Dual-clutch hybrid power tractor transmission system and control method

Abstract

The invention discloses a dual-clutch hybrid power tractor transmission system and a control method, and relates to the technical field of agricultural vehicle control, the technical scheme is that the dual-clutch hybrid power tractor transmission system comprises an engine and a driving motor, and the output end of the driving motor is connected with an even gear input shaft of a dual-clutch automatic transmission; the output end of the engine is connected with an odd-number gear input shaft of the double-clutch automatic transmission; the input end of the driving motor is connected with the output end of the inverter, and the input end of the inverter is connected with the storage battery; the storage battery, the dual-clutch automatic transmission, the driving motor and the engine are respectively controlled by a battery energy management unit, a dual-clutch automatic transmission control unit, a motor control unit and an engine control unit, so that the engine and the driving motor can provide power for the tractor independently or jointly; the engine can continuously operate in the efficient interval, meanwhile, the whole vehicle controller can adjust the working mode in real time according to the load working condition of the tractor, and energy utilization is relatively good.

Description

Technical Field

[0001] This invention relates to the field of agricultural vehicle control technology, and more specifically, to a dual-clutch hybrid tractor transmission system and control method. Background Technology

[0002] Tractors are an important component of agricultural production equipment, capable of performing various operations such as plowing, rotary tilling, sowing, and spraying. Traditional tractors are generally powered by internal combustion engines. Under extensive working conditions, they have low traction efficiency, high energy consumption, and large emissions of particulate matter, which seriously pollute the environment. Therefore, the development of low-energy-consumption, low-emission, and high-efficiency new energy tractors is urgent. In recent years, with the development and maturation of hybrid vehicle technology, hybrid tractors have gradually entered the field of agricultural machinery.

[0003] However, the existing parallel hybrid tractor power system cannot be matched with the working conditions in real time, resulting in high energy consumption and poor economic benefits. In addition, the traditional parallel hybrid system requires both a power coupling device and a manual transmission, which has low transmission efficiency, slow shifting speed and poor driving experience. Therefore, a dual-clutch automatic transmission parallel hybrid tractor power system and control method are proposed to solve the above problems. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a control method for a dual-clutch hybrid tractor transmission system.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A dual-clutch hybrid tractor transmission system includes an engine and a drive motor. The output of the drive motor is connected to the even-numbered gear input shaft of the dual-clutch automatic transmission. The output of the engine is connected to the odd-numbered gear input shaft of the dual-clutch automatic transmission. The input of the drive motor is connected to the output of an inverter. The input of the inverter is connected to a battery. The battery, dual-clutch automatic transmission, drive motor, and engine are controlled by a battery energy management unit, a dual-clutch automatic transmission control unit, a motor control unit, and an engine control unit, respectively. These control units are connected to a vehicle controller via a controller area network (CLAN) bus. The vehicle controller acquires tractor operation data and sends relevant control commands via the CLAN bus. The vehicle controller is also connected to a model prediction management unit via the CLAN bus. The vehicle controller uploads the acquired tractor operation data to the model prediction management unit, and the model prediction management unit feeds back the real-time optimized control strategy to the vehicle controller, achieving data interaction and enabling control of the tractor transmission system.

[0006] The data processing unit in the model prediction management unit removes missing and invalid data, cleans noise, and filters the operating data uploaded by the vehicle controller. It extracts the kinematic features of the hybrid tractor, performs dimensionality reduction on the data fragments of multiple kinematic features to obtain a parameter set, and then uses the model judgment unit to match the parameter set with samples in the loading condition library to obtain the optimal control parameters under the corresponding operating conditions.

[0007] Preferably, the dual-clutch automatic transmission includes odd-numbered gear shafts and even-numbered gear shafts connected to the engine and drive motor, odd-numbered gear clutches, even-numbered gear clutches, a reverse intermediate shaft, and a power confluence device for transmitting power from the engine and drive motor to the rear axle and pulse train output shaft.

[0008] Preferably, the drive modes of the dual-clutch hybrid tractor transmission system further include a hybrid drive mode, an engine-independent drive mode, an electric motor-independent drive mode, a driving-charging mode, and an energy recovery mode. The hybrid drive mode is used when the tractor is under heavy load; the power provided by the engine and electric motor is coupled to the tractor via the drive shaft. The engine-independent drive mode is used when the tractor is performing medium-to-low load operations such as tilling and harrowing; the engine efficiently provides traction, and the engine independently drives the tractor. The electric motor-independent drive mode is used when the tractor is under low load operations such as starting and transporting; the electric motor efficiently provides traction, and the motor's output power is transmitted to the drive wheels via the transmission system to drive the tractor. In the driving-charging mode, when the tractor is in engine-independent drive mode and the battery is low, the engine drives the tractor normally, and the remaining power charges the battery through the generator. In the energy recovery mode, when the tractor is in a deceleration and braking condition at high speed, the electric motor becomes a generator, converting the mechanical energy fed back from the tractor into electrical energy to charge the battery.

[0009] Preferably, the present invention also provides a control method for a dual-clutch hybrid tractor transmission system, specifically as follows: the model judgment unit establishes a judgment model, including the following steps: A judgment model is established, and a model judgment controller is constructed based on the judgment model. In each control cycle, the model judgment controller uses the current estimated value of the system state variables as the initial state to judge the future operating condition information and obtain the operating condition information value. The operating condition information value is input into the judgment model to output the control instruction set of the finite time domain optimization problem. The control command set includes the engine optimal torque command, the electric motor optimal torque command, the dual-clutch optimal pressure command, and the optimal target gear. The control command set is sent to the engine controller, motor controller and transmission controller; the actual output measurement value of the system is obtained and compared with the operating condition information value for feedback and correction, so as to realize rolling optimization control.

[0010] The model-based decision controller adopts a hierarchical structure, including: The upper-level energy management model judges the controller, whose control cycle is T1 and the judgment time domain is Np1, where 100ms≤T1≤500ms and 5s≤Np1×T1≤30s; The upper-level controller takes determining the total equivalent fuel consumption in the time domain as its core objective and solves for the engine reference torque command and the motor reference torque command. The lower-level dynamic coordination model judges the controller, whose control cycle is T2 and the judgment time domain is Np2, where 10ms≤T2≤50ms and 0.2s≤Np2×T2≤2s; The lower-level controller receives the output commands from the upper-level controller and optimizes the final actuator command with multiple objectives, including tracking the reference command, maintaining drive torque, clutch slippage work, and ensuring smooth actuator operation.

[0011] The lower-level dynamic coordination model determines the specific optimization objective function J of the controller as follows: ; in, T total T represents the actual driving torque of the entire vehicle. req This represents the total required torque. T motor and T eng T represents the actual torque of the engine and drive motor. engref and T motorref The reference torque provided by the upper-level controller; Δω1 and Δω2 are the slip friction velocities of the odd-gear clutch and the even-gear shaft clutch, respectively, and ΔT motor This represents the rate of change of the motor torque. Φ(gear) is a penalty function related to gear selection, used to avoid cyclic shifting and to incentivize economical gears based on the judgment of operating conditions; a1 to a6 are the weight coefficients of each optimization term.

[0012] The lower-level dynamic coordination model determines that the controller uses an enumeration-based optimization method when processing discrete control variables, specifically including: Based on the current vehicle speed, driver requirements, and instructions from higher levels, a set of candidate gear sequences is generated; The candidate gear sequence is calculated by comparing the candidate gear sequence in the candidate gear sequence set with the fixed gear variable to obtain the target value of the candidate sequence; Compare the target values ​​of all candidate gear sequences, determine the candidate gear sequence with the smallest objective function value as the optimal sequence, and output the first gear command in the optimal sequence as the optimal target gear at the current moment.

