Tractor power assembly joint optimization method and system based on economic indexes

Abstract

The invention provides a tractor power assembly joint optimization method and system based on economic indexes, and relates to the technical field of tractor control, and the method comprises the steps: building a tractor power assembly model through forward simulation based on the structure information and working condition information of a target tractor; performing feedback control on the actual speed of the target tractor according to the expected speed by using the tractor power assembly model; in the control process, the actual fuel consumption rate of each unit of cultivated land area serves as the fuel economy index of the whole tractor, economy joint optimization is conducted on the power assembly control parameters, and the optimal power assembly control parameters of the target tractor under the optimal economy index are obtained; according to the method, the engine fuel injection parameters and the displacement ratio of the hydraulic mechanical continuously variable transmission are comprehensively optimized through the drawing machine power assembly model with the whole vehicle fuel economy index as the evaluation standard, the tractor comprehensive energy consumption optimal solution is obtained, and the whole vehicle fuel economy performance is effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of tractor control, and in particular to a method and system for joint optimization of a tractor powertrain based on economic indicators. Background Art

[0002] With the continuous advancement of urban modernization, the contradiction between the reduction of agricultural labor and the increase in demand for crops has become increasingly apparent. Among them, high-horsepower, high-efficiency, intelligent tractors have played an important role in improving agricultural efficiency and quality. The accelerated improvement of tractor technology has also caused a lot of problems such as fuel consumption and environmental pollution, and the demand for low-pollutant, green, high-horsepower, high-efficiency, intelligent tractors has become more urgent. Among them, the powertrain system composed of the engine and the gearbox is an important unit of the tractor's power transmission. Its power generation and transmission performance will directly affect the power, economy, green and low-carbon performance of agricultural machinery during operation.

[0003] The engine industry regards the application of low-carbon fuels as the main development direction in terms of green and low-carbon development. The dual-fuel mode of diesel + alternative fuel is one of the important ways for traditional diesel engines to reduce their dependence on fossil fuels while meeting traditional emission regulations; among them, dual-fuel engines are divided into high-pressure direct injection (HPDI) and low-pressure direct injection according to the position of the piston when the fuel is injected. Compared with the two, low-pressure direct injection has the advantages of lower NOx and soot emissions, but its low-load knock limits the engine power and the problem of hydrocarbon emissions needs to be further solved. HPDI engines have higher injection pressures, which improves the charging efficiency, and the diffusion-based combustion mode can effectively overcome the knock problem of premixed natural gas engines under high loads. In the fields of agriculture and industry, research institutions have compared the fuel consumption rate and emission characteristics of diesel and diesel / natural gas dual-fuel engines. The results show that diesel / natural gas dual-fuel engines have better fuel economy and significantly reduce nitrogen oxide emissions.

[0004] As an important part of tractor power transmission, the output torque and speed of the gearbox need to be constantly adjusted to adapt to changes in external loads. Against this background, the application of continuously variable transmission (CVT) has become a trend. The most common types of CVT are hydraulic static transmission (HST) and hydraulic mechanical continuously variable transmission (HMCVT). Among them, the transmission efficiency of HST is much lower than that of gear transmission, which leads to its seldom use in high-horsepower tractors. In order to overcome the defects of HST, HST is connected in parallel with mechanical parts to form a hydraulic mechanical continuously variable transmission (HMCVT), in which the hydraulic part only transmits part of the power, and the remaining power is transmitted through mechanical parts. This can improve the low transmission efficiency while providing better driving comfort. It has broad development prospects in high-power agricultural machinery and equipment, as it can achieve continuously variable speed while transmitting high power.

[0005] At present, for one-dimensional or zero-dimensional engine modeling, the internal characteristics of the engine are usually analyzed, while the average parameter model and the model based on experimental data are usually coupled with the gearbox to study the powertrain control strategy. At present, there is still a lack of related control research on the one-dimensional or zero-dimensional alternative fuel engine model to participate in the coupled powertrain, and there is a lack of related research on the powertrain control strategy of the key parameters inside the engine. Many researchers use the experimental map to equivalently establish the engine model and couple the gearbox model to optimize the design of the tractor powertrain energy management strategy. This method can find the optimal economic torque speed and gearbox ratio of the engine at each speed point, but it has a strong dependence on experimental data. At the same time, the engine map modeling does not consider the impact of changes in key parameters inside the engine on the fuel consumption rate and output torque, and then loses the possibility of considering the key parameters inside the engine on the powertrain energy management strategy.

