AMT low fuel consumption gear configuration system and method

Abstract

The application discloses an AMT low-oil-consumption gear configuration system and method, and relates to the field of automatic transmission control.The system comprises a gear shift control module, which is configured with a preset correspondence table of throttle opening and gear, is used for dynamically generating upshift point and downshift point signals according to the throttle opening and the current gear, a simulation running module, which is configured with heavy commercial vehicle test working condition parameters, and is used for simulating actual road conditions to optimize gear configuration, and a sensor module, which is used for collecting vehicle speed, engine speed and throttle opening data in real time and feeding back to the gear shift control module.The application can systematically improve the fuel economy, power continuity and working condition adaptability of the electrically controlled mechanical automatic transmission, and solves the core defects of the traditional AMT under complex road conditions.

Description

Technical Field

[0001] This invention relates to the field of automatic transmission control, and in particular to an AMT low fuel consumption gear configuration system and method. Background Technology

[0002] An automated manual transmission (AMT) is a mechanical gearbox that uses an electronic control system to automatically shift gears, building upon the foundation of a traditional manual transmission. Its core advantages lie in inheriting the high transmission efficiency and low cost of a manual transmission, while simultaneously improving driving comfort and fuel economy through electronically optimized shift logic.

[0003] However, the application of AMT in medium and heavy-duty commercial vehicles faces significant challenges, including: traditional fuel consumption testing relies on large-scale sites and fuel consumption, resulting in high costs and difficulty in simulating real-world fuel consumption under all road conditions; existing AMT shifting strategies are mostly based on fixed MAP tables or PID control, making it difficult to adapt to dynamic operating conditions, causing the engine to deviate from its optimal fuel efficiency range; AMT requires power to be cut off during shifting, resulting in a brief power interruption, affecting driving smoothness, and the limited response speed of hydraulic or electric actuators exacerbates shifting delays. Therefore, based on the above challenges, this invention proposes an AMT low-fuel-consumption gear configuration system and method. Summary of the Invention

[0004] Technical Purpose

[0005] To address the aforementioned issues, the present invention aims to provide an AMT low-fuel-consumption gear configuration system and method. This system addresses the technical bottlenecks of high fuel consumption testing costs, insufficient adaptive shifting logic, and power interruption in AMT systems for medium and heavy-duty commercial vehicles under real-world road conditions. It ensures that the engine continuously operates within its optimal fuel efficiency range and reduces the discrepancy between simulation and actual fuel consumption through intelligent algorithms, ultimately achieving a reduction in overall vehicle fuel consumption and improving shifting smoothness and system robustness.

[0006] Technical solution

[0007] To achieve the above objectives, this invention provides an AMT low-fuel-consumption gear configuration system and method. This system constructs a dynamic simulation environment by integrating real-time engine operating conditions, transmission ratios, rear axle torque distribution, and road spectrum parameters, and controls simulation errors through iterative calculations. A dynamic relationship table between throttle opening and gear position is established, with upshift / downshift speed thresholds increasing non-linearly with throttle opening to ensure efficient engine range matching. Using fuel consumption rate and shift smoothness as multi-objective functions, shift logic parameters are optimized and embedded into the electronic control unit. The clutch and shift point controller are linked to precisely cut off power transmission during shifts, and shift delays are corrected in real-time based on vehicle speed and load feedback, reducing power interruption duration. This solution, through collaborative hardware and algorithm design, systematically improves the economy and reliability of AMT under complex operating conditions.

[0008] In a first aspect, the present invention provides an AMT low-fuel-consumption gear configuration system, including an engine, an electronically controlled mechanical automatic transmission, a clutch, and a rear axle, and further including:

[0009] The shift control module is equipped with a preset table of correspondence between throttle opening and gear position. It is used to dynamically generate upshift and downshift signals based on the throttle opening and the current gear position, so as to dynamically adjust the transmission shift logic and make the engine work in the optimal fuel efficiency range.

[0010] The simulation operation module is configured with test condition parameters for heavy commercial vehicles, which is used to simulate actual road conditions and optimize gear configuration.

[0011] The sensor module is used to collect vehicle speed, engine speed and throttle opening data in real time and feed them back to the shift control module.

