Electric brake energy recovery control system of new energy tractor

Abstract

The present application relates to the field of agricultural machinery and new energy vehicle control technology, specifically to a new energy tractor electric brake energy recovery control system, comprising: a multi-source state perception module synchronously collects kinematics and dynamics parameters to obtain original working state data; a soil characteristic observation module performs slip rate calculation and filtering processing on the data to extract soil adhesion limit characteristics; a machine coupling analysis module identifies the dynamics interaction state of the mounted machine and constructs the dynamic stability domain boundary of electric brake recovery; a strategy distribution module compares the driver's demand with the boundary to generate torque distribution instructions; a collaborative execution module responds to the instructions to collaboratively adjust the motor and mechanical brake to execute energy recovery; an adaptive evolution module captures the time-varying degradation trend of soil adhesion and performs shrinkage correction on the dynamic stability domain boundary; the present application introduces soil mechanics and machine dynamics into the control closed loop, achieving the overall optimization of working safety, agronomic protection and energy recovery efficiency.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery and new energy vehicle control technology, specifically to an electric braking energy recovery control system for a new energy tractor. Background Technology

[0002] With the development of agricultural modernization and electrification technology, new energy tractors have become an important research direction in the field of agricultural machinery. In order to achieve energy conservation, emission reduction and range improvement of tractors, the research on electric braking energy recovery control system has become particularly important. In the design of tractor braking systems, directly adopting the regenerative braking strategy of electric vehicles shows certain potential due to its maturity; however, the working environment of tractors is special. The soft foundation characteristics of farmland soil make it difficult to directly obtain the tire adhesion limit and it is very easy to experience time-varying fading; in actual field operation conditions, tractors are usually loaded with heavy agricultural implements. Considering the dynamic interaction between the implements and the vehicle body, such as pushing or dragging, if the control logic of conventional vehicles is simply transplanted, it is often unable to effectively adapt to the complex unstructured road surface of farmland. This can easily lead to excessive wheel slippage, damage to the soil structure, and even cause the whole vehicle to turn and become unstable. Therefore, it is crucial to ensure the safe and efficient operation of new energy tractors in complex farmland environments by utilizing multi-source sensing data to observe the interaction between soil adhesion limit characteristics and machine dynamics in real time, and to construct a dynamic stability domain boundary in combination with the system operation mechanism in order to formulate a torque distribution strategy that takes into account both operational safety and agronomic protection. Summary of the Invention

[0003] To solve the above-mentioned technical problems, the present invention provides an electric braking energy recovery control system for a new energy tractor. Specifically, the technical solution of the present invention includes: The multi-source state perception module is configured to synchronously collect kinematic and dynamic parameters of the new energy tractor during operation in order to obtain raw operation state data. The soil property observation module is configured to perform slip ratio calculation and Kalman filtering on the original operating state data to extract the soil adhesion limit features that characterize the current road surface adhesion ability. The implement coupling analysis module is configured to perform dynamic decoupling on the load force signal in the original operation state data, identify the implement dynamic interaction state of the attached agricultural implements, and construct a dynamic stability domain boundary as the upper limit of electric braking recovery in combination with the soil adhesion limit characteristics. The strategy allocation module is configured to acquire the driver's braking demand, compare it numerically with the boundary of the dynamic stability domain, and generate a torque allocation command. The collaborative execution module is configured to, in response to the torque distribution command, coordinate the adjustment of the motor regenerative braking torque and the mechanical braking pressure to perform energy recovery actions when no instability warning is generated; The adaptive evolution module is configured to perform time-series analysis on multiple soil adhesion limit characteristics under the same work site, capture the time-varying decay trend of soil adhesion, and perform shrinkage correction on the boundary of the dynamic stable domain based on the time-varying decay trend.

[0004] Preferred methods for obtaining raw job status data include: Real-time acquisition of multi-channel wheel speed signals and ground-based radar speed signals; Differential processing is performed on the multi-channel wheel speed signals to obtain the drive wheel speed data; Use the ground-based radar speed signal as the actual vehicle speed data; Real-time acquisition of force sensor signals from the suspension system to obtain traction force data; Real-time acquisition of motor speed, current, and battery state of charge data completes the acquisition of raw operating status data.

[0005] Preferably, the method for extracting the soil adhesion limit characteristics of the current road surface is configured as follows: Based on the drive wheel speed data and the actual vehicle speed data, the real-time slip ratio is calculated; Establish a nonlinear observer model based on the tire-soil interaction mechanism; The real-time slip ratio and the driving or braking torque of the motor are input into the nonlinear observer model for iterative estimation; In each iteration, the estimated error covariance matrix is ​​calculated, and the Kalman gain is updated according to the preset noise covariance parameter. The soil peak adhesion coefficient is corrected using the updated Kalman gain, and the corrected soil peak adhesion coefficient is defined as the soil adhesion limit characteristic.