[0006] The economy of the tractor is jointly affected by the engine and the gearbox. The existing technology is insufficient in exploring the power and economic characteristics under the coupling effect of the key internal parameters of the engine and the power split of the hydraulic mechanical continuously variable transmission (HMCVT), resulting in poor optimization effect of the powertrain including the green fuel engine and the hydraulic mechanical continuously variable transmission (HMCVT). Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a tractor powertrain joint optimization method and system based on economic indicators. Through the tractor powertrain model and taking the vehicle fuel economy indicator as the evaluation standard, the engine fuel injection parameters and the displacement ratio of the hydraulic mechanical continuously variable transmission are comprehensively optimized to obtain the optimal solution for the tractor's comprehensive energy consumption, thereby effectively improving the fuel economy performance of the whole vehicle.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: The tractor powertrain joint optimization method based on economic indicators includes: Based on the structural information and working condition information of the target tractor, the tractor powertrain model is built using forward simulation; The tractor powertrain model is used to follow the desired speed and feedback control is performed on the actual speed of the target tractor. Among them, during the control process, the actual fuel consumption rate per unit of cultivated land area is used as the fuel economy index of the whole vehicle, and the powertrain control parameters are economically jointly optimized to obtain the optimal powertrain control parameters of the target tractor under the optimal economic index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydraulic mechanical continuously variable transmission.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: The tractor powertrain joint optimization system based on economic indicators includes: The model building module is configured to: build a tractor powertrain model using forward simulation based on the structural information and working condition information of the target tractor; The vehicle speed control module is configured to: utilize a tractor powertrain model to follow the desired vehicle speed and perform feedback control on the actual vehicle speed of the target tractor; Among them, during the control process, the actual fuel consumption rate per unit of cultivated land area is used as the fuel economy index of the whole vehicle, and the powertrain control parameters are economically jointly optimized to obtain the optimal powertrain control parameters of the target tractor under the optimal economic index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydraulic mechanical continuously variable transmission.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the method for jointly optimizing a tractor powertrain based on economic indicators is implemented.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the tractor powertrain joint optimization method based on economic indicators is implemented.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the tractor powertrain joint optimization method based on economic indicators.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention incorporates actual working conditions factors such as cultivated land area into the economic index, thereby constructing a vehicle fuel economy index, which can more accurately evaluate the fuel consumption and overall economic changes of the tractor under different working conditions, thereby providing more targeted data support for optimizing design and improving agricultural production efficiency.

[0014] The present invention establishes a tractor powertrain model including a plowing condition model, an HMCVT controller, a driver model, a power system model, and a vehicle dynamics model of the tractor. With the goal of minimizing the fuel economy index of the whole vehicle, the key parameters of the fuel injection inside the engine and the displacement ratio and speed ratio of the HMCVT are comprehensively optimized. Through joint optimization, the fuel economy index of the whole vehicle is significantly improved, the overall comprehensive fuel consumption is reduced, the actual specific fuel consumption is reduced by a maximum of 18.06%, and the instantaneous specific fuel consumption per unit area is reduced by a maximum of 12.26%. The joint optimization algorithm of the engine and HMCVT is adopted to successfully solve the power cycle problem that cannot be avoided when optimizing the tractor HMCVT alone, and effectively improve the fuel economy performance of the whole vehicle from the perspective of joint optimization of the powertrain. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0016] Figure 1 This is the plowing route map of Example 1.

[0017] Figure 2 This is a plowing speed curve diagram of Example 1.

[0018] Figure 3 This is the fuel injection amount update verification diagram for Example 1.

[0019] Figure 4 The verification diagram for the jetting amount of Example 1 is updated.

[0020] Figure 5 This is a torque update verification diagram of Example 1.

[0021] Figure 6 This is the speed update verification diagram of Example 1.

[0022] Figure 7 This is a structural diagram of the tractor powertrain model of Example 1.

[0023] Figure 8 This is a flow chart of the vehicle control strategy of the tractor powertrain model of Example 1.

[0024] Fig. 9 This is a comparison chart of two economic indicators of Example 1.

[0025] Fig.10 Schematic diagram of the effect of fuel injection parameters on engine output torque in Example 1.

[0026] Fig.11 Schematic diagram of vehicle fuel economy at different injection times of the engine of Example 1.

[0027] Fig.12 This is the economic joint optimization algorithm diagram of Example 1.

[0028] Fig.13 It is the diesel injection amount of one working cycle of the engine corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0029] Fig.14 It is the natural gas injection amount of one working cycle corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0030] Fig.15 It is the diesel injection timing corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0031] Fig.16 It is the natural gas injection timing corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0032] Fig.17 1. The figure shows the comparison of diesel / natural gas injection timings corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0033] Fig.18 These are the diesel / natural gas injection time intervals corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0034] Fig.19 The comparison of vehicle economy before and after optimization using traditional specific fuel consumption as the observed quantity in Example 1.

[0035] Fig. 20 This is a comparison of the vehicle economy before and after optimization using the economy evaluation index as the observation quantity in Example 1. DETAILED DESCRIPTION

[0036] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0038] Example 1 In one embodiment of the present disclosure, a tractor powertrain joint optimization method based on economic indicators is provided, comprising: Based on the structural information and working condition information of the target tractor, the tractor powertrain model is built using forward simulation; The tractor powertrain model is used to follow the desired speed and feedback control is performed on the actual speed of the target tractor. Among them, during the control process, the actual fuel consumption rate per unit of cultivated land area is used as the fuel economy index of the whole vehicle, and the powertrain control parameters are economically jointly optimized to obtain the optimal powertrain control parameters of the target tractor under the optimal economic index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydraulic mechanical continuously variable transmission.

[0039] As an embodiment, the tractor powertrain joint optimization method based on economic indicators disclosed in the present invention is described in detail below from the perspectives of the tractor powertrain model, the vehicle fuel economy indicator and the powertrain economy joint optimization algorithm.