[0012] Furthermore, the rear axle integrates a differential module, which is used to dynamically adjust the driving force distributed to the left and right rear wheels according to the torque output by the transmission, in order to adapt to simulation parameters under different road conditions.

[0013] Furthermore, the shift control module is connected to the clutch signal, and during the shift process, it disconnects the power connection between the engine and the transmission through the clutch, and optimizes the shift timing based on real-time feedback of vehicle speed, engine speed and load status.

[0014] Furthermore, in the table showing the correspondence between throttle opening and gear position, the upshift and downshift points increase non-linearly with the increase of throttle opening, and when the throttle opening is ≥50%, the upshift point is at least 20% higher than the low throttle opening state of the same gear.

[0015] Within the 50%-80% throttle opening range, by dynamically adjusting the upshift point speed threshold, the engine operating range is optimized to the high-efficiency zone, improving fuel efficiency by at least 8.4%. Under incline conditions, this strategy reduces shift frequency by at least 35%, shortening power interruption time to 0.54 seconds. This non-linear design effectively balances power demand and economy under high loads, avoiding fuel consumption fluctuations caused by frequent shifting.

[0016] Furthermore, the preset parameters of the shift control module include multi-dimensional constraints, specifically:

[0017] The speed threshold range for upshifting points for each gear is from 1 km / h to 140 km / h.

[0018] The downshift point speed threshold range is from 1 km / h to 100 km / h;

[0019] The speed threshold difference between upshifting and downshifting in adjacent gears is ≥3km / h under different throttle openings.

[0020] Furthermore, the simulation operation module integrates resistance coefficient, slope parameters, and inertial load data that match actual road conditions, and reduces the error between simulated fuel consumption and actual test to within 5% through iterative calculation.

[0021] Furthermore, the simulation operation module performs multi-objective optimization of the shift logic parameters using a genetic algorithm. The objective functions include fuel consumption rate, shift smoothness, and engine emission indicators. The optimized parameters are written into the storage module of the electronic control unit.

[0022] Under urban start-stop conditions, this algorithm reduces vehicle fuel consumption by at least 6.2% and nitrogen oxide emissions by at least 12%. Through iterative calculations, the objective function error is reduced from the initial 15% to less than 5%, and the optimized parameter set covers more than 90% of real-world road conditions.

[0023] Furthermore, it also includes a driver operation simulation module, which is used to input accelerator pedal opening, braking signals and steering commands, and uses a closed-loop control algorithm to make the simulation model consistent with actual driving behavior.

[0024] Furthermore, it also includes a torque distribution optimization module. By collecting real-time data on vehicle gradient, road surface friction coefficient, and wheel slip ratio during driving, and combining this data with the transmission output torque and rear axle differential characteristics, a dynamic torque distribution model is established. The ideal torque distribution ratio is then calculated based on road condition parameters, as shown in the following formula:

[0025]

[0026] In the formula, For the ideal torque distribution ratio; and The coefficient of friction between the left and right wheels and the road surface; This is the weighting coefficient for the slope; The weighting coefficient for the yaw rate; Slope; This is the absolute value of the yaw rate.

[0027] It collects real-time data on slope, road surface friction coefficient, and wheel slip ratio, dynamically adjusts the rear axle torque distribution ratio, suppresses unilateral wheel slippage, and optimizes driving force utilization. This improves traction efficiency while reducing energy loss, enabling the vehicle to maintain stable power output under complex road conditions and significantly reducing additional fuel consumption caused by uneven torque distribution.

[0028] Furthermore, it also includes a shift delay compensation module, which trains a strategy network using historical shift data and real-time driving conditions to predict the optimal shift timing and control the clutch action in advance. The reward function is:

[0029]

[0030] In the formula, Output for the reward function; Optimize the weights for time; Assigned weight to fuel consumption; This refers to the actual shift delay time. The base delay time; This represents the actual fuel consumption during the current shift cycle. This is the baseline fuel consumption.

[0031] Based on deep reinforcement learning algorithms, the system predicts the optimal shift point using historical data and real-time driving conditions, and pre-controls clutch action to compensate for actuator delays. This reduces power interruption time, decreases the proportion of transient engine conditions during shifting, thereby improving shift smoothness, reducing fuel consumption, and enhancing the system's adaptability to dynamic operating conditions.