[0013] The process of determining future operating conditions using the current estimated values ​​of system state variables as the initial state to obtain operating condition information values ​​includes the following steps: By using the vehicle-mounted GPS receiver and pre-stored high-precision map data, the sequence of road slope changes within the time domain can be obtained for judgment. A multidimensional time series dataset was obtained by using the positioning trajectory and slope information from the Global Navigation Satellite System. Soil moisture and surface hardness are obtained by meteorological sensors, and geological characteristic parameters are obtained by combining vehicle dynamics data with synchronized timestamps. The soil resistance parameters and working depth of the farm implements for the current working field are obtained from the farm management server through the vehicle-mounted communication module. The working resistance value is obtained by judging the working resistance change sequence of the road slope change sequence. Based on the current accelerator pedal opening and its historical changes, combined with multi-dimensional time series datasets, geological feature parameters, and operational resistance values, the operating condition information value is obtained by determining the driver's demand torque sequence in the short term.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In this invention, by establishing a parameter optimization model with the equivalent power consumption of the hybrid tractor as the objective function and the tractor's power performance as the constraint condition, and by optimizing the parameters, the economy of the hybrid tractor is improved.

[0015] 2. In this invention, power coupling is achieved through the transmission, and power is output to the pulse train output shaft and the rear axle. The rear axle drives the drive wheel to rotate. When the tractor is actually working, the engine and the drive motor can provide power to the tractor individually or together, which allows the engine to continuously operate in its high-efficiency range. At the same time, the vehicle controller can adjust the working mode in real time according to the load conditions of the tractor, resulting in relatively good energy utilization.

[0016] 3. In this invention, a parallel hybrid power system for the tractor is adopted. This system consists of an engine, a drive motor, a dual-clutch automatic transmission, a pulse train output shaft, a rear axle, and related components of the control system. This power system replaces the original power coupling device and manual transmission with a dual-clutch automatic transmission, resulting in higher transmission efficiency, significantly reduced shift time, enhanced shift smoothness, and a greatly improved driving experience. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the parallel hybrid power system in this invention; Figure 2 This is a schematic diagram of the control logic structure of the present invention; Figure 3 This is a flowchart of the transmission system control method of the present invention.

[0018] In the diagram: 1. Odd-numbered gear shaft, 1st gear; 2. Odd-numbered gear shaft, 4th gear; 3. Odd-numbered gear shaft, 2nd gear; 4. Odd-numbered gear shaft, 3rd gear; 5. Even-numbered gear shaft, 1st gear; 6. Even-numbered gear shaft, 2nd gear. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0022] Reference Figures 1-3 As shown.

[0023] Example 1 further illustrates the dual-clutch hybrid tractor transmission system and control method proposed in this invention.

[0024] A dual-clutch hybrid tractor transmission system includes an engine and a drive motor; the output end of the drive motor is connected to the even-numbered gear input shaft of the dual-clutch automatic transmission; the output end of the engine is connected to the odd-numbered gear input shaft of the dual-clutch automatic transmission; the input end of the drive motor is connected to the output end of an inverter; and the input end of the inverter is connected to a battery. The battery, dual-clutch automatic transmission, drive motor, and engine are controlled by the battery energy management unit, dual-clutch automatic transmission control unit, motor control unit, and engine control unit, respectively. The above control units are connected to the vehicle controller via a controller area network bus. The vehicle controller obtains data information on the tractor's operation through the controller area network bus and sends relevant control commands. The vehicle controller is also connected to the model prediction management unit via the controller area network bus. The vehicle controller uploads the collected tractor operation data to the model prediction management unit, and the model prediction management unit feeds back the real-time optimized control strategy to the vehicle controller to realize data interaction and control the tractor transmission system.

[0025] The engine, as the traditional power core, connects directly to the odd-numbered gear input shaft of the dual-clutch automatic transmission, providing stable power under medium-to-high load conditions. The drive motor, serving as auxiliary power and pure electric drive unit, connects to the even-numbered gear input shaft of the transmission, functioning during starting, low-speed driving, or power compensation. The odd and even gear shafts connect to different power sources, laying the structural foundation for the dual-clutch transmission's rapid gear shifting and uninterrupted power transmission. The drive motor's power supply and regulation rely on an electrical energy system composed of an inverter and a battery. The battery converts stored DC power into AC power required by the drive motor via the inverter. Simultaneously, the inverter can also convert AC power back to DC power during regenerative braking to charge the battery, thus achieving energy recycling.

[0026] The hierarchical management and coordinated operation of control units are the core of the system's precise control. Each key component is equipped with a dedicated control unit. Among them, the battery energy management unit is responsible for monitoring the battery's status and regulating its energy, keeping track of the battery's state of charge, charging status, voltage, and temperature parameters in real time to ensure that the battery operates efficiently within a safe range. The dual-clutch automatic transmission control unit focuses on the transmission's gear shifting logic control, precisely controlling the engagement and disengagement of the clutch and gear shifting according to power requirements and operating conditions. The motor control unit is responsible for adjusting the speed and torque of the drive motor, achieving precise control under different operating conditions such as motor start-up, operation, and regenerative braking. The engine control unit regulates the engine's core parameters, such as speed and fuel injection quantity, to ensure that the engine operates under optimal conditions.

[0027] During the data acquisition phase, the vehicle controller uses the Controller Area Network (CLAN) bus to acquire various key data about the tractor's operation in real time. This includes status parameters of each power source and transmission component, such as engine speed, motor torque, battery state of charge, and the current gear of the transmission, as well as tractor operating condition information, such as travel speed, load size, and operating mode. In the command issuance phase, the vehicle controller generates preliminary control commands based on the acquired real-time data and preset control logic, and sends them to the corresponding control units via the CLAN bus to achieve coordinated control of the engine, drive motor, transmission, and battery.

[0028] The data processing unit in the model prediction management unit removes missing and invalid data, cleans noise, and filters the operating data uploaded by the vehicle controller. It extracts the kinematic features of the hybrid tractor and performs dimensionality reduction on data fragments of multiple kinematic features to obtain a parameter set. The model judgment unit then matches the reduced parameter set with samples from the loading condition library to obtain the optimal control parameters under the corresponding operating conditions. The model prediction management unit further enhances the real-time performance and optimization capabilities of the control strategy, forming a closed-loop control process of data acquisition, strategy optimization, and command execution. After acquiring the tractor's operating data, the vehicle controller uploads this real-time data to the model prediction management unit via the controller area network bus. The model prediction management unit incorporates an advanced control strategy optimization model, which can dynamically optimize the initial control strategy based on real-time operating data, historical operating experience, and the needs of different operating scenarios. For example, it optimizes the motor drive ratio to reduce fuel consumption under light load conditions, coordinates the power output of the engine and motor to improve operating efficiency under heavy load conditions, and adjusts the energy recovery strategy to quickly replenish electrical energy when the battery's state of charge is low. The optimized control strategy is fed back to the vehicle controller by the model prediction management unit through the controller area network bus. The vehicle controller then updates the control commands according to the optimized strategy and sends them to each control unit, thereby achieving precise and efficient control of the tractor transmission system and ensuring that the entire system always operates in the optimal state.

