and the target state, while taking into account the smoothness of the control quantity (i.e., minimization of the torque change rate) and minimization of energy consumption, and must strictly meet the constraints related to parking safety, such as the maximum parking force, the minimum release force, and the maximum allowable slip during parking. Adaptiveness is reflected in the fact that the parameters of the model are corrected in each control cycle, so that the controller always predicts and optimizes based on the model that is closest to the actual working conditions.

[0015] In order to clearly illustrate the dynamic characteristics of the vehicle during parking, the present invention adopts the following simplified vehicle longitudinal dynamics model as the basis for model predictive control: in, It represents the total mass of the electric vehicle, which is a variable parameter and is affected by the number of passengers and cargo; Indicates the longitudinal acceleration of the vehicle; Indicates the equivalent parking force applied by the parking mechanism on the wheel; represents the gravitational acceleration constant; Indicates the slope angle of the current road surface, with uphill being positive and downhill being negative; Indicates the road rolling resistance; Represents air resistance. This formula describes the longitudinal motion of the vehicle after overcoming the slope gravity component, rolling resistance and air resistance under the action of parking force. In the stable parking state, Approaching zero.

[0016] Furthermore, the adaptive model predictive control (AMPC) strategy determines the optimal parking motor torque sequence by solving an online optimization problem. The objective function of the optimization problem can be expressed as: in, In the current control cycle The cost function that needs to be minimized; is the length of the prediction horizon; is the length of the control time domain; Indicates at time Predicted future moments Vehicle status output (such as vehicle speed or parking force); is the corresponding reference target state; is the weighted matrix of state error, which is used to measure the deviation penalty between the predicted state and the target state; Indicates the future moment The change in the parking motor torque command, i.e. ; It is a weighted matrix of the control variable change rate, which is used to penalize the severity of the control action to ensure stability; Indicates the estimated energy consumption of the parking motor during this control cycle; Is the weight coefficient of energy consumption. This objective function takes into account parking accuracy, stability and energy efficiency. By adjusting the weight matrix and and coefficients , a trade-off can be made between these performance metrics.

[0017] In practical applications, there is a transmission ratio relationship between the torque output by the parking motor and the vehicle's parking force. To achieve precise parking force control, it is necessary to reversely calculate the torque required by the parking motor based on the parking force requirement obtained from the optimization problem above. This relationship can be approximately expressed as: in, The torque output by the parking motor; is the desired parking force applied to the wheel; is the effective radius of the wheel; is the reduction ratio of the parking mechanism; is the transmission efficiency of the parking mechanism. In actual operation, the reduction ratio and transmission efficiency of the parking mechanism may fluctuate slightly due to wear or temperature changes. The present invention ensures the accuracy of torque conversion by incorporating real-time estimation and updating of these parameters into the vehicle-environment coupled dynamic model.

[0018] Compared with the prior art, the electric vehicle parking motor torque adjustment method provided by the present invention has the following beneficial effects by introducing multi-source information fusion, a dynamic vehicle-environment coupling model, and an adaptive model predictive control strategy: High Precision and High Adaptability: The present invention can sense vehicle status and environmental changes in real time, and dynamically update vehicle model parameters, so that the parking motor torque adjustment can accurately match the actual working conditions. In particular, it demonstrates excellent adaptability and high-precision parking capabilities in complex environments such as slopes, different road adhesion coefficients, and load changes.

[0019] Improved safety: By predicting the vehicle's future motion trends and performing proactive control, it effectively avoids accidental slippage caused by insufficient parking force or impact caused by excessive parking force, significantly improving the safety and reliability of the parking process.

[0020] Optimized smoothness and comfort: The adaptive model predictive control strategy optimizes the rate of change of the control variable, making the application and release of parking torque smoother, reducing vehicle vibration and impact, and thus significantly improving the parking comfort experience for drivers and passengers.

[0021] Optimizing Energy Efficiency and Extending Component Life: By minimizing energy consumption within the objective function, this invention avoids unnecessary excessive torque output, effectively reducing the energy consumption of the parking system. Furthermore, smooth and precise torque regulation reduces wear on mechanical components, extending the service life of the parking system.

