A heavy electric engineering equipment intelligent driving control method and device

Abstract

The application discloses a kind of heavy electric engineering equipment intelligent travel control method, device, the method includes the following steps: by road condition acquisition device, real-time acquisition equipment front road surface information and road condition information;Self-learning drive torque control model is constructed according to operating data, road surface and road condition information are comprehensively analyzed, select the driving control mode suitable for current driving condition, analyze the power and speed feedback of drive system, optimize decision to output torque, calculate to obtain safe torque limit boundary value, simultaneously to energy scheduling is carried out to energy-consuming equipment, realizes energy consumption management;According to safe torque limit boundary, the maximum available torque of vehicle, the information of vehicle-mounted traction motor driver, battery management system constructs and continuously corrects torque output value, is transmitted to traction motor driver by whole vehicle controller, controls permanent magnet synchronous motor output corresponding torque.The application realizes torque accurate output, improves the efficiency of complete machine, adaptability, driving safety, reduces the difficulty of personnel operation.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular, to an intelligent driving control method and device for heavy-duty electric engineering equipment. Background Art

[0002] Compared with electric passenger vehicles, heavy-duty electric engineering equipment has the characteristics of large weight, high center of gravity, and low speed. Its application scenarios are harsh, which puts higher requirements on the safety, stability, and reliability of the driving control of engineering equipment.

[0003] The current driving control strategies of heavy-duty electric engineering equipment mostly follow the torque control systems of passenger cars, lacking the ability to coordinate with high loads and unable to effectively identify road environments. In addition, existing heavy-duty electric engineering equipment has many practical problems, such as poor torque control capabilities under complex working conditions and lack of accurate control of slip rate. At the same time, the equipment also suffers from unstable speed and excessive power consumption when operating on slippery roads, uphill at large angles, or downhill over long distances.

[0004] Therefore, it is necessary to develop an intelligent driving control method to improve the energy utilization and driving safety of heavy-duty electric engineering equipment, which has important practical significance and broad market prospects. Summary of the Invention

[0005] On the one hand, the present application provides an intelligent driving control method for heavy-duty electric engineering equipment to solve the technical problems of insufficient energy utilization, driving safety and stability of existing heavy-duty electric engineering equipment.

[0006] This application is implemented through the following scheme:

[0007] A method for intelligent driving control of heavy electric engineering equipment, comprising the steps of:

[0008] Through the road condition collection device, the road surface information and road condition information in front of the equipment can be obtained in real time;

[0009] A self-learning drive torque control model is constructed. The self-learning drive torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to optimize the output torque and calculate the safe torque limit boundary value. At the same time, energy consumption of energy-consuming equipment is dispatched during driving to achieve energy consumption management for all operating conditions of the equipment.

[0010] The torque limit boundary is constructed based on the safety torque limit boundary, the vehicle's maximum available torque, the on-board traction motor driver, and the information of the battery management system to obtain the torque request limit value. The torque output value of the electric drive system is generated based on the torque request limit value, and the torque output value is continuously corrected. The torque output value is then transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque.

[0011] Furthermore, the method further comprises the steps of:

[0012] The various sensors and measurement units of the entire vehicle are integrated to build status monitoring and fault diagnosis functions, monitor the operating status of the equipment in real time, and perform fault diagnosis and prompt corresponding treatment measures when an abnormal situation is detected.

[0013] Furthermore, the method further comprises the steps of:

[0014] Provides an interactive interface between the driver and the equipment, allowing users to view the equipment's operating status, set driving parameters, and receive safety reminders through local display devices or mobile terminals.

[0015] Furthermore, the road surface information includes asphalt, hardened, wetland, ice and snow, and the road condition information is a combination of road surface conditions and road conditions. The road surface conditions include hardened, slippery, sandy, ice and snow, and the road conditions include flat roads, potholes, ramps, and obstacles.