[0029] Because the operational data collected by various sensors during tractor operation in fields or on roads may be affected by complex environmental interference, such as sudden anomalies in speed sensor data due to bumpy roads, or sporadic data loss during signal transmission, the accuracy of subsequent control decisions may be affected. Therefore, the primary task of the data processing unit is to remove missing and invalid data. For example, it identifies and removes sudden zero values ​​or values ​​far exceeding the normal range from continuously collected speed data to ensure data integrity. Subsequently, noise cleaning and filtering are performed. High-frequency interference signals mixed in with key parameters such as engine speed and motor torque are smoothed using filtering algorithms to restore the true trend of parameter changes. After data purification, the data processing unit extracts kinematic features reflecting the tractor's operating status from massive amounts of operational data, including the tractor's instantaneous speed, acceleration, braking frequency, and the power output ratio of the engine and motor. After acquiring a series of continuous kinematic feature data segments, to reduce the complexity of subsequent matching calculations and highlight core information, the data processing unit uses a dimensionality reduction algorithm to compress the high-dimensional feature data, ultimately obtaining a set of dimensionality-reduced parameters that accurately characterize the tractor's current operating status.

[0030] The model prediction management unit pre-loads a working condition library covering various typical tractor operating scenarios. This library stores a large amount of practically validated sample data, with each sample corresponding to the optimal control parameters under specific working conditions. For example, the library might include samples from different scenarios such as deep plowing in wheat fields and weeding in orchards. The sample for deep plowing in wheat fields corresponds to an operating state with high soil resistance and the need for continuous high power output. Its optimal control parameters are set to a power mode where the engine is the primary driver and the electric motor is the auxiliary driver, while the dual-clutch transmission maintains low-gear, high-torque output. When the model judgment and optimization unit receives the reduced parameter set transmitted by the data processing unit, it uses a feature comparison algorithm to match the current parameter set with the samples in the working condition library one by one, identifying the working condition type that best matches the tractor's current operating state. Upon successful matching, the model judgment and optimization unit calls upon the optimal control parameters corresponding to that working condition, specifically including the engine's target speed, the drive motor's torque distribution ratio, the dual-clutch transmission's gear shifting timing, and the battery's charging and discharging control thresholds. Finally, the optimal control parameters are fed back to the vehicle controller, which then converts them into specific control commands and sends them to each execution unit to ensure that the tractor operates with optimal power distribution and transmission efficiency under the current working conditions, thus ensuring both work quality and energy savings.

[0031] The dual-clutch automatic transmission includes odd-numbered gear shafts and even-numbered gear shafts connected to the engine and drive motor, odd-numbered gear clutches, even-numbered gear clutches, a reverse intermediate shaft, and a power confluence device for transmitting power from the engine and drive motor to the rear axle and pulse train output shaft.

[0032] Furthermore, the drive modes of this dual-clutch hybrid tractor transmission system also include hybrid drive mode, engine independent drive mode, electric motor independent drive mode, driving charging mode, and energy recovery mode.

[0033] Hybrid drive mode is used when the tractor is under heavy load conditions, where the power provided by the engine and the electric motor is provided to the tractor through the drive shaft coupling. The engine-independent drive mode is used for tractors to perform medium- and low-load operations such as tillage and harrowing, where the engine efficiently provides traction and drives the tractor independently. The independent motor drive mode is used when the tractor is in a low-load condition of starting, driving, or transporting, and the motor efficiently provides working traction. The motor output power is transmitted to the drive wheels through the transmission system to drive the tractor.

[0034] When the model prediction management unit determines, based on multi-source data, that the tractor is under heavy load conditions such as deep tillage or heavy-duty sowing, the vehicle controller immediately activates this mode. In the power transmission path, the engine outputs power through the odd-numbered input shaft of the dual-clutch automatic transmission, while the drive motor outputs power through the even-numbered input shaft. The two power sources are coupled at the power coupling mechanism inside the transmission and transmitted to the drive wheels via the drive shaft, forming a combined driving force. At this time, the upper-level energy management controller allocates the torque ratio between the engine and the motor based on the goal of minimizing total equivalent fuel consumption, ensuring that power requirements are met while maintaining economy. For example, in deep tillage operations, where soil resistance is 2500 N, the tractor needs to output a total drive torque of 1800 N·m to ensure stable working depth. The engine's efficient torque range under this operating condition is 1200-1400 Nm, and the motor's rated torque is 600 Nm. The upper-level controller calculates and determines the engine's reference torque to be 1300 Nm and the motor's reference torque to be 500 Nm. The total torque after coupling the two meets the total drive torque requirement, fully satisfying the heavy load requirements of deep tillage operations. At the same time, the lower-level controller precisely controls the clutch pressure and torque change rate to ensure smooth coupling of the two power paths without shock fluctuations.

[0035] The engine-independent drive mode is suitable for low-to-medium load operations such as tillage and harrowing. It utilizes the engine's high efficiency within this load range to provide traction independently, avoiding energy conversion losses caused by the electric motor's involvement. When the model prediction and management unit detects that the tractor's load F is between 800-1500 N and the engine is operating in the high-efficiency zone of the efficiency MAP, the vehicle controller switches to this mode. At this time, the drive motor stops outputting power and idles, the inverter cuts off the power supply to the motor, and the dual-clutch automatic transmission engages only the odd-numbered gear clutch. The engine's power is transmitted to the drive wheels via the odd-numbered gear input shaft, the transmission gears, and the output shaft, forming a single power drive path. Taking tillage as an example, the tractor needs to maintain a stable speed of 5 km / h, with a load of 1200 N, corresponding to a transmission output torque requirement of 1000 N·m. The engine achieves its lowest fuel consumption rate at 1800 rpm and a torque of 1050 N·m. The upper-level controller sets the engine reference torque to 1050 N·m, higher than the required torque, to cope with load fluctuations. The engine's actual output torque is achieved by adjusting the fuel injection quantity through the engine control unit. Simultaneously, the dual-clutch transmission control unit controls the even-numbered gear clutches to disengage and the odd-numbered gear clutches to fully engage, maintaining pressure at 1.6 MPa to ensure zero slippage loss in power transmission. This design is suitable for long-term, low-to-medium load operation, effectively reducing operating costs.

[0036] The independent motor drive mode is designed for low-load conditions such as starting, short-distance transport, and other tasks. It leverages the advantages of a motor—fast start-up response, high efficiency under low load, and low operating noise—to provide power solely to the tractor. When the model prediction management unit identifies scenarios such as the tractor starting, transporting, or moving between tasks, the vehicle controller activates this mode. In this mode, the engine stops or idles. The clutch control unit disengages the odd-gear clutch and engages the even-gear clutch. The battery converts DC power to AC power via an inverter to supply the drive motor. The motor's output power is transmitted to the drive wheels via the even-gear input shaft, gearbox, and drive shaft, achieving pure electric drive. Taking starting a tractor and transporting farm implements for a short distance as an example, the starting speed increases from 0 to 3 km / h, the load is 500 N, and the required torque is 600 N·m. The motor's efficiency can reach over 90% within the speed range of 0-1500 rpm, higher than the engine's efficiency at low speeds. There is no engine noise or exhaust emissions during operation, making it suitable for short-distance mobile operations within farms.

[0037] The switching between the three drive modes is automatically completed by the vehicle controller based on the operating condition information, and the switching process is smooth and without power interruption. For example, when the tractor switches from deep tillage to inter-row cultivation, the model prediction and management unit detects that the load has dropped from 2500 N to 1200 N and immediately feeds this information back to the vehicle controller. The controller first gradually reduces the motor torque to 0 while maintaining stable engine torque. After the motor stops, it controls the even-numbered gear clutch to disengage. The entire process is completed within 0.5 seconds. Subsequently, the engine torque is adjusted to 1050 N·m, achieving a seamless mode switch. If transportation needs to be started after inter-row cultivation, the controller first allows the engine to idle, controls the odd-numbered gear clutch to disengage, engages the even-numbered gear clutch, and simultaneously starts the motor and gradually increases the torque, completing the switch to the motor-independent drive mode, ensuring the continuity and smoothness of tractor operation.