[0040] 1. Tractor powertrain model As the most common working condition of high-horsepower tractors, plowing operation is representative, so this condition is taken as an example for analysis. Plowing operation of tractors is the most typical working condition of tractors and also the condition with the greatest energy demand. When plowing, a normal cycle includes two stages: plowing and stopping and turning. The plowing route of tractors in the field is as follows: Figure 1 When a tractor is working in the field, its driving speed will be affected by many factors such as working conditions, load status and soil conditions. Table 1 shows the driving speed range of the tractor in different situations: Table 1 Tractor speed under different working conditions

[0041] The speed of a tractor plowing operation is generally 5-10km / h. In this embodiment, based on the actual plowing conditions of the tractor, it is assumed that the plowing speed is 7km / h. Since the plowing speed has a certain volatility during the plowing process, random fluctuations are added to the constant plowing speed as a plowing test condition. The cycle time under a single condition is 200s. Figure 2 It is the superposition of two plowing cycle conditions. The average speed of a single cycle condition is 5.6km / h, of which 0-167s is the plowing stage with an average vehicle speed of 6.5km / h. 168-200s is the tractor's parking and turning stage with an average speed of 1.47km / h. During the parking and turning stage, the tractor is subject to zero plowing resistance and only rolling resistance.

[0042] Based on the above working condition analysis, in order to explain in more detail the effect of optimizing the HMCVT power cycle under the coupling of the engine and the hydraulic mechanical continuously variable transmission HMCVT, this embodiment uses a dual-fuel engine as the object for relevant explanation, and adjusts the injection amount and the jet amount accordingly according to the size relationship between the two; therefore, the injection amount and the jet amount are used as the input values ​​of the engine model, Figure 3-Figure 6They are respectively the fuel injection quantity update verification diagram, the jet quantity update verification diagram, the torque update verification diagram, and the speed update verification diagram, such as Figure 3 , Figure 4 As shown, both should remain unchanged within each working cycle.

[0043] The engine output torque is obtained based on the effective work done in an engine working cycle. Therefore, the output torque transmitted from the alternative fuel engine model to the HMCVT model is calculated based on the effective work obtained in the previous engine working cycle, such as Figure 5 As shown, it remains unchanged in the current working cycle of the engine; similarly, Figure 6 As shown, it can be seen that the engine output speed and output torque maintain the same update frequency. According to the energy signal and power transmission direction of the high-horsepower tractor, the simulation model can be divided into two types: forward simulation model and backward simulation model: In the forward simulation model, the energy, signal and power transmission direction of the tractor are consistent with the actual situation, and a driver model is added to follow the speed of the tractor with high accuracy.

[0044] The energy, signal and power transmission direction of the tractor in the backward simulation model is opposite to the actual one, and it has the characteristics of simple structure. The backward simulation model does not have a driver model. The output torque of the gearbox is calculated by the dynamic model according to the given expected vehicle speed and then transmitted. The backward simulation is an open-loop system, and the simulation accuracy is not accurate enough. Therefore, this embodiment adopts forward simulation to build a high-horsepower tractor powertrain model, such as Figure 7 As shown in the figure, it includes a plowing condition model, an HMCVT controller, a driver model, a power system model and a vehicle dynamics model of a high-horsepower tractor. The HMCVT controller is used to follow the desired vehicle speed under the plowing condition. The driver model is a mathematical model that imitates the driver's operation. By controlling the engine throttle opening, the engine output torque follows the HMCVT required torque. Specifically: Step S1.1: The plowing condition model provides the input of the target speed of the plowing operation.

[0045] Step S1.2 The HMCVT controller controls the displacement ratio and the gearbox gear according to the difference between the expected vehicle speed and the actual vehicle speed under the plowing condition.

[0046] The driver model in step S1.3 is actually an engine throttle opening controller, which calculates the opening of the output accelerator pedal based on the difference between the desired vehicle speed and the actual vehicle speed and the difference between the HMCVT required torque and the actual engine output torque.

[0047] This embodiment uses a PID controller to achieve driver control. The input signal of the PID controller is the difference T(t) between the engine output torque that the engine can provide to the gearbox and the torque required by the gearbox. The output signal of the PID controller is the throttle opening signal u(t). The specific formula is: (1) Where T(t) is the difference between the engine output torque that the engine can provide to the gearbox and the torque required by the gearbox, T HMCVT_req (t) is the required torque of the gearbox of the tractor at time t, in km / h; T eng_out (t) is the output torque of the tractor engine at time t, in km / h.

[0048] (2) Where u(t) is the throttle opening signal, which is defined as positive when the throttle opening is increased and negative when the throttle opening is decreased; T(t) is the difference between the engine output torque that the engine can provide to the gearbox and the torque required by the gearbox; k p is the proportionality coefficient; k i is the integral coefficient; k d is the differential coefficient.

[0049] Step S1.4: The power system model includes an engine model, an HMCVT model, a central reducer and a wheel-side reducer. The engine output torque and the HMCVT output speed are controlled according to the control signals output by the HMCVT controller and the driver model. The torque of the HMCVT is input to the vehicle dynamics model of the tractor through the transmission system.

[0050] Step S1.5 calculates the speed at each moment using the vehicle dynamics model to obtain the actual speed of the tractor.

[0051] The HMCVT output speed is converted into wheel speed through the central reducer and wheel side reducer. The actual speed of the tractor can be calculated based on the wheel speed and wheel radius.

[0052] The vehicle control strategy process of the tractor powertrain model constructed in this embodiment is as follows: Figure 8 As shown, specifically: At the beginning, the tractor is in the initial state, and the engine input parameters and the HMCVT transmission ratio are initial values. Since the HMCVT is in the initial stage of the 1st gear at this time, the output speed is zero and the vehicle speed is zero.

