Tractor electric drive intelligent control system based on energy recovery

Abstract

The invention provides a tractor electric drive intelligent control system based on energy recovery. The system comprises an energy recovery module for collecting and storing redundant kinetic energy, an electric drive module for electric drive, an intelligent control module for collecting information, planning a strategy and distributing energy, and a system integration module for integrating power and farm tool control and reusing energy. Through cooperative work of multiple modules, energy recovery, driving and distribution are accurately regulated and controlled according to various threshold values and coefficients, for example, the threshold values such as speed and load are determined based on tests, the coefficients are determined according to actual requirements, and the purposes of improving the energy utilization rate of the tractor, optimizing the driving performance and improving the working efficiency and the intelligent level are achieved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural machinery, and in particular to an intelligent control system for electric drive of a tractor based on energy recovery. Background Art

[0002] With the acceleration of agricultural modernization, tractors are increasingly used in agricultural production. Simultaneously, advancements in electronics, sensors, and energy storage technologies are providing technical support for the intelligent and efficient upgrades of tractors. Increasing demands for agricultural production efficiency, energy efficiency, and environmental protection are driving the development of more advanced tractor technology.

[0003] Traditional tractor control systems rely on fuel, resulting in low energy efficiency and wasted energy during braking and downhill descents. Furthermore, these drive systems lack precise control, making it difficult to adjust power output to suit varying operating conditions. Furthermore, these systems lack intelligence and are unable to collect and process real-time information about operating conditions and the environment, resulting in low operating efficiency and high energy consumption.

[0004] Therefore, it is necessary to design an intelligent control system for tractor electric drive based on energy recovery to solve the problems of serious energy waste, inaccurate drive control and low intelligence in the existing technology. Summary of the Invention

[0005] In view of this, the present invention proposes an intelligent control system for tractor electric drive based on energy recovery, which aims to solve the problems of how to improve the energy utilization rate of tractors, optimize driving performance and enhance the level of intelligent operation.

[0006] In one aspect, the present invention provides an intelligent control system for electric drive of a tractor based on energy recovery, comprising:

[0007] An energy recovery module, which is used to collect and store excess kinetic energy generated by the tractor;

[0008] Electric drive module, used to drive the tractor to travel and work with electricity;

[0009] Intelligent control module, which collects information about the tractor's operating conditions and working environment, and plans driving strategies and energy distribution;

[0010] System integration module, used to integrate the power system and agricultural implement control, and to distribute and reuse energy across modules.

[0011] Furthermore, the excess kinetic energy is generated by the tractor when braking and going downhill;

[0012] The initial excess kinetic energy is configured as E0, and the actual recoverable energy is E rThe tractor's speed during braking or downhill driving is v, and the speed threshold is v0, where the speed threshold v0 is the lowest speed at which effective energy recovery can be achieved by performing multiple braking and downhill driving tests on the tractor at different speeds.

[0013] If v>v0, then E r =k1×E0, if v≤v0, then E r =k2×E0, where k1 and k2 are energy recovery coefficients, and 0<k1<1, 0<k2<1, k1>k2;

[0014] The current remaining capacity of the energy storage device is configured as C r ;

[0015] When E r ≤C r When all E r store;

[0016] When E r >C r When storing C r The size of energy.

[0017] Furthermore, the load of the tractor is configured as L, and the load threshold is L0, wherein the load threshold L0 is determined by comprehensively considering the power of the tractor's power system, the load capacity of the transmission system, and the working efficiency of the energy recovery device, conducting multiple groups of tests under different loads, and analyzing the energy recovery effect, to determine the maximum load that can enable the energy recovery system to operate stably and efficiently;

[0018] When v>v0, if L>L0, then k1=a1×v+b1×L+c1, if L≤L0, then k1=a2×v+b2×L+c2;

[0019] When v≤v0, if L>L0, then k2=a3×v+b3×L+c3, if L≤L0, then k2=a4×v+b4×L+c4, where 0<a i ,b i ,c i <1(i=1,2,3,4);

[0020] The temperature of the energy storage device is configured as T, the temperature threshold is T0, and the actual stored energy is E s , wherein the temperature threshold T0 is determined by performing charge and discharge tests on the energy storage device under different temperature environments and monitoring the performance degradation and safety indicators of the device to ensure the long-term stable operation of the device;

[0021] When T>T0, E s =E r×(1-d×(T-T0)), where d is the temperature influence coefficient and 0<d<1.