[0038] It also includes a driving charging mode, where when the tractor is in engine-driven mode and the battery is low, the engine drives the tractor to work normally, and the remaining power is used to charge the battery through the generator. In energy recovery mode, when the tractor is decelerating and braking at high speeds, the motor turns into a generator, converting the mechanical energy fed back from the tractor into electrical energy to charge the battery.

[0039] The driving charging mode is a mode where, after the engine meets the power requirements for normal tractor operation, the excess power is converted into electrical energy through the motor to replenish the battery. The activation of this mode is automatically determined by the vehicle controller based on the battery state of charge value and the engine operating status. Usually, when the battery state of charge is below 30% and the engine is in the medium-low load high-efficiency operating range, the controller will activate the driving charging mode. In the power transmission and energy conversion path, the engine outputs power through the odd-numbered input shaft of the dual-clutch automatic transmission to drive the tractor to complete normal operations such as tillage and harrowing. At the same time, the engine's surplus power drives the motor to rotate through the power splitting mechanism, switching the motor from driving mode to generating mode. The AC power generated by the motor is rectified into DC power by the inverter and then sent to the battery for charging, realizing the synchronization of operation and charging. Taking the tractor tillage operation as an example, at this time the tractor is in the engine independent drive mode, the power required by the working load is 35 kilowatts, the engine output power at the current speed of 1800 rpm is 50 kilowatts, and the battery's current SOC is 28%, which meets the starting conditions of the driving charging mode. This does not affect the normal operation of tillage and completes the energy replenishment of the battery.

[0040] In driving charging mode, the vehicle controller dynamically balances the distribution of operating power and charging power to ensure that the two do not interfere with each other. If the tractor encounters increased local soil resistance during tillage, and the operating power demand suddenly rises to 45 kW, the controller will immediately adjust the power distribution strategy, reducing the generator power to prioritize the engine's surplus power to meet the operating needs. At this time, the surplus power is 8.6 kW, the generator power drops to 7.57 kW, and the charging current drops to 19.9 amps. Once the resistance recovers, the charging power is gradually increased, always prioritizing the maintenance of normal operation.

[0041] The energy recovery mode is suitable for deceleration and braking conditions when the tractor is operating at high speeds. It converts the mechanical energy wasted during deceleration and braking into electrical energy through a generator, achieving energy recovery and reuse. When the tractor needs to decelerate and brake during highway transport or long-distance work intervals, the driver presses the brake pedal or the controller automatically triggers a deceleration command based on the predicted working conditions. At this time, the motor immediately switches from drive mode to generator mode, connecting to the drive wheels through the transmission system. The tractor's inertia drives the motor to rotate and generate electricity. The electromagnetic resistance generated by the motor creates braking torque, assisting the tractor in deceleration, while simultaneously converting mechanical energy into electrical energy. After processing by the inverter, this electrical energy is stored in the battery, achieving both energy recovery and enhanced braking performance.

[0042] The present invention also provides a control method for a dual-clutch hybrid tractor transmission system, as detailed below: The model judgment unit establishes a judgment model, including the following steps: A judgment model is established, and a model judgment controller is constructed based on the judgment model. In each control cycle, the model judgment controller uses the current estimated value of the system state variables as the initial state to judge the future operating condition information and obtain the operating condition information value. The operating condition information value is input into the judgment model to output the control instruction set of the finite time domain optimization problem. The control command set includes the engine optimal torque command, the electric motor optimal torque command, the dual-clutch optimal pressure command, and the optimal target gear. The control command set is sent to the engine controller, motor controller and transmission controller; the actual output measurement value of the system is obtained and compared with the operating condition information value for feedback and correction, so as to realize rolling optimization control.

[0043] The model-based decision controller uses rolling optimization as its core, breaking down the entire control process into multiple continuous and interconnected control cycles. Each control cycle is set to 100 to 500 milliseconds, with the specific duration dynamically adjusted according to the complexity of the tractor's operating scenario. For example, the cycle is shortened to improve response speed during precision field operations, while the cycle is appropriately extended to reduce computational load during highway driving. At the beginning of each control cycle, the model-based decision controller first obtains the current estimates of the system state variables, including the current engine speed, current drive motor torque, current battery state of charge, current dual-clutch transmission gear, tractor speed, and operating load. These values ​​are uploaded in real time via the controller's local area network bus and filtered to obtain the initial basis for the controller's decision.

[0044] Starting with the initial state variables, the model-based judgment controller predicts the operating conditions in the near future and obtains operating condition values ​​based on reasonable extrapolations from current operating trends and historical operating condition characteristics. For example, when a tractor is sowing in a wheat field, if the current state variables show a stable speed of 3 km / h, a load of 800 N, and an engine speed of 1500 rpm, and the load fluctuation amplitude is less than 5% in the previous three control cycles, the model-based judgment controller will predict that the uniform speed and medium load sowing condition will continue for the next second. The corresponding operating condition values ​​are then set as speed 3 ± 0.2 km / h, load 800 ± 40 N, and continuous sowing mode. The operating condition values ​​are then input into the pre-established judgment model, which solves the optimization problem using the time-limited optimization objective function and constraints built into the judgment model, and outputs the control command set for the current control cycle.

[0045] The control command set encompasses optimal engine torque commands, optimal motor torque commands, optimal dual-clutch pressure commands, and optimal target gears. Each command is closely matched to the tractor's current and anticipated operating conditions. For example, when a tractor switches from field operations to road transport, the anticipated operating conditions are: speed increases from 3 km / h to 15 km / h, and load decreases from 800 N to 300 N. The judgment model generates corresponding commands based on the optimization objective of minimizing energy loss. The calculation of the optimal engine torque command incorporates fuel consumption characteristics; its core logic is to use the target torque to keep the engine operating in the lowest fuel consumption range, ensuring the engine outputs power at economical speeds.

[0046] The optimal torque command for the drive motor needs to balance power assistance and energy recovery. When the tractor accelerates, the motor provides auxiliary torque; when decelerating or maintaining a constant speed, it can switch to generator mode to replenish the battery, working in tandem with the engine torque to improve acceleration performance. The optimal pressure command for the dual clutch directly determines the smoothness of gear shifting. The pressure of the odd-numbered and even-numbered clutches needs to be dynamically adjusted according to the relationship between the target gear and the current gear. For example, when shifting from even-numbered 2nd gear to odd-numbered 3rd gear, the even-numbered clutch pressure linearly decreases from 1.2 MPa to 0.1 MPa, while the odd-numbered clutch pressure increases from 0.1 MPa to 1.3 MPa. The rate of pressure change is optimized by the judgment model based on the speed difference to avoid shift shock. The optimal target gear is determined based on the predicted speed and load, combined with the transmission ratio characteristics. When the speed increases to 15 km / h, the target gear shifts from 2nd gear to 4th gear to reduce the engine and motor speeds and improve operating efficiency.

