An adaptive low-power transmission control system and control method

Abstract

The application discloses a kind of self-adapting low-power transmission control system and control method, comprising: S1, the parameter of real-time acquisition engine;S2, according to the parameter of acquisition, electronic control system judges the working condition of engine;S3, according to the result of judgment, future short time engine load change trend is predicted by machine learning algorithm in combination with historical engine data;S4, control module according to engine load change trend, adjust the shifting logic of transmission, transmission ratio and the occasion of minimum power output.The application can realize the optimization configuration of transmission lower power under different engine load, not only improve the response speed and accuracy of transmission, make transmission can better adapt to engine load change, while ensuring the coherence and stability of power output, but also significantly reduce power loss, realize high efficient power transmission and fuel economy under low power.

Description

Technical Field

[0001] This invention relates to the field of transmission control technology, and in particular to an adaptive low-power transmission control system and control method. Background Technology

[0002] With the increasing demand for energy conservation and environmental protection, reducing vehicle energy consumption and improving fuel economy have become important goals for the automotive industry. Through adaptive low-power control, the transmission can dynamically adjust its operating parameters according to actual driving needs and operating conditions to achieve more efficient energy utilization and reduce unnecessary power consumption.

[0003] However, existing technologies for adaptive control of transmissions have problems: they cannot integrate and optimize the transmission and engine as a whole, cannot balance the engine load, resulting in jerking, insufficient power or over-response during driving, and cannot accurately reflect the transmission's shift logic, gear ratio and power output.

[0004] Therefore, it is necessary to improve the existing adaptive low-power transmission control method to solve the above problems. Summary of the Invention

[0005] This invention overcomes the shortcomings of the prior art and provides an adaptive low-power transmission control system and control method.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: an adaptive low-power transmission control system, comprising the following steps:

[0007] S1. Real-time acquisition of engine speed, fuel injection quantity, turbine pressure, coolant temperature and oil temperature parameters, and transmission to the electronic control system;

[0008] S2. Based on the parameters collected in S1, the electronic control system determines the engine's operating status.

[0009] S3. Based on the judgment results in S2 and combined with historical engine data, predict the engine load change trend in the near future using machine learning algorithms.

[0010] S4. The control module adjusts the transmission's shift logic, gear ratio, and the timing of minimum power output based on the engine load change trend in S3.

[0011] In a preferred embodiment of the present invention, in S1, a crankshaft position sensor, a pressure sensor, a flow sensor, and a temperature sensor are provided on the engine side.

[0012] In a preferred embodiment of the present invention, in step S1, when evaluating the engine operating state using the engine speed, fuel injection quantity, turbo pressure, coolant temperature, and oil temperature, a comprehensive evaluation is performed using the following formula: , where is the engine operating state evaluation value, is the engine speed, is the maximum engine speed, is the fuel injection quantity, is the maximum injection quantity, is the turbo pressure, is the maximum turbo pressure, is the coolant temperature, is the maximum coolant operating temperature, is the oil temperature, is the maximum oil operating temperature. is the weight coefficient, which is used to adjust the influence degree of each parameter on the engine operating state.

[0013] In a preferred embodiment of the present invention, when 0 < EWCE < 0.2, the engine is in a light load state; when 0.2 ≤ EWCE < 0.5, the engine is in a medium load state; when 0.5 ≤ EWCE < 0.8, the engine is in a heavy load state; when 0.8 ≤ EWCE < 1, the engine is in a full load state.

[0014] In a preferred embodiment of the present invention, step S3 further includes the following sub-steps, specifically including:

[0015] S31. Extract relevant historical features, including the engine operating state evaluation values corresponding to the engine speed, fuel injection quantity, turbo pressure, coolant temperature, and oil temperature;

[0016] S32. Use the historical features to train the prediction model;<确定

[0017] S33. Take the engine operating state evaluation value collected in real time as the input, input it into the trained prediction model for prediction, and obtain the engine load change trend in the short term in the future.