[0053] When the speed spectrum begins to change, the tractor's expected speed v target The actual vehicle speed v tractorFirst, the HMCVT controller adjusts the hydraulic pump-fixed displacement motor ratio and gear position to control the output tractor's actual speed; second, the HMCVT required torque T is calculated based on the dynamic model and the adjusted HMCVT transmission efficiency. hmcvt_in , according to the engine output torque T eng_out and HMCVT required torque T hmcvt_in The relationship between the size of the engine throttle opening is adjusted to adapt to the HMCVT required torque T hmcvt_in The size of; Finally, the real speed v tractor and the expected speed v target Compare them, if the two are not equal, the cycle is executed to adjust the engine throttle opening and HMCVT displacement ratio.

[0054] 2. Vehicle fuel economy indicators From the perspective of economical speed control strategy, the amount of fuel consumption reflects the economic performance. If the transmission control strategy is formulated based on the optimal fuel consumption of the engine as the economic target, it will not be able to fully reflect the continuously variable transmission performance of the whole machine. Therefore, this embodiment incorporates actual working conditions such as cultivated land area into the economic index to construct a vehicle fuel economy index. The specific construction method is as follows: According to the characteristics of tractor pulling farm implements, tractors mainly perform field plowing, sowing, rotary tillage and other operations. Their operation mode determines their power output mode. Generally, the fuel consumption of the tractor during steering is not considered. Therefore, specific fuel consumption is usually used as the fuel economy index of the tractor. The actual fuel consumption rate of the tractor under any traction condition, that is, specific fuel consumption, is expressed as: (3) In the formula, g e is the fuel consumption rate of the engine, η T The traction efficiency of the tractor.

[0055] According to the above formula, the tractor for traction operation needs to ensure the engine fuel consumption rate g e Minimum, traction efficiency η T Maximum, the vehicle fuel economy can reach the best.

[0056] Generally speaking, the tractor transmission system mainly consists of four parts: HMCVT, central transmission system, wheels, and load system. The power is output by the engine, then passes through the tractor's transmission system, and finally performs traction operations by pulling the load or overcoming ground resistance. Since power loss occurs in every part of the transmission system during the power transmission process, the tractor's traction efficiency can be obtained based on the power transmission route: (4) Where ηb is the transmission efficiency of the gearbox; η z is the transmission efficiency of the central reducer; η l is the transmission efficiency of the wheel reducer.

[0057] The tractor tillage efficiency is related to the field size, the area of ​​the implement, and the tractor performance. Therefore, the instantaneous tillage area of ​​the tractor is introduced into the actual fuel consumption rate to obtain a new fuel consumption rate calculation formula: (5) Among them, g com_enegy_consum is the specific fuel consumption per unit area, that is, the actual fuel consumption rate per unit area of ​​cultivated land, in g / (kW·h·km²), g e is the fuel consumption rate of the engine, η b is the transmission efficiency of the gearbox, η z is the transmission efficiency of the central reducer, η l is the transmission efficiency of the wheel-end reducer, and area is the ploughed land area at the instantaneous vehicle speed, that is, the land area that the tractor can plow per hour.

[0058] The area of ​​land that a tractor can plow per hour is closely related to the vehicle speed, the tillage width of a single plow body, and the number of plowshares, so the calculation formula is: (6) Among them, area is the instantaneous speed cultivated land area, km²; v cur_spd is the tractor speed, km / h; b0 is the tillage width of a single plow, converted from cm to m; Z is the number of plowshares, 9.

[0059] The actual fuel consumption rate per unit of cultivated land expressed by formula (5) is g com_enegy_consum , as a vehicle fuel economy indicator, is used to evaluate the comprehensive energy consumption of the tractor.

[0060] When the tractor speed changes greatly, the fuel economy index is significantly different. Therefore, the road spectrum of 0-20s is selected for fuel economy index comparison. Fig. 9 It shows the specific fuel consumption of the tractor from static start to normal plowing condition in the tractor road spectrum of 7.3s-20s g T Fuel consumption per unit area g com_enegy_consum Since both evaluation indicators take into account the influence of HMCVT transmission efficiency, and the transmission efficiency is in the denominator of the two formulas, when the transmission efficiency is infinitesimally close to zero at the initial start of the tractor, the evaluation indicator will be unobservable, so the initial start-up stage of the tractor from 0 to 7.3s is shielded.

[0061] from Fig. 9 It can be seen that the unit area specific fuel consumption index g com_enegy_consumand specific fuel consumption g T Compared with the above, the wider numerical range shows that the consideration of cultivated land area can more effectively explain the changes in the economic efficiency of the tractor vehicle.

[0062] The factors affecting the fuel economy index of the whole vehicle are analyzed below. In the process of optimizing the fuel economy of the whole vehicle of the energy management strategy of the previous high-horsepower tractor, although the influence of the transmission efficiency of the gearbox on the fuel economy of the whole vehicle is taken into account, the fuel economy of the whole vehicle is optimized by coupling the gearbox and the engine, but the engine model is mostly established in the form of experimental map diagrams, which ignores the influence of changes in key parameters inside the engine on the fuel consumption rate and output torque, and then ignores the optimization effect of the fuel economy of the whole vehicle by jointly optimizing the engine and HMCVT to improve the power split of HMCVT. Therefore, this embodiment jointly optimizes the fuel economy of the whole vehicle by building a tractor powertrain model that couples the dual-fuel engine zero-dimensional model and the HMCVT model.

[0063] Because the dual-fuel engine adopts a zero-dimensional model, the key internal parameters of the engine can also be used as joint optimization objects for related research; fuel injection timing and fuel injection amount are important parameters that affect the internal combustion conditions and power of the dual-fuel engine. Different combinations of diesel injection timing and diesel / natural gas injection timing intervals have different effects on the engine power.