[0022] Furthermore, the basic energy required to drive the tractor is configured as E d1 The energy required to drive the agricultural implement is E d2 , the total driving energy requirement is E d , and E d =E d1 +E d2 ;

[0023] For the electric drive module, when E s ≥E d When using E s Drive; when E s <E d When the energy consumption is insufficient, the shortfall is obtained from other energy sources;

[0024] The friction coefficient of the road surface on which the tractor is traveling is set to μ, and the friction coefficient threshold is μ0. The friction coefficient threshold μ0 is determined by measuring the friction coefficient of various typical farmland and working road surfaces, combining the tractor's drive wheel structure and power transmission characteristics, and analyzing the tractor's driving stability and energy consumption under different friction coefficients. The minimum friction coefficient that ensures the tractor's normal driving and reasonable energy consumption is determined;

[0025] When μ>μ0, E d1 =e1×μ+f1; when μ≤μ0, E d1 =e2×μ+f2; where 0<e1, e2<1, f1 and f2 are positive numbers determined according to actual energy demand;

[0026] The operating intensity of the agricultural implement is set to I, and the operating intensity threshold is I0. The operating intensity threshold I0 is determined by testing and evaluating the power consumption, wear, and operating quality of the agricultural implement under different operating intensities, combined with the power output capacity of the tractor. It is the minimum operating intensity that allows the agricultural implement to operate efficiently without exceeding the tractor's power load.

[0027] When I>I0, E d2 =g1×I+h1; when I≤I0, E d2 =g2×I+h2; where 0<g1, g2<1, h1 and h2 are positive numbers determined according to actual energy demand.

[0028] Furthermore, the tractor's driving acceleration is configured as a, and the acceleration threshold is a0, wherein the acceleration threshold a0 is determined by conducting acceleration performance tests on the tractor under different road conditions and load conditions, combining the driver's operating comfort and the vehicle's mechanical structure tolerance, and analyzing the power requirements and component stress conditions under different accelerations to ensure the maximum acceleration of the tractor that can ensure safe and comfortable acceleration;

[0029] When μ>μ0, if a>a0, then If a≤a0, then Among them, 0<e3,e4<1, f3, f4, and It is a positive number determined according to actual energy demand;

[0030] When μ≤μ0, if a>a0, then If a≤a0, then Among them, 0<e5,e6<1, f5, f6, and It is a positive number determined according to actual energy demand;

[0031] The duration of the agricultural implement operation is set to t, and the duration threshold is set to t0. The duration threshold t0 is determined by testing the continuous operation performance of the agricultural implement, monitoring its temperature, wear, and performance changes under different operation times, and combining the operation efficiency and maintenance cycle to determine the longest continuous operation time that can keep the agricultural implement in good working condition.

[0032] When I>I0, if t>t0, then E d2 =g3×I+h3×t+m1; if t≤t0, then E d2 =g4×I+h4×t+m2, where 0<g3, g4<1, h3, h4, m1 and m2 are positive numbers determined according to actual energy requirements.

[0033] Furthermore, the operating condition information is configured as The operating environment information is

[0034] The comprehensive index of working condition and environment is Among them, ω i and p j is the weight coefficient, and 0<ω i <1, 0<p j <1;

[0035] Set the comprehensive indicator threshold to S0;

[0036] When S>S0, the driving strategy is A1 and the energy allocation scheme is P1;

[0037] When S≤S0, the driving strategy is A2 and the energy allocation scheme is P2;

[0038] The comprehensive index threshold S0 is determined based on the statistical analysis results of historical working conditions and environmental data, and the correlation between the driving strategy and the energy distribution plan is determined according to the tractor operation efficiency optimization goal.

[0039] Furthermore, the tractor's driving direction is set to θ, and the circumferential direction is divided into N intervals, each interval angle is

[0040] The driving direction is set to θ, and the weight coefficient related to the driving direction in the working condition information is ω j , configure the slope to be α and the slope threshold to be α0, wherein the slope threshold α0 is a suitable slope limit value determined by testing the tractor's power performance at different slopes and analyzing actual operation requirements;

[0041] When α>α0, ω j =ω j1 ×(1+β×α);

[0042] When α≤α0, ω j =ω j2 ×(1+γ×α), where ω j1 and ω j2 is the initial weight, and 0<ω j1 ,ω j2 <1, β and γ are slope influence coefficients, and 0 < β, γ < 1;

[0043] Configure the light intensity of the working area to be L i , the light intensity threshold is L0, and the weight coefficient related to light in the working environment information is p k , wherein the light intensity threshold L0 is an appropriate light intensity limit value determined by comprehensively evaluating the operating difficulty of the driver when the tractor is operating under different light intensities, the working efficiency of the agricultural implement, and the impact of the environment on the system;

[0044] When L i >L0, p k =p k1 ×(1-δ×(L i -L0));

[0045] When L i When ≤L0, p k =p k2 ×(1-ξ×(L0-L i )), where pk1 and p k2 is the initial weight, and 0<p k1 , p k2 <1, δ and ξ are slope influence coefficients, and 0 < δ, ξ < 1;

[0046] The direction change amount is configured as Δθ, the illumination change amount is ΔL, the direction change threshold is Δθ0, and the illumination change threshold is ΔL0, wherein the direction change threshold Δθ0 is a suitable angle change limit value determined by analyzing the stability and effectiveness of the system control strategy adjustment under different driving direction change amplitudes of the tractor, and the illumination change threshold ΔL0 is a suitable light intensity change limit value determined by testing and analyzing the system's environmental adaptability and operating performance under different light intensity change amplitudes;

[0047] When Δθ>Δθ0 or ΔL>ΔL0, the corresponding weight coefficient is re-determined to calculate S, and the driving strategy and energy distribution plan are planned according to the new S value.