[0032] Secondly, the present invention also provides an AMT low fuel consumption gear configuration method, the method being based on the system described in the first aspect above, comprising:

[0033] A multi-parameter coupled simulation model was established based on the test conditions of heavy commercial vehicles;

[0034] The vehicle speed thresholds for upshift and downshift points are dynamically matched based on the preset throttle opening and gear mapping relationship.

[0035] During gear shifting, power transmission is cut off via the clutch, and the shift delay time is adjusted based on the real-time engine operating conditions.

[0036] The optimized gear configuration scheme is output through simulation model.

[0037] Thirdly, the present invention also provides a computer device, including a management platform and a memory, wherein the management platform is connected to the memory, the memory is used to store computer programs, and the management platform is used to execute the computer programs stored in the memory, so that the computer device executes the aforementioned AMT low fuel consumption gear configuration method.

[0038] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a management platform, implements the aforementioned AMT low-fuel-consumption gear configuration method.

[0039] This invention integrates engine operating conditions, transmission ratios, rear axle torque distribution, and road spectrum parameters to construct a high-precision simulation environment, reducing the deviation between simulation and actual fuel consumption. Through the dynamic mapping relationship between throttle opening and gear position, it adaptively adjusts the upshift / downshift speed thresholds to ensure the engine continuously operates within its optimal fuel efficiency range. Using genetic algorithms and deep reinforcement learning, it optimizes the shifting strategy with fuel consumption rate and shift smoothness as multiple objectives, and embeds this into the electronic control unit for adaptive control. The clutch and shift controller are linked, and combined with real-time feedback, precisely cut off power transmission, reducing shift delay and power interruption duration. This solution systematically solves the core problems of traditional AMTs, such as low fuel consumption testing efficiency, insufficient shift logic robustness, and poor power continuity under complex road conditions. It significantly improves fuel economy, optimizes power distribution efficiency, and enhances adaptability to dynamic operating conditions, providing innovative technical support for the energy efficiency and driving performance of medium and heavy-duty commercial vehicles.

[0040] Beneficial effects

[0041] By implementing the AMT low fuel consumption gear configuration system and method provided by the present invention, the following technical effects are achieved:

[0042] (1) This application constructs a high-precision dynamic simulation environment by integrating engine operating conditions, transmission ratios, rear axle torque distribution, and road spectrum parameters. This model significantly improves the consistency between simulation results and actual tests, provides reliable data support for shift strategy optimization, ensures that the engine continues to operate in the high-efficiency range across the entire operating condition range, and reduces the deviation between simulation and actual vehicle testing.

[0043] (2) Based on a preset dynamic relationship table between throttle opening and gear position, the upshift / downshift speed threshold is nonlinearly adjusted to precisely match the shift logic with the engine's optimal fuel efficiency range. This technology effectively avoids the limitations of traditional fixed threshold strategies, optimizes power transmission efficiency under dynamic load and throttle changes, improves overall vehicle economy, and reduces the interference of shift frequency on driving smoothness.

[0044] (3) Real-time data collection of slope, road surface friction coefficient, and wheel slip ratio is used to dynamically adjust the rear axle torque distribution ratio, suppress unilateral wheel slippage, and optimize driving force utilization. This improves traction efficiency while reducing energy loss, enabling the vehicle to maintain stable power output under complex road conditions and significantly reducing additional fuel consumption caused by uneven torque distribution.

[0045] (4) Based on deep reinforcement learning algorithms, the optimal shifting time is predicted through historical data and real-time driving conditions, and the clutch action is controlled in advance to compensate for the delay of the actuator. This can shorten the power interruption time, reduce the proportion of transient engine conditions during the shifting process, thereby improving shifting smoothness and reducing fuel consumption, and enhancing the system's adaptability to dynamic conditions. Attached Figure Description

[0046] To make the above-described AMT low fuel consumption gear configuration system and method of the present invention more obvious and understandable, the drawings used in the specific embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the AMT low fuel consumption gear configuration system;

[0048] Figure 2 This is a flowchart illustrating the method for configuring low-fuel-consumption gears in an AMT (Automated Manual Transmission) system. Detailed Implementation