[0016] Furthermore, a self-learning driving torque control model is constructed. The self-learning driving torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to make an optimized decision on the output torque and calculates the safe torque limit boundary value. Specifically, the model includes the following steps:

[0017] A self-learning driving torque control model is constructed, and preset speed limits and preset slip ratios for different road surfaces are set according to the acquired forward road surface information and road condition information as preset parameters of the self-learning driving torque control model;

[0018] When the vehicle first solves and outputs the requested torque, the self-learning driving torque control model uses the preset parameters, rolling resistance F f , air resistance F w is the theoretical calculated value, obtained by looking up the table;

[0019] The vehicle controller first obtains the current parameters of the target vehicle, including vehicle speed, pedal opening, slope angle, vehicle mass, total reduction ratio, transmission system mechanical efficiency, wheel rolling radius, and specific rolling resistance, all of which are preset values. Based on the obtained parameters, real-time calculations are performed according to the following formula:

[0020] F t =F f +F w +F i +F j

[0021] Calculate the torque request value under the current road conditions, slope and slip ratio;

[0022] in: is the driving force, F f 、F w are rolling resistance and air resistance, F i =mgsinθ、F j =ma are slope resistance and acceleration resistance respectively; T d is the driving torque, i T is the total reduction ratio, η T is the mechanical efficiency of the transmission system, r d is the wheel rolling radius; m is the vehicle mass, g is the acceleration of gravity, θ is the road slope angle, and a is the acceleration;

[0023] When the vehicle speed is stable and the slip rate is less than the preset slip rate, the vehicle controller calculates the comprehensive resistance F based on the current traction motor driver feedback torque and speed. r = rolling resistance F f + Air resistance F w ;

[0024] Obtain the required key information, including real-time road slope angle, slip rate, road surface information and road condition information, and build the vehicle's driving torque T under the current road conditions d The corresponding relationship between and acceleration a;

[0025] Similarly, based on diverse road slope angles, slip rates, road surface information, and road condition information, the self-learning drive torque control model is updated in real time based on the identified and solved information, building a multi-dimensional control model between expected acceleration and drive torque:

[0026] a=f(T d ,θ,F f ,F w );

[0027] When road condition recognition fails, the self-learning drive torque control model uses the preset vehicle speed limit for control;

[0028] If the vehicle speed change is greater than the set value or the slip rate is greater than the preset slip rate, the vehicle controller reduces the torque output value at a constant rate until the slip rate meets the preset slip rate, and records the acceleration limit under the current road slope angle, slip rate, road surface information and road condition information;

[0029] Repeat the above steps to obtain the acceleration limit under different vehicle speeds n Constructing acceleration limit boundaries at full speed

[0030] According to T d The linear relationship between the multi-dimensional control model and a is used to calculate the safe torque limit boundary value As the upper limit of the torque request value under any working condition of the vehicle controller, it ensures that the motor output torque is stable and controllable under various road conditions.

[0031] Furthermore, the preset slip rate is:

[0032] Hardened road surface: preset slip rate is 15%;

[0033] Wet road: preset slip rate is 30%;

[0034] Icy and snowy roads: The preset slip rate is 60%;

[0035] Gravel road: preset slip rate is 85%;

[0036] The preset speed limits are:

[0037] Hardened road surface: operating speed is 100% of the maximum speed;

[0038] On wet and slippery roads: the operating speed is 70% to 80% of the maximum speed;

[0039] On icy and snowy roads: the operating speed is 40% to 50% of the maximum speed;

[0040] Gravel road surface: operating speed is 20% to 30% of the maximum speed.

[0041] Furthermore, a torque limit boundary is constructed based on the safe torque limit boundary, the maximum available torque of the vehicle, the onboard traction motor driver, and information from the battery management system to obtain a torque request limit value. A torque output value of the electric drive system is generated based on the torque request limit value, and the torque output value is continuously corrected. The torque output value is then transmitted to the traction motor driver via the vehicle controller to control the permanent magnet synchronous motor to output a corresponding torque. Specifically, the steps include:

[0042] The torque request limit value is obtained by constructing a torque limit boundary based on the safe torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, the real-time acquired power battery SOC, and the allowable charge and discharge power of the power battery;

[0043] Based on the torque request limit value and model predictive control, the torque output value for several future cycles is generated and the torque output value is continuously corrected. The vehicle controller transmits the torque output value to the traction motor driver via CAN to control the permanent magnet synchronous motor to output the corresponding torque.