[0048] Furthermore, the energy requirement of the power system is configured as E p , the energy requirement for the agricultural implement control is E a , the total energy that can be distributed by the system is E t , the importance level of the power system is I p The importance level of the agricultural implement control is 1 a , where I p and I a It is a positive number determined based on a combination of relevant factors;

[0049] When E p +E a ≤E t When , the energy allocated to the power system is The energy allocated to the implement control is

[0050] When E p +E a >E t When I p >I a , then the energy demand of the power system is met first; if I p a , the energy demand of the agricultural implement control is met first.

[0051] Furthermore, the task urgency of configuring the power system is U p (0≤U p ≤1), the task complexity of the power system is C p (0≤C p ​≤1), the urgency of the task of agricultural implement control is U a (0≤U a ≤1), the task complexity of the agricultural implement control is C a (0≤C a ≤1);

[0052] I p =x1U p +x2C p ,I a =y1U a +y2C a , where x1, x2, y1, and y2 are weight coefficients, and x1+x2=1, 0≤x1≤1, 0≤x2≤1, y1+y2=1, 0≤y1≤1, 0≤y2≤1.

[0053] Compared with the prior art, the beneficial effects of the present invention are that the energy recovery-based tractor electric drive intelligent control system of the present invention collects and stores excess kinetic energy during tractor braking and downhill driving through the energy recovery module, thereby improving energy utilization; the electric drive module realizes electric drive and accurately controls power output. The intelligent control module plans the drive strategy and energy distribution based on the collected working conditions and environmental information, combined with multiple thresholds and coefficients. The system integration module integrates the power system with agricultural implement control, optimizing energy distribution and cross-module reuse. Overall, the energy utilization efficiency, drive performance and intelligent operation level of the tractor are improved, meeting the demand for efficient, energy-saving and intelligent equipment in agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0055] Figure 1 This is a functional block diagram of an intelligent control system for electric drive of a tractor based on energy recovery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the implementation regulations.

[0057] Reference Figure 1 As shown, in some embodiments of the present application, a tractor electric drive intelligent control system based on energy recovery includes:

[0058] An energy recovery module, which is used to collect and store excess kinetic energy generated by the tractor;

[0059] Electric drive module, used to drive the tractor to travel and work with electricity;

[0060] Intelligent control module, which collects information about the tractor's operating conditions and working environment, and plans driving strategies and energy distribution;

[0061] System integration module, used to integrate the power system and agricultural implement control, and to distribute and reuse energy across modules.

[0062] In some embodiments of the present application, the excess kinetic energy is generated when the tractor brakes and goes downhill; the initial excess kinetic energy is configured as E0, and the actual recoverable energy is E r , the tractor's speed during braking or downhill is v, and the speed threshold is v0, where the speed threshold v0 is the lowest speed at which effective energy recovery can be achieved by performing multiple braking and downhill tests on the tractor at different speeds; if v>v0, then E r =k1×E0, if v≤v0, then E r =k2×E0, where k1 and k2 are energy recovery coefficients, and 0<k1<1, 0<k2<1, k1>k2; the current remaining capacity of the energy storage device is configured as C r ; when E r ≤C r When all E r Storage; when E r >C r When storing C r The size of energy.

[0063] Specifically, accurate judgment of the timing of energy recovery is the key to improving recovery efficiency. The best time for energy recovery is determined by real-time monitoring of the tractor's operating status. By continuously collecting data such as speed and acceleration, it is determined whether the tractor is in a braking or downhill state. Dynamic optimization of the speed threshold is a guarantee for adapting to different operating scenarios. The optimization of the speed threshold uses an algorithm based on big data analysis. A large amount of tractor operating data under different operating conditions is sorted and analyzed, and the speed threshold is adjusted according to different operating road surfaces, load conditions and other factors. The performance evaluation of the energy recovery device is a prerequisite for ensuring effective energy storage. The performance evaluation of the energy recovery device uses a multi-dimensional testing method, which tests from multiple dimensions such as energy conversion efficiency and storage stability. At the same time, a reliability evaluation algorithm is used to process the test data to determine the reliability of the device and ensure stable operation of the energy recovery and storage links.