[0022] Improved intelligence and automation: This invention can autonomously make optimal decisions based on complex environmental changes without human intervention, demonstrating a higher level of parking automation and intelligence, and laying a solid foundation for future smart parking and advanced driver assistance systems for electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the overall architecture of the electric vehicle parking motor torque adjustment system of the present invention; Figure 2 This is a main flow chart of the electric vehicle parking motor torque adjustment method of the present invention; Figure 3 Detailed structural diagram of the data acquisition module of the present invention; Figure 4 A schematic diagram of the vehicle-environment coupling dynamic model construction and parameter updating module of the present invention; Figure 5 Schematic diagram of the adaptive model predictive control strategy execution module of the present invention. DETAILED DESCRIPTION

[0024] Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0025] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0026] In addition, numerous specific details are provided in the following detailed description to better illustrate the present application. Those skilled in the art will appreciate that the present application can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0028] The present invention provides a method for adjusting the torque of a parking motor of an electric vehicle. Figures 1 to 5 As shown, this method aims to achieve optimal torque management for electric vehicles in various parking scenarios by constructing an intelligent control framework capable of real-time perception, prediction, and adaptive adjustment. The core concept of this method is to integrate multi-source information to establish and dynamically update a coupled model of the vehicle and environment. Then, using an adaptive model predictive control strategy, it proactively calculates the optimal torque command for the parking motor, achieving high precision, high safety, high stability, and high energy efficiency during the parking process.

[0029] In one specific embodiment, the entire logic flow for regulating the parking motor torque in an electric vehicle begins with the data acquisition phase. This phase forms the perception foundation of the entire control system, continuously acquiring multi-dimensional, real-time data streams from the electric vehicle's various subsystems and installed sensors. For example, vehicles are typically equipped with an inertial measurement unit (IMU), which can output the vehicle's three-axis acceleration information (including longitudinal, lateral, and vertical acceleration) and three-axis angular velocity information (including pitch, roll, and yaw angular velocity) at high frequency. After appropriate processing and filtering, this raw data can be used to accurately estimate the vehicle's real-time attitude, such as pitch and roll angles, which is crucial for determining the road grade angle on which the vehicle is located. Furthermore, each wheel is typically equipped with a wheel speed sensor to monitor its rotational speed in real time. By comparing the rotational speeds of different wheels, or combining this with the vehicle's overall motion state, it is possible to infer whether the vehicle is prone to slip, which is crucial for estimating the road adhesion coefficient and determining parking safety. In addition, the pressure sensor inside the brake system provides information on the current hydraulic or air pressure in the brake line, which is directly related to the actual braking force applied to the brake disc or brake drum, providing important input for precise control of parking force.

[0030] In addition to the aforementioned sensors that directly measure vehicle motion and posture, the method of the present invention also utilizes the vehicle's internal Controller Area Network (CAN) bus to obtain deeper system status information. For example, the battery management system (BMS) provides information such as the battery's state of charge (SOC), current, voltage, and temperature via the CAN bus. This data is crucial for evaluating the parking system's energy consumption, optimizing energy efficiency, and ensuring battery safety. Simultaneously, the drive motor controller (DMC) also reports the drive motor's output torque, speed, and operating mode in real time via the CAN bus. This data helps coordinate the parking motor and drive motor during release, ensuring a smooth vehicle start. Regarding environmental perception data, in addition to the preliminary slope information provided by the IMU, the present invention can also utilize data from advanced driver assistance system (ADAS) sensors such as onboard cameras and / or millimeter-wave radar for auxiliary judgment. Using image processing and computer vision algorithms, onboard cameras can identify road surface texture, lane markings, road signs, and surrounding stationary or moving objects, further refining perception of road conditions (e.g., wet, dry, gravel, etc.) and slope. Millimeter-wave radar can provide highly accurate distance and relative speed information, assisting in detecting obstacles in front of or behind the vehicle and, in certain scenarios, assisting with slope estimation. Furthermore, combined with high-precision map data, it can provide precise altitude information of the vehicle's location and preset road slope information, further enhancing the robustness and accuracy of slope angle estimation.

[0031] Next comes the data preprocessing and feature extraction phase. All raw data collected from various sensors and the CAN bus must undergo rigorous preprocessing before direct use due to varying sampling frequencies, data formats, units, and potential noise. The first step is noise filtering. For example, to address the high-frequency noise and random jitter that may be present in IMU data, algorithms such as sliding average filtering, median filtering, or more complex Kalman filtering and extended Kalman filtering can be used to obtain smooth and accurate vehicle posture and kinematic data. Time synchronization is another critical step. Because data from different sensors may be collected at different timestamps, a timestamp alignment algorithm must be used to synchronize all data onto a unified timeline. This ensures that all data in subsequent calculations and model inputs corresponds to the vehicle's true state at the same moment. Unit conversion is also essential, for example, converting raw pulse signals from wheel speed sensors into actual wheel angular velocity or linear velocity, and converting brake pressure into actual force values.