[0047] The model-based decision controller adopts a hierarchical structure, including: The upper-level energy management model judges the controller, whose control cycle is T1 and the judgment time domain is Np1, where 100ms≤T1≤500ms and 5s≤Np1×T1≤30s; The upper-level controller takes determining the total equivalent fuel consumption in the time domain as its core objective and solves for the engine reference torque command and the motor reference torque command. The lower-level dynamic coordination model judges the controller, whose control cycle is T2 and the judgment time domain is Np2, where 10ms≤T2≤50ms and 0.2s≤Np2×T2≤2s; The lower-level controller receives the output commands from the upper-level controller and optimizes the final actuator command with multiple objectives, including tracking the reference command, maintaining drive torque, clutch slippage work, and ensuring smooth actuator operation.

[0048] The upper-level energy management model determines that the controller prioritizes long-term energy economy as its core control objective. The controller's control cycle T1 is set between 100 and 500 milliseconds, while the product of the judgment time domain Np1 and T1 remains within the range of 5 to 30 seconds. This means the upper-level controller generates a long-term control strategy every 100 to 500 milliseconds, and simultaneously predicts the trend of operating conditions within the next 5 to 30 seconds based on the current state, providing sufficient decision-making basis for energy allocation. For example, when a tractor is performing continuous rotary tillage in the field, the upper-level controller sets T1 to 300 milliseconds and Np1 to 100, ensuring the judgment time domain covers 30 seconds. This allows for the complete capture of the cyclical pattern of load changes with soil hardness during rotary tillage, avoiding frequent decision changes due to an excessively short control cycle or the inability to cope with cyclical fluctuations in operating conditions due to insufficient judgment time.

[0049] The core task of the upper-level controller is to determine the engine reference torque command and the motor reference torque command within the time domain, with the goal of minimizing total equivalent fuel consumption. Total equivalent fuel consumption includes not only the fuel directly consumed by the engine but also the fuel consumption after converting the electrical energy consumed or recovered by the motor according to the energy equivalence principle, ensuring the comprehensiveness of energy calculation. Its decision-making process comprehensively considers factors such as the battery state of charge, the tractor's operating mode, and predicted load changes. Taking the scenario of a tractor switching from field operations to road transport as an example, at the initial stage of the switch, the upper-level controller predicts that the tractor will switch from a low-speed, high-load state to a high-speed, low-load state within the next 20 seconds, and the current battery state of charge is 75%, which is at a relatively high level. The controller generates a phased torque distribution strategy: For the first 5 seconds after switching, the motor drive is the main driver, the engine reference torque command is set to a low value to maintain idle speed, and the motor reference torque command is set to 80 Nm. Electric power is used to achieve rapid acceleration and reduce fuel consumption. In the middle 10 seconds, the engine reference torque command is increased to 100 Nm, the engine undertakes the main power output, and the motor assists in adjustment to maintain the engine operating under economic conditions. In the last 5 seconds, if it is predicted that the destination is about to be reached, the motor reference torque command turns to a negative value and enters the power generation mode to recover braking energy to recharge the battery, ensuring that the total equivalent fuel consumption reaches the optimal level throughout the entire judgment time domain.

[0050] The lower-level dynamic coordination model determines that the control cycle and decision time domain of the controller are much shorter than those of the upper-level controller. The control cycle T2 is set between 10 and 50 milliseconds, and the product of the decision time domain Np2 and T2 is maintained within the range of 0.2 to 2 seconds. This short-cycle, short-time-domain parameter setting aims to achieve rapid tracking and precise control of the upper-level commands. The lower-level controller focuses on receiving the engine reference torque command and motor reference torque command output from the upper-level controller. Based on this, it constructs a multi-objective optimization model and ultimately generates specific control commands for each actuator. The primary objective is to accurately track the reference torque command given by the upper level to ensure that the power output conforms to the energy management strategy. At the same time, it is necessary to maintain a stable drive torque to avoid tractor driving or operating vibrations caused by torque fluctuations. It is also necessary to strictly control the slippage work of the clutch to reduce clutch wear and extend service life. Finally, it is necessary to ensure the smoothness of actuator actions such as gear shifting and clutch engagement and disengagement to improve driving comfort and operational stability.

[0051] For the engine reference torque, the lower-level controller calculates the corresponding fuel injection adjustment parameters based on the engine's current speed of 1800 rpm, generating engine execution torque commands to ensure that the actual output torque quickly approaches 100 Nm, while minimizing torque fluctuations through fine-tuning. For the motor reference torque of 30 Nm, the controller sends voltage and current adjustment commands to the motor inverter to precisely control the motor's output torque, while also considering the battery's charging status during adjustment to avoid impacting the battery.

[0052] If the tractor is at the critical point of gear shifting, the upper-level command implies a need to shift to a higher gear, and the lower-level controller will prioritize the coordinated control of clutch action. Assuming the current gear is 3rd and the target gear is 4th, the controller will first calculate the speed difference between odd and even gears in the dual-clutch transmission, and optimize the clutch pressure change curve accordingly. At the initial stage of gear shifting, the pressure of the currently operating clutch is slowly reduced from 1.4 MPa to 0.3 MPa, while the pressure of the target gear clutch is gradually increased from 0.1 MPa to 1.5 MPa. The entire pressure adjustment process is completed within 5 control cycles. By precisely controlling the rate of pressure change, the clutch slippage work is kept below 150 joules, avoiding excessive wear caused by slippage and ensuring the continuity of power transmission. The drive torque fluctuation is controlled below 5 Nm, achieving smooth gear shifting.

[0053] The upper-level controller updates the reference torque command once every T1 cycle, while the lower-level controller receives the upper-level command and performs dynamic optimization every T2 cycle. Within one T1 cycle, the lower-level controller can complete 7 to 8 adjustments to the actuator command, which not only ensures the foresight and economy of the energy management strategy, but also responds to dynamic changes in the execution process in a timely manner, effectively solving the contradiction between long-term energy optimization and short-term dynamic control.

[0054] The lower-level dynamic coordination model determines the specific optimization objective function J of the controller as follows: ; in, T total T represents the actual driving torque of the entire vehicle. req This represents the total required torque. T motor and T eng T represents the actual torque of the engine and drive motor. engref and T motorref The reference torque provided by the upper-level controller; Δω1 and Δω2 are the slip friction velocities of the odd-gear clutch and the even-gear shaft clutch, respectively, and ΔT motor This represents the rate of change of the motor torque. Φ(gear) is a penalty function related to gear selection, used to avoid cyclic shifting and to incentivize economical gears based on the judgment of operating conditions; a1 to a6 are the weight coefficients of each optimization term.

[0055] The components of the objective function J correspond to different control requirements, and a dynamic balance between these requirements is achieved through weighting coefficients a1 to a6. These weighting coefficients are adaptively adjusted based on the tractor's current operating mode, such as field work, road transport, or hill climbing. Specifically, the first term a1(T)... total - T req )² is the core indicator for ensuring the power output of the entire vehicle, used to control the actual driving torque T of the entire vehicle. total With total demand torque T req The deviation, total demand torque T req Generated by the upper-level controller based on predicted working conditions, it is directly related to the tractor's operating efficiency and power performance. The penalty for larger deviations is amplified through a squared term to ensure T... total Closely track T req To avoid operational stagnation due to insufficient power or energy waste due to excessive power, such as in wheat field sowing operations, T req The temperature needs to be stabilized at 800 N to ensure uniform seeding depth. If T total If the deviation reaches 750 N, the penalty value will increase significantly, prompting the controller to adjust the power output.