[0018] In a preferred embodiment of the present invention, step S4 further includes the following sub-steps, specifically including:

[0019] S41. According to the predicted engine load change trend, the control module calculates the target gear of the transmission;

[0020] S42. The control module adjusts the shift logic of the transmission according to the current vehicle operating state and the driver's operation intention; <确定

[0021] S43. Based on the predicted engine load change trend and the adjusted shift logic, the control module calculates the target gear ratio of the transmission.

[0022] S44. Based on the target gear ratio and the current state of the transmission, the control module adjusts the timing of the transmission's minimum power output to ensure that the transmission outputs the minimum power at the appropriate time.

[0023] In a preferred embodiment of the present invention, step S42 specifically includes:

[0024] S421. Receive information on the current vehicle operating status and the driver's operating intentions;

[0025] S422. Based on the target gear calculated in S41, as well as the current vehicle operating status and the driver's operating intention, determine the appropriate gear shifting time.

[0026] S423. Based on the determined shift timing and target gear, adjust the transmission's shift logic, including changing the shift point settings and adjusting the upshift and downshift logic.

[0027] In a preferred embodiment of the present invention, step S43 specifically includes:

[0028] S431. Receive the predicted engine load change trend and shift logic information, including the prediction of future engine load and the shift logic adjusted according to step S42.

[0029] S432. Based on the received information and the current state of the transmission, the control module determines a suitable target gear ratio. The selection of the target gear ratio is mainly based on the engine load requirements and the vehicle's operating state.

[0030] S433: The control module adjusts the state of the transmission according to the target value, including changing the state of the oil pressure and solenoid valves, to adjust the transmission gear ratio.

[0031] In a preferred embodiment of the present invention, step S44 specifically includes:

[0032] S441, Receive target gear ratio and current status information: The control module receives the target gear ratio information from step S43 and the current status data of the transmission, including: gear position, oil pressure, and solenoid valve status.

[0033] S442. Based on the target gear ratio and the current state of the transmission, the control module analyzes and outputs the optimal timing for the lowest power.

[0034] S443, The control module adjusts the timing of the transmission's minimum power output to ensure that the transmission outputs the minimum power at the appropriate time.

[0035] The present invention also provides an adaptive low-power transmission control system, based on the control method described above, comprising:

[0036] Data acquisition module: used to collect parameters such as engine speed, fuel injection quantity, turbine pressure, coolant temperature and oil temperature in real time;

[0037] Electronic control system: used to receive data transmitted from the data acquisition module and determine the engine's operating status;

[0038] Machine learning algorithm module: Used to predict engine load change trends in the near future based on historical engine data and current operating status;

[0039] Control module: Used to receive the prediction results from the machine learning algorithm module and adjust the shift logic, gear ratio and timing of minimum power output of the transmission.

[0040] This invention addresses the shortcomings of the prior art and has the following beneficial effects:

[0041] (1) This invention provides an adaptive low-power transmission control method. By collecting multiple parameters of the engine in real time, the engine's operating state is determined. Combined with the use of machine learning algorithms to predict the engine load change trend in the near future, the transmission's shift logic, gear ratio, and timing of minimum power output are adjusted. This enables optimized configuration of the transmission at lower power under different engine loads. While ensuring the continuity and smoothness of power output, it not only improves the transmission's response speed and accuracy, allowing the transmission to better adapt to engine load changes, but also significantly reduces power loss, achieving efficient power transmission and fuel economy under low power conditions, thus achieving energy saving and emission reduction.

[0042] (2) This invention provides a method for evaluating engine operating status. By adjusting the weighting coefficients, the influence of each parameter on the evaluation result is adjusted, and the current operating status of the engine, such as low load, medium load, high load, or full load, can be accurately determined. This evaluation method helps the transmission to more intelligently adjust the shift logic, gear ratio, and power output, thereby optimizing power and fuel economy. It realizes a precise and adaptive engine operating status evaluation method, providing strong support for the intelligent control of the transmission.