[0049] Example 1:

[0050] A low-fuel-consumption gear configuration system and method for an automatic transmission (AMT) are provided, the system as follows: Figure 1 As shown, the system includes an engine, an electromechanical automatic transmission, a clutch, and a rear axle. It also includes: a shift control module configured with a preset throttle opening and gear correspondence table, used to dynamically generate upshift and downshift signals based on the throttle opening and the current gear, thereby dynamically adjusting the transmission's shift logic to ensure the engine operates within its optimal fuel efficiency range; a simulation operation module configured with heavy-duty commercial vehicle test condition parameters, used to simulate actual road conditions and optimize gear configuration; and a sensor module used to collect vehicle speed, engine speed, and throttle opening data in real time and feed them back to the shift control module. The method flow is as follows: Figure 2 As shown. Details are as follows.

[0051] The rear axle integrates a differential module, which is used to dynamically adjust the driving force distributed to the left and right rear wheels according to the torque output by the transmission, in order to adapt to simulation parameters under different road conditions.

[0052] The shift control module is connected to the clutch signal. During the shift process, it disconnects the power connection between the engine and the transmission through the clutch and optimizes the shift timing based on real-time feedback of vehicle speed, engine speed and load status.

[0053] In the table showing the correspondence between throttle opening and gear position, the upshift and downshift points increase non-linearly with the increase of throttle opening. When the throttle opening is ≥50%, the upshift point is at least 20% higher than the low throttle opening state of the same gear, as shown in Table 1.

[0054] Table 1. Correspondence between throttle opening and gear position

[0055] gear Throttle opening Upshift point Downshift point 1 0 1 1 1 10 1 1 1 25 1 1 1 50 5 2 1 90 6 4 1 100 7 5 2 0 5 4 2 10 5 4 2 25 5 4 2 50 8 5 2 90 10 6 2 100 12 8 3 0 10 7 3 10 10 7 3 25 10 7 3 50 12 8 3 90 15 10 3 100 18 12 4 0 15 11 4 10 15 11 4 25 15 11 4 50 17 14 4 90 20 16 4 100 23 18 5 0 23 20 5 10 23 20 5 25 23 20 5 50 25 21 5 90 27 23 5 100 30 25 6 0 32 28 6 10 32 28 6 25 32 28 6 50 35 30 6 90 38 33 6 100 42 35 7 0 45 30 7 10 45 30 7 25 45 30 7 50 50 34 7 90 54 37 7 100 58 40 8 0 65 50 8 10 65 50 8 25 65 50 8 50 70 54 8 90 75 58 8 100 79 60 9 0 90 65 9 10 90 65 9 25 90 65 9 50 95 70 9 90 100 75 9 100 108 80 10 0 80 85 10 10 80 85 10 25 90 85 10 50 95 90 10 90 120 95 10 100 140 100

[0056] The preset parameters of the shift control module include multi-dimensional constraints, specifically:

[0057] The speed threshold range for upshifting points for each gear is from 1 km / h to 140 km / h.

[0058] The downshift point speed threshold range is from 1 km / h to 100 km / h;

[0059] The speed threshold difference between upshifting and downshifting in adjacent gears is ≥3km / h under different throttle openings.

[0060] The simulation operation module integrates resistance coefficient, slope parameters and inertial load data that match actual road conditions, and reduces the error between simulated fuel consumption and actual test to within 5% through iterative calculation.

[0061] The simulation operation module uses a genetic algorithm to perform multi-objective optimization of the shift logic parameters. The objective functions include fuel consumption rate, shift smoothness, and engine emission indicators. The optimized parameters are written into the storage module of the electronic control unit.

[0062] It also includes a driver operation simulation module, which is used to input accelerator pedal opening, braking signals and steering commands, and uses a closed-loop control algorithm to make the simulation model consistent with actual driving behavior.

[0063] Example 2:

[0064] Building upon the aforementioned embodiments, a torque distribution optimization algorithm based on dynamic road condition feedback is added. This algorithm collects real-time data on vehicle gradient, road surface friction coefficient, and wheel slip ratio during driving, and combines this data with the transmission output torque and rear axle differential characteristics to establish a dynamic torque distribution model. Its core function is to dynamically adjust the torque distribution ratio between the left and right rear wheels according to road conditions, reducing energy loss caused by wheel slippage on one side or uneven load, while simultaneously improving driving force utilization.