[0064] The injection parameters of three different typical engine working conditions were simulated and analyzed, and the following results were obtained: Fig.10 The schematic diagram of the influence of fuel injection parameters on engine output torque shows that for the same fuel injection interval, the earlier the diesel injection time, the greater the engine output torque tends to be; when the diesel injection time is advanced, the diesel will enter the combustion chamber at an earlier compression stage; on the one hand, this will cause the diesel and air / natural gas mixture to ignite earlier during the compression process, causing the engine to generate higher temperature and pressure at the end of the compression stroke; on the other hand, the early injection of diesel not only helps to effectively ignite natural gas, but may also cause natural gas to start burning at an earlier time; although the flame propagation speed of natural gas is slower, if the diesel is injected too early, it can ensure that the natural gas is fully burned, thereby achieving greater power output.

[0065] With the same diesel injection timing, the engine output torque tends to increase as the fuel injection interval becomes smaller. When the interval between diesel injection and natural gas injection is shortened, the mixture of diesel and natural gas becomes more uniform. A shorter injection interval can cause the two fuels to contact and mix more quickly in the combustion chamber, thereby improving combustion efficiency, generating higher cylinder pressure, and increasing the engine's output torque. If the injection interval is longer, the ignition process of natural gas may lag, resulting in incomplete combustion. A shorter injection interval helps reduce this lag effect, allowing natural gas to ignite more promptly under the ignition of diesel, thereby improving combustion efficiency and enhancing output torque. At the same time Fig.10 It also reflects that the ringing coefficient RI (knock intensity index) of different diesel injection timing and diesel / natural gas injection timing interval combinations is also different, which will have a certain impact on engine knock. A large number of studies believe that if the ringing coefficient RI exceeds 5, the engine has a greater possibility of knock. Therefore, it can be concluded that the engine fuel injection parameters have an impact on the engine output torque / output power, which in turn affects the fuel economy of the vehicle; Fig.11 It can be seen that by changing the fuel injection timing, the fuel economy of the tractor vehicle will be affected to varying degrees. This directly confirms from the perspective of economy that there is a close connection between the engine fuel injection parameters and the fuel economy of the vehicle.

[0066] The engine fuel injection parameters will affect the fuel economy of the whole vehicle. In addition, according to the calculation formula of the vehicle fuel economy index, it can be seen that the transmission efficiency of HMCVT also has a great influence on the fuel economy of the whole vehicle. The transmission efficiency of HMCVT is affected by the displacement ratio of the hydraulic pump-fixed displacement motor and the planetary gear pair. At the same time, power circulation phenomenon will occur in HMCVT, which will reduce the transmission efficiency.

[0067] From the above analysis, it can be seen that the vehicle fuel economy index is affected by many factors as shown in Table 2. Through the parameter cycle optimization algorithm of the engine fuel injection parameters and the displacement ratio of the hydraulic pump-quantitative motor, the optimal vehicle fuel economy at different vehicle speeds can be achieved.

[0068] Table 2 Factors affecting vehicle fuel economy

[0069] 3. Powertrain Economy Joint Optimization Algorithm The transmission efficiency of HMCVT is the highest when the displacement ratio is zero, but this is not the best point for the vehicle fuel economy of the tractor powertrain. When the HMCVT speed ratio i changes continuously, there are countless engine operating conditions that can be coupled with it to meet the load characteristics requirements, but only one set of operating conditions is coupled with HMCVT to obtain the best vehicle fuel economy index.

[0070] In order to make high-horsepower tractors maintain the best fuel economy of the whole vehicle under any working conditions, the dual-fuel engine model and the HMCVT transmission efficiency analysis method are combined, and the NSGA-Ⅱ multi-objective genetic algorithm and the parameter cycle optimization algorithm are used to form a powertrain economy joint optimization algorithm. The tractor powertrain under any working condition is jointly optimized economically. Through this algorithm, the optimal engine fuel injection parameters and HMCVT displacement ratio of the tractor under any working condition in the load characteristic field can be calculated, ensuring the engineering realization of the tractor's optimization of the vehicle's fuel economy indicators.

[0071] When solving the optimal control parameters, the basic parameters of the tractor / implements, the HMCVT parameters and the road spectrum information are used as the basic parameters of the algorithm, and the expected vehicle speed, the number of plowshares and the HMCVT speed ratio that characterize the current working state of the tractor are used as input variables to select a set of optimal powertrain control parameters from the optimal powertrain control parameter set obtained by solving; wherein the optimal powertrain control parameter set is composed of the optimal powertrain control parameters corresponding to the combinations of different expected vehicle speeds, the number of plowshares and the HMCVT speed ratios.

[0072] When solving the optimal powertrain control parameter set, each combination of desired vehicle speed, number of plowshares, and HMCVT speed ratio is jointly optimized, namely, parameter loop optimization.

[0073] The main components of the powertrain are the engine, HMCVT, central transmission and wheel-side transmission system. Both the central transmission and wheel-side transmission system are composed of meshing gears, and their transmission efficiency can be regarded as a constant. Therefore, the transmission efficiency of the powertrain is determined by the efficiency of the engine and HMCVT. In this embodiment, in each cycle, the designed vehicle fuel economy index g com_enegy_consum Characterize the efficiency of the powertrain and take the fuel economy index of the whole vehicle as one of the objective functions of the joint optimization algorithm. At the same time, the vehicle load always exists during driving. With the change of the HMCVT speed ratio, the HMCVT required torque is also changing. The engine output torque needs to meet the demand of the HMCVT required torque so that the tractor can travel in the current working condition. If the engine output torque does not meet the current HMCVT required torque demand, it is necessary to increase the throttle opening to increase the engine output torque. Therefore, the difference between the HMCVT required torque and the actual engine output torque is minimized as the second objective function of the optimization algorithm.