[0043] (3) This invention adjusts the transmission's shift logic, gear ratio, and timing of minimum power output based on engine load variation trends, which helps achieve adaptive low-power control of the transmission. By predicting engine load changes and adjusting transmission parameters in advance, the power consumption of the transmission can be effectively reduced, energy efficiency improved, and engine operating conditions improved, further enhancing the vehicle's fuel economy and power performance. This control method can achieve optimal power output and efficiency under different operating conditions, providing drivers with a smoother and more economical driving experience. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of an adaptive low-power transmission control method according to a preferred embodiment of the present invention;

[0046] Figure 2 This is a flowchart illustrating the prediction model of a preferred embodiment of the present invention for predicting the trend of engine load changes in the near future.

[0047] Figure 3 This is a flowchart illustrating the control module adjusting the transmission according to a preferred embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] like Figure 1 As shown, the present invention provides an adaptive low-power transmission control method, comprising the following steps:

[0050] S1. Real-time acquisition of engine speed, fuel injection quantity, turbine pressure, coolant temperature and oil temperature parameters, and transmission to the electronic control system;

[0051] S2. Based on the parameters collected in S1, the electronic control system determines the engine's operating status.

[0052] S3. Based on the judgment results in S2 and combined with historical engine data, predict the engine load change trend in the near future using machine learning algorithms.

[0053] S4. The control module adjusts the transmission's shift logic, gear ratio, and the timing of minimum power output based on the engine load change trend in S3.

[0054] In step S1, the present invention includes a crankshaft position sensor, a pressure sensor, a flow sensor, and a temperature sensor installed on one side of the engine.

[0055] Engine speed is measured by a crankshaft position sensor, which is typically installed inside the crankcase. It measures engine speed by detecting the crankshaft's rotation angle and rotational speed. The crankshaft position sensor uses either magnetic induction or Hall effect principles to monitor crankshaft rotation in real time and transmits the speed signal to the electronic control system.

[0056] Fuel injection quantity is measured by a flow sensor on the fuel injector. The flow sensor is installed at the outlet of the fuel injector and calculates the fuel injection quantity by measuring the fuel flow rate and volume. The flow sensor uses a differential pressure or turbine operating principle to accurately measure the fuel injection quantity and transmit the data to the electronic control system.

[0057] Turbine pressure is measured by a pressure sensor installed at the turbocharger's intake or exhaust port. The sensor monitors turbine pressure by measuring the intake or exhaust pressure. Using strain gauges or piezoelectric principles, the pressure sensor can monitor turbine pressure in real time and transmit the data to the electronic control system.

[0058] Coolant and oil temperatures are measured using temperature sensors. These sensors are installed in the engine's cooling water or oil passages and measure the engine's coolant and oil temperatures by detecting the temperature of the coolant or engine oil. The temperature sensors operate on the principle of thermistor or thermocouple, enabling real-time monitoring of temperature changes and transmitting the data to the electronic control system.

[0059] These sensors can monitor the engine's operating status in real time and transmit the data to the electronic control system for analysis and processing. By measuring these parameters, a better understanding of the engine's operating status and performance can be achieved, thereby enabling precise control and optimization of the engine.

[0060] In step S2, when evaluating the engine operating condition using engine speed, fuel injection quantity, turbine pressure, coolant temperature, and oil temperature, the following formula is used for comprehensive evaluation: ,in, This is an evaluation value for the engine's operating condition. Engine speed, is the maximum engine speed, is the fuel injection quantity, is the maximum injection quantity, is the turbo pressure, is the maximum turbo pressure, is the coolant temperature, is the maximum working temperature of the coolant, is the oil temperature, is the maximum working temperature of the oil. is the weight coefficient, used to adjust the influence degree of each parameter on the engine working state.

[0061] Through the above comprehensive evaluation formula, comprehensively considering the influence of engine speed, fuel injection quantity, turbo pressure, coolant temperature and oil temperature on the engine working state, an evaluation value of the comprehensive engine working state is obtained, and the value range of the engine working state evaluation value is between 0 and 1.