[0065] Inertial measurement units and wheel speed sensors are pre-deployed on the vehicle to obtain the vehicle's pitch angle, yaw rate, and left and right rear wheel slip ratios in real time.

[0066] The ideal torque distribution ratio is calculated based on road condition parameters, using the following formula:

[0067]

[0068] In the formula, For the ideal torque distribution ratio; and The coefficient of friction between the left and right wheels and the road surface; This is the weighting coefficient for slope, typically 0.15; This is the weighting coefficient for the yaw rate, typically 0.08; Slope; This is the absolute value of the yaw rate.

[0069] The torque distribution is adjusted in real time by the rear axle electronic differential to ensure that the wheels on the high-traction side receive greater torque and to suppress the wheel spin-off of the wheels on the low-traction side.

[0070] Verification shows that, while achieving an average error similar to the aforementioned embodiments, optimized torque distribution results in a reduction of at least 3.2% in overall fuel consumption during simulation tests. Under an 8% gradient, the drive wheel slip ratio is reduced by at least 28%, and the climbing time is shortened by at least 12%. The results demonstrate that this algorithm, by dynamically adjusting the rear axle torque distribution ratio, significantly suppresses wheel slippage and improves the effective utilization of driving force under complex road conditions. Comparative analysis shows that the optimized torque distribution reduces energy loss caused by unilateral wheel spin, enabling the vehicle to maintain stable traction performance under asymmetrical loads or slippery surfaces. Simultaneously, the improved energy transfer efficiency directly reduces ineffective engine work, thus reflecting improved fuel economy in comprehensive road condition tests.

[0071] Example 3:

[0072] Building upon the aforementioned embodiments, a shift delay compensation model based on deep reinforcement learning is added. This model trains a strategy network using historical shift data and real-time driving conditions to predict the optimal shift timing and control the clutch action in advance, compensating for the inherent delay of the hydraulic actuator and thus reducing the duration of power interruption.

[0073] The system collects data on throttle opening, engine speed, load, and shift delay time during vehicle operation, and constructs a state vector.

[0074] Define the action space as the clutch disengagement time advance, and the reward function as:

[0075]

[0076] In the formula, Output for the reward function; The weight for time optimization is typically 0.6; As a weighting factor for fuel consumption, it is generally 0.4; This is the actual shift delay time, which is the time difference between clutch disengagement and power reconnection. This is the baseline delay time, typically 0.8 seconds. This represents the actual fuel consumption during the current shift cycle. This is the baseline fuel consumption, typically the average fuel consumption of a conventional shifting strategy under specific operating conditions, used to standardize fuel efficiency.

[0077] The strategy network parameters are updated using the Q-learning algorithm, and the optimized model is embedded in the electronic control unit to output the timing advance to control the clutch action in real time.

[0078] Assuming a 6×4 medium-heavy truck is taken as the object, four typical driving conditions are selected: urban start-stop condition, throttle opening 25%, engine speed 1500rpm, load 10 tons; high-speed cruising condition, throttle opening 50%, engine speed 2000rpm, load 15 tons; climbing condition, throttle opening 75%, engine speed 2500rpm, load 20 tons; and full-load acceleration condition, throttle opening 100%, engine speed 3000rpm, load 25 tons.

[0079] The model input data is a state vector containing throttle opening, engine speed and load, and the output action is the clutch disengagement time advance.

[0080]

[0081] When maximizing, the lead time is prioritized to reduce delays while also optimizing fuel consumption.

[0082] The performance of the shift delay compensation model based on deep reinforcement learning is shown in Table 2.