[0006] Preferably, the method for identifying the dynamic interaction state of mounted agricultural implements is configured as follows: The time-domain features of the mounted traction force data are extracted to obtain longitudinal force feature values; The longitudinal force characteristic value is compared with a preset zero force reference: if the longitudinal force characteristic value is greater than the zero force reference, the dynamic interaction state of the machine is determined to be a resistance dragging state; if the longitudinal force characteristic value is less than the zero force reference, the dynamic interaction state of the machine is determined to be an inertial push state. Based on the dynamic interaction state of the implement, its gain factor or attenuation factor on the longitudinal stability of the whole vehicle is determined according to the preset mapping rules.

[0007] Preferred methods for constructing the boundaries of dynamically stable domains include: Obtain the maximum charging power limit corresponding to the battery state of charge data and convert it into the maximum regenerative torque of the motor; Based on the soil adhesion limit characteristics, calculate the maximum tire braking grip torque under the current slip ratio threshold; By combining attenuation factors or gain factors, the maximum braking grip torque of the tire is corrected to obtain the safe braking limit; The smaller value between the maximum regenerative torque of the motor and the safe braking limit is selected and defined as the boundary of the dynamic stability domain of electric braking regeneration.

[0008] Preferably, the method for generating torque distribution commands includes: Convert the driver's braking demand into total braking torque demand; The total required braking torque is numerically compared with the boundary of the dynamic stability domain; If the total required braking torque is less than or equal to the boundary of the dynamic stability domain, a full regenerative braking command is generated, and the total required braking torque is fully allocated to the motor. If the total required braking torque is greater than the boundary of the dynamic stability domain, a composite braking command is generated. The value corresponding to the boundary of the dynamic stability domain is allocated to the motor as regenerative braking torque, and the difference between the total required braking torque and the boundary of the dynamic stability domain is allocated to the hydraulic mechanical braking system.

[0009] Preferably, the collaborative execution module is also configured with agronomic auxiliary braking logic, which includes: When generating a compound braking command, the battery state-of-charge data is detected; If the battery state of charge data is higher than the preset saturation threshold, or the motor temperature is higher than the preset thermal protection threshold, the regenerative braking torque of the motor will be limited. At the same time, the system sends an instruction to the tractor's hydraulic suspension system to adjust the attitude of the implements, thereby controlling the load to increase the depth of penetration into the soil by a preset range. This utilizes the soil cutting resistance to compensate for the lack of braking force and maintain a constant deceleration of the entire vehicle.

[0010] Preferably, the method for capturing the time-varying decay trend of soil adhesion is configured as follows: Multiple soil adhesion limit features calculated within a continuous time window within the current work area are obtained to form an adhesion feature sequence; A sliding window regression analysis was performed on the adhesion feature sequence to calculate the slope of the adhesion feature sequence as a function of operation time. If the slope of the change is negative and its absolute value exceeds the preset decay rate threshold, the soil is determined to be in a shear strength decay trend. Based on the change slope, the soil adhesion attenuation at the next moment is predicted, and the attenuation is fed back to the soil property observation module for numerical shrinkage correction of the dynamic stability domain boundary at the next moment.

[0011] Preferably, an electric braking energy recovery control system for a new energy tractor further includes: Instability protection module, used for real-time monitoring of slip rate; If the real-time slip ratio exceeds the preset safe slip limit, regardless of the torque distribution command, the regenerative braking torque of the motor will be immediately cut off, and the ABS anti-lock braking logic will be triggered until the real-time slip ratio returns to the safe range.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves accurate reconstruction of road surface adhesion capability in complex farmland environments through the collaborative work of a multi-source state perception module and a soil characteristic observation module. By introducing ground radar as a reference benchmark for real vehicle speed and combining it with a high-frequency pulse wheel speed differential algorithm, it effectively solves the problem of speed estimation distortion caused by wheel micro-slippage on soft ground, providing a reliable basis for slip ratio calculation. Utilizing a nonlinear observer model based on the tire-soil interaction mechanism, the real-time slip ratio and motor torque are substituted into an extended Kalman filter algorithm for iterative estimation, and the observation noise covariance is dynamically adjusted according to vehicle speed, ensuring that the soil adhesion limit characteristics characterizing the current peak road surface adhesion capability can still be accurately extracted even under low-speed, high-noise conditions. This physically clear closed-loop observation method overcomes the difficulty of traditional agricultural machinery in obtaining dynamic soil parameters in real time, laying a solid data foundation for subsequent stable control. 2. This invention significantly improves the longitudinal stability of tractors during loading operations by deeply integrating the implement coupling analysis module with the dynamic stability domain boundary construction method. This invention can extract time-domain features and decouple dynamics from the load force signal in real time, accurately identifying whether the loaded implement is in a state of resistance dragging or inertial push-heading, and quantifying its gain or attenuation factor on the overall vehicle stability accordingly. Based on this, the system further combines a center of gravity height model that dynamically changes with the traction load to calculate the vertical load on the drive axle in real time, thereby constructing a dynamic stability domain boundary for electric braking recovery that conforms to physical limits. This mechanism breaks the limitation of existing technologies that ignore the dynamic coupling effect of implements, effectively preventing vehicle folding and loss of control caused by trailer push-heading, while appropriately relaxing the braking limit when the implement generates resistance, achieving a comprehensive optimization of operational safety and energy recovery efficiency. 3. This invention achieves maximum energy recovery and agronomic protective braking under safety constraints through the intelligent cooperation of the strategy allocation module and the collaborative execution module. The system can not only generate composite braking commands including the motor and hydraulic mechanical system according to the driver's needs and the dynamic stability domain boundary, realizing the control logic of recovering as much as possible and compensating for any shortfall, but also innovatively configures agronomic auxiliary braking logic. Under extreme conditions such as battery saturation or motor overheating, the soil specific drag coefficient is identified online through a trial calibration strategy, and the suspension system is controlled to increase the depth of the implement into the soil, using the additional soil cutting resistance to compensate for the lack of braking force. This method not only ensures the constant deceleration of the whole vehicle and avoids the safety hazards caused by the thermal fade of the braking system, but also cleverly utilizes the characteristics of agricultural operations to solve the technical pain point of limited braking ability of traditional electric agricultural machinery when fully charged. 4. This invention constructs a dual safety defense line from prediction and prevention to a bottom-line guarantee through an adaptive evolution module and an instability protection module. On the one hand, by performing sliding window regression analysis on the soil adhesion limit characteristics within a continuous time window, the system can keenly capture the time-varying decay trend of soil shear strength caused by repeated compaction, and predict the adhesion attenuation amount at the next moment. It actively shrinks and corrects the boundary of the dynamic stability domain, realizing a leap from passive anti-skid to predictive anti-skid, effectively avoiding the risk of soil structure collapse. On the other hand, the instability protection module, as the last line of defense, can forcibly cut off the regenerative torque and trigger anti-lock logic when the slip ratio exceeds the safety limit. This ensures that the system can still maintain the bottom-line safety of the vehicle under extreme fault conditions such as sensor failure or sudden road surface changes, preventing complete loss of control due to algorithm iteration delays. Attached Figure Description