[0032] During this phase, critical environmental state estimation and vehicle parameter identification are also performed. For example, slope angle estimation utilizes a multi-source fusion approach, combining pitch angle information from the IMU, altitude rate data from the GPS, and road slope information pre-stored in high-precision maps. Weighted fusion, Kalman filtering, or other sensor fusion algorithms can effectively compensate for the shortcomings of single sensors in complex environments (such as bumpy roads and areas with unstable GPS signals), improving the accuracy and robustness of slope estimation. Estimating the road adhesion coefficient is a challenging aspect of parking force control. This approach is achieved through the following methods: One method utilizes real-time correlation analysis between tire slip and driving / braking torque. When driving or braking torque is applied, the road adhesion coefficient is identified online by monitoring the changing trend of wheel slip and combining it with known tire models. Another method utilizes the vehicle's vibration characteristics and suspension dynamic response, indirectly estimating the road adhesion coefficient by analyzing the vibration spectrum or suspension displacement data when the vehicle travels on different road surfaces. Furthermore, estimating the vehicle's total mass is crucial for parking force calculation, as changes in vehicle load significantly affect the required parking force. The vehicle's gross mass can be estimated online using a variety of methods. For example, during the vehicle's launch or acceleration phase, the output torque of the drive motor and the vehicle's acceleration can be measured and inversely calculated using the vehicle's dynamics equations. Alternatively, if the vehicle is equipped with load sensors, the sensor data can be directly used to update the vehicle's gross mass. Accurate, real-time estimation of these parameters provides a solid data foundation for subsequent dynamic model construction and control strategy execution.

[0033] Entering the vehicle-environment coupling dynamic model construction and parameter update phase, this paper establishes a multivariable, nonlinear dynamic model designed to comprehensively describe the longitudinal and lateral dynamic behavior of electric vehicles during parking and unparking. This model fully considers the transmission characteristics of the parking mechanism and the impact of environmental factors (such as slope and road adhesion coefficient) on vehicle motion. The core of this model lies in its parameter adaptability. That is, the key parameters of the model are not fixed but dynamically updated based on real-time estimation results to ensure that the model always closely matches the actual vehicle and environmental conditions.

[0034] The model primarily consists of the following submodules: a vehicle dynamics model, which describes the vehicle's motion response to various forces (such as parking force, gravity, rolling resistance, and air resistance), including longitudinal acceleration, vehicle speed, and potential lateral slip. A parking mechanism model, which details how the parking motor's output torque is converted through the reduction mechanism into the actual parking force applied to the wheels, taking into account nonlinear characteristics such as transmission efficiency, clearance, and friction. An environmental model, which incorporates real-time estimated environmental parameters such as slope angle, road adhesion coefficient, and wind speed into the vehicle dynamics calculations, enabling the model to accurately reflect the forces acting on the vehicle under different environmental conditions.

[0035] The most important feature of this model is the online real-time update of its parameters. For example, the total mass of the vehicle The road's rolling resistance coefficient is affected by the road surface type and temperature; the air resistance coefficient is affected by vehicle speed and air density; and the actual transmission efficiency of the parking mechanism may fluctuate slightly due to wear or temperature changes. The present invention utilizes advanced system identification and state estimation algorithms, such as recursive least squares (RLS), extended Kalman filtering (EKF), or unscented Kalman filtering (UKF), to iteratively correct the aforementioned key parameters based on the difference between the actual vehicle motion response (such as speed and acceleration) and the model's predicted output. In this way, the system always operates on a dynamic model that closely matches the actual vehicle and environment, thereby ensuring the accuracy of the vehicle's future state predictions and providing a reliable foundation for adaptive model predictive control.

[0036] Here, the longitudinal motion equation of the vehicle under the action of the parking force is the core component of the vehicle-environment coupled dynamic model of the present invention and the basis for predicting the vehicle's dynamic behavior. This equation can be expressed as: in, Indicates that at the current moment The estimated gross mass of the electric vehicle. This is a dynamic parameter that updates in real time based on changes in the vehicle's load (such as the number of occupants and cargo weight) to accurately reflect the vehicle's inertial characteristics.

[0037] Indicates that at the current moment The vehicle's longitudinal acceleration. This value approaches zero in a stable parking state. It describes the vehicle's speed trend during parking or releasing the vehicle.

[0038] Indicates that the parking mechanism is at the current moment The equivalent parking force applied to the wheels. This is the actual braking force applied to the wheels after the parking motor outputs torque, converted through the reduction mechanism and braking system. It is the core variable that requires precise control in this method.

[0039] represents the gravitational acceleration constant, which is usually taken as approximately .

[0040] Indicates that at the current moment Estimated road slope angle. Positive values ​​for uphill slopes and negative values ​​for downhill slopes. This parameter reflects the component of gravity in the longitudinal direction of the vehicle and has a decisive influence on the parking force required.

[0041] Indicates that at the current moment The rolling resistance of the road surface to which the vehicle is subjected is related to the road surface type, tire characteristics, vehicle speed, and vehicle load. The present invention dynamically calculates this value based on the real-time estimated road adhesion coefficient and vehicle speed.

