A hybrid power distribution method for hybrid tractor based on multi-domain joint control

Abstract

The present application relates to the field of agricultural machinery control technology, and particularly relates to a hybrid power tractor intelligent power distribution method based on multi-domain joint control, comprising the following steps: S1, collecting power domain, chassis domain and operation domain data of the hybrid power tractor, and constructing a multi-domain perception graph reflecting current working condition requirements; S2, based on the multi-domain perception graph and power data, fusing operation energy-saving priority and traction load, and constructing a multi-source power supply and demand mapping model; S3, according to the supply and demand mapping model, selecting an optimal driving mode, combining the efficiency, energy and thermal load of each power source, outputting driving switching logic and power distribution instructions, and realizing intelligent power scheduling. The present application realizes intelligent driving mode switching and efficient power distribution of the hybrid power tractor under different working conditions, and improves operation energy efficiency and system response capability.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery control technology, and in particular to an intelligent power distribution method for hybrid tractors based on multi-domain joint control. Background Technology

[0002] With the continuous improvement of the intelligence level of agricultural machinery, hybrid tractors have shown broad application prospects in terms of energy saving, consumption reduction and adaptability to complex working conditions due to their advantages of multi-power source coordinated drive. In actual operation, tractors need to cope with different terrain slopes, load changes and diversified agricultural implement operation modes, which puts forward higher requirements on the response capability, energy efficiency level and thermal management capability of the power system. In order to improve operating efficiency and energy utilization, the design of a coordinated operation strategy among electric motors, engines and generators has become a research hotspot.

[0003] However, existing power distribution methods for hybrid tractors mostly rely on single-domain data or static threshold drive strategies, lacking a comprehensive understanding of the operating environment, traction load, and power system status. This results in problems such as sluggish response and insufficient energy efficiency matching in power distribution, making it difficult to achieve dynamic switching of drive modes and energy consumption optimization under multiple operating conditions. In addition, some solutions ignore boundary factors such as thermal load limits and remaining power / fuel during the power coordination process between power sources, which can easily lead to redundant power output or energy supply bottlenecks, affecting system stability and operational continuity. Summary of the Invention

[0004] This invention provides an intelligent power distribution method for hybrid tractors based on multi-domain joint control. By constructing a multi-domain perception map and fusing power, chassis and operation information, it dynamically generates power demand distribution, establishes a multi-source power supply and demand mapping model, and combines the efficiency characteristics of each power source, residual energy and thermal load constraints to intelligently select the drive mode and generate joint control commands. This achieves high adaptability of power distribution and energy consumption optimization under multiple working conditions, and improves the overall intelligent level of tractor operation and energy utilization efficiency.

[0005] A method for intelligent power distribution in a series-parallel hybrid tractor based on multi-domain joint control includes the following steps:

[0006] S1 collects power domain data, chassis domain data and work domain data of the hybrid tractor during operation, and constructs a multi-domain perception map based on the power domain data, chassis domain data and work domain data to characterize the power response requirements and work environment constraints under the current working conditions.

[0007] S2, based on the multi-domain perception map, the operation energy-saving priority in the operation domain data and the traction load in the chassis domain data are integrated to dynamically generate a power demand distribution map, and combined with the power domain data, a multi-source power supply and demand mapping model reflecting the power supply and demand relationship between the electric motor, generator and engine is constructed.

[0008] S3, based on the multi-source power supply and demand mapping model, selects the current optimal driving mode through power adaptability criteria, and outputs joint control commands for controlling the driving mode switching logic and the power allocation parameters of a single power source according to the load efficiency curve, energy residual and heat load limit of each power source, so as to realize intelligent power allocation under different working conditions.

[0009] Optionally, S1 includes:

[0010] S11 collects real-time power domain data of the engine, electric motor and generator through the tractor's onboard control system and sensor network, including engine output power, electric motor output power and generator energy conversion efficiency.

[0011] S12 collects chassis domain data, including real-time traction load, gradient angle, and drive wheel speed difference, through chassis sensors (traction sensor, IMU, and wheel speed sensor).