[0064] It can be understood that accurately defining the timing of energy recovery enables the energy recovery module to more efficiently collect excess kinetic energy during braking and downhill; the precise speed threshold determination method enhances the adaptability and stability of the system; the setting of the energy recovery coefficient under different conditions can optimize the energy recovery efficiency according to actual working conditions; the energy storage rules based on the remaining capacity of the storage device ensure the rationality and safety of energy storage, and overall improve the energy management capabilities and practicality of the system.

[0065] In some embodiments of the present application, the load of the tractor is configured to be L, and the load threshold is L0, wherein the load threshold L0 is determined by comprehensively considering the power of the tractor power system, the carrying capacity of the transmission system, and the working efficiency of the energy recovery device, conducting multiple groups of tests under different loads, and analyzing the energy recovery effect to determine the maximum load that can enable the energy recovery system to work stably and efficiently; when v>v0, if L>L0, then k1=a1×v+b1×L+c1, if L≤L0, then k1=a2×v+b2×L+c2; when v≤v0, if L>L0, then k2=a3×v+b3×L+c3, if L≤L0, then k2=a4×v+b4×L+c4, wherein 0<a i ,b i ,c i <1 (i=1,2,3,4); the temperature of the energy storage device is configured as T, the temperature threshold is T0, and the actual stored energy is E s , wherein the temperature threshold T0 is determined by performing charge and discharge tests on the energy storage device under different temperature environments, monitoring the performance degradation and safety indicators of the device, and ensuring the long-term stable operation of the device; when T>T0, E s =E r ×(1-d×(T-T0)), where d is the temperature influence coefficient and 0<d<1.

[0066] Specifically, precise calibration of the load threshold is crucial for ensuring the stable and efficient operation of the energy recovery system. This calibration utilizes a method that simulates diverse operating scenarios, simulating various load conditions in both laboratory and field environments. A multivariate regression analysis algorithm integrates multiple variables, including tractor powertrain power, transmission load capacity, and energy recovery device efficiency, to determine the optimal load threshold. Dynamic adjustment of the temperature threshold is crucial for ensuring the long-term stable operation of the energy storage device. This adjustment utilizes environmental simulation experiments, testing the energy storage device in diverse seasonal and regional temperature environments. An adaptive control algorithm also utilizes real-time operating data and performance feedback to automatically adjust the temperature threshold, ensuring the device consistently operates within a safe and stable temperature range. Optimizing the energy recovery coefficient is crucial for improving energy recovery effectiveness. This determination utilizes a multi-factor cross-testing approach, combining multiple factors, including tractor speed, load, and energy storage device temperature. A machine learning algorithm analyzes and analyzes extensive test data to optimize the energy recovery coefficient and enhance its accuracy and efficiency.

[0067] It's clear that by precisely determining the load threshold, the system optimizes energy recovery based on the tractor's actual load and the performance of the energy recovery device, avoiding overload or inefficient recovery. Clearly defining temperature thresholds and corresponding strategies ensures stable operation of the energy storage device at optimal temperatures, minimizing performance degradation and potential safety hazards. The detailed breakdown of energy recovery coefficients under varying load and temperature conditions improves energy recovery accuracy, enhances the overall system's energy efficiency, and ensures stable and efficient tractor operation under complex operating conditions.

[0068] In some embodiments of the present application, the basic energy required to drive the tractor is configured as E d1 The energy required to drive the agricultural implement is E d2 , the total driving energy requirement is E d , and E d =E d1 +E d2 For the electric drive module, when E s ≥E d When using E s Drive; when E s <E dThe insufficient part is obtained from other energy sources; the friction coefficient of the road surface on which the tractor is traveling is configured to be μ, and the friction coefficient threshold is μ0, wherein the friction coefficient threshold μ0 is determined by measuring the friction coefficient of various typical farmlands and working roads, combining the tractor's drive wheel structure and power transmission characteristics, and analyzing the tractor's driving stability and energy consumption under different friction coefficients to ensure the tractor's normal driving and reasonable energy consumption. When μ>μ0, E d1 =e1×μ+f1; when μ≤μ0, E d1 =e2×μ+f2; where 0<e1, e2<1, f1 and f2 are positive numbers determined according to actual energy demand; the operating intensity of the agricultural implement is configured as I, and the operating intensity threshold is I0, where the operating intensity threshold I0 is determined by testing and evaluating the power consumption, wear and operation quality of the agricultural implement under different operating intensities, combined with the power output capacity of the tractor, to achieve the minimum operating intensity that enables the agricultural implement to operate efficiently and does not exceed the tractor's power load; when I>I0, E d2 =g1×I+h1; when I≤I0, E d2 =g2×I+h2; where 0<g1, g2<1, h1 and h2 are positive numbers determined according to actual energy demand.