[0062] According to the above formula, the present invention can obtain the state of the current engine. When 0 < EWCE < 0.2, the engine is in a small load state; 0.2 ≤ EWCE < 0.5, the engine is in a medium load state; 0.5 ≤ EWCE < 0.8, the engine is in a large load state; 0.8 ≤ EWCE < 1, the engine is in a full load state.

[0063] For example, there is a gasoline engine, XYZ - 2000. The measured actual speed is 1000 revolutions per minute, the maximum speed is 6000 revolutions per minute, the fuel injection quantity is 0.5 g / s, the maximum injection quantity is 2 g / s, the turbo pressure is 1.5 bar, the maximum turbo pressure is 2.5 bar, the coolant temperature is 80 °C, the maximum working temperature of the coolant is 120 °C, the oil temperature is 90 °C, and the maximum working temperature of the oil is 150 °C. is selected as 0.3, is selected as 0.2, is selected as 0.2, is selected as 0.1, is selected as 0.2. Based on the above calculation formula, the evaluation value of the engine working state of this gasoline engine is 0.29. Therefore, the current working state of this gasoline engine is in a medium load state.

[0064] It should be noted that in actual applications, it may be necessary to adjust according to the specific engine model, characteristics and actual working conditions. At the same time, the setting of the weight coefficient will also affect the final evaluation value, thus affecting the judgment of the load state.

[0065] As Figure 2 shown, step S3 in the present invention further includes, specifically:

[0066] S31. Extract relevant historical features, including engine operating status evaluation values ​​corresponding to engine speed, fuel injection quantity, turbine pressure, coolant temperature, and oil temperature;

[0067] S32. Use historical features to train the prediction model;

[0068] S33. The real-time collected engine operating status evaluation value is used as input and fed into the trained support vector machine (SVM) model for prediction to obtain the engine load change trend in the near future.

[0069] By following the steps above, historical data and support vector machine (SVM) models can be used to predict the engine load change trend in the near future, providing a more accurate adjustment basis for the adaptive low-power transmission control method, thereby improving the system's efficiency and performance.

[0070] In step S31, features related to engine load change trends are extracted from historical data. These features will be used for subsequent model training and prediction. Specifically, this includes: selecting data records similar to the current engine model, configuration, and operating conditions from historical data, and extracting historical data on engine speed, fuel injection quantity, turbine pressure, coolant temperature, and oil temperature from the records; calculating the engine operating condition evaluation value for each historical record according to the engine operating condition evaluation formula; and storing these features and the corresponding engine operating condition evaluation values ​​as a training dataset.

[0071] In step S32, a prediction model is trained using a support vector machine (SVM) algorithm based on historical features and engine operating status evaluation values. Specifically, this includes: importing an SVM library, such as Scikit-learn; using the extracted historical features as input and the corresponding engine operating status evaluation values ​​as the target output; organizing this data into a dataset where each record contains input features and a corresponding label (target output); and using the SVM algorithm to fit the training data to build a prediction model.

[0072] Cross-validation is used to evaluate the model and adjust hyperparameters to improve prediction performance. For example, hyperparameters such as the kernel type and penalty coefficient C can be adjusted. Through cross-validation, the model performance score under each hyperparameter combination can be obtained. Based on the score, the optimal hyperparameter combination is selected, and the support vector machine (SVM) model is trained.

[0073] In step S33, a trained support vector machine model is used to predict the load change trend in the near future based on the real-time engine operating status assessment values. Specifically, this includes: inputting the real-time engine operating status assessment values ​​into the trained support vector machine model, and the model outputting the load change trend in the near future (e.g., 10s-15s).

[0074] like Figure 3 As shown, in step S4, the control module adjusts the transmission's shift logic, gear ratios, and the timing of minimum power output based on the predicted engine load change trend. Specifically, this includes the following sub-steps:

[0075] S41. Based on the predicted engine load change trend, the control module calculates the target gear of the transmission;

[0076] S42. The control module adjusts the transmission shift logic according to the current vehicle operating status and the driver's operating intention;

[0077] S43. Based on the predicted engine load change trend and the adjusted shift logic, the control module calculates the target gear ratio of the transmission.