[0012] S13 collects work domain data through the work control unit and implement load monitoring system, including the current work mode, implement load level and work energy saving priority;

[0013] S14 integrates the collected power domain data, chassis domain data, and operating domain data to construct a multi-domain perception map describing the current operating status of the tractor. .

[0014] Optionally, S2 includes:

[0015] S21, by integrating the energy-saving priority of the operation in the operation domain data with the traction load in the chassis domain data, the power demand of the current operation task is evaluated and the operation demand index is dynamically calculated.

[0016] S22, based on the calculated work demand index and power domain data, constructs a multi-source power supply and demand mapping model to describe the power supply and demand relationship between electric motors, generators and engines.

[0017] Optionally, S21 includes:

[0018] S211, based on the collected data on energy-saving priorities and traction load, dynamically calculates the work demand index. This indicates the power requirement of the current task;

[0019] S212, based on the calculated job demand index Generate a power demand distribution map This is used to describe the power demand distribution of the current job task.

[0020] Optionally, S22 includes:

[0021] S221, the job demand index After normalization, a standardized demand index is obtained. ;

[0022] S222, based on engine output power, electric motor output power, and generator energy conversion efficiency acquired from power domain data, constructs a power source supply capacity vector for supply-demand mapping. ;

[0023] S223, Standardized Operations Demand Index Vector of power source supply capacity A multi-source power supply and demand mapping model was constructed.

[0024] Optionally, the multi-source power supply and demand mapping model in S223 adopts a penalty-weighted linear model, which includes:

[0025] S2231, based on the standardized operation demand index Set the activation weight coefficients for electric motors and engines to dynamically adjust the participation intensity of different power sources under the current operating conditions, and strengthen the electric drive priority under energy-saving conditions and the engine main power supply logic under high load conditions.

[0026] S2232, Design and Generator Energy Conversion Efficiency An inversely proportional generation path penalty term is used to automatically suppress the power participation ratio of the generation path when generation efficiency is low;

[0027] S2233, based on the standardized operation demand index Adjusted electric motor and engine activation weight coefficients, combined with electric motor output power Engine output power And auxiliary power supply items driven by electric motors and converted by generators By introducing a power generation path penalty term, a weighted nonlinear energy supply model is constructed, and the multi-source supply capacity value is output.

[0028] Optionally, S3 includes:

[0029] S31, based on the multi-source power supply and demand mapping model, combined with the current work demand index and the power supply capacity value of each power source, calculates the power adaptability index under various driving modes such as pure electric, series, parallel and hybrid, and selects the driving mode that meets the power demand and has the best energy consumption as the current optimal execution mode.

[0030] S32, based on the selected drive mode, integrates the load efficiency curves, remaining power or fuel levels and upper limit of thermal load of each power source, extracts the corresponding power supply boundary conditions, and forms a set of power allocation parameters for a single power source that meets the current energy efficiency constraints and safety boundaries.

[0031] S33 integrates the drive mode selection result with the power allocation parameters of a single power source to generate joint control commands, including drive mode switching logic control quantities and power source power allocation ratio commands, and outputs them synchronously to the power domain controller and chassis controller to realize intelligent power scheduling closed loop.

[0032] Optionally, S31 includes:

[0033] S311, based on the current operating conditions, integrates the output power and limiting conditions (boundary factors affecting the actual power supply capacity of the power source, including the battery state of charge and the thermal load of the motor / engine), and calculates the power supply capacity values ​​of the motor, engine and power generation path;

[0034] S312 calculates the power adaptability index of various driving modes (pure electric PE, series SE, parallel HE, and hybrid ME) based on the energy supply capacity value and the current operation demand index.

[0035] S313 compares the power supply and demand adaptability and energy consumption level of each drive mode, and selects the drive mode with the lowest energy consumption under the premise of meeting the working power requirements as the current optimal execution drive mode.