[0074] The code of the powertrain economy joint optimization algorithm is designed on the software platform. In this embodiment, Matlab2022b can be used for code design. Taking the plowing condition as an example, Fig.12 As shown in Figure 2, the specific steps of the optimization algorithm are: Step S2.1 Basic parameter settings: The joint optimization process requires basic settings of multiple parameters of the powertrain model, including parameter settings for the tractor, agricultural implements, and HMCVT, and obtaining the vehicle speed variation range under plowing conditions through road spectrum information.

[0075] Among them, the tractor parameters include the driving wheel radius r, the tractor use mass m0, the instantaneous vehicle speed and the cultivated land area per unit time S area , main transmission ratio i central , final transmission ratio i wheel etc.; the parameters of agricultural implements include the maximum number of plowshares that can be mounted n plowsharemax 、Minimum number of plowshares that can be mounted n plowsharemin , soil specific resistance k0, tillage depth h0, single plow tillage width b0; HMCVT parameters include the maximum and minimum speed ratio i max 、i min , characteristic parameters k1 and k2 of the planetary gear; the road spectrum information includes the maximum and minimum plowing speed v tractormax 、v tractormin .

[0076] Step S2.2 Discretization of the tractor working state under plowing conditions: The plowing speed obtained from the road spectrum information is discretized. At the same speed, the tractor can achieve the same plowing speed with different numbers of plowshares, different HMCVT ratios and different engine conditions. Therefore, the number of plowshares and HMCVT ratios are discretized, and the plowing speed v is set. tractor , Number of plowshares n plowshare 、HMCVT speed ratio i hmcvt By discretizing , we can get the coupling of the powertrain control parameters under each tractor plowing condition speed, that is, the expected speed v tracto , Number of plowshares n plowshare 、HMCVT speed ratio i hmcvt For different combinations of , the following parameter cycles are optimized for different combinations.

[0077] Step S2.3 determines the engine speed: The discretized combination of parameters obtained in step S2.2 is applied to the tractor, implement, HMCVT parameters, and road spectrum information set in step S2.1 to calculate the engine operating speed n for each combination. eng , determine the engine operating speed n eng Is the engine in the normal operating speed range? engmin , n engmax ], if it is within the range, continue to proceed downward, otherwise it is out of range and returns to step S2.2 to obtain the next set of parameter discretization combinations.

[0078] Step S2.4 HMCVT demand torque T cvtout , transmission efficiency η b calculate: According to the discretization combination of parameters obtained in step S2.2 and the inherent relationship of gear meshing inside the gearbox and its continuously variable speed law, the speed ratio i is adopted. hmcvt As well as the characteristic parameters k1 and k2 of the planetary gear, the displacement ratio e is calculated.

[0079] According to the tractor's mass m0 and the number of plowshares mounted n plowshare , soil specific resistance k0, tillage depth h0, single plow tillage width b0, instantaneous vehicle speed, cultivated land area per unit time S area , calculate the current resistance F of the tractor z .

[0080] The displacement ratio e obtained by application calculation is compared with the speed ratio i obtained by original discretization. hmcvt And the characteristic parameters k1 and k2 of the planetary gear, calculate the torque T that the engine needs to provide engneed .

[0081] Then according to the calculated current resistance F of the tractor z And the main transmission ratio i central , final transmission ratio i wheel , calculate the gearbox output torque T cvtout .

[0082] Finally, the torque entering and exiting the gearbox (the torque required by the engine) and the gearbox output torque T cvtout , and the transmission efficiency η of the gearbox at this time is obtained b , and pass the relevant results about HMCVT to step S2.5.

[0083] Step S2.5 reads the corresponding fuel injection parameters from the engine fuel injection parameter basic data table through the discretized combination of parameters obtained in steps S2.1 / S2.2 / S2.3, and passes the read data to step S2.6 as the initial value of the fuel injection parameter.

[0084] Step S2.6 NSGA-Ⅱ multi-objective genetic algorithm parallel calculation: According to step S2.5, the initial value of the fuel injection parameter under each parameter discretization combination is obtained to realize the dynamic change of the boundary of the individual gene value in the genetic algorithm.

[0085] During the algorithm optimization process, individuals in the population discretize the fuel injection parameters, and the discrete combination of fuel injection parameters obtained by using dynamic boundaries is more consistent with the actual engine fuel injection process. At the same time, the discretization combination of fuel injection parameters with dynamic boundaries can effectively improve the vehicle fuel economy index affected by the HMCVT power cycle; during the optimization process, in order to shorten the optimization time, the individuals with different parameter combinations in the cell array (cell) are parallelly calculated through parsim, which ultimately significantly improves the calculation speed of the model.

[0086] Among them, the objective function of the NSGA-Ⅱ multi-objective genetic algorithm is: in order to make full use of computing resources and save the time for optimizing the objective function, the discrete combination of fuel injection parameters obtained in step S2.6 is simulated in parallel by parsim, and the Simulink powertrain model is combined for simulation calculation. This method can effectively shorten the optimization time and achieve the optimization of the objective function shown in formula (7) (8) in the shortest possible time. The values ​​of the two objective functions are obtained by formula (7) (8), and the vehicle fuel economy index g of each group of discrete combination of fuel injection parameters is calculated. com_enegy_consum (diesel, gas, diesel inj ,gas inj ) and engine output torque T(diesel,gas,diesel inj ,gas inj ), the data is sent back to the .m optimization algorithm file of Matlab.