[0056] The second term a2 ((T) eng + T engref )-(T engref +T motorref ))² focuses on the command tracking accuracy of the power source, specifically targeting the torque control of the engine and drive motor. T engref and T motorrefThe reference torque allocated to the upper-level energy management controller based on the goal of minimizing total equivalent fuel consumption ensures the actual torque T of the engine and electric motor. eng T motor Maintaining the energy allocation strategy set by the upper layer is fundamental to achieving long-term energy consumption optimization. Taking highway transportation as an example, the upper layer sets T... engref =100 N·m, T motorref =30 N·m, if the actual torque T of the motor is... motor When the value fluctuates to 40 N·m, the third penalty value will increase, and the controller will adjust the inverter output current to make T... motor The energy efficiency is improved by returning to near the reference value while ensuring the stability of engine torque, thus avoiding the impact of fluctuations in a single power source on the overall energy optimization effect.

[0057] The third and fourth terms, a3(Δω1)² and a4(Δω2)², are key indicators for clutch wear control. Δω1 and Δω2 represent the slippage speeds of the odd-numbered and even-numbered clutches, respectively. Slippage speed is directly related to clutch slippage work; the greater the slippage work, the more severe the clutch disc wear and the shorter the service life. This project uses the square of the sum of the slippage speeds of the two clutches as a penalty term to minimize clutch slippage, especially during gear shifting. The controller precisely controls the rate of change of clutch pressure to reduce the values ​​of Δω1 and Δω2. For example, when shifting from 2nd to 3rd gear, the controller first slowly reduces the even-numbered clutch pressure, gradually increasing Δω2, while simultaneously gradually increasing the odd-numbered clutch pressure, causing Δω1 to gradually decrease from a larger value. By controlling the rhythm of these changes, the service life of the clutch is effectively extended.

[0058] The fifth term a5(ΔT) motor The focus is on controlling the rate of change of motor torque, a parameter that directly affects the smoothness of actuator operation. As a power source with a relatively fast response time, sudden torque changes in the motor can cause shocks in the transmission system, affecting the tractor's operational stability or driving comfort. By controlling ΔT... motor As a penalty, the controller limits the rate of motor torque adjustment, typically keeping it below 50 Nm / s. For example, when the motor switches from drive mode to generator mode, the torque needs to decrease from 30 Nm to -20 Nm. The controller will adjust this gradually over five control cycles, ensuring that the torque ΔT decreases by a certain amount each cycle. motor The torque should be kept around 10 N·m to avoid shocks caused by sudden torque changes.

[0059] The sixth term, a6Φ(gear), is an auxiliary optimization term for gear control. Φ(gear) is a penalty function related to gear selection, and its core function is to avoid cyclic shifting and encourage the selection of economical gears. The value of the penalty function Φ(gear) changes dynamically according to the current gear, the target gear, and the characteristics of the operating conditions: when the system shows a tendency to frequently switch between two gears, the value of Φ(gear) increases, suppressing cyclic shifting through the penalty term; when the current gear deviates significantly from the economical gear based on the operating conditions, Φ(gear) also increases, pushing the controller to switch to a more economical gear. For example, in highway transportation conditions, when the speed is stable at 15 km / h, gear 4 is the economical gear. If the current gear is gear 3, Φ(gear) is 8 (8 when the gear deviates by 1 gear and 20 when it deviates by 2 gears). If there is frequent switching between gear 3 and gear 4, Φ(gear) will jump to 30. Through the weighted penalty of a6, the controller is stabilized in gear 4, which avoids shift shock and reduces energy consumption.

[0060] The dynamic adjustment of weighting coefficients a1 to a6 is key to achieving multi-objective balance, and the priority differences of each objective under different working conditions are reflected by the weights. In precision field operations, power smoothness and output accuracy are crucial, and the weights of a1 (0.3) and a5 (0.25) are significantly higher than other coefficients. During long-distance road transport, economy and clutch protection take priority, increasing the weights of a2 (0.2), a3 (0.2), and a4 (0.2). In uphill conditions, power output is the core requirement, increasing the weight of a1 to 0.4, while the weights of other coefficients decrease accordingly.

[0061] The lower-level controller's operation can be divided into four steps: instruction reception, objective function calculation, optimization solution, and instruction output. First, the controller receives the T signal sent from the upper layer every 20 milliseconds. engref T motorref and system status parameters; secondly, combined with the T data collected by the sensors. total T eng T motor Using actual data such as Δω1 and Δω2, the values ​​of each term and the total value of the objective function J are calculated. Then, the control parameters that minimize J are solved by a quadratic programming algorithm, including the engine fuel injection adjustment value, the motor inverter voltage and current value, the clutch pressure change curve, and the gear shifting command. Finally, these parameters are converted into actuator commands and sent to each controller to achieve precise control.

[0062] The lower-level dynamic coordination model determines that the controller uses an enumeration-based optimization method when processing discrete control variables, specifically including: Based on the current vehicle speed, driver requirements, and instructions from higher levels, a set of candidate gear sequences is generated; The candidate gear sequence is calculated by comparing the candidate gear sequence in the candidate gear sequence set with the fixed gear variable to obtain the target value of the candidate sequence; Compare the target values ​​of all candidate gear sequences, determine the candidate gear sequence with the smallest objective function value as the optimal sequence, and output the first gear command in the optimal sequence as the optimal target gear at the current moment.

[0063] Based on the current vehicle speed, driver requirements, and upper-level instructions, a set of candidate gear sequences is generated. The rationality of the candidate sequences directly determines the effectiveness of subsequent optimization results. The current vehicle speed *v* is the core basis for gear matching. Different vehicle speeds correspond to the efficient gear ratio range of the transmission. For example, when a tractor is equipped with a 6-speed dual-clutch transmission, speeds of 3-6 km / h correspond to gears 1-2, 6-12 km / h to gears 2-4, and 12-20 km / h to gears 4-6. Driver requirements are reflected through operating handle signals or load sensor data. The engine and motor reference torques in the upper-level instructions indirectly reflect the direction of energy optimization. For example, when the motor torque ratio is high in the upper-level instructions, gears that allow the motor to operate in its efficient range should be prioritized.

[0064] The candidate gear sequence contains gear combinations that may be used in the current and near future. The sequence length is usually matched with the decision time domain of the lower-level controller. Assuming the lower-level controller's control cycle T2 = 20 milliseconds and the decision time domain Np2 × T2 = 1 second (meaning the decision time domain contains 50 control cycles), the gear sequence length is set to 3. Taking a tractor with a current speed of 10 km / h, a driver's demand for increased power, and upper-level instructions favoring economical operation as an example, the current gear is 3rd gear. Combining the speed range and demand, the generated candidate gear sequence set includes four sets: [3,3,4], [3,4,4], [3,4,5], and [4,4,5]. During the generation process, unreasonable sequences are automatically excluded, such as [3,2,3] which contains downshifts followed immediately by upshifts, and [3,5,6] which has an excessively large span and is prone to power interruption, ensuring the validity of the candidate set.

[0065] The candidate gear sequence is calculated by combining the candidate gear sequence and the fixed gear variable in the candidate gear sequence set to obtain the target value of the candidate sequence. The target value of the candidate sequence is a comprehensive evaluation index calculated based on the optimization objective function J of the lower-level controller and the power transmission characteristics corresponding to the gear sequence. During the calculation, for each gear in each candidate sequence, the corresponding parameters of the vehicle drive torque Ttotal and clutch slip speed Δω are calculated in combination with the current system state, and then substituted into the calculation formula of the optimization objective function J.

[0066] The target value J_seq for the entire sequence is obtained by calculating the target value for each gear in the gear sequence step by step and then weighting it by time. For example, when calculating the target value of the candidate sequence [3,4,4], first calculate J1 corresponding to gear 3 at the current time, then calculate J2 during the process of the first judgment step size switching to gear 4 in the future, and finally calculate J3 during the process of the second judgment step size maintaining gear 4 in the future. The total target value of the sequence J_seq = 0.4×J1 + 0.3×J2 + 0.3×J3, where the weight coefficients are set according to the importance of each time period, with the current time having the highest weight.