[0078] S44. Based on the target gear ratio and the current state of the transmission, the control module adjusts the timing of the transmission's minimum power output to ensure that the transmission outputs the minimum power at the appropriate time.

[0079] Through the above sub-steps, the control module can adaptively adjust the transmission's shift logic, gear ratio, and timing of minimum power output based on the predicted engine load change trend, in order to achieve efficient power transmission and fuel economy under low power conditions.

[0080] In step S42, the transmission's shift logic is adjusted based on the current vehicle operating status, the driver's intention, and the target gear. The specific operation is as follows:

[0081] S421. Receive information on the current vehicle operating status and the driver's operating intention, including but not limited to the current vehicle speed, engine speed, accelerator pedal position, and brake pedal status.

[0082] S422. Determine the target gear: Based on the target gear calculated in S41, as well as the current vehicle operating status and the driver's operating intention, determine the appropriate gear shifting time;

[0083] S423. Adjusting Shift Logic: Based on the determined shift timing and target gear, adjust the transmission's shift logic, including changing the shift point settings and adjusting upshift and downshift logic. For example, if an increase in engine load is predicted, it may be necessary to upshift earlier to maintain appropriate power output; conversely, if a decrease in engine load is predicted, it may be necessary to delay upshifting to optimize fuel economy.

[0084] S424. Record and store shift logic: Record and store the shift logic in the control module.

[0085] In step S43, based on the predicted engine load change trend and the adjusted shift logic, the control module calculates the target gear ratio of the transmission. The specific operation is as follows:

[0086] S431. Receive the predicted engine load change trend and shift logic information, including the prediction of future engine load and the shift logic adjusted according to step S42.

[0087] S432. Analyze and determine the target gear ratio: Based on the received information and the current state of the transmission, the control module determines a suitable target gear ratio. The selection of the target gear ratio is mainly based on the engine load demand and the vehicle's operating state. For example, if it is predicted that the engine load will increase, a smaller gear ratio may be selected to provide greater power; conversely, if it is predicted that the engine load will decrease, a larger gear ratio may be selected to optimize fuel economy.

[0088] S433, Adjust transmission status: The control module will adjust the transmission status according to the target value, including changing the oil pressure and solenoid valve status, to achieve the adjustment of the transmission gear ratio.

[0089] S434. Record and store the target gear ratio: To obtain the target gear ratio, it is necessary to record and store it in the control module.

[0090] Through the above steps, S43 can calculate the appropriate target gear ratio of the transmission based on the predicted engine load change trend and the adjusted shift logic, thereby achieving optimal power and fuel economy while meeting engine load requirements.

[0091] Step S44 involves the control module adjusting the minimum power output timing of the transmission based on the target gear ratio and the current state of the transmission. Specific sub-steps include:

[0092] S441, Receive target gear ratio and current status information: The control module receives the target gear ratio information from step S43 and the current status data of the transmission, including: gear position, oil pressure, and solenoid valve status.

[0093] S442. Analyze the timing of minimum power output: Based on the target gear ratio and the current state of the transmission, the control module analyzes and outputs the optimal timing for minimum power output.

[0094] S443. Adjusting the minimum power output timing: The control module adjusts the minimum power output timing of the transmission to ensure that the transmission outputs the minimum power at the appropriate time.

[0095] S444 Record and store the adjustment results: Record and store the timing of the lowest power output in the control module.

[0096] Based on the above control method, this invention also provides an adaptive low-power transmission control system, comprising:

[0097] Data acquisition module: used to collect parameters such as engine speed, fuel injection quantity, turbine pressure, coolant temperature and oil temperature in real time;

[0098] Electronic control system: used to receive data transmitted from the data acquisition module and determine the engine's operating status;

[0099] Machine learning algorithm module: Uses historical engine data and current operating status to predict engine load change trends in the near future using machine learning algorithms;

[0100] Control module: Used to receive the prediction results from the machine learning algorithm module and adjust the shift logic, gear ratio and timing of minimum power output of the transmission.