[0042] Indicates that at the current moment Air resistance on a vehicle. This resistance is primarily proportional to the vehicle's frontal area, the drag coefficient, and the square of the vehicle's speed. It is generally small when the vehicle is parked or stationary at low speeds, but becomes significant when the vehicle is released from parking and begins to move.

[0043] The above equations describe how a vehicle's longitudinal motion, generated by a parking force, overcomes or is constrained by the slope's gravity component, road rolling resistance, and air resistance. By updating various parameters online, the model accurately predicts the vehicle's dynamic response under various complex conditions.

[0044] The next step is to determine the target parking force requirement. This stage is to calculate the ideal parking force required to keep the vehicle stationary or in steady motion, based on the driver's intention and the real-time status of the vehicle and the environment. When the driver issues a parking request through various means (such as pressing the parking button, shifting the gear lever into P gear, or receiving a command from the automatic parking system), the system will respond immediately. After receiving the parking request, the system will calculate the target parking force required based on the current estimated slope angle. and road adhesion coefficient, combined with the vehicle-environment coupling dynamic model, calculate the minimum parking force required to at least overcome the current slope gravity component and road rolling resistance. For example, in an uphill scenario, the minimum parking force must be sufficient to offset the tendency of the vehicle to slide backward due to gravity; in a downhill scenario, it is necessary to resist the tendency of the vehicle to slide forward. In order to maximize parking safety, the present invention usually adds a preset safety margin on the basis of the calculated minimum parking force. This safety margin is an additional braking force designed to deal with uncertainties such as model estimation errors, sudden changes in road conditions, or external sudden shocks, to ensure that the vehicle can be parked securely even in extreme cases. Ultimately, the minimum parking force plus the safety margin constitutes the target parking force at this time. .

[0045] When the driver requests to release the parking brake, the target parking force determination logic is completely different. Instead of being immediately reduced to zero, the target parking force gradually decreases from its current value to zero according to a preset, gentle curve. This gradual reduction strategy is designed to ensure a smooth vehicle start and avoid the jerk or shock that can occur during release with traditional parking systems. Based on a vehicle-environment coupling dynamic model, the system predicts the vehicle's starting smoothness at different torque reduction rates and selects the optimal decay curve. For example, when releasing the parking brake on a downhill slope, to prevent sudden acceleration due to gravity, the system may maintain a weak auxiliary braking force until the driver applies sufficient driving torque to overcome the slope's resistance and the vehicle begins stable forward motion. Only then will the parking force be fully released. This refined target parking force determination mechanism is key to achieving the highly stable parking and release characteristics of the present invention.

[0046] In the core adaptive model predictive control (AMPC) strategy execution phase, the present invention uses the vehicle-environment coupling dynamic model updated in the previous phase and the determined target parking force requirement to solve an optimization problem online to determine the future period (i.e., the prediction time domain). ) The optimal torque command sequence of the parking motor. This optimization process is performed in each control cycle will be re-performed, which reflects the "adaptive" nature of the controller.

[0047] The basic idea of ​​AMPC is that in each control cycle, the controller uses the most accurate vehicle-environment coupling dynamic model to predict the possible motion state of the vehicle in multiple time steps in the future. On this basis, an optimization algorithm is used to calculate the possible motion state of the vehicle in the future finite control time domain. A series of parking motor torque commands are generated to ensure that the predicted vehicle state approaches the target parking state (for example, zero longitudinal velocity and no slip) as closely as possible while meeting a series of performance indicators and safety constraints. These performance indicators typically include minimizing the deviation between the parking state and the target state (i.e., achieving high-precision parking), minimizing the rate of change of the controlled variable (i.e., ensuring smooth torque output and reducing shock), and minimizing energy consumption.

[0048] Specifically, the objective function for online optimization of the present invention can be written as: in, It is at the present moment The cost function to be minimized. The smaller its value, the closer the predicted parking performance is to the ideal state.

[0049] Indicates that at the current moment Future moments predicted based on the updated vehicle-environment coupled dynamic model The vehicle state vector is a vector of the vehicle's velocity and dynamics. This vector typically contains several key vehicle kinematic and dynamic states, such as the vehicle's longitudinal velocity, longitudinal acceleration, the state of the parking mechanism (e.g., the relative position or pressure of the brake disc and brake pad), and other internal state variables that may affect the parking force.

[0050] is the corresponding reference target state vector, which defines the vehicle's The desired ideal parking state. In a fully parked vehicle scenario, this typically means the vehicle's longitudinal velocity and acceleration are both zero, and the parking mechanism is fully applied to counteract external disturbance forces.