[0044] Furthermore, a torque limit boundary is constructed based on the safe torque limit boundary, the maximum available torque of the vehicle, the onboard traction motor driver, and information from the battery management system to obtain a torque request limit value. A torque output value of the electric drive system is generated based on the torque request limit value, and the torque output value is continuously corrected. The torque output value is then transmitted to the traction motor driver via the vehicle controller to control the permanent magnet synchronous motor to output a corresponding torque. Specifically, the steps include:

[0045] When the sensor reports the vehicle's tilt angle value and determines that the vehicle is climbing a steep slope, the vehicle controller automatically reduces the power demand of auxiliary systems, including the air conditioning system, and increases the drive system's heat dissipation capacity by increasing the flow rate of the cooling water pump and turning on the cooling fan.

[0046] When the sensor feeds back the vehicle's tilt angle value and the vehicle is in a long-term regenerative braking state, determining that the vehicle is in a long-distance downhill slope, or the driver actively enters the steep slope descent mode, the vehicle controller sends a preset speed limit to the traction motor driver to maintain a relatively stable vehicle speed, reduce the axle brake pressure, and avoid axle brake overheating and failure.

[0047] Furthermore, when the sensor feeds back the vehicle's tilt angle value and the vehicle is in a long-term regenerative braking state, determining that the vehicle is in a long-distance downhill slope, or the driver actively enters the steep slope descent mode, the vehicle controller sends a preset speed limit to the traction motor driver to maintain a relatively stable vehicle speed, reduce the axle brake pressure, and avoid axle brake overheating and failure. Specifically, the steps include:

[0048] The traction motor driver performs regenerative braking according to the received preset speed limit to fully feed back energy;

[0049] The vehicle controller obtains information from the electromagnetic management system. If the power battery SOC exceeds the set threshold of 85%, it issues an energy consumption request and sends a start signal to the auxiliary traction motor driver. The 30kW oil pump motor consumes the regenerative energy to prevent the SOC from being too high and unable to generate regenerative braking torque.

[0050] The oil pump motor converts the regenerative energy into heat energy of the hydraulic system, which increases the temperature of the hydraulic oil. When the hydraulic oil temperature increases, the cooling device is activated to dissipate heat, accelerating the consumption of the feedback energy.

[0051] When the consumed energy is greater than the regenerative energy, the preset speed limit is increased; when the consumed energy is less than the regenerative energy, the preset speed limit is decreased.

[0052] On the other hand, the present application also provides an intelligent driving control device for heavy-duty electric engineering equipment, comprising:

[0053] Environmental perception module, used to obtain real-time road and road condition information in front of the equipment through the road condition collection device;

[0054] A decision-making and planning module is used to build a self-learning drive torque control model. The self-learning drive torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to optimize the output torque and calculate the safe torque limit boundary value. At the same time, energy consumption of energy-consuming equipment is dispatched during driving to achieve energy consumption management for all equipment operating conditions.

[0055] The control execution module is used to establish a torque limit boundary based on the safety torque limit boundary, the vehicle's maximum available torque, the on-board traction motor driver, and information from the battery management system to obtain a torque request limit value, generate a torque output value of the electric drive system based on the torque request limit value, continuously correct the torque output value, and then transmit the torque output value to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque.

[0056] Furthermore, it also includes:

[0057] The status detection and fault diagnosis module is used to integrate the various sensors and measurement units of the vehicle to build status monitoring and fault diagnosis functions, monitor the operating status of the equipment in real time, and perform fault diagnosis and prompt corresponding treatment measures when an abnormal situation is detected.

[0058] Furthermore, it also includes:

[0059] The human-computer interaction module is used to provide an interactive interface between the driver and the equipment, allowing the user to view the equipment's operating status, set driving parameters, and receive safety prompts through a local display device or mobile terminal.

[0060] On the other hand, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the intelligent driving control method for heavy electric engineering equipment when executing the computer program.

[0061] On the other hand, the present application also provides a storage medium, which includes a stored program, and when the program is running, controls the device where the storage medium is located to execute the steps of the intelligent driving control method for heavy electric engineering equipment.