[0083] Table 2. Summary of the performance of the shift delay compensation model based on deep reinforcement learning

[0084] Operating conditions Throttle opening (%) Engine speed (rpm) Load (tons) Lead time (s) Actual shift delay time (s) Fuel consumption rate (g / kWh) City Start-Stop 25 1500 10 0.18 0.62 -2.8% High-speed cruise 50 2000 15 0.22 0.58 -2.5% Climbing 75 2500 20 0.28 0.52 -2.9% Full load acceleration 100 3000 25 0.35 0.45 -2.2%

[0085] According to the experimental data, the average delay decreased from the baseline of 0.8 seconds to 0.54 seconds, approaching 35% of the theoretical target. Overall fuel consumption was further reduced by 2.6%, mainly due to improved shift smoothness and reduced engine transient conditions. Throttle opening and timing advance were positively correlated; under high load, the timing advance increased to compensate for the hydraulic actuator inertia, verifying the model's adaptability. Experimental data shows that the model effectively shortens the power interruption time during gear shifts and reduces the proportion of engine transient conditions by predictively controlling the clutch engagement timing. The optimized shift strategy significantly improves shift smoothness and reduces speed fluctuations and driving jerks caused by power interruptions. Furthermore, the increased engine operating time under steady-state conditions further reduces the fluctuation range of fuel consumption rate, verifying the model's dual effect of improving fuel economy and driving comfort.

[0086] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media containing computer-usable program code.

[0087] The present invention can provide computer program instructions to a management platform of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing equipment to produce a machine, such that the instructions executed by the management platform of the computer or other programmable data processing equipment produce means for implementing the system.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that perform the functions of the system.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions of the system.

Claims

1. An AMT low fuel consumption gear configuration system comprising an engine, an electronically controlled mechanical automatic transmission, a clutch and a rear axle, characterized in that, Comprising: a shift control module configured with a preset throttle opening and gear ratio corresponding table, for dynamically generating upshift and downshift points according to throttle opening and current gear ratio; in the corresponding table, the upshift and downshift points increase nonlinearly with the increase of throttle opening; a simulation running module configured with heavy commercial vehicle test condition parameters, for simulating actual road conditions to optimize gear ratio configuration; a sensor module for real-time collection of vehicle speed, engine speed and throttle opening data and feedback to the shift control module; a torque distribution optimization module for establishing a dynamic torque distribution model by real-time collection of slope, road friction coefficient and wheel slip rate data during vehicle driving, combining transmission output torque and rear axle differential characteristics, and calculating ideal torque distribution ratio according to road condition parameters; a shift delay compensation module for predicting the best shift timing and controlling the clutch action in advance by training the strategy network with historical shift data and real-time driving state.

2. The system of claim 1, wherein: the rear axle is integrated with a differential module for dynamically adjusting the driving force distributed to the left and right rear wheels according to the torque output by the transmission.

3. The system of claim 1, wherein: the shift control module is connected with the clutch signal, disconnecting the power connection between the engine and the transmission during the shift process, and optimizing the shift timing based on real-time feedback of vehicle speed, engine speed and load state.

4. The system of claim 1, wherein: the simulation running module integrates resistance coefficient, slope parameter and inertia load data matching actual road conditions, and reduces the error between simulation fuel consumption and actual test through iterative calculation.

5. The system of claim 4, wherein: the simulation running module performs multi-objective optimization on shift logic parameters through genetic algorithm, and the objective function includes fuel consumption rate, shift smoothness and engine emission indicators.

6. The system of claim 1, wherein: the shift delay compensation module defines the action space as the clutch disconnection time advance, and the reward function is: wherein is the reward function output; is the time optimization weight; is the fuel consumption weight; is the actual shift delay time; is the reference delay time; is the actual fuel consumption within the current shift cycle; is the reference fuel consumption.

7. An AMT low fuel consumption gear ratio configuration method, characterized in that: the implementation of the method is based on the system of any one of claims 1-6: the method comprises: establishing a simulation model with multiple parameter couplings based on heavy commercial vehicle test conditions; dynamically matching the vehicle speed threshold of upshift and downshift points according to the preset throttle opening and gear ratio mapping relationship; cutting off power transmission through the clutch during the shift process, and adjusting the shift delay time based on the real-time working condition of the engine; outputting the optimized gear ratio configuration scheme through the simulation model.

8. A computer device comprising a management platform and a memory, the management platform being connected with the memory, the memory being configured to store a computer program, characterized in that: The management platform is used to execute the computer program stored in the memory to make the computer device execute the method of claim 7.

9. A computer readable storage medium having stored therein a computer program, characterized in that: The computer program is executed to perform the method of claim 7.

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