[0069] Specifically, accurate assessment of driving energy demand is key to ensuring efficient tractor operation. A working condition simulation test method was used to assess the energy required to drive the tractor and implements. Various driving conditions and implement operation scenarios were simulated in the laboratory, and energy consumption under different conditions was recorded. A cluster analysis algorithm was also used to classify similar operating conditions to determine the energy demand range under each condition. Dynamic determination of the friction coefficient threshold is crucial for ensuring tractor driving safety and reasonable energy consumption. This determination utilizes a combination of field measurements and data analysis. Field friction coefficient measurements were conducted on a variety of typical farmland and working road surfaces, collecting data from different seasons and weather conditions. A data fitting algorithm was used to fit the relationship curve between the friction coefficient, driving stability, and energy consumption based on the measured data, thereby determining an appropriate friction coefficient threshold. Optimizing the setting of the operating intensity threshold is the key to ensuring efficient operation of agricultural implements without exceeding the tractor's power load. This setting utilizes operating process monitoring and performance evaluation methods, monitoring the power consumption, wear, and operating quality of agricultural implements during operation. The Analytic Hierarchy Process (AHP) is used to comprehensively consider factors such as the tractor's power output capacity, agricultural implement design parameters, and operating standards to determine the operating intensity threshold for different agricultural implements, making energy distribution more scientific and reasonable.

[0070] It is understandable that key elements in the system, such as driving energy requirements, road friction coefficient, and operating intensity thresholds, must be clearly defined to accurately define the energy requirements for tractor travel and implement operation, ensuring proper energy allocation. By determining the friction coefficient threshold, the tractor can be both stable and energy-efficient when operating on various road surfaces. Clarifying the operating intensity threshold allows for efficient implement operation without exceeding the tractor's power load, improving operational efficiency and avoiding equipment damage. This comprehensively enhances the system's adaptability and energy efficiency in different operating scenarios, ultimately achieving optimized operation of the tractor's electric drive system.

[0071] In some embodiments of the present application, the tractor's driving acceleration is configured as a, and the acceleration threshold is a0, wherein the acceleration threshold a0 is determined by conducting acceleration performance tests on the tractor under different road conditions and load conditions, combining the driver's operating comfort and the vehicle's mechanical structure bearing capacity, and analyzing the power requirements and component stress conditions under different accelerations to ensure the maximum acceleration of the tractor that can ensure safe and comfortable acceleration; when μ>μ0, if a>a0, then If a≤a0, then Among them, 0<e3,e4<1, f3, f4, and is a positive number determined according to actual energy demand; when μ≤μ0, if a>a0, then If a≤a0, then Among them, 0<e5,e6<1, f5, f6, and is a positive number determined according to the actual energy demand; the duration of the agricultural implement operation is set to t, and the duration threshold is t0, where the duration threshold t0 is determined by testing the continuous operation performance of the agricultural implement, monitoring its temperature, wear and performance changes under different operation times, and combining the operation efficiency and maintenance cycle to determine the longest continuous operation time that can keep the agricultural implement in good working condition; when I>I0, if t>t0, then E d2 =g3×I+h3×t+m1; if t≤t0, then E d2 =g4×I+h4×t+m2, where 0<g3, g4<1, h3, h4, m1 and m2 are positive numbers determined according to actual energy requirements.

[0072] Specifically, real-time monitoring of the system's operating status is fundamental to ensuring system stability and efficiency. This monitoring utilizes a multi-sensor fusion approach, installing different types of sensors at key locations on the tractor, such as speed sensors, temperature sensors, and power sensors, to collect system operating data in real time. Rapid and accurate fault diagnosis is key to reducing downtime. System fault diagnosis utilizes fault feature extraction and matching methods, extracting fault features from collected operating data. Expert system algorithms are also employed to match the extracted features with pre-set fault patterns to quickly determine the fault type and location. Dynamic adjustment of the control strategy is crucial for adapting to varying operating conditions. This adjustment utilizes operating condition identification and decision-making methods, identifying the current operating condition based on real-time monitoring data. Fuzzy control algorithms are then employed to dynamically adjust the system's control strategy based on the operating condition identification results, ensuring optimal performance under all operating conditions.

[0073] It's understandable that multi-sensor fusion, which monitors the system's operating status in real time, enables timely access to operating information from all system components. Fault diagnosis, using fault feature extraction and matching combined with expert system algorithms, allows for rapid and accurate fault location, reducing downtime for repairs. Dynamic adjustment of control strategies based on operating condition identification and decision-making, coupled with the use of fuzzy control algorithms, enables the system to flexibly adapt to varying operating conditions. These three elements work together to effectively improve the system's operational stability and reliability, ensuring efficient tractor operation in complex operating environments, reducing equipment failure rates, optimizing energy efficiency, and enhancing the practicality and intelligence of the entire control system.