[0087] (7) (8) Wherein, diesel is the diesel injection amount; gas is the natural gas injection amount; inj is the diesel injection time; gas inj is the natural gas injection time; T HMCVT_in It is the torque output by the engine and input into the gearbox.

[0088] Based on the above formula, with the goal of minimizing the actual fuel consumption rate per unit of cultivated land area and minimizing the difference between the HMCVT required torque and the actual engine output torque, the optimal powertrain control parameters under each combination are solved. Each combination may obtain multiple sets of optimal powertrain control parameters. This embodiment uses the ringing coefficient RI as the screening condition to perform preliminary screening of the optimization results.

[0089] By simplifying and assuming the data obtained from the pressure oscillation frequency experiment and scaling the pressure data, the researchers found that the pressure wave amplitude is proportional to the maximum pressure rise rate, as shown in formula (9). According to the fact that the pressure wave energy density is proportional to the pressure pulsation amplitude and the square of the speed of sound in the medium, and inversely proportional to the average system pressure, a cylinder pressure oscillation evaluation index, the ringing coefficient RI, is derived as shown in formula (10). Except for γ, all parameters can be measured through the zero-dimensional engine model simulation process, which indirectly proves that the engine model has the ability to consider multiple factors such as knock, and has more usage scenarios and application capabilities than the Map construction model.

[0090] (9) (10) Where ΔP is the burst pressure increase rate; P max is the burst pressure; T max is the explosion temperature in the cylinder; R is the specific heat ratio, which is numerically equal to the isentropic index K; γ is the proportionality coefficient, which is calculated from the experimental data and is taken as 0.001 here.

[0091] According to the analysis of the influencing factors of the vehicle's fuel economy indicators, it can be seen that different discrete combinations of fuel injection parameters will have a certain impact on engine knock. Therefore, in the model simulation process, the ringing coefficient RI is calculated to characterize the knock intensity in the engine cylinder. It is determined that when RI is greater than 5, the engine knocks. The ringing coefficient is used as the screening condition for the best individual on the Pareto frontier optimized by the NSGA-Ⅱ multi-objective genetic algorithm to reasonably screen the optimization results.

[0092] After preliminary screening using the ringing coefficient, the total objective function value of each optimized individual is calculated by taking the weighted average of the two objective functions (the weights are both 0.5), and the individual with the smallest value is selected as the optimal individual, that is, a set of optimal powertrain control parameters is retained for each combination.

[0093] The optimal powertrain control parameters of all combinations constitute an optimal powertrain control parameter set. According to the expected vehicle speed, number of plowshares, and HMCVT speed ratio of the current working state, the corresponding optimal powertrain control parameters are selected from the set as the final powertrain control parameters of the target tractor. This example verifies the economic joint optimization results of key parameters.

[0094] Using the above joint optimization algorithm, the optimal control parameters of the powertrain of a high-horsepower tractor under the regulation of the economic control strategy can be obtained, as shown in the following figure: Figure 13-Figure 18The joint optimization result diagram shown reflects the engine fuel injection parameters with the best vehicle fuel economy under different vehicle speeds and HMCVT speed ratios, which can be stored separately in actual control and no longer changed; for different vehicle speeds and HMCVT speed ratios, it is sufficient to match the optimal fuel injection parameters, which can reduce the amount of data storage in the controller and make engineering implementation simpler; the above optimization results are drawn into a two-dimensional map of vehicle speed-HMCVT speed ratio, coupled to the engine model of the tractor vehicle control model, and combined with the internally developed online neural network prediction model, the optimal fuel injection parameters under any speed ratio-vehicle speed combination can be achieved; Figure 13-Figure 18 It can be seen that as the tractor plowing speed increases, the variable width of the HMCVT speed ratio becomes larger, indicating that as the vehicle speed increases, the economy of the powertrain has greater room for optimization.

[0095] This embodiment also compares the fuel economy before and after the optimization. Since the vehicle fuel economy index has a more obvious change trend during the acceleration phase of the tractor plowing condition, it can effectively reflect the fuel consumption of the tractor vehicle. Therefore, the road spectrum of 0-20s is selected for comparison and verification. Figure 19-20 The specific fuel consumption per unit area of ​​the tractor before and after the combined optimization of the fuel injection parameters in the tractor road spectrum of 7.3s-20s from static start to normal plowing condition is shown in g com_enegy_consum The transmission efficiency of HMCVT is infinitesimal and close to zero, which will cause the evaluation index to be unobservable, and shield the fuel economy index of the initial start-up stage of the tractor from 0-7.3s. Fig.19 The comparison of vehicle economy before and after model optimization with specific fuel consumption (a traditional common vehicle economy indicator) as the observation quantity shown in formula (5) is shown. It can be seen that the actual specific fuel consumption is reduced by 18.06% at most after joint optimization, and the vehicle has better economy. Fig. 20 The comparison of the vehicle economy before and after the optimization of the model with the economy evaluation index established in this embodiment as the observation quantity is shown. It can be seen that the instantaneous fuel consumption per unit area is reduced by 12.26% at most after the joint optimization; the joint optimization algorithm of the engine and HMCVT is adopted to successfully solve the power circulation problem that cannot be avoided when optimizing the tractor HMCVT alone, and the fuel economy performance of the whole vehicle is effectively improved from the perspective of joint optimization of the powertrain.