[0051] is the weighted matrix of the state error, which is a positive definite or semi-positive definite matrix used to measure the predicted state vector With the reference target state vector By adjusting The values ​​of different diagonal elements in the matrix can balance the tracking accuracy of different state variables (such as velocity error and acceleration error) and give higher weights to more important states.

[0052] Indicates the future moment The change in the parking motor torque command, i.e. This item is designed to penalize drastic changes in the parking motor torque command to ensure smooth control action during parking, reducing mechanical shock and user discomfort.

[0053] It is a weighted matrix of the control variable change rate, which is also a positive definite or semi-positive definite matrix used to penalize the torque command. By adjusting The elements of the matrix can be used to trade off between the response speed of the control and the output smoothness. A higher value will cause the controller to generate a smoother torque command sequence.

[0054] is the length of the prediction horizon, which represents the number of time steps the controller uses to predict the future vehicle state.

[0055] is the length of the control time domain, which represents the number of time steps the controller uses to calculate the optimal torque command for the parking motor in the future. Less than or equal to the prediction time domain .

[0056] This optimization process is typically implemented by solving a quadratic programming (QP) problem. This optimization problem not only incorporates the aforementioned objective function but is also subject to a series of stringent constraints. These constraints include the maximum and minimum torque output limits of the parking motor itself, physical limits on the torque change rate (to prevent motor overload or overly rapid response, causing shock), and safety constraints such as the maximum allowable vehicle slip distance during parking. For example, when parking on a slope, the system constrains the parking force to be sufficient to overcome gravity and rolling resistance, and the vehicle slip distance must remain within a minimal, safe range. The "adaptive" nature of AMPC is reflected in the fact that before each optimization run, the parameters of the vehicle-environment coupled dynamic model are updated online based on the latest observations, ensuring that the controller always makes predictions and decisions based on the most accurate vehicle dynamic characteristics. This dynamic adjustment ensures that the controller provides the optimal torque command under various operating conditions.

[0057] In practical applications, there's a critical transmission ratio relationship between the torque output by the parking motor and the vehicle's parking force. While AMPC's optimization goal is typically to directly generate the optimal parking motor torque command, understanding and accurately mastering the conversion relationship between parking force and motor torque is crucial when designing the control strategy. This relationship can be approximately expressed as: in, The torque that the parking motor needs to output in the current control cycle. This is the direct output of the AMPC strategy and is used to drive the parking motor. The desired equivalent parking force applied to the wheels. This force is derived from the target parking force requirement and represents the actual force required to prevent the vehicle from moving. Within AMPC, this desired force guides motor torque optimization, either through back-calculation or as part of a constraint. is the effective radius of the wheel. It converts the force on the wheel into torque, or the torque of the wheel into force, and is an important geometric parameter in mechanical conversion. The reduction ratio of the parking mechanism. The parking motor typically uses a multi-stage gear or worm gear reduction mechanism to convert the high-speed, low-torque motor output into a low-speed, high-torque braking force. This reduction ratio determines the torque amplification factor. The transmission efficiency of the parking mechanism. In actual physical transmission, due to factors such as friction and energy loss, the input torque is not completely converted into output torque. The transmission efficiency is a coefficient less than 1, reflecting the effectiveness of energy conversion.

[0058] In actual operation, the reduction ratio of the parking mechanism Usually a fixed value, but its transmission efficiency Slight fluctuations may occur due to mechanical wear, temperature changes, lubrication conditions, and load. By incorporating real-time estimation and updating of these parameters into the vehicle-environment coupled dynamic model, the present invention ensures that the conversion from parking force to motor torque is accurate at all times, thereby guaranteeing that the optimal torque command calculated by the AMPC accurately achieves the required parking force. Although this formula is not a direct calculation step when the AMPC outputs motor torque, it describes the physical characteristics of the parking mechanism and serves as the fundamental physical constraints and knowledge used by the AMPC's internal model for prediction and optimization.

[0059] Once the optimal torque command sequence Once calculated, the parking motor drive stage will send the optimal torque command in the current control cycle. Sent to the parking motor controller. The parking motor controller is the executive core of the parking system, responsible for converting the abstract torque command provided by the high-level control unit (i.e., the AMPC) into actual motor drive signals. Specifically, the controller uses advanced control algorithms such as current closed-loop control, field-oriented control (FOC), or direct torque control (DTC) depending on the motor type (for example, if it is a stepper motor, a brushless DC motor, or a permanent magnet synchronous motor). These algorithms precisely regulate the current and voltage flowing through the motor windings, driving the parking motor to precisely output the desired torque.