[0062] Compared with the existing technology, this application has the following beneficial effects:

[0063] The intelligent driving control method of this application realizes intelligent control of heavy-duty electric engineering equipment by integrating environmental perception, decision-making planning, and control execution, and has the following advantages:

[0064] 1) Improve overall machine energy efficiency: By building a self-learning drive torque control model, based on the electric drive system's comprehensive perception of load conditions, precise torque output is achieved, improving energy utilization efficiency;

[0065] 2) Enhanced equipment adaptability: This system can avoid mechanical overloads caused by excessive changes in output torque during human operation, overcome the influence of factors such as mechanical wear, hardware deviation, and road condition changes, and automatically perform corrections;

[0066] 3) Improved driving safety: By comprehensively analyzing the power and speed feedback of the drive system, the system optimizes the output torque and calculates the safe torque limit boundary to obtain the control boundary of the slip rate and output torque. This optimizes the hill driving control logic, improves the regenerative braking intervention capability, and reduces the risk of axle brake overheating and failure.

[0067] 4) Reduce the difficulty of manual operation: The self-learning drive torque control model and control execution can adapt to complex road conditions, intelligently dispatch and smooth torque output, reducing the difficulty of manual operation.

[0068] In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail below with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings that constitute a part of this application are used to provide further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute improper limitations on this application.

[0070] Figure 1 This is a flow chart of the intelligent driving control method for heavy electric engineering equipment according to the preferred embodiment of the present application.

[0071] Figure 2 This is a schematic diagram of the working principle of heavy-duty electric engineering equipment.

[0072] Figure 3 This is a flow chart of an intelligent driving control method for heavy electric engineering equipment according to another preferred embodiment of the present application.

[0073] Figure 4 This is a flow chart of an intelligent driving control method for heavy electric engineering equipment according to another preferred embodiment of the present application.

[0074] Figure 5This is a flowchart of the sub-steps of step S2 in another preferred embodiment of the present application.

[0075] Figure 6 This is a flowchart of the sub-steps of step S3 in another preferred embodiment of the present application.

[0076] Figure 7 This is a flowchart of the sub-steps of step S3 in another preferred embodiment of the present application.

[0077] Figure 8 This is a flowchart of the sub-steps of step S34 in another preferred embodiment of the present application.

[0078] Figure 9 This is a flowchart of the sub-steps of step S34 in another preferred embodiment of the present application.

[0079] Figure 10 This is a schematic diagram of the module of the intelligent driving control device for heavy-duty electric engineering equipment according to the preferred embodiment of the present application.

[0080] Figure 11 This is a schematic diagram of a module of an intelligent driving control device for heavy-duty electric engineering equipment according to another preferred embodiment of the present application.

[0081] Figure 12 This is a schematic diagram of a module of an intelligent driving control device for heavy-duty electric engineering equipment according to another preferred embodiment of the present application.

[0082] Figure 13 This is a schematic block diagram of an electronic device entity according to a preferred embodiment of the present application.

[0083] Figure 14 It is a diagram of the internal structure of a computer device according to a preferred embodiment of the present application. DETAILED DESCRIPTION

[0084] The embodiments of the present application are described in detail below with reference to the accompanying drawings, but the present application can be implemented in a variety of different ways defined and covered below.

[0085] Example 1

[0086] like Figure 1 As shown, the preferred embodiment of the present application provides an intelligent driving control method for heavy electric engineering equipment, comprising the steps of:

[0087] S1. Obtaining real-time road and road condition information ahead of the equipment through a road condition collection device installed on the vehicle;

[0088] S2. Constructing a self-learning drive torque control model. The self-learning drive torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to optimize the output torque, calculates the safe torque limit boundary value, and simultaneously performs energy scheduling on energy-consuming equipment during driving to achieve energy consumption management for all operating conditions of the equipment;

[0089] S3. Construct a torque limit boundary based on the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and information from the battery management system to obtain a torque request limit value. Generate a torque output value of the electric drive system based on the torque request limit value, continuously correct the torque output value, and then transmit the torque output value to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque.

[0090] Figure 2 This is a schematic diagram of the working principle of heavy-duty electric engineering equipment. The drive system includes a power battery pack, a permanent magnet synchronous motor, a traction motor drive (Motor Control Unit (MCU), a speed reducer, front and rear drive axles, and a vehicle control unit (VCU). The heavy-duty electric engineering equipment also includes an auxiliary motor (30kW oil pump motor), an on-board charger, a cooling water pump, and a cooling fan. External charging uses a DC charging station.