[0067] The target values ​​of all candidate gear sequences are compared, and the candidate gear sequence with the smallest objective function value is selected as the optimal sequence. The first gear command in this sequence is then extracted as the optimal target gear output for the current moment. Assume the target values ​​of the other candidate sequences are: J_seq=235.1 for [3,3,4], J_seq=218.7 for [3,4,5], and J_seq=256.3 for [4,4,5]. By comparison, it can be seen that J_seq=209.06 for [3,4,4] is the minimum value. Therefore, this sequence is determined as the optimal sequence, and its first gear is 3. The controller then outputs 3 as the optimal target gear for the current moment, while using the subsequent gear change trends in the sequence as predictive information to prepare for gear adjustment in the next control cycle.

[0068] Although the enumeration-based optimization method requires calculation of multiple candidate sequences, the computational load remains within a controllable range due to the short decision time domain of the lower-level controller and the reasonable screening of candidate sequences. It can meet the control cycle requirement of T2=10-50 milliseconds, and can comprehensively consider the continuity and subsequent impact of gear changes, avoiding the limitations of gear selection at a single moment. For example, in the switching of tractor working conditions from field operations to road transport, this method can accurately generate a sequence that gradually transitions from low gear to high gear, which not only meets the initial power demand, but also smoothly switches to the economic gear, achieving a balance between power and economy.

[0069] The process of determining future operating conditions using the current estimated values ​​of system state variables as the initial state to obtain operating condition information values ​​includes the following steps: By using the vehicle-mounted GPS receiver and pre-stored high-precision map data, the sequence of road slope changes within the time domain can be obtained for judgment. A multidimensional time series dataset was obtained by using the positioning trajectory and slope information from the Global Navigation Satellite System. Soil moisture and surface hardness are obtained by meteorological sensors, and geological characteristic parameters are obtained by combining vehicle dynamics data with synchronized timestamps. The soil resistance parameters and working depth of the farm implements for the current working field are obtained from the farm management server through the vehicle-mounted communication module. The working resistance value is obtained by judging the working resistance change sequence of the road slope change sequence. Based on the current accelerator pedal opening and its historical changes, combined with multi-dimensional time series datasets, geological feature parameters, and operational resistance values, the operating condition information value is obtained by determining the driver's demand torque sequence in the short term.

[0070] The vehicle-mounted GPS receiver can output the tractor's latitude, longitude, and altitude location information in real time, ensuring the continuity of location data. The pre-stored high-precision map data includes the altitude and slope parameters corresponding to each latitude and longitude coordinate within the work area. The controller matches the real-time positioning coordinates with the high-precision map to extract altitude change data along the tractor's trajectory within the time domain, and then calculates the road slope change sequence. For example, when the tractor is working on terraced fields, the positioning system obtains the coordinates of its trajectory for the next 20 seconds. After matching the map, the altitude gradually increases from 120 meters to 125 meters and then decreases to 122 meters, corresponding to horizontal distances of 50 meters and 40 meters respectively. The calculated slope sequence is 1 degree and -0.75 degrees, providing a basis for subsequent judgment of changes in working resistance.

[0071] Global Navigation Satellite Systems (GNSS) not only provide location information but also output dynamic parameters such as speed and heading angle. Combined with slope information, these parameters are synchronized and integrated according to timestamps to form a multidimensional time-series dataset containing time, location, speed, heading angle, and slope. The time step of the multidimensional time-series dataset is consistent with the sampling frequency of the positioning system. For example, in wheat harvesting operations, the multidimensional time-series data within a certain second might be: at 0 seconds, coordinates X1Y1, speed 4 km / h, heading angle 180 degrees, and slope 0.3 degrees; at 0.1 seconds, coordinates X2Y2, speed 3.9 km / h, heading angle 180 degrees, and slope 0.35 degrees, and so on, forming a continuous dataset.

[0072] The vehicle-mounted weather sensor includes a soil moisture sensor and a surface hardness sensor. The soil moisture sensor measures soil moisture content through contact measurement, with a measurement range of 0 to 100%. The surface hardness sensor measures the compressive strength of the soil surface layer through a pressure probe. Vehicle dynamics data, including wheel speed, drive torque, and suspension system vibration frequency, are collected in real time by the vehicle-mounted sensors. The controller correlates the soil moisture and surface hardness data acquired by the weather sensor with the vehicle dynamics data at the same time stamp, removing outliers to form geological characteristic parameters. For example, when working in a cornfield, at 10:05:00, soil moisture is 65% and surface hardness is 800 kPa. Simultaneously, vehicle dynamics data shows a wheel speed of 120 rpm and a drive torque of 110 N·m. Together, these constitute the geological characteristic parameters at that moment, reflecting the impact of soil conditions on the tractor's driving resistance.

[0073] The vehicle-mounted communication module obtains soil resistance parameters and implement working depth for the current work area from the farm management server. This data, combined with a road slope change sequence, is used to determine the working resistance value based on the change sequence. The vehicle-mounted communication module utilizes 5G or IoT technology to achieve real-time data interaction with the farm management server, which stores historical soil resistance data for each field and the implement working depth in the current work plan. Soil resistance is positively correlated with implement working depth; the greater the working depth, the greater the soil resistance. Slope changes also affect working resistance; resistance increases uphill and decreases downhill. For example, the benchmark soil resistance provided by the farm server is 1500 N, the benchmark working depth is 15 cm, the current working depth of the farm implement is 18 cm, and combined with the previously obtained slope sequences of 1 degree and -0.75 degrees, taking k=1.0, the working resistance values ​​are calculated to be 1500×(18 / 15)×(1+1.0×1°×π / 180)≈1831 N and 1500×(18 / 15)×(1+1.0×(-0.75°)×π / 180)≈1769 N, forming a working resistance change sequence.

[0074] Accelerator pedal opening is the most direct parameter reflecting driver demand. It is acquired through an accelerator pedal position sensor, with an opening range of 0 to 100%. A larger opening indicates a greater torque demand from the driver. The controller analyzes the history of accelerator pedal changes. For example, if the opening increases from 30% to 50% within the past 2 seconds, it indicates a driver's need for increased power. Combining this with driving speed from a multi-dimensional time-series dataset, soil moisture and surface hardness from geological features, and operating resistance values, a fuzzy logic algorithm is used to determine the driver's torque demand sequence.