[0013] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0015] Example 1: Please see Figure 1 An electric braking energy recovery control system for a new energy tractor includes: a multi-source state sensing module configured to synchronously collect kinematic and dynamic parameters of the new energy tractor during operation to obtain raw operation state data; The soil property observation module is configured to perform slip ratio calculation and Kalman filtering on the original operating state data to extract the soil adhesion limit features that characterize the current road surface adhesion ability. The implement coupling analysis module is configured to perform dynamic decoupling on the load force signal in the original operation state data, identify the dynamic interaction state of the attached agricultural implements, and construct a dynamic stability domain boundary as the upper limit of electric braking recovery by combining soil adhesion limit characteristics. The strategy allocation module is configured to acquire the driver's braking demand, compare it numerically with the dynamic stability domain boundary, and generate a torque allocation command. The collaborative execution module is configured to respond to torque distribution commands and collaboratively adjust the regenerative braking torque of the motor and the mechanical braking pressure to perform energy recovery actions when no instability warning is generated. The adaptive evolution module is configured to perform time-series analysis on multiple soil adhesion limit characteristics under the same work site, capture the time-varying decay trend of soil adhesion, and perform shrinkage correction on the boundary of the dynamic stable domain based on the time-varying decay trend.

[0016] This embodiment elaborates on the architecture logic of the above system. The system aims to solve the technical problems of existing electric tractors directly adopting the braking strategy of electric vehicles, ignoring the weak foundation characteristics of farmland soil and the dynamic coupling effect of attached agricultural implements, which leads to wheel slippage, soil structure damage and vehicle instability during operation. The multi-source state perception module constructs a full-dimensional digital mapping of the working conditions, which synchronously collects kinematic parameters such as wheel speed, ground speed, and yaw rate, as well as dynamic parameters such as motor torque and attachment force. The soil property observation module solves the problem of soil parameters being difficult to measure directly. It extracts the soil adhesion limit feature that characterizes the current road surface adhesion ability in real time through algorithms. This feature refers to the maximum longitudinal grip coefficient that the tires can provide without excessive slippage under the current soil conditions. The implement coupling analysis module quantifies the impact of attached agricultural implements on tractor stability and identifies implement dynamic interaction states such as push-head or towing. Based on this, the strategy allocation module maximizes energy recovery under safety constraints and generates torque allocation instructions that include the ratio of motor and hydraulic mechanical braking system; the cooperative execution module executes specific braking actions when no instability warning is generated; the adaptive evolution module addresses the time-varying nature of soil properties and shrinks and corrects the boundary of the dynamic stability domain based on the time-varying decay trend. This embodiment, through the coordinated operation of the aforementioned modules, breaks through the limitations of traditional energy recovery systems that only focus on battery status and motor capacity, by introducing soil mechanical properties and implement dynamics into the control closed loop. This ensures that when the tractor performs energy recovery in soft, muddy farmland, it can dynamically adjust the braking force according to the soil's tolerance limit, avoiding wheel slippage and soil compaction caused by excessive braking force, while also preventing vehicle loss of control due to trailer oversteer, thus achieving a holistic optimization of operational safety, agronomic protection, and energy recovery efficiency.