[0060] The parking motor is typically a small, high-power-density electric motor whose output shaft is connected to the vehicle's braking system via a high-precision reduction mechanism. This reduction mechanism converts the motor's high-speed, low-torque rotational motion into low-speed, high-torque braking action to meet the required parking force. The reduction mechanism may take various forms, such as planetary gears, worm gears, or screw nuts. Its design goal is to provide sufficient reduction ratios and transmission efficiency while ensuring a compact structure and good durability. The decelerated torque is ultimately transmitted to the braking system, for example, acting on the brake disc or drum, which clamps the friction pads or pushes the brake shoes, applying sufficient braking force to the wheels to park the vehicle. The response speed and precision of the entire drive chain directly impact the effectiveness of the AMPC strategy and the smoothness of the ultimate parking experience.

[0061] Performance evaluation and model updates are conducted continuously throughout the parking and release processes. This closed-loop feedback mechanism continuously monitors parking system performance and, when necessary, adjusts and optimizes model parameters to address changing operating conditions. The system monitors key parking conditions in real time. For example, high-precision wheel speed sensors continuously monitor individual wheels for minor slip (even when parked, minor slip can occur due to insufficient road adhesion or errors in parking force calculations). Furthermore, accelerometers on the vehicle chassis or brake calipers monitor for any jerks or unwanted vibrations during parking and release.

[0062] If there is a significant deviation between the actual parking effect (such as vehicle slip or starting shock) and the target state predicted by AMPC, this indicates that certain parameters of the vehicle-environment coupled dynamic model may contain significant errors, or that the external environment (such as the road adhesion coefficient) has undergone drastic changes that have not been accurately perceived. In this case, the system will trigger further online re-identification and updating of model parameters. For example, if a slight slip is detected while parking on a slope, the system will infer that the current estimate of the road adhesion coefficient may be too high and will adjust the road adhesion coefficient estimation algorithm or its output. Similarly, if a noticeable jerk is felt during vehicle launch, the system may fine-tune the estimated transmission efficiency of the parking mechanism or modify the algorithm for estimating the total vehicle mass to improve the model's prediction accuracy in the next control cycle. This continuous feedback, error detection, and adaptive adjustment mechanism ensures that the method of the present invention maintains excellent performance under various dynamically changing operating conditions, effectively enhancing the system's robustness and long-term adaptability.

[0063] Furthermore, the present invention fully demonstrates its advantages of refined regulation during the park release process. Traditional parking systems often release the braking force abruptly during release, causing the vehicle to experience a noticeable jerk, especially on slopes. The AMPC system of the present invention, however, adopts a more intelligent and smooth release strategy. When the driver issues a park release request, the AMPC system does not immediately reduce the parking torque to zero. Instead, it combines the currently updated vehicle-environment coupling dynamic model to proactively predict the vehicle's starting smoothness at different torque reduction rates. Based on these predictions, the system calculates an optimal torque attenuation curve. This curve ensures that the parking force can be gradually reduced from the current value to complete release smoothly within a set time, without any shock, thereby ensuring that the vehicle can start with extremely high smoothness when the driver steps on the accelerator pedal. For example, in the special condition of a downhill start, to prevent the vehicle from suddenly accelerating due to gravity after the parking force is fully released, the system may maintain a weak braking torque during the release process, allowing the vehicle to move only in a controlled, slow glide state until the driver applies sufficient driving torque to overcome the slope resistance and the vehicle begins to steadily accelerate. Only then will the parking force be fully released. This "hill start" or slow release strategy significantly improves the comfort of the driver and passengers during the release process, avoiding the discomfort caused by traditional parking systems.

[0064] The method of the present invention demonstrates strong universality and excellent performance in multiple application scenarios: Scenario 1: Parking on a flat road. In this ideal working condition, the road slope angle The vehicle's gravity component is close to zero, minimizing the impact of gravity on the vehicle. At this point, the vehicle-environment coupled dynamic model identifies the primary force to be overcome as road rolling resistance. Based on the vehicle's current state and a pre-set safety margin, the AMPC strategy calculates a smaller, optimal parking motor torque command. This torque effectively ensures stable parking while minimizing parking system energy consumption and avoiding unnecessary overload. When releasing the parking brake, AMPC smoothly releases the parking torque to zero according to a pre-set gentle torque decay curve, ensuring a smooth, impact-free launch on flat roads.

[0065] Scenario 2: Uphill parking. When the vehicle is on an uphill road, the slope angle If the value is positive, the vehicle will be subjected to a rearward gravity component, causing it to have a tendency to slide backward. In the method of the present invention, the data acquisition module and the data preprocessing module will accurately estimate the current uphill slope angle. Based on this precise slope angle, the vehicle-environment coupled dynamic model will calculate the minimum parking force sufficient to overcome the rearward gravity component and rolling resistance. The AMPC strategy will add a safety margin to this minimum parking force and output a larger parking motor torque command. During the entire uphill parking process, the performance evaluation and model update module will continuously monitor the wheel speed sensor data. Once any slight backward sliding tendency is detected, the AMPC will respond immediately and fine-tune the parking motor torque, ensuring that the vehicle is always firmly parked on the uphill road with a millisecond-level response speed without unexpected slippage.