[0075] The inputs to the fuzzy logic algorithm include accelerator pedal opening α, driving speed v, soil moisture θ, ground hardness H, and working resistance F, and the output is the driver's required torque Treq. For example, the fuzzy subset of accelerator pedal opening α is set to three levels: small, medium, and large, corresponding to ranges of 0-30%, 30%-70%, and 70%-100%; the fuzzy subset of driving speed v is set to low, medium, and high, corresponding to 0-5 km / h, 5-15 km / h, and 15-25 km / h; the fuzzy subset of soil moisture θ is set to moist, moderate, and dry, corresponding to 60%-100%, 30%-60%, and 0-30%; the fuzzy subset of ground hardness H is set to soft, medium, and hard, corresponding to 0-500 kPa, 500-1000 kPa, and 1000-1500 kPa; and the fuzzy subset of working resistance F is set to small, medium, and large, corresponding to 0-1000 N, 1000-2000 N, and 2000-3000 N. By using preset fuzzy rules, such as Treq being medium when α is medium, v is low, θ is wet, H is medium, and F is medium, and then undergoing defuzzification processing, specific torque values ​​are obtained. This comprehensively reflects the tractor's operational needs in the near future. The model-based controller uses this as a basis to solve for control commands in conjunction with energy optimization objectives, making the control strategy more forward-looking. For example, it can increase the engine and motor torque in advance to cope with upcoming gradient increases and avoid operational interruptions due to insufficient power.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dual-clutch hybrid tractor transmission system, characterized in that, The dual-clutch hybrid tractor transmission system includes an engine and a drive motor; the output end of the drive motor is connected to the even-numbered gear input shaft of the dual-clutch automatic transmission; the output end of the engine is connected to the odd-numbered gear input shaft of the dual-clutch automatic transmission; the input end of the drive motor is connected to the output end of an inverter; and the input end of the inverter is connected to a battery. The battery, dual-clutch automatic transmission, drive motor, and engine are controlled by the battery energy management unit, dual-clutch automatic transmission control unit, motor control unit, and engine control unit, respectively. The above control units are connected to the vehicle controller via a controller area network bus. The vehicle controller obtains data information on the tractor's operation through the controller area network bus and sends relevant control commands. The vehicle controller is also connected to the model prediction management unit via the controller area network bus. The vehicle controller uploads the collected tractor operation data to the model prediction management unit, and the model prediction management unit feeds back the real-time optimized control strategy to the vehicle controller to realize data interaction and control the tractor transmission system.

2. The dual-clutch hybrid tractor transmission system according to claim 1, characterized in that, The data processing unit in the model prediction management unit removes missing and invalid data, cleans noise, and filters the operating data uploaded by the vehicle controller. It extracts the kinematic features of the hybrid tractor, performs dimensionality reduction on the data fragments of multiple kinematic features to obtain a parameter set, and then uses the model judgment unit to match the parameter set with samples in the loading condition library to obtain the optimal control parameters under the corresponding operating conditions.

3. The dual-clutch hybrid tractor transmission system according to claim 1, characterized in that, The dual-clutch automatic transmission includes odd-numbered gear shafts and even-numbered gear shafts connected to the engine and drive motor, odd-numbered gear clutches, even-numbered gear clutches, a reverse intermediate shaft, and a power confluence device for transmitting power from the engine and drive motor to the rear axle and pulse train output shaft.

4. The dual-clutch hybrid tractor transmission system according to claim 1, characterized in that, The dual-clutch hybrid tractor transmission system also includes hybrid drive mode, engine independent drive mode, motor independent drive mode, driving charging mode, and energy recovery mode.

5. A dual-clutch hybrid tractor transmission system according to claim 4, characterized in that, The hybrid drive mode is used when the tractor is under heavy load, and the power provided by the engine and the motor is provided to the tractor through the drive shaft coupling. The engine-independent drive mode is used for tractors to perform medium- and low-load operations such as tillage and harrowing, and the engine efficiently provides working traction, with the engine driving the tractor independently. The independent motor drive mode is used when the tractor is in a low-load working condition of starting, driving, and transporting, and the motor efficiently provides working traction. The motor output power is transmitted to the drive wheels through the transmission system to drive the tractor. In the driving charging mode, when the tractor is in engine-driven independent operation and the battery power is insufficient, the engine drives the tractor to operate normally, and the remaining power is used to charge the battery through the generator. In the energy recovery mode, when the tractor is in a deceleration and braking condition at high speed, the motor turns into a generator, converting the mechanical energy fed back by the tractor into electrical energy to charge the battery.

6. A control method applied to a dual-clutch hybrid tractor transmission system as described in claims 1-5, characterized in that, The model judgment unit establishes a judgment model, including the following steps: A judgment model is established, and a model judgment controller is constructed based on the judgment model. In each control cycle, the model judgment controller uses the current estimated value of the system state variables as the initial state to judge the future operating condition information and obtain the operating condition information value. The operating condition information value is input into the judgment model to output the control instruction set of the finite time domain optimization problem. The control command set includes the engine optimal torque command, the electric motor optimal torque command, the dual-clutch optimal pressure command, and the optimal target gear. The control command set is sent to the engine controller, motor controller and transmission controller; the actual output measurement value of the system is obtained and compared with the operating condition information value for feedback and correction, so as to realize rolling optimization control.

7. The control method for a dual-clutch hybrid tractor transmission system according to claim 6, characterized in that, The model-based decision controller adopts a hierarchical structure, including: The upper-level energy management model judges the controller, whose control cycle is T1 and the judgment time domain is Np1, where 100ms≤T1≤500ms and 5s≤Np1×T1≤30s; The upper-level controller takes determining the total equivalent fuel consumption in the time domain as its core objective and solves for the engine reference torque command and the motor reference torque command. The lower-level dynamic coordination model judges the controller, whose control cycle is T2 and the judgment time domain is Np2, where 10ms≤T2≤50ms and 0.2s≤Np2×T2≤2s; The lower-level controller receives the output commands from the upper-level controller and optimizes the final actuator command with multiple objectives, including tracking the reference command, maintaining drive torque, clutch slippage work, and ensuring smooth actuator operation.

8. The control method for a dual-clutch hybrid tractor transmission system according to claim 7, characterized in that, The lower-level dynamic coordination model determines the specific optimization objective function J of the controller as follows: ； in, T total T represents the actual driving torque of the entire vehicle. req This represents the total required torque. T motor and T eng T represents the actual torque of the engine and drive motor. engref and T motorref The reference torque provided by the upper-level controller; Δω1 and Δω2 are the slip friction velocities of the odd-gear clutch and the even-gear shaft clutch, respectively, and ΔT motor This represents the rate of change of the motor torque. Φ(gear) is a penalty function related to gear selection, used to avoid cyclic shifting and to incentivize economical gears based on the judgment of operating conditions; a1 to a6 are the weight coefficients of each optimization term.

9. The control method for a dual-clutch hybrid tractor transmission system according to claim 7, characterized in that, The lower-level dynamic coordination model determines that the controller uses an enumeration-based optimization method when processing discrete control variables, specifically including: Based on the current vehicle speed, driver requirements, and instructions from higher levels, a set of candidate gear sequences is generated; The candidate gear sequence is calculated by comparing the candidate gear sequence in the candidate gear sequence set with the fixed gear variable to obtain the target value of the candidate sequence; Compare the target values ​​of all candidate gear sequences, determine the candidate gear sequence with the smallest objective function value as the optimal sequence, and output the first gear command in the optimal sequence as the optimal target gear at the current moment.

10. The control method for a dual-clutch hybrid tractor transmission system according to claim 6, characterized in that, The process of determining future operating conditions using the current estimated values ​​of system state variables as the initial state to obtain operating condition information values ​​includes the following steps: By using the vehicle-mounted GPS receiver and pre-stored high-precision map data, the sequence of road slope changes within the time domain can be obtained for judgment. A multidimensional time series dataset was obtained by using the positioning trajectory and slope information from the Global Navigation Satellite System. Soil moisture and surface hardness are obtained by meteorological sensors, and geological characteristic parameters are obtained by combining vehicle dynamics data with synchronized timestamps. The soil resistance parameters and working depth of the farm implements for the current working field are obtained from the farm management server through the vehicle-mounted communication module. The working resistance value is obtained by judging the working resistance change sequence of the road slope change sequence. Based on the current accelerator pedal opening and its historical changes, combined with multi-dimensional time series datasets, geological feature parameters, and operational resistance values, the operating condition information value is obtained by determining the driver's demand torque sequence in the short term.

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