[0091] The intelligent driving control method of this embodiment achieves intelligent control of heavy-duty electric engineering equipment by integrating environmental perception, decision-making planning, and control execution. It has the following advantages:

[0092] 1) Improve overall machine energy efficiency: By building a self-learning drive torque control model, based on the electric drive system's comprehensive perception of load conditions, precise torque output is achieved, improving energy utilization efficiency;

[0093] 2) Enhanced equipment adaptability: This system can avoid mechanical overloads caused by excessive changes in output torque during human operation, overcome the influence of factors such as mechanical wear, hardware deviation, and road condition changes, and automatically perform corrections;

[0094] 3) Improved driving safety: By comprehensively analyzing the power and speed feedback of the drive system, the system optimizes the output torque and calculates the safe torque limit boundary to obtain the control boundary of the slip rate and output torque. This optimizes the hill driving control logic, improves the regenerative braking intervention capability, and reduces the risk of axle brake overheating and failure.

[0095] 4) Reduce the difficulty of manual operation: The self-learning drive torque control model and control execution can adapt to complex road conditions, intelligently dispatch and smooth torque output, reducing the difficulty of manual operation.

[0096] Example 2

[0097] like Figure 3 As shown, in another preferred embodiment of the present application, the intelligent driving control method for heavy electric engineering equipment further includes the steps of:

[0098] S4. Integrate the various sensors and measurement units of the vehicle to build status monitoring and fault diagnosis functions, monitor the operating status of the equipment in real time, and perform fault diagnosis and prompt corresponding treatment measures when abnormal conditions are detected.

[0099] The control method of this embodiment monitors the operating status of the equipment in real time, and when an abnormal situation is detected, performs fault diagnosis and prompts corresponding treatment measures to ensure that the equipment can promptly perform fault diagnosis, shut down safely or take corresponding measures under abnormal circumstances.

[0100] Example 3

[0101] like Figure 4 As shown, in another preferred embodiment of the present application, the intelligent driving control method for heavy electric engineering equipment further includes the steps of:

[0102] S5. Provide an interactive interface between the driver and the equipment, so that the user can view the operating status of the equipment, set driving parameters, and receive safety prompts through a local display device or mobile terminal.

[0103] This embodiment provides an interactive interface between the driver and the equipment. The user can view the operating status of the equipment, set driving parameters, receive safety prompts and other information through a local display device or mobile terminal, thereby improving the human-computer interaction performance and allowing the user to promptly understand real-time information such as system status parameters to ensure safe and controllable operation of the system.

[0104] Example 4

[0105] In another preferred embodiment of the present application, the road surface information includes asphalt, hardened, wetland, ice and snow, and the road condition information is a combination of road surface conditions and road conditions. The road surface conditions include hardened, slippery, sandy, ice and snow, and the road conditions include flat roads, potholes, ramps, and obstacles.

[0106] This embodiment provides a variety of common road surface information and road condition information, thereby improving the applicability and flexibility of the entire control method and ensuring that the control method meets the control requirements under different current working conditions.

[0107] Example 5

[0108] In another preferred embodiment of the present application, a self-learning driving torque control model is constructed. The self-learning driving torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information, and selects a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to optimize the output torque and calculate the safe torque limit boundary value. Specifically, the steps include:

[0109] S21, constructing a self-learning driving torque control model, and setting preset speed limits and preset slip ratios for different road surfaces based on the acquired forward road surface information and road condition information as preset parameters of the self-learning driving torque control model;

[0110] S22, when the vehicle first solves and outputs the requested torque, the self-learning driving torque control model uses the preset parameters, rolling resistance F f , air resistance F w is the theoretical calculated value, obtained by looking up the table;

[0111] S23. The vehicle controller first obtains the current parameters of the target vehicle, including vehicle speed, pedal opening, slope angle, vehicle mass, total reduction ratio, transmission system mechanical efficiency, wheel rolling radius, and specific rolling resistance, all of which are preset values. Based on the obtained parameters, the vehicle controller performs real-time calculations according to the following formula:

[0112] F t =F f +F w +F i +F j

[0113] Calculate the torque request value under the current road conditions, slope and slip ratio;