[0036] Optionally, S32 includes:

[0037] S321, based on the selected drive mode, extract the load efficiency curves of the electric motor and engine, and obtain their optimal efficiency range under the current operational requirements. ;

[0038] S322, considering the remaining capacity of the battery or fuel tank, limits the continuous power supply range, specifically including:

[0039] For electric motors:

[0040] ;

[0041] For the engine:

[0042] ;

[0043] in, Remaining battery power This represents the remaining oil volume. The lower heating value of fuel per unit volume. For the estimated remaining operation time;

[0044] S323 limits the maximum allowable power under the current temperature rise condition, expressed as:

[0045] ;

[0046] in, The rated maximum power of power source m The current temperature. For the rated operating temperature, This is the upper limit of the temperature range;

[0047] S324, for each power source m, the final power allocation range is obtained by fusion. , is represented as:

[0048] ;

[0049] in, This represents the power range that the power source m is allowed to output under the current constraints of electrical charge or remaining fuel.

[0050] Optionally, S33 includes:

[0051] S331, based on the selected optimal drive mode Generate the corresponding drive control logic quantity This is used to switch the command-level control system to pure electric, series, parallel, or hybrid modes, and is represented as:

[0052] ;

[0053] S332, based on the final power allocation range Normalize its maximum power limit and calculate the power allocation ratio coefficient. ;

[0054] S333, Integrated drive mode control quantity With power distribution ratio coefficient Construct a joint control command vector and will The power outputs are respectively sent to the power domain controller (used to adjust the power output of each power source) and the chassis controller (used to match the traction characteristics and driving strategy in the corresponding mode to realize intelligent power scheduling closed-loop control).

[0055] The beneficial effects of this invention are:

[0056] This invention, by constructing a multi-domain perception map that integrates multi-source data from the power domain, chassis domain, and operating domain, can comprehensively characterize the operating load characteristics and environmental constraints of a series-parallel hybrid tractor under different working conditions. It enables dynamic perception and response to multi-source operating condition factors, providing a decision-making basis for power demand identification and adaptive switching of drive modes, and significantly improving the accuracy and real-time performance of system perception.

[0057] This invention designs a multi-source power supply and demand mapping model based on the task demand index, and introduces a motor and engine activation weight adjustment mechanism, a power generation path penalty term, and a weighted nonlinear power supply synthesis method. This achieves precise matching between the power demand of the task and the power supply capacity of the three power sources, and can adaptively cope with various complex working conditions such as energy saving priority and high load, thereby improving the response coordination and energy efficiency adaptability of the power system.

[0058] This invention constructs a closed-loop control joint control command output mechanism by combining the logic control quantity of the joint drive mode switching and the power distribution ratio parameter. This mechanism can effectively guide each power source to flexibly participate in the output according to the working condition matching principle, realize the dynamic switching and intelligent power scheduling of pure electric, series, parallel and hybrid drive modes, improve the operating efficiency and fuel economy of hybrid tractors, and at the same time ensure operational safety and control system stability. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic diagram of the allocation method according to an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of the power distribution control process according to an embodiment of the present invention;

[0062] Figure 3 This is a logic block diagram of an embodiment of the present invention. Detailed Implementation

[0063] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0064] like Figures 1-3As shown, a method for intelligent power distribution in a series-parallel hybrid tractor based on multi-domain joint control includes the following steps:

[0065] S1 collects power domain data, chassis domain data and work domain data of the hybrid tractor during operation, and constructs a multi-domain perception map based on the power domain data, chassis domain data and work domain data to characterize the power response requirements and work environment constraints under the current working conditions.

[0066] S2, based on a multi-domain perception map, integrates the energy-saving priority of the operation domain data and the traction load of the chassis domain data to dynamically generate a power demand distribution map, and combines the power domain data to construct a multi-source power supply and demand mapping model that reflects the power supply and demand relationship between electric motors, generators and engines.

[0067] S3, based on a multi-source power supply and demand mapping model, selects the current optimal driving mode through power adaptability criteria, and outputs joint control commands for controlling the driving mode switching logic and the power allocation parameters of a single power source according to the load efficiency curve, energy residual and heat load limit of each power source, so as to realize intelligent power allocation under different operating conditions.