[0101] Next, to illustrate the technical solution of the present invention, an actual test will be used as an example.

[0102] The engine is started, and its parameters, including engine speed, fuel injection quantity, turbo pressure, coolant temperature, and oil temperature, are collected in real time and transmitted to the electronic control system. The electronic control system determines the engine's operating status based on the collected parameters. Based on the determination results and historical engine data, it uses machine learning algorithms to predict the engine load change trend in the near future. The electronic control system adjusts the transmission's shift logic, gear ratios, and the timing of minimum power output according to the predicted load change trend. The above steps are repeated to continuously collect data and adjust parameters to achieve the best control effect.

[0103] The performance of the transmission during the experiment was analyzed, including shift smoothness, power, and fuel economy. The control effects before and after adjustment were compared, and the adaptability and low-power performance of the proposed scheme were evaluated. Based on the experimental results, the control scheme was optimized and improved to enhance the transmission's performance. The adjustment strategy is shown in the table below.

[0104]

[0105] In the table above, the adjusted values ​​for engine speed, fuel injection quantity, turbo pressure, coolant temperature, and oil temperature are the actual values ​​of the engine after a period of time.

[0106] Experimental Results Analysis: Under the adjusted control scheme, engine speed and fuel injection quantity increased, improving vehicle power performance. Observation of transmission gear changes revealed that the adjusted control scheme resulted in smoother gear shifts without noticeable jerking, indicating better shift smoothness. Although the fuel injection quantity increased, the engine load was better matched due to the adjusted gear ratio, improving fuel economy. The adjusted control scheme enabled the transmission to output minimum power at appropriate times, improving low-power performance.

[0107] Based on the experimental results, it can be concluded that the adaptive low-power transmission control scheme is feasible and effective. By acquiring engine parameters in real time and predicting load change trends, the electronic control system can adaptively adjust the transmission's shift logic, gear ratio, and the timing of minimum power output to achieve better power performance and fuel economy.

[0108] Based on the preferred embodiments of the present invention described above, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. An adaptive low-power transmission control method, characterized in that, It includes the following steps: S1. Collect the parameters of the engine speed, fuel injection volume, turbo pressure, coolant temperature, and oil temperature in real time and transmit them to the electronic control system; S2. Based on the parameters collected in S1, the electronic control system judges the working state of the engine; S3. Based on the judgment result in S2 and combined with historical engine data, predict the engine load change trend in the short term in the future through a machine learning algorithm; S4. The control module adjusts the shift logic, gear ratio, and the timing of the minimum power output of the transmission according to the engine load change trend in S3; Step S3 also includes the following sub-steps, specifically including: S31. Extract relevant historical features, including the engine working state evaluation values corresponding to the engine speed, fuel injection volume, turbo pressure, coolant temperature, and oil temperature; S32. Use the historical features to train the prediction model; S33. Take the engine working state evaluation value collected in real time as the input and input it into the trained prediction model for prediction to obtain the engine load change trend in the short term in the future; Step S4 also includes the following sub-steps, specifically including: S41. According to the predicted engine load change trend, the control module calculates the target gear of the transmission; S42. The control module adjusts the shift logic of the transmission according to the current vehicle running state and the driver's operation intention; S43. According to the predicted engine load change trend and the adjusted shift logic, the control module calculates the target gear ratio of the transmission; S44. According to the target gear ratio and the current state of the transmission, the control module adjusts the timing of the minimum power output of the transmission to ensure that the transmission outputs the minimum power at an appropriate time.

2. The adaptive low-power transmission control method according to claim 1, characterized in that: In S1, a crankshaft position sensor, a pressure sensor measurement, a flow sensor, and a temperature sensor measurement are set on one side of the engine.