[0017] Example 2: The methods for obtaining raw operational status data include: real-time acquisition of multi-channel wheel speed signals and ground radar speed signals; differential processing of multi-channel wheel speed signals to obtain drive wheel speed data; using ground radar speed signals as actual vehicle speed data; real-time acquisition of force sensor signals from the suspension system to obtain mounted traction force data; and real-time acquisition of motor speed, current, and battery state of charge data to complete the acquisition of raw operational status data.

[0018] This embodiment details the data acquisition process of the multi-source state perception module. The system executes kinematic data acquisition steps, acquiring real-time signals from wheel speed sensors installed on each drive wheel. These signals are typically high-frequency pulse sequences C output as the wheels rotate, as well as speed signals from a ground-based Doppler radar or high-precision RTK-GPS installed under the vehicle. The system performs differential processing on the multi-channel wheel speed signals to obtain drive wheel rotation speed data. ; The specific differential processing algorithm is configured as follows: read the current sampling time. Compared to the previous moment wheel speed sensor cumulative pulse count value Here, the subscript k explicitly represents the current discrete sampling period, and the subscript k-1 represents the previous discrete sampling period. This is combined with the preset sensor pulse-per-revolution resolution N and the sampling period. The unit is seconds, expressed by the formula:

[0019] Calculate the instantaneous angular velocity of each wheel, and take the arithmetic mean of the angular velocities of the left and right drive wheels to eliminate the effect of differential speed, thus obtaining the final drive wheel speed data. Meanwhile, in order to eliminate speed measurement errors caused by wheel slippage, the ground radar speed signal is directly used as the real vehicle speed data v; the system executes the implement interaction force acquisition step, and collects the pin-type force sensor signal located on the tractor's three-point suspension device or traction hook in real time to obtain the attached traction force data F, which includes the longitudinal tension or thrust applied by the attached implement to the tractor. The system reads the drive motor's speed n, current I, and battery state-of-charge data S reported by the battery management system in real time via the CAN bus. This embodiment effectively solves the problem of inaccurate speed estimation caused by the common slight slippage of wheels on soft farmland ground by introducing ground radar as a reference benchmark for the actual vehicle speed. It also clarifies the method of obtaining speed based on pulse time difference, providing reliable data support for accurately judging the dynamic impact of implements on the whole vehicle.

[0020] Example 3: The method for extracting the soil adhesion limit characteristics of the current road surface is configured as follows: Based on the drive wheel speed data and actual vehicle speed data, calculate the real-time slip ratio; establish a nonlinear observer model based on the tire-soil interaction mechanism; input the real-time slip ratio and the motor's drive or braking torque into the nonlinear observer model for iterative estimation; in each iteration, calculate the estimation error covariance matrix and update the Kalman gain according to the preset noise covariance parameter; use the updated Kalman gain to correct the peak soil adhesion coefficient, and define the corrected peak soil adhesion coefficient as the soil adhesion limit characteristic. This embodiment details the algorithm implementation of the soil property observation module; the system is based on drive wheel speed data. Calculate the real-time slip ratio using the tire rolling radius r and the actual vehicle speed data v. The calculation formula is as follows:

[0021] in, The data represents the drive wheel speed, sourced from the wheel speed sensor; r is the tire rolling radius, a preset constant in meters, obtained by consulting the tire technical manual or through vehicle calibration; v is the actual vehicle speed data, sourced from ground-based radar. The system establishes a nonlinear observer model based on the tire-soil interaction mechanism. In this model and subsequent iterative formulas, the subscript k is explicitly defined as the current discrete time step, and the subscript k-1 as the previous discrete time step. Specifically, the peak soil adhesion coefficient is selected as the state variable. Construct discretized state equations:

[0022] in, Assuming process noise, the variation of the adhesion coefficient conforms to a Gaussian white noise sequence with zero mean; due to the state variables in this model For a one-dimensional variable, its corresponding process noise covariance Q is configured as a dimensionless scalar, with typical values ​​such as... Simultaneously, observation equations are constructed based on the single-wheel dynamic equilibrium equations:

[0023] in, For observation noise, it represents the random disturbance term introduced by sensor measurements and model simplification, and the observed values. The calculation incorporates parameter corrections for the transmission system, and the specific formula is as follows:

[0024] in, Provides real-time torque output for the motor; This refers to the total mechanical transmission ratio from the motor to the drive wheel; For the mechanical efficiency of the transmission system; The formula for calculating the equivalent moment of inertia referred to the drive wheel end is configured as follows:

[0025] in, For the moment of inertia of the driving wheel, The moment of inertia of the motor rotor; It is the wheel angular acceleration; it is obtained by analyzing the speed data of the drive wheels. Obtain by performing difference calculations: ; To address the issue of the algorithm's inability to reproduce the real-time vertical load on the drive wheels due to the absence of this crucial state variable, the system is configured with the following dynamic formula for real-time calculation:

[0026] in, The total vehicle weight is expressed in kg. L is the distance from the front axle to the center of gravity, in meters (m); L is the wheelbase, in meters (m). The center of gravity height is not fixed, but is configured to dynamically adjust with the traction force of the mounted load. The calculation logic is as follows:

[0027] in, The static center of gravity height of the vehicle is 1.05m, and L is the wheelbase in meters. This is the center of gravity height sensitivity coefficient, in meters, for example, 0.15m; this dynamic calculation ensures the accuracy of vertical load estimation. Where is the hook height, and F is the traction force of the load. This represents the vehicle's longitudinal acceleration, the value of which is derived from the derivative of the ground-detection radar velocity; here, the absolute value is used. To characterize the shaft load transfer effect caused by inertial forces during acceleration and deceleration; Among them, longitudinal acceleration The specific acquisition logic configuration is as follows: based on the acquired real vehicle speed data v from the ground radar, using the differential formula... The calculated value is then processed by a first-order RC low-pass filter to suppress noise interference, where the filter coefficients are... Configured to 0.05; nonlinear observation function The improved Burckhardt tire model is used for description, and its calculation formula is as follows:

[0028] To ensure state variables It can directly characterize the peak adhesion coefficient of soil, thus achieving a closed loop in physical terms, and the system clearly defines... These are the normalized and calibrated empirical coefficients, where The maximum value of the adhesion coefficient curve is determined. Determines the slope of the linear region of the curve. The downward trend after the peak of the curve is determined; the specific acquisition method is as follows: offline collection of slip rate-adhesion coefficient curves of typical farmland soils, normalization of their maximum values, and fitting of the shape function. , making The global maximum value is always equal to 1; for example, for typical dryland soils in northern China, the normalized parameters obtained through offline calibration are as follows: , , Therefore, in the model This is a direct scaling estimate of the current peak adhesion capacity of the road surface; Based on this, the system will display the real-time slip ratio. With torque Substituting into the above model, iterative estimation is performed using the Extended Kalman Filter (EKF) algorithm; in each iteration, the prior error covariance is calculated. and Jacobi matrix Simultaneously, the current observed noise covariance is dynamically calculated based on vehicle speed. :

[0029] in, The pre-defined basic observation noise covariance is dimensionless. This is the velocity-related attenuation coefficient, configured in units of s / m, used to reduce the velocity term. This is converted into a dimensionless noise increment coefficient, with a value of, for example, 0.002. To prevent the noise covariance matrix from being singular or too small, a numerical stability correction factor is used, for example... ; This dynamic adjustment strategy aims to reflect the increased observation uncertainty caused by the reduced signal-to-noise ratio of wheel speed signals at low speeds; utilizing the updated... The estimated value from the previous time step is corrected to obtain the optimal estimated value for the current time step. ,Should This refers to the soil adhesion limit characteristic mu.

[0030] Example 4: The method for identifying the dynamic interaction state of attached agricultural implements is configured as follows: Time-domain features are extracted from the attached traction force data to obtain longitudinal force characteristic values; the longitudinal force characteristic values ​​are compared with a preset zero-force benchmark; if the longitudinal force characteristic value is greater than the zero-force benchmark, the dynamic interaction state of the implement is determined to be a resistance-driven dragging state; if the longitudinal force characteristic value is less than the zero-force benchmark, the dynamic interaction state of the implement is determined to be an inertial push-head state; based on the dynamic interaction state of the implement, its gain factor or attenuation factor for the longitudinal stability of the entire vehicle is determined according to a preset mapping rule.

[0031] This embodiment details the state recognition logic of the implement coupling analysis module; the system processes the attached traction force data. Moving average filtering and time-domain feature extraction are performed to filter out high-frequency noise caused by ground bumps, thereby obtaining longitudinal force characteristic values. The system will use longitudinal force characteristic values With the preset zero force reference Perform numerical comparison; respond to The system determines that the dynamic interaction state of the implement is a dragging state, in which the implement exerts a pulling force on the tractor; in response to The system determines that the implement's dynamic interaction state is an inertial push state, in which the implement pushes the tractor due to inertia; based on the identified state, the system determines the gain factor or attenuation factor for the longitudinal stability of the entire vehicle. The calculation formula is as follows:

[0032] The system will use longitudinal force characteristic values With the preset zero force reference The unit is N, and it is usually set to 0 or a tiny dead zone threshold for numerical comparison; in response to Call during calculation As a resistance gain weight; in response to Call during calculation Both are dimensionless coefficients used as weights for pusher attenuation and are configured differently depending on the type of equipment. To make appropriate use of the machine's resistance, To strictly limit the risk of push-head movement; and the system is equipped with safety cutoff logic to enforce restrictions. To prevent loss of braking ability due to excessive decay of the calculation factor or instability due to excessive gain; G is the total weight of the tractor, which is the product of the total mass m and the gravitational acceleration g. This embodiment accurately identifies whether the mounted equipment acts as a deceleration chute or a booster by real-time monitoring of the stress state at the attachment point; by introducing factors... When the implement pushes the head, it actively reduces the expected braking limit to prevent vehicle folding due to excessive energy recovery; when the implement generates resistance, it appropriately relaxes the braking limit to improve recovery efficiency, thus achieving decoupling and utilization of the implement effect.

[0033] Example 5: The method for constructing the dynamic stability domain boundary includes: obtaining the maximum charging power limit corresponding to the battery state of charge data and converting it into the maximum regenerative torque of the motor; calculating the maximum tire braking grip torque under the current slip ratio threshold based on the soil adhesion limit characteristics; correcting the maximum tire braking grip torque by combining the attenuation factor or gain factor to obtain the safe braking limit; and selecting the smaller value between the maximum regenerative torque of the motor and the safe braking limit as the dynamic stability domain boundary of electric braking recovery.