[0068] S1 includes:

[0069] S11, through the tractor's onboard control system and sensor network, collects real-time power domain data of the engine, electric motor, and generator, including engine output power, electric motor output power, and generator energy conversion efficiency, expressed as:

[0070] ;

[0071] in, This refers to the engine's output power. For engine output torque, The engine angular velocity;

[0072] ;

[0073] in, This refers to the output power of the electric motor. For the electric motor to respond to torque in real time, This represents the current angular velocity of the motor.

[0074] ;

[0075] in, The real-time energy conversion efficiency of the generator. For the generator to output electrical power, This refers to the generator torque. The generator's angular velocity;

[0076] S12 collects chassis domain data through chassis sensors (traction sensor, IMU, and wheel speed sensor), including real-time traction load, gradient angle, and drive wheel speed difference, expressed as:

[0077] ;

[0078] in, To provide real-time load traction, The traction force measured by the traction sensor. The radius of the drive wheel;

[0079] ;

[0080] in, The slope angle, Let Z represent the acceleration component along the Z-axis, indicating the acceleration in the vertical direction. The acceleration component along the X-axis represents the acceleration in the horizontal direction;

[0081] ;

[0082] in, Due to the speed difference of the drive wheels, The linear velocity of the left drive wheel. The linear velocity of the right drive wheel;

[0083] S13 collects work domain data through the work control unit and implement load monitoring system, including the current work mode, implement load level, and work energy saving priority, as shown below:

[0084] ;

[0085] in, This is the work mode;

[0086] ;

[0087] in, For agricultural implement load rating, This refers to the actual load on the agricultural implements. This represents the maximum load-bearing capacity of agricultural implements.

[0088] ;

[0089] in, Prioritize energy conservation in operations. The energy-saving weight corresponding to the operating mode. This refers to the maximum load capacity of agricultural implements.

[0090] S14 integrates the collected power domain data, chassis domain data, and operating domain data to construct a multi-domain perception map describing the current operating status of the tractor. , is represented as:

[0091] ;

[0092] in, For the i-th dynamic domain data, For the i-th chassis domain data, Let N be the data for the i-th working domain, and N be the total number of data points in the multi-domain sensing map.

[0093] S2 includes:

[0094] S21, by integrating the energy-saving priority of the operation in the operation domain data with the traction load in the chassis domain data, the power demand of the current operation task is evaluated and the operation demand index is dynamically calculated.

[0095] S22, based on the calculated work demand index and power domain data, constructs a multi-source power supply and demand mapping model to describe the power supply and demand relationship between electric motors, generators and engines.

[0096] S21 includes:

[0097] S211, based on the collected data on energy-saving priorities and traction load, dynamically calculates the work demand index. , representing the power requirement of the current task, is expressed as:

[0098] ;

[0099] in, Weights for job modes;

[0100] S212, based on the calculated job demand index Generate a power demand distribution map This describes the power demand distribution of the current job task, expressed as:

[0101] ;

[0102] in, Power demand represents the power output required for the current operation. This represents the total number of job requirement samples collected or calculated.

[0103] S22 includes:

[0104] S221, in order to ensure that power requirements under different operating scenarios have a uniform scale, the operating demand index is... After normalization, a standardized demand index is obtained. , is represented as:

[0105] ;

[0106] in, , These are the minimum and maximum demand indices, respectively, based on historical statistics or established parameters.

[0107] S222, based on engine output power, electric motor output power, and generator energy conversion efficiency acquired from power domain data, constructs a power source supply capacity vector for supply-demand mapping. , is represented as:

[0108] ;

[0109] S223, Standardized Operations Demand Index Vector of power source supply capacity A multi-source power supply and demand mapping model was constructed.