[0066] Scenario 3: Downhill parking. When the vehicle is on a downhill road, the slope angle If the value is negative, the vehicle will be subjected to a forward-rolling gravity component, causing it to slide forward. Similar to uphill parking, the system accurately estimates the downhill slope angle. The vehicle-environment coupled dynamic model calculates a parking force sufficient to offset the forward gravity component and rolling resistance. The AMPC strategy outputs the appropriate parking motor torque based on this requirement. Downhill parking release is a key feature of the present invention. To prevent sudden acceleration due to gravity after the parking force is released, the AMPC system prioritizes a smooth release of torque. It may employ a "hill start" strategy. This strategy involves not immediately releasing the parking force completely when releasing the parking force. Instead, as the vehicle begins to slowly glide forward while still under control, the parking force is gradually reduced based on the driver's driving demand and the slope until the driver's driving torque is sufficient to stably control the vehicle's forward acceleration. This ensures safety and stability during downhill starts, effectively avoiding the dangers of sudden acceleration.

[0067] Scenario 4: Parking under different loads. When the load of an electric vehicle changes, such as adding passengers or loading cargo, the total mass of the vehicle The total vehicle mass is one of the key parameters that affect the calculation of parking force. Traditional parking systems are often unable to dynamically adapt to such changes. The present invention can update the total vehicle mass in real time through the online parameter identification mechanism in its vehicle-environment coupling dynamic model. For example, during vehicle acceleration, the inverse dynamics model accurately estimates the current total mass using real-time measurements of the drive motor torque and vehicle acceleration. Consequently, during AMPC optimization, the controller consistently uses the most accurate total vehicle mass model for predictions and decisions. This avoids issues such as insufficient parking force (vehicle slip) or excessive parking force (energy waste and component wear) caused by inaccurate mass estimation, ensuring precise and safe parking under all load conditions.

[0068] This invention also incorporates mechanisms for handling system failures or anomalies, further enhancing system robustness and safety. For example, if a critical sensor (such as an IMU or wheel speed sensor) fails, resulting in data loss or anomalies, the system will immediately activate a backup data source (if available) or employ model prediction methods to estimate the missing data to maintain basic parking functionality. Simultaneously, the system will clearly warn the driver of the sensor failure and recommend appropriate action. More importantly, if the AMPC controller, while executing its optimization algorithm, detects that it cannot find a parking motor torque command sequence that fully meets all safety requirements within the currently configured constraints (e.g., maximum parking force, minimum release force, and maximum allowable slip distance), the system prioritizes parking safety. In this case, the controller abandons the pursuit of optimal performance and instead immediately applies a pre-set, rigorously validated maximum safe parking force, preventing the vehicle from slipping or entering an unsafe state. The system then issues another warning to the driver. This fail-safe mechanism ensures the highest level of parking safety is maintained even in the event of a system anomaly.

[0069] In summary, the present invention provides a method for regulating the parking motor torque of an electric vehicle. By incorporating multi-source information fusion technology to perceive the vehicle and environmental conditions in real time, constructing and dynamically updating a high-precision vehicle-environment coupling dynamic model, and utilizing an adaptive model predictive control strategy for forward-looking torque management, this method achieves high-precision, adaptable, safe, stable, and energy-efficient regulation of the parking motor torque. This method significantly improves the parking performance and user experience of electric vehicles, effectively addressing the existing issues of insufficient parking force control accuracy, delayed response, and inefficient energy consumption under complex operating conditions.

Claims

1. A method for adjusting the torque of a parking motor of an electric vehicle, characterized in that: include: Real-time collection of multi-dimensional vehicle operating status data, driver operation instructions and environmental perception data; Based on the above real-time collected data, a coupled dynamic model of the current vehicle and the environment is established, and the parameters of the coupled dynamic model are updated in real time; determining a target parking force requirement based on the driver's operating command and a coupled dynamic model of the vehicle and the environment; Utilizing an adaptive model predictive control strategy, based on a coupled dynamic model of the vehicle and the environment and the target parking force requirement, the vehicle's future motion state is predicted within a preset prediction time domain, and an optimal torque command for the parking motor is optimally calculated within a current control cycle. as well as The optimal torque command is sent to the parking motor controller to drive the parking motor to generate corresponding torque to achieve vehicle parking or parking release.