[0034] This embodiment elaborates on the dynamic stability domain boundary. The specific calculation logic; the system executes the electrical limit conversion step to obtain the maximum charging power limit corresponding to the battery state-of-charge data. Combined with the current motor speed n, it is converted into the motor's maximum regenerative torque. ;

[0035] in, The maximum charging power limit is in kW; n is the current motor speed in r / min. The overall efficiency coefficient of the motor and inverter is dimensionless; in this embodiment, a fixed value of 0.92 is used, or it can be obtained by looking up a table based on the motor efficiency MAP chart. The system performs physical limit calculations based on soil adhesion limit characteristics. Corresponding to Example 3 Calculate the maximum braking grip torque of the tire under the current optimal slip ratio threshold. To ensure that the calculated physical limit is realistically achievable, the system solves for the optimal slip ratio based on the zero point of the first derivative of the Burckhardt model. ;Will Substitute the values ​​into the observation equation to calculate the maximum adhesion coefficient that the current road surface can actually provide. Given that the model parameters have been normalized and constrained in Example 3, the calculated parameters here are... Numerically, it closely approximates the state estimate. However, this calculation step rigorously preserves the nonlinear characteristics of the model; This model clarifies the mathematical relationship between the center of gravity height and traction load, replacing the previous rough estimation and ensuring... Completeness of calculated parameters; final calculation of maximum tire braking grip torque: The system performs a coupling correction step, combining the attenuation factor or gain factor. The maximum braking grip torque of the tires is corrected to obtain the safe braking limit. The corrected formula is as follows:

[0036] in, The basic safety margin coefficient; its value range is configured as follows: In this embodiment, a value of 0.85 is preferred to allow for a safety margin in response to sudden changes in road surface conditions; the system selects the maximum regenerative torque of the motor. With safe braking limits The smaller value in the equation is defined as the boundary of the dynamic stability domain of electric braking regeneration. .

[0037] Example 6: The method for generating torque distribution commands includes: converting the driver's braking demand into total braking torque demand; comparing the total braking torque demand with the boundary of the dynamic stability domain; if the total braking torque demand is less than or equal to the boundary of the dynamic stability domain, generating a full regenerative braking command to distribute all the total braking torque demand to the motor; if the total braking torque demand is greater than the boundary of the dynamic stability domain, generating a composite braking command, distributing the value corresponding to the boundary of the dynamic stability domain to the motor as regenerative braking torque, and distributing the difference between the total braking torque demand and the boundary of the dynamic stability domain to the hydraulic mechanical braking system.

[0038] This embodiment details the execution logic of the strategy allocation module; the system acquires the brake pedal travel signal and converts it into the total required braking torque according to the preset braking characteristic curve. The system will With the boundary of the dynamic stable region Perform numerical comparisons; respond to The system generates a full regenerative braking command, setting the motor's regenerative torque equal to the total required braking torque and the mechanical braking torque to zero. This indicates that the current braking demand neither exceeds the battery's charging capacity nor breaches the safety limits of the soil and equipment; in response to The system generates a composite braking command, locking the motor's regenerative torque within a safe boundary. The difference is then allocated to the hydraulic mechanical braking system. This embodiment achieves intelligent control that recovers energy as much as possible and supplements it if necessary. Under low-intensity braking or when the soil is well-adhered, the motor is used first to recover energy. Under high-intensity braking or when the soil is slippery or the implement is pushing, the electric braking amplitude is strictly limited, and the mechanical braking is used to supplement the braking force due to its fast response and high reliability, thus balancing energy saving and safety.

[0039] Example 7: The collaborative execution module is also equipped with agronomic auxiliary braking logic, which includes: detecting battery state of charge data when generating compound braking commands; if the battery state of charge data is higher than a preset saturation threshold, or the motor temperature is higher than a preset thermal protection threshold, limiting the motor regenerative braking torque; at the same time, sending implement attitude adjustment commands to the tractor hydraulic suspension system to control the attached implements to increase the soil penetration depth by a preset amount, using soil cutting resistance to compensate for the lack of braking force and maintain constant vehicle deceleration.

[0040] This embodiment details the specific execution steps of the agronomic-assisted braking logic; when the system generates a composite braking command, it detects the battery state-of-charge data S and the motor temperature H; if the constraints are met, the system calculates the difference between the current total required braking torque and the actual output braking torque. And convert it into the compensating resistance required for the entire vehicle:

[0041] Where r is the tire radius; the system sends implement attitude adjustment commands to the tractor's hydraulic suspension system; based on the preset tillage depth-traction resistance linear model... Here, the model parameters are explicitly defined: W is the working width of the mounted agricultural machinery, in meters; h is the soil penetration depth, in meters. For traction resistance, the soil specific drag coefficient is used. To address the challenges of varying land plots and difficulty in pre-setting control strategies, the system is configured with a trial-calibration closed-loop control strategy: During the trial phase, the agricultural machinery is controlled with the minimum preset step size. For example, 1cm, buried in the soil; observation and calibration: real-time acquisition of resistance increments fed back from the force sensor. The current soil resistivity is identified online, and its calculation formula is as follows:

[0042] in The cutting resistance per unit volume of earthwork is represented by units of 1. ; Target execution: based on the identified The calculation can generate compensating resistance. Required target depth It also drives the suspension device to adjust to that depth; simultaneously, the system is equipped with travel protection logic: real-time comparison of the current soil penetration depth. Maximum mechanical stroke of agricultural machinery ,like If so, then the issuance of depth-increasing commands will be forcibly stopped; Through the above closed-loop logic, the increased soil cutting resistance generated by the increased depth of the agricultural implement is used as an auxiliary braking force to compensate for the lack of braking force, thereby maintaining a constant deceleration of the entire vehicle.

[0043] Example 8: The method for capturing the time-varying decay trend of soil adhesion is configured as follows: multiple soil adhesion limit features calculated within a continuous time window in the current work area are obtained to form an adhesion feature sequence; a sliding window regression analysis is performed on the adhesion feature sequence to calculate the slope of the adhesion feature sequence as a function of work time. If the slope of change is negative and its absolute value exceeds the preset decay rate threshold, the soil is determined to be in a shear strength decay trend. Based on the slope of change, the soil adhesion decay amount at the next moment is predicted, and the decay amount is fed back to the soil property observation module for numerical shrinkage correction of the dynamic stability domain boundary at the next moment.

[0044] This embodiment details the trend analysis logic of the adaptive evolution module; the system obtains multiple soil adhesion limit characteristics calculated within a continuous time window W (in seconds) within the current work area. This constitutes the attachment feature sequence M; The system performs sliding window linear regression analysis on the attachment feature sequence M to calculate the slope k of the sequence as a function of operation time. The calculation formula is as follows:

[0045] in, These are the mean values ​​of time and adhesion coefficient, respectively, where the subscript i represents the index of the data point sequence within the current sliding window; The system executes the decay determination step; in response to and If the preset decay rate threshold is exceeded, the system determines that the soil is in a shear strength decay trend. This situation is common after multiple compactions of the same furrow, which damages the soil structure. The system predicts the soil adhesion decay at the next moment based on the changing slope k. In this process, the system control period is explicitly defined as follows: For example, calculate the attenuation over 10ms. ; This attenuation amount is then fed back to the logic layer, while a preset safety margin factor is introduced. Its value must satisfy For example, a value of 1.2 is used to construct a modified adhesion coefficient. ;Should The original measured values ​​will be used as the boundary of the dynamic stability domain at the next time step. The calculation enables numerical contraction correction of the boundary, thus avoiding the risk of instability caused by soil structure collapse in advance. This embodiment captures the subtle decline trend caused by soil thixotropy through time series analysis and actively contracts and brakes the boundary before slippage occurs, realizing the leap from passive anti-slip to predictive anti-slip, and further ensuring the continuity of operation.

[0046] Example 9: The system also includes an instability protection module, which monitors the real-time slip ratio in real time. If the real-time slip ratio exceeds the preset safe slip limit, the regenerative braking torque of the motor will be immediately cut off, and the ABS anti-lock braking logic will be triggered, until the real-time slip ratio returns to the safe range.

[0047] This embodiment details the execution logic of the instability protection module; this module is configured as a safety defense line of the system; the system monitors the real-time slip rate calculated by the multi-source state perception module in real time. ; in response Breaking the preset safety sliding limit For example, if the torque distribution is 0.2% or 20%, the system immediately cuts off the regenerative braking torque of the motor and sets it to zero, regardless of the current torque distribution command; the system triggers the ABS anti-lock braking logic, performing high-frequency intermittent braking control on the hydraulic mechanical braking system until the real-time slip ratio is reached. Return to a safe range; This embodiment ensures that when sensors fail, algorithms estimate errors, or sudden changes in road surface cause a sharp increase in slip ratio, the module has the highest priority to forcibly take over vehicle control, preventing vehicle loss of control due to algorithm iteration delays and ensuring the system's bottom-line safety under extreme fault conditions.