[0110] The multi-source power supply and demand mapping model in S223 adopts a penalized term weighted linear model, which includes:

[0111] S2231, based on the standardized operation demand index The activation weight coefficients for the electric motor and engine are set to dynamically adjust the participation intensity of different power sources under the current operating conditions, strengthening the electric drive priority under energy-saving conditions and the engine main power supply logic under high-load conditions, expressed as:

[0112] ;

[0113] ;

[0114] ;

[0115] in, For the activation degree of the electric motor, For engine activation level, This indicates that the power generation path is always active by default;

[0116] S2232 is designed to address the large fluctuations in power generation path efficiency and is aligned with generator energy conversion efficiency. An inversely proportional generation path penalty term is used to automatically suppress the power participation ratio of the generation path when generation efficiency is low, thereby avoiding increased energy consumption and system fluctuations. It is expressed as:

[0117] ;

[0118] in, This is a penalty item for the power generation path. As a penalty factor;

[0119] S2233, based on the standardized operation demand index Adjusted electric motor and engine activation weight coefficients, combined with electric motor output power Engine output power And auxiliary power supply items driven by electric motors and converted by generators By introducing a power generation path penalty term, a weighted nonlinear energy supply model is constructed, and the multi-source supply capacity value is output, expressed as:

[0120] ;

[0121] in, This is the composite value of multiple energy sources. , , These are the basic weights for the electric motor, power generation path, and engine, respectively.

[0122] S3 includes:

[0123] S31, based on the multi-source power supply and demand mapping model, combined with the current work demand index and the power supply capacity value of each power source, calculates the power adaptability index under various driving modes such as pure electric, series, parallel and hybrid, and selects the driving mode that meets the power demand and has the best energy consumption as the current optimal execution mode.

[0124] S32, based on the selected drive mode, integrates the load efficiency curves, remaining power or fuel levels and upper limit of thermal load of each power source, extracts the corresponding power supply boundary conditions, and forms a set of power allocation parameters for a single power source that meets the current energy efficiency constraints and safety boundaries.

[0125] S33 integrates the drive mode selection result with the power allocation parameters of a single power source to generate joint control commands, including drive mode switching logic control quantities and power source power allocation ratio commands, and outputs them synchronously to the power domain controller and chassis controller to realize intelligent power scheduling closed loop.

[0126] S31 includes:

[0127] S311, based on the current operating conditions, combining the output power and limiting conditions (boundary factors affecting the actual power supply capacity of the power source, including battery state of charge and thermal load of the motor / engine), calculate the power supply capacity values ​​of the motor, engine, and power generation path, expressed as:

[0128] ;

[0129] ;

[0130] ;

[0131] in, , , These represent the power supply capacity values ​​for the electric motor, engine, and power generation path, respectively. , These represent the current output power of the electric motor and engine, respectively, while SOC represents the current state of battery charge. , These are the thermal loads of the electric motor and the engine, respectively. , , , , These are the corresponding system parameters;

[0132] S312, based on the energy supply capacity value and the current operational demand index, calculates the power adaptability index for various driving modes (pure electric PE, series SE, parallel HE, and hybrid ME), expressed as:

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] in, , , , These are the power adaptability indicators corresponding to various driving modes. This represents the current standardized operation power demand index. , , , The unit power consumption for different driving modes is as follows: , , , These are the corresponding energy consumption penalty weights. , , These are the weights of each power source in the hybrid mode;

[0138] S313 compares the power supply and demand adaptability and energy consumption level of each drive mode, and selects the drive mode with the lowest energy consumption under the premise of meeting the working power requirements as the current optimal execution drive mode.

[0139] S32 includes:

[0140] S321, based on the selected drive mode, extract the load efficiency curves of the electric motor and engine, and obtain their optimal efficiency range under the current operational requirements. , is represented as:

[0141] ;

[0142] in, Let m be the efficiency value of the power source m at the output power p. The minimum efficiency threshold is given, where mot is the power source for the electric motor. As the engine's power source;

[0143] ;

[0144] in, Let m be the average efficiency of the power source under operating load. This represents the 95th percentile efficiency of power source m in the historical efficiency distribution. As the benchmark adjustment coefficient, Assign energy-saving priority weights to the current task.

[0145] S322, considering the remaining capacity of the battery or fuel tank, limits the continuous power supply range, specifically including:

[0146] For electric motors:

[0147] ;

[0148] For the engine:

[0149] ;

[0150] in, Remaining battery power This represents the remaining oil volume. The lower heating value of fuel per unit volume. For the estimated remaining operation time;

[0151] S323 limits the maximum allowable power under the current temperature rise condition, expressed as:

[0152] ;

[0153] in, The rated maximum power of power source m The current temperature. For the rated operating temperature, This is the upper limit of the temperature range;

[0154] S324, for each power source m, the final power allocation range is obtained by fusion. , is represented as:

[0155] ;

[0156] in, This represents the power range that the power source m is allowed to output under the current constraints of electrical charge or remaining fuel.