3. The adaptive low-power transmission control method according to claim 1, characterized in that: In step S1, when evaluating the engine operating condition using engine speed, fuel injection quantity, turbine pressure, coolant temperature, and oil temperature, a comprehensive evaluation is performed using the following formula: ,in, This is an evaluation value for the engine's operating condition. Engine speed, This is the engine's maximum speed. This refers to the fuel injection quantity. For maximum injection volume, For turbine pressure, For maximum turbine pressure, Here, represents the coolant temperature; represents the maximum operating temperature of the coolant; represents the oil temperature; and represents the maximum operating temperature of the oil. These are weighting coefficients used to adjust the degree of influence of each parameter on the engine's operating state.

4. The adaptive low-power transmission control method according to claim 3, characterized in that: When 0 < EWCE < 0.2, the engine is in a small load state; 0.2 ≤ EWCE < 0.5, the engine is in a medium load state; 0.5 ≤ EWCE < 0.8, the engine is in a large load state; 0.8 ≤ EWCE < 1, the engine is in a full load state.

5. An adaptive low-power transmission control method according to claim ①, characterized in that: In step S42, it specifically includes: S421. Receive the information of the current vehicle running state and the driver's operation intention; S422. According to the target gear calculated in S41, as well as the current vehicle running state and the driver's operation intention, determine the appropriate shift timing; S423. Based on the determined shift timing and target gear, adjust the shift logic of the transmission, including changing the setting of the shift point and adjusting the logic of upshifting and downshifting.

6. An adaptive low-power transmission control method according to claim ①, characterized in that: In step S43, it specifically includes: S431. Receive the predicted engine load change trend and shift logic information, including the prediction of the future engine load and the shift logic adjusted according to step S42; S432. Based on the received information and the current state of the transmission, the control module determines a suitable target gear ratio. The selection of the target gear ratio is mainly based on the engine load requirements and the vehicle's operating state. S433: The control module adjusts the state of the transmission according to the target value, including changing the state of the oil pressure and solenoid valves, to adjust the transmission gear ratio.

7. The adaptive low-power transmission control method according to claim 1, characterized in that: Step S44 specifically includes: S441, Receive target gear ratio and current status information: The control module receives the target gear ratio information from step S43 and the current transmission status data, including: gear position, oil pressure, and solenoid valve status; S442. Based on the target gear ratio and the current state of the transmission, the control module analyzes and outputs the optimal timing for the lowest power. S443, The control module adjusts the timing of the transmission's minimum power output to ensure that the transmission outputs the minimum power at the appropriate time.

8. An adaptive low-power transmission control system, based on the control method according to any one of claims 1-7, characterized in that, include: Data acquisition module: used to collect parameters such as engine speed, fuel injection quantity, turbine pressure, coolant temperature and oil temperature in real time; Electronic control system: used to receive data transmitted from the data acquisition module and determine the engine's operating status; Machine learning algorithm module: Uses historical engine data and current operating status to predict engine load change trends in the near future; the machine learning algorithm module is also used to implement the following steps: Extract relevant historical features, including engine operating status assessment values ​​corresponding to engine speed, fuel injection quantity, turbine pressure, coolant temperature, and oil temperature; Use historical features to train the prediction model; The real-time collected engine operating status assessment value is used as input and fed into the trained prediction model to predict the engine load change trend in the near future. Control module: Used to receive the prediction results from the machine learning algorithm module and adjust the transmission's shift logic, gear ratio, and timing of minimum power output; the control module is also used to implement the following steps: Based on the predicted trend of engine load change, the control module calculates the target gear of the transmission; The control module adjusts the transmission's shift logic based on the current vehicle operating status and the driver's operating intentions; Based on the predicted engine load change trend and the adjusted shift logic, the control module calculates the target gear ratio of the transmission. Based on the target gear ratio and the current state of the transmission, the control module adjusts the timing of the transmission's minimum power output to ensure that the transmission outputs the minimum power at the appropriate time.

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