[0074] In some embodiments of the present application, the operating condition information is configured as The operating environment information is The comprehensive index of working condition and environment is Among them, ω i and p j is the weight coefficient, and 0<ω i <1, 0<p j <1; configure the comprehensive index threshold to S0; when S>S0, the driving strategy is A1 and the energy distribution plan is P1; when S≤S0, the driving strategy is A2 and the energy distribution plan is P2; the comprehensive index threshold S0 is determined based on the statistical analysis results of historical working conditions and environmental data, and the correlation between the driving strategy and the energy distribution plan is determined according to the tractor operation efficiency optimization goal. Configure the tractor's driving direction to be θ, divide the circumferential direction into N intervals, and the angle of each interval is The driving direction is set to θ, and the weight coefficient related to the driving direction in the working condition information is ω j, configure the slope to be α, and the slope threshold to be α0, wherein the slope threshold α0 is the appropriate slope limit value determined by the power performance test of the tractor at different slopes and the analysis of actual operation requirements; when α>α0, ω j =ω j1 ×(1+β×α); when α≤α0, ω j =ω j2 ×(1+γ×α), where ω j1 and ω j2 is the initial weight, and 0<ω j1 ,ω j2 <1, β and γ are slope influence coefficients, and 0 < β, γ < 1; the light intensity of the operating area is configured as L i , the light intensity threshold is L0, and the weight coefficient related to light in the working environment information is p k , wherein the light intensity threshold L0 is a suitable light intensity limit value determined by comprehensively evaluating the driver's operating difficulty, the working efficiency of the agricultural implements and the impact of the environment on the system when the tractor is operating under different light intensities; when L i >L0, p k =p k1 ×(1-δ×(L i -L0)); when L i When ≤L0, p k =p k2 ×(1-ξ×(L0-L i )), where p k1 and p k2 is the initial weight, and 0<p k1 , p k2 <1, δ and ξ are slope influence coefficients, and 0<δ, ξ<1; configure the direction change amount to Δθ, the illumination change amount to ΔL, the direction change threshold to Δθ0, and the illumination change threshold to ΔL0, wherein the direction change threshold Δθ0 is determined by analyzing the stability and effectiveness of the system control strategy adjustment under different driving direction changes of the tractor, and the illumination change threshold ΔL0 is determined by testing and analyzing the system's ability to adapt to the environment and the impact on operating performance under different illumination intensity changes. When Δθ>Δθ0 or ΔL>ΔL0, redetermine the corresponding weight coefficient to calculate S, and plan the driving strategy and energy distribution plan based on the new S value. Configure the energy demand of the power system to be E p , the energy requirement for the agricultural implement control is E a , the total energy that can be distributed by the system is E t , the importance level of the power system is I p The importance level of the agricultural implement control is 1 a, where I p and I a It is a positive number determined based on relevant factors; when E p +E a ≤E t When , the energy allocated to the power system is The energy allocated to the implement control is When E p +E a >E t When I p >I a , then the energy demand of the power system is met first; if I p a , the energy demand of the agricultural implement control is met first.

[0075] Specifically, deep integration of the power system and implement control is key to improving operational synergy. This integration utilizes modular design and interface standardization, unifying the design of the power system and implement control modules to ensure smooth data and energy transfer between modules. Optimizing cross-module energy reuse is key to improving energy utilization. This management utilizes energy flow monitoring and dynamic allocation methods, providing real-time monitoring of energy flow between modules. A greedy algorithm prioritizes energy allocation to the modules most in need, while meeting the basic needs of each module, to achieve efficient energy utilization. Ensuring system integration stability is fundamental to maintaining reliable system operation. This is achieved through redundant design and fault-tolerant processing. Redundant modules are deployed in key locations, allowing them to take over immediately if a module fails. A self-healing algorithm automatically repairs minor faults, ensuring stable operation after system integration.

[0076] It is understandable that through modular design and interface standardization, the deep integration of power system and agricultural implement control is achieved, making the coordination of various links of tractor operation more efficient; using energy flow monitoring and greedy algorithm to optimize cross-module energy reuse management, greatly improving energy utilization and reducing waste; with the help of redundant design, fault-tolerant processing and fault self-healing algorithm, the stability of system integration is guaranteed, the risk of failure is reduced, and downtime is shortened. The three work together to significantly enhance the operating efficiency, energy saving effect and reliability of the tractor electric drive intelligent control system, providing better equipment support for agricultural production.

[0077] In some embodiments of the present application, the urgency of the task of configuring the power system is U p (0≤U p ≤1), the task complexity of the power system is C p (0≤C p ​≤1), the urgency of the task of agricultural implement control is U a (0≤U a ≤1), the task complexity of the agricultural implement control is C a (0≤C a ≤1); I p =x1U p +x2C p ,I a =y1U a +y2C a , where x1, x2, y1, and y2 are weight coefficients, and x1+x2=1, 0≤x1≤1, 0≤x2≤1, y1+y2=1, 0≤y1≤1, 0≤y2≤1.