2. The electric vehicle parking motor torque adjustment method according to claim 1, characterized in that: The real-time collection of multi-dimensional vehicle operating status data, driver operation instructions and environmental perception data includes: Obtain the vehicle's real-time posture, kinematics, and brake pressure information through internal vehicle sensors; Obtaining battery state of charge data provided by the battery management system and drive motor torque and speed information provided by the drive motor controller through the vehicle network; and The estimated value of the road slope angle and the estimated value of the road adhesion coefficient are obtained through the vehicle-mounted environmental perception sensor and / or high-precision map information.

3. The electric vehicle parking motor torque adjustment method according to claim 2, characterized in that: The acquisition of the vehicle's real-time posture, kinematic information, and brake pressure information through internal vehicle sensors includes: The vehicle's three-axis acceleration and three-axis angular velocity information is obtained through the inertial measurement unit, and the vehicle's pitch angle and roll angle are calculated; Obtaining the real-time rotation speed of each wheel through the wheel speed sensor; and The hydraulic or air pressure information in the brake line is obtained through the brake system pressure sensor.

4. The electric vehicle parking motor torque adjustment method according to claim 2, characterized in that: The obtaining of the estimated road slope angle and the estimated road adhesion coefficient by using the vehicle-mounted environment perception sensor and / or high-precision map information includes: Comprehensively utilize the pitch angle information of the inertial measurement unit, the altitude change rate data of the global positioning system, and the road slope information stored in the high-precision map to estimate the current road slope angle; and The road adhesion coefficient is estimated online based on real-time correlation analysis of tire slip rate and driving / braking torque, or vehicle vibration characteristics and suspension dynamic response.

5. The electric vehicle parking motor torque adjustment method according to claim 1, characterized in that: The step of establishing a coupled dynamic model between the current vehicle and the environment and updating parameters of the coupled dynamic model in real time includes: Establish a coupled dynamic model including vehicle dynamics model, parking mechanism model and environment model; The vehicle dynamics model describes the longitudinal motion response of the vehicle under the effects of parking force, gravity, rolling resistance and air resistance; The parking mechanism model describes the transmission characteristics of converting the output torque of the parking motor into the parking force applied to the wheels; and The environmental model incorporates real-time estimates of bank angle and road adhesion into vehicle dynamics calculations.

6. The electric vehicle parking motor torque adjustment method according to claim 5, characterized in that: The real-time updating of the parameters of the coupled dynamic model includes: The system identification and state estimation algorithm of the recursive least squares method, extended Kalman filter or unscented Kalman filter is used to iteratively correct the vehicle gross mass, rolling resistance coefficient, air resistance coefficient and transmission efficiency of the parking mechanism in the coupled dynamic model in combination with the difference between the actual vehicle motion response and the model prediction output.

7. The electric vehicle parking motor torque adjustment method according to claim 1, characterized in that: Determining the target parking force requirement includes: When a parking request is received, calculating a minimum parking force required to maintain the vehicle stationary based on the currently estimated road slope angle and road adhesion coefficient, and adding a preset safety margin to the minimum parking force to determine a target parking force; and When a parking release request is received, the vehicle's starting smoothness is predicted based on a coupled dynamic model of the vehicle and the environment, and an optimal torque decay curve is calculated so that the target parking force is smoothly reduced from the current value to zero according to the optimal torque decay curve.

8. The electric vehicle parking motor torque adjustment method according to claim 1, characterized in that: The method of using the adaptive model predictive control strategy to predict the future motion state of the vehicle and to optimize and calculate the optimal torque command of the parking motor in the current control cycle includes: In each control cycle, using the updated coupled dynamic model of the vehicle and the environment, predicting the future motion state of the vehicle in the prediction time domain; An optimal torque command sequence for the parking motor in a future control time domain is obtained by solving a quadratic programming problem, wherein the objective function of the quadratic programming problem includes a state tracking error term and a control variable change rate term; The state tracking error term penalizes deviations between the predicted vehicle state and the target parking state; and The control variable change rate term penalizes a sharp change in the parking motor torque command.

9. The electric vehicle parking motor torque adjustment method according to claim 8, characterized in that: Solving the quadratic programming problem is also subject to a series of constraints, including: Parking motor maximum torque output limit and minimum torque output limit; Physical limitations on the rate of change of parking motor torque; and A safety constraint on the maximum allowable slip distance of a vehicle during parking.

10. The electric vehicle parking motor torque adjustment method according to claim 1, characterized in that: The optimal torque command is sent to a parking motor controller to drive the parking motor to generate a corresponding torque to achieve parking or releasing the vehicle, further comprising: The parking motor controller drives the parking motor using a current closed-loop control or a field-oriented control algorithm according to the optimal torque command; and The system monitors the parking status in real time, including the vehicle's slip and impact conditions. If there is a significant deviation between the actual parking effect and the predicted state, it triggers the online identification and update of the parameters of the coupled dynamic model of the vehicle and the environment to improve the model's prediction accuracy.

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