[0048] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A control system for the electric braking energy recovery of a new energy tractor, characterized in that, include: The multi-source state perception module is configured to synchronously collect kinematic and dynamic parameters of the new energy tractor during operation in order to obtain raw operation state data. The soil property observation module is configured to perform slip ratio calculation and Kalman filtering on the original operating state data to extract the soil adhesion limit features that characterize the current road surface adhesion ability. The implement coupling analysis module is configured to perform dynamic decoupling on the load force signal in the original operation state data, identify the implement dynamic interaction state of the attached agricultural implements, and construct a dynamic stability domain boundary as the upper limit of electric braking recovery in combination with the soil adhesion limit characteristics. The strategy allocation module is configured to acquire the driver's braking demand, compare it numerically with the boundary of the dynamic stability domain, and generate a torque allocation command. The collaborative execution module is configured to, in response to the torque distribution command, coordinate the adjustment of the motor regenerative braking torque and the mechanical braking pressure to perform energy recovery actions when no instability warning is generated; The adaptive evolution module is configured to perform time-series analysis on multiple soil adhesion limit characteristics under the same work site, capture the time-varying decay trend of soil adhesion, and perform shrinkage correction on the boundary of the dynamic stable domain based on the time-varying decay trend. Methods for obtaining raw job status data include: Real-time acquisition of multi-channel wheel speed signals and ground-based radar speed signals; Differential processing is performed on the multi-channel wheel speed signals to obtain the drive wheel speed data; Use the ground-based radar speed signal as the actual vehicle speed data; Real-time acquisition of force sensor signals from the suspension system to obtain traction force data; Real-time acquisition of motor speed, current and battery state of charge data to obtain raw operating status data; The method for identifying the dynamic interaction state of mounted agricultural implements is configured as follows: The time-domain features of the mounted traction force data are extracted to obtain longitudinal force feature values; The longitudinal force characteristic value is compared with a preset zero force reference: if the longitudinal force characteristic value is greater than the zero force reference, the dynamic interaction state of the machine is determined to be a resistance dragging state; if the longitudinal force characteristic value is less than the zero force reference, the dynamic interaction state of the machine is determined to be an inertial push state. Based on the dynamic interaction state of the implement, its gain factor or attenuation factor on the longitudinal stability of the whole vehicle is determined according to the preset mapping rules. Methods for constructing the boundary of a dynamically stable domain include: Obtain the maximum charging power limit corresponding to the battery state of charge data and convert it into the maximum regenerative torque of the motor; Based on the soil adhesion limit characteristics, calculate the maximum tire braking grip torque under the current slip ratio threshold; By combining attenuation factors or gain factors, the maximum braking grip torque of the tire is corrected to obtain the safe braking limit; The smaller value between the maximum regenerative torque of the motor and the safe braking limit is selected and defined as the boundary of the dynamic stability domain of electric braking regeneration.

2. The electric braking energy recovery control system for a new energy tractor according to claim 1, characterized in that, The method for extracting the soil adhesion limit characteristics of the current road surface is configured as follows: Based on the drive wheel speed data and the actual vehicle speed data, the real-time slip ratio is calculated; Establish a nonlinear observer model based on the tire-soil interaction mechanism; The real-time slip ratio and the driving or braking torque of the motor are input into the nonlinear observer model for iterative estimation; In each iteration, the estimated error covariance matrix is ​​calculated, and the Kalman gain is updated according to the preset noise covariance parameter. The soil peak adhesion coefficient is corrected using the updated Kalman gain, and the corrected soil peak adhesion coefficient is defined as the soil adhesion limit characteristic.

3. The electric braking energy recovery control system for a new energy tractor according to claim 1, characterized in that, Methods for generating torque distribution commands include: Convert the driver's braking demand into total braking torque demand; The total required braking torque is numerically compared with the boundary of the dynamic stability domain; If the total required braking torque is less than or equal to the boundary of the dynamic stability domain, a full regenerative braking command is generated, and the total required braking torque is fully allocated to the motor. If the total required braking torque is greater than the boundary of the dynamic stability domain, a composite braking command is generated. The value corresponding to the boundary of the dynamic stability domain is allocated to the motor as regenerative braking torque, and the difference between the total required braking torque and the boundary of the dynamic stability domain is allocated to the hydraulic mechanical braking system.

4. The electric braking energy recovery control system for a new energy tractor according to claim 3, characterized in that, The collaborative execution module is also configured with agronomic auxiliary braking logic, which includes: When generating a compound braking command, the battery state-of-charge data is detected; If the battery state of charge data is higher than the preset saturation threshold, or the motor temperature is higher than the preset thermal protection threshold, the regenerative braking torque of the motor will be limited. At the same time, the system sends an instruction to the tractor's hydraulic suspension system to adjust the attitude of the implements, thereby controlling the load to increase the depth of penetration into the soil by a preset range. This utilizes the soil cutting resistance to compensate for the lack of braking force and maintain a constant deceleration of the entire vehicle.

5. The electric braking energy recovery control system for a new energy tractor according to claim 1, characterized in that, The method for capturing the time-varying decline trend of soil adhesion is configured as follows: Multiple soil adhesion limit features calculated within a continuous time window within the current work area are obtained to form an adhesion feature sequence; A sliding window regression analysis was performed on the adhesion feature sequence to calculate the slope of the adhesion feature sequence as a function of operation time. If the slope of the change is negative and its absolute value exceeds the preset decay rate threshold, the soil is determined to be in a shear strength decay trend. Based on the change slope, the soil adhesion attenuation at the next moment is predicted, and the attenuation is fed back to the soil property observation module for numerical shrinkage correction of the dynamic stability domain boundary at the next moment.

6. The electric braking energy recovery control system for a new energy tractor according to claim 1, characterized in that, The system also includes: Instability protection module, used for real-time monitoring of slip rate; If the real-time slip ratio exceeds the preset safe slip limit, regardless of the torque distribution command, the regenerative braking torque of the motor will be immediately cut off, and the ABS anti-lock braking logic will be triggered until the real-time slip ratio returns to the safe range.

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