[0078] It should be noted that:

[0079] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0080] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features and not other features included in other embodiments, the combination of features from different embodiments is meant to be within the scope of this application and to form different embodiments.

[0081] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An intelligent control system for electric drive of tractors based on energy recovery, characterized in that: include: An energy recovery module, which is used to collect and store excess kinetic energy generated by the tractor; Electric drive module, used to drive the tractor to travel and work with electricity; Intelligent control module, which collects information about the tractor's operating conditions and working environment, and plans driving strategies and energy distribution; System integration module, used to integrate the power system and agricultural implement control, and to distribute and reuse energy across modules.

2. The intelligent control system for electric drive of tractors based on energy recovery according to claim 1, characterized in that: The excess kinetic energy is generated by the tractor when braking and going downhill; The initial excess kinetic energy is configured as E0, and the actual recoverable energy is E r The tractor's speed during braking or downhill driving is v, and the speed threshold is v0, where the speed threshold v0 is the lowest speed at which effective energy recovery can be achieved by performing multiple braking and downhill driving tests on the tractor at different speeds. If v>v0, then E r =k1×E0, if v≤v0, then E r =k2×E0, where k1 and k2 are energy recovery coefficients, and 0<k1<1, 0<k2<1, k1>k2; The current remaining capacity of the energy storage device is configured as C r ; When E r ≤C r When all E r store; When E r >C r When storing C r The size of energy.

3. The intelligent control system for electric drive of tractors based on energy recovery according to claim 2, characterized in that: The load of the tractor is set to L, and the load threshold is set to L0. The load threshold L0 is determined by comprehensively considering the power of the tractor's power system, the load capacity of the transmission system, and the working efficiency of the energy recovery device, and conducting multiple tests under different loads to analyze the energy recovery effect. The maximum load that enables the energy recovery system to operate stably and efficiently is determined; When v>v0, if L>L0, then k1=a1×v+b1×L+c1, if L≤L0, then k1=a2×v+b2×L+c2; When v≤v0, if L>L0, then k2=a3×v+b3×L+c3, if L≤L0, then k2=a4×v+b4×L+c4, where 0<a i ,b i ,c i <1(i=1,2,3,4); The temperature of the energy storage device is configured as T, the temperature threshold is T0, and the actual stored energy is E s , wherein the temperature threshold T0 is determined by performing charge and discharge tests on the energy storage device under different temperature environments and monitoring the performance degradation and safety indicators of the device to ensure the long-term stable operation of the device; When T>T0, E s =E r ×(1-d×(T-T0)), where d is the temperature influence coefficient and 0<d<1.

4. The intelligent control system for electric drive of tractors based on energy recovery according to claim 3, characterized in that: The basic energy required to drive the tractor is E d1 , the energy required to drive the agricultural implement is E d2 , the total driving energy requirement is E d , and E d =E d1 +E d2 ; For the electric drive module, when E s ≥E d When using E s Drive; when E s <E d When the energy consumption is insufficient, the shortfall is obtained from other energy sources; The friction coefficient of the road surface on which the tractor is traveling is set to μ, and the friction coefficient threshold is μ0. The friction coefficient threshold μ0 is determined by measuring the friction coefficient of various typical farmland and working road surfaces, combining the tractor's drive wheel structure and power transmission characteristics, and analyzing the tractor's driving stability and energy consumption under different friction coefficients. The minimum friction coefficient that ensures the tractor's normal driving and reasonable energy consumption is determined; When μ>μ0, E d1 =e1×μ+f1; when μ≤μ0, E d1 =e2×μ+f2; where 0<e1, e2<1, f1 and f2 are positive numbers determined according to actual energy demand; The operating intensity of the agricultural implement is set to I, and the operating intensity threshold is I0. The operating intensity threshold I0 is determined by testing and evaluating the power consumption, wear, and operating quality of the agricultural implement under different operating intensities, combined with the power output capacity of the tractor. It is the minimum operating intensity that allows the agricultural implement to operate efficiently without exceeding the tractor's power load. When I>I0, E d2 =g1×I+h1; when I≤I0, E d2 =g2×I+h2; where 0<g1, g2<1, h1 and h2 are positive numbers determined according to actual energy demand.