[0157] S33 includes:

[0158] S331, based on the selected optimal drive mode Generate the corresponding drive control logic quantity This is used to switch the command-level control system to pure electric, series, parallel, or hybrid modes, and is represented as:

[0159] ;

[0160] S332, based on the final power allocation range Normalize its maximum power limit and calculate the power allocation ratio coefficient. , is represented as:

[0161] ;

[0162] in, is the upper limit of the final power allocation range for the i-th power source, and n is the number of power sources participating in power allocation under the current driving mode;

[0163] S333, Integrated drive mode control quantity With power distribution ratio coefficient Construct a joint control command vector and will The outputs are respectively sent to the power domain controller (used to adjust the power output of each power source) and the chassis controller (used to match the traction characteristics and driving strategy in the corresponding mode to achieve intelligent power scheduling closed-loop control), represented as:

[0164] ;

[0165] in, , , These represent the power distribution ratio coefficients of the power sources (such as electric motors, engines, and generators) participating in this driving mode.

[0166] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0167] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A hybrid power distribution method for a hybrid tractor based on multi-domain joint control, characterized in that, The method comprises the following steps: S1, collecting power domain data, chassis domain data and work domain data of the series-parallel hybrid tractor during operation, and constructing a multi-domain perception graph based on the power domain data, the chassis domain data and the work domain data, for depicting power response demand and work environment constraints under the current working condition; S2, based on the multi-domain perception graph, fusing the work energy-saving priority in the work domain data and the traction load in the chassis domain data, dynamically generating a power demand distribution graph, and combining the power domain data to construct a multi-source power supply and demand mapping model reflecting the power supply and demand relationship between the motor, generator and engine, specifically comprising: S21, by fusing the work energy-saving priority in the work domain data and the traction load in the chassis domain data, the power demand of the current work task is evaluated, and the work demand index is dynamically calculated, specifically comprising: S211, dynamically calculate the work demand index based on the collected work energy-saving priority and the traction load , representing the power demand of the current work task, is represented as: ； ； wherein, is a work mode weight, is a work energy saving priority, is a real-time traction load, is a maximum load capacity of the implement, is an energy saving weight corresponding to the work mode, is an implement load level, is a maximum load capacity of the implement; S212, generating a power demand distribution map according to the calculated job demand index , generating a power demand distribution map , for describing the power demand distribution of the current job task; S22, based on the calculated work demand index and the power domain data, a multi-source power supply and demand mapping model is constructed to describe the power supply and demand relationship between the motor, generator and engine, specifically comprising: S221, the job demand index The normalization processing is performed to obtain the standardized demand index ; S222, based on the engine output power, the motor output power and the generator energy conversion efficiency of the power domain data acquisition, the power supply capacity vector for supply and demand mapping is constructed ; S223, constructing a normalized job demand index with the power supply capability vector , constructing a multi-source power supply and demand mapping model S3, based on the multi-source power supply and demand mapping model, the current optimal driving mode is selected through the power adaptability criterion, and the joint control instruction for controlling the driving mode switching logic and the single power source power distribution parameter is output according to the load efficiency curve, energy residual amount and thermal load limit of each power source, to realize intelligent power distribution under different working conditions.

2. The hybrid power distribution method for hybrid tractor based on multi-domain joint control according to claim 1, characterized in that, The S1 comprises: S11, collecting the power domain data of the engine, motor and generator in real time through the vehicle-mounted control system and sensor network of the tractor, including the engine output power, motor output power and generator energy conversion efficiency; S12, collecting the chassis domain data through the chassis sensor, including real-time traction load, slope angle and driving wheel speed difference; S13, collecting the work domain data through the work control unit and implement load monitoring system, including the current work mode, implement load level and work energy-saving priority; S14, integrate the collected power domain data, chassis domain data and work domain data to construct a multi-domain perception graph for describing the current running state of the tractor .