[0114] in: is the driving force, F f 、F w are rolling resistance and air resistance, F i =mgsinθ、F j =ma are slope resistance and acceleration resistance respectively; T d is the driving torque, i T is the total reduction ratio, η T is the mechanical efficiency of the transmission system, r d is the wheel rolling radius; m is the vehicle mass, g is the acceleration of gravity, θ is the road slope angle, and a is the acceleration;

[0115] S24, when the vehicle speed is stable and the slip rate is less than the preset slip rate, the vehicle controller calculates the comprehensive resistance F based on the current traction motor driver feedback torque and speed. r = rolling resistance F f + Air resistance Fw ;

[0116] S25, obtain the required key information, including real-time road slope angle, slip rate, road surface information and road condition information, and construct the vehicle's driving torque T under the current road conditions d The corresponding relationship between and acceleration a;

[0117] S26. Similarly, based on diverse road slope angles, slip rates, road surface information, and road condition information, the self-learning drive torque control model is updated in real time based on the identification and solution information to construct a multi-dimensional control model between the expected acceleration and drive torque:

[0118] a=f(T d ,θ,F f ,F w );

[0119] S27. When the road condition recognition fails, the self-learning driving torque control model uses the preset vehicle speed limit for control;

[0120] S28. If the vehicle speed change is greater than a set value or the slip ratio is greater than a preset slip ratio, the vehicle controller reduces the torque output at a constant rate until the slip ratio meets the preset slip ratio, and records the acceleration limit under the current road slope angle, slip ratio, road surface information, and road condition information;

[0121] S29. Repeat the above steps to obtain the acceleration limit under different vehicle speeds n. Constructing acceleration limit boundaries at full speed

[0122] S210, according to T d The linear relationship between the multi-dimensional control model and a is used to calculate the safe torque limit boundary value As the upper limit of the torque request value of the vehicle controller under any working condition, the torque request value of the VCU under any working condition cannot be greater than T max , ensuring that the motor output torque is stable and controllable under various road conditions.

[0123] The self-learning drive torque control model, through self-learning and optimization, can more precisely control torque output, improving system performance and efficiency. Based on input information such as current slip rate, slope angle, and vehicle weight, and real-time feedback such as requested torque and acceleration, the model identifies the current equipment load and automatically calculates the required torque to overcome resistance. This improves torque control accuracy and overall system performance, and can automatically adjust for factors such as mechanical wear, hardware deviations, and changing road conditions.

[0124] The self-learning drive torque control model is not limited to a specific application scenario and can be adjusted and optimized according to different application requirements. It can effectively prevent mechanical overload problems caused by excessive torque, improve system safety, reduce unnecessary energy consumption, improve energy efficiency, and reduce driving pressure on personnel.

[0125] In summary, this embodiment constructs a self-learning drive torque control model, and calculates the safe torque limit boundary value between the slip rate and the output torque under different road information, road condition information, and different vehicle speeds based on the comprehensive perception of the electric drive system on the load conditions as the upper limit of the torque request value of the vehicle controller under any working condition. The torque request value of the VCU under any working condition cannot be greater than T max , ensuring that the motor output torque is stable and controllable under various road conditions, avoiding mechanical overload problems caused by excessive changes in output torque during human operation, overcoming the influence of factors such as mechanical wear, hardware deviation, and road condition changes, automatically correcting and achieving precise torque output, so that the equipment has good grip performance and improves equipment safety; improving energy utilization efficiency, being able to adapt to complex road conditions, intelligent scheduling, smooth torque output, and reducing the difficulty of manual operation (see Figure 5 ).

[0126] Example 6

[0127] In another preferred embodiment of the present application, the preset slip rate is:

[0128] Hardened road surface: preset slip rate is 15%;

[0129] Wet road: preset slip rate is 30%;

[0130] Icy and snowy roads: The preset slip rate is 60%;

[0131] Gravel road: The preset slip rate is 85%.

[0132] This embodiment initializes and sets preset slip rates suitable for different road surfaces, thereby ensuring that the slip rate of the self-learning driving torque control model in the initial control stage can adapt to the needs of the current road surface, and provides optimal raw data for subsequent comprehensive perception calculations to obtain safe torque limit boundary values ​​between slip rates and output torque under different road surface information, road condition information, and different vehicle speeds, thereby avoiding excessive data fluctuations and ensuring smooth torque output during the control process