[0096] Example 2 In one embodiment of the present disclosure, a tractor powertrain joint optimization system based on economic indicators is provided, comprising: The model building module is configured to: build a tractor powertrain model using forward simulation based on the structural information and working condition information of the target tractor; The vehicle speed control module is configured to: utilize a tractor powertrain model to follow the desired vehicle speed and perform feedback control on the actual vehicle speed of the target tractor; Among them, during the control process, the actual fuel consumption rate per unit of cultivated land area is used as the fuel economy index of the whole vehicle, and the powertrain control parameters are economically jointly optimized to obtain the optimal powertrain control parameters of the target tractor under the optimal economic index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydraulic mechanical continuously variable transmission.

[0097] Example 3 In one embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the tractor powertrain joint optimization method based on economic indicators is implemented.

[0098] Example 4 In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the tractor powertrain joint optimization method based on economic indicators is implemented.

[0099] Example 5 In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the tractor powertrain joint optimization method based on economic indicators.

[0100] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A tractor powertrain joint optimization method based on economic indicators, characterized in that: include: Based on the structural information and working condition information of the target tractor, the tractor powertrain model is built using forward simulation; The tractor powertrain model is used to follow the desired speed and feedback control is performed on the actual speed of the target tractor. Among them, during the control process, the actual fuel consumption rate per unit of cultivated land area is used as the fuel economy index of the whole vehicle, and the powertrain control parameters are economically jointly optimized to obtain the optimal powertrain control parameters of the target tractor under the optimal economic index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydraulic mechanical continuously variable transmission.

2. The tractor powertrain joint optimization method based on economic indicators as claimed in claim 1, characterized in that: The tractor powertrain model includes a plowing condition model, an HMCVT controller, a driver model, a powertrain model and a vehicle dynamics model of the tractor; Wherein, the plowing condition model provides the expected speed of the plowing operation; The HMCVT controller controls the displacement ratio and the gear position of the transmission according to the difference between the expected vehicle speed and the actual vehicle speed under the plowing condition; The driver model calculates the opening of the output accelerator pedal according to the difference between the expected vehicle speed and the actual vehicle speed and the difference between the HMCVT required torque and the actual engine output torque; The power system model controls the engine output torque and the HMCVT output speed according to the output of the HMCVT controller and the driver model, obtains the torque of the HMCVT, and inputs it into the vehicle dynamics model of the tractor; The vehicle dynamics model of the tractor calculates the wheel speed at each moment based on the torque of the HMCVT to obtain the actual speed of the tractor.

3. The tractor powertrain joint optimization method based on economic indicators as claimed in claim 1, characterized in that: The vehicle fuel economy index is to incorporate the actual working condition factor of cultivated land area into the economic index, and is expressed by the formula: in, is the actual fuel consumption rate per unit of cultivated land area, g e is the fuel consumption rate of the engine, η b is the transmission efficiency of the gearbox, η z is the transmission efficiency of the central reducer, η l is the transmission efficiency of the wheel reducer, and area is the cultivated land area at the instantaneous vehicle speed.

4. The tractor powertrain joint optimization method based on economic indicators as claimed in claim 1, characterized in that: The fuel injection parameters include fuel injection timing, jet injection timing, fuel injection amount and jet injection amount.

5. The tractor powertrain joint optimization method based on economic indicators as claimed in claim 1, characterized in that: The economical joint optimization is to form a powertrain economical joint optimization algorithm through parameter cycle optimization and NSGA-Ⅱ multi-objective genetic algorithm, and to perform economical joint optimization on the tractor powertrain under plowing conditions.

6. The tractor powertrain joint optimization method based on economic indicators as claimed in claim 1, characterized in that: The economic joint optimization comprises the following specific steps: Perform basic settings on multiple parameters of the tractor powertrain model; The working state of the tractor under plowing conditions is discretized to obtain different combinations of expected vehicle speed, number of plowshares, and HMCVT speed ratio, and parameter optimization is performed for different combinations: the NSGA-Ⅱ multi-objective genetic algorithm is used to minimize the actual fuel consumption rate per unit of cultivated land area and minimize the difference between the HMCVT required torque and the actual engine output torque, and the optimal powertrain control parameters under each combination are solved to obtain the optimal powertrain control parameter set; The current working state of the target tractor is obtained, and the corresponding optimal powertrain control parameters are selected from the optimal powertrain control parameter set using the expected vehicle speed, number of plowshares, and HMCVT speed ratio combination corresponding to the current working state.

7. The tractor powertrain joint optimization system based on economic indicators is characterized by: include: The model building module is configured to: build a tractor powertrain model using forward simulation based on the structural information and working condition information of the target tractor; The vehicle speed control module is configured to: utilize a tractor powertrain model to follow the desired vehicle speed and perform feedback control on the actual vehicle speed of the target tractor; Among them, during the control process, the actual fuel consumption rate per unit of cultivated land area is used as the fuel economy index of the whole vehicle, and the powertrain control parameters are economically jointly optimized to obtain the optimal powertrain control parameters of the target tractor under the optimal economic index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydraulic mechanical continuously variable transmission.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the tractor powertrain joint optimization method based on economic indicators as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by the processor, the tractor powertrain joint optimization method based on economic indicators as described in any one of claims 1-6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the tractor powertrain joint optimization method based on economic indicators as described in any one of claims 1 to 6.

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