3. The hybrid power distribution method for hybrid tractor based on multi-domain joint control according to claim 2, characterized in that, The multi-source power supply and demand mapping model in S223 adopts a penalty term weighted linear model, which comprises: S2231, according to the standardized job demand index The activation weight coefficient of the motor and the engine is set to dynamically adjust the participation intensity of different power sources in the current working condition, and to strengthen the logic of electric drive priority in energy-saving working condition and engine main supply in high-load working condition. S2232, design and generator energy conversion efficiency Inversely proportional to the power generation path penalty term for automatically suppressing the power generation path power participation ratio when the power generation efficiency is low; S2233, based on the standardized job demand index The adjusted motor and engine activation weight coefficients are combined with the motor output power , the engine output power , and the auxiliary energy supply item driven by the motor and converted by the generator , a power generation path penalty term is introduced, a weighted nonlinear energy supply model is constructed, and a multi-source supply capacity value is output.

4. The hybrid power distribution method for hybrid tractor based on multi-domain joint control according to claim 3, characterized in that, The S3 comprises: S31, based on the multi-source power supply and demand mapping model, combining the current work demand index and the energy supply capacity value of each power source, calculating the power adaptability index of multiple driving modes such as pure electric, series, parallel and series-parallel, and selecting the driving mode that meets the power demand and has the optimal energy consumption as the current optimal execution mode; S32, according to the selected driving mode, fusing the load efficiency curve, power or oil remaining level and thermal load upper limit of each power source, extracting the corresponding power supply boundary conditions, and forming a single power source power distribution parameter set that meets the current energy efficiency constraints and safety boundary; S33, integrating the driving mode selection result and the power distribution parameter of the single power source to generate a joint control instruction, including the driving mode switching logic control amount and the power source power distribution proportion instruction, and synchronously outputting to the power domain controller and the chassis controller to realize intelligent power scheduling closed loop.

5. The hybrid power distribution method for hybrid tractor based on multi-domain joint control according to claim 4, characterized in that, The S31 comprises: S311, according to the current working condition, the output power and the limit condition are fused to calculate the energy supply capability value of the motor, the engine and the power generation path; S312, based on the energy supply capability value and the current operation demand index, the power adaptability index of multiple driving modes is calculated; S313, by comparing the power supply and demand adaptability and the energy consumption level of each driving mode, the driving mode with the lowest energy consumption under the premise of meeting the operation power demand is selected as the current optimal execution driving mode.

6. The hybrid power distribution method for hybrid tractor based on multi-domain joint control according to claim 5, characterized in that, The S32 includes: S321, according to the selected driving mode, extracting the load efficiency curve of the motor and the engine, and obtaining the optimal efficiency interval thereof under the current operation demand ; S322, considering the remaining capacity of the battery or the oil tank, the continuous energy supply range is limited, specifically including: For the motor: ； For the engine: ； wherein, is the remaining power, is the remaining oil, is the low calorific value of the fuel per unit volume, is the estimated remaining operation time; S323, the maximum power allowed in the current temperature rise state is limited, which is expressed as: ； wherein, a power source a rated maximum power, a current temperature, a rated operating temperature, a temperature upper limit; S324, for each power source , the final power distribution interval is obtained by fusion , is expressed as: ； wherein, a power source a power interval that allows output under a current charge or fuel remaining constraint.

7. The hybrid power distribution method for hybrid tractor based on multi-domain joint control according to claim 6, characterized in that, The S33 includes: S331, generating corresponding driving control logic quantity according to the selected optimal driving mode , generating corresponding driving control logic quantity , for instructing the level control system to switch to the pure electric, series, parallel or hybrid mode, represented as: ； S332, based on the final power allocation interval , the maximum power upper limit is normalized, and a power allocation proportion coefficient is calculated ; S333, the integrated driving mode control quantity with the power distribution proportionality coefficient , the joint control instruction vector is constructed , and the is respectively output to the power domain controller and the chassis controller.

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