5. The intelligent control system for electric drive of tractors based on energy recovery according to claim 4, characterized in that: The tractor's driving acceleration is set to a, and the acceleration threshold is a0. The acceleration threshold a0 is determined by conducting acceleration performance tests on the tractor under different road conditions and loads, combining the driver's operating comfort and the vehicle's mechanical structure tolerance, and analyzing the power requirements and component stress conditions under different accelerations to ensure the tractor's safe and comfortable acceleration. When μ>μ0, if a>a0, then If a≤a0, then Among them, 0<e3,e4<1, f3, f4, and It is a positive number determined according to actual energy demand; When μ≤μ0, if a>a0, then If a≤a0, then Among them, 0<e5,e6<1, f5, f6, and It is a positive number determined according to actual energy demand; The duration of the agricultural implement operation is set to t, and the duration threshold is set to t0. The duration threshold t0 is determined by testing the continuous operation performance of the agricultural implement, monitoring its temperature, wear, and performance changes under different operation times, and combining the operation efficiency and maintenance cycle to determine the longest continuous operation time that can keep the agricultural implement in good working condition. When I>I0, if t>t0, then E d2 =g3×I+h3×t+m1; if t≤t0, then E d2 =g4×I+h4×t+m2, where 0<g3, g4<1, h3, h4, m1 and m2 are positive numbers determined according to actual energy requirements.

6. The intelligent control system for electric drive of tractors based on energy recovery according to claim 5, characterized in that: Configure the working condition information as The operating environment information is The comprehensive index of working condition and environment is Among them, ω i and p j is the weight coefficient, and 0<ω i <1, 0<p j <1; Set the comprehensive indicator threshold to S0; When S>S0, the driving strategy is A1 and the energy allocation scheme is P1; When S≤S0, the driving strategy is A2 and the energy allocation scheme is P2; The comprehensive index threshold S0 is determined based on the statistical analysis results of historical working conditions and environmental data, and the correlation between the driving strategy and the energy distribution plan is determined according to the tractor operation efficiency optimization goal.

7. The intelligent control system for electric drive of tractors based on energy recovery according to claim 6, characterized in that: Assign the tractor's driving direction to θ, divide the circumference into N intervals, and each interval has an angle The driving direction is set to θ, and the weight coefficient related to the driving direction in the working condition information is ω j , configure the slope to be α and the slope threshold to be α0, wherein the slope threshold α0 is a suitable slope limit value determined by testing the tractor's power performance at different slopes and analyzing actual operation requirements; When α>α0, ω j =ω j1 ×(1+β×α); When α≤α0, ω j =ω j2 ×(1+γ×α), where ω j1 and ω j2 is the initial weight, and 0<ω j1 ,ω j2 <1, β and γ are slope influence coefficients, and 0 < β, γ < 1; Configure the light intensity of the working area to be L i , the light intensity threshold is L0, and the weight coefficient related to light in the working environment information is p k , wherein the light intensity threshold L0 is an appropriate light intensity limit value determined by comprehensively evaluating the operating difficulty of the driver when the tractor is operating under different light intensities, the working efficiency of the agricultural implement, and the impact of the environment on the system; When L i >L0, p k =p k1 ×(1-δ×(L i -L0)); When L i When ≤L0, p k =p k2 ×(1-ξ×(L0-L i )), where p k1 and p k2 is the initial weight, and 0<p k1 , p k2 <1, δ and ξ are slope influence coefficients, and 0 < δ, ξ < 1; The direction change amount is configured as Δθ, the illumination change amount is ΔL, the direction change threshold is Δθ0, and the illumination change threshold is ΔL0, wherein the direction change threshold Δθ0 is a suitable angle change limit value determined by analyzing the stability and effectiveness of the system control strategy adjustment under different driving direction change amplitudes of the tractor, and the illumination change threshold ΔL0 is a suitable light intensity change limit value determined by testing and analyzing the system's environmental adaptability and operating performance under different light intensity change amplitudes; When Δθ>Δθ0 or ΔL>ΔL0, the corresponding weight coefficient is re-determined to calculate S, and the driving strategy and energy distribution plan are planned according to the new S value.

8. The intelligent control system for electric drive of tractors based on energy recovery according to claim 7, characterized in that: The energy requirement of the power system is configured as E p , the energy requirement for the agricultural implement control is E a , the total energy that can be distributed by the system is E t , the importance level of the power system is I p The importance level of the agricultural implement control is 1 a , where I p and I a It is a positive number determined based on a combination of relevant factors; When E p +E a ≤E t When , the energy allocated to the power system is The energy allocated to the implement control is When E p +E a >E t When I p >I a , then the energy demand of the power system is met first; if I p a , the energy demand of the agricultural implement control is met first.​ 9. The intelligent control system for electric drive of tractors based on energy recovery according to claim 8, characterized in that: The urgency of the task of configuring the power system is U p (0≤U p ≤1), the task complexity of the power system is C p (0≤C p ≤1), the urgency of the task of agricultural implement control is U a (0≤U a ≤1), the task complexity of the agricultural implement control is C a (0≤C a ≤1); I p =x1U p +x2C p ,I a =y1U a +y2C a , where x1, x2, y1, and y2 are weight coefficients, and x1+x2=1, 0≤x1≤1, 0≤x2≤1, y1+y2=1, 0≤y1≤1, 0≤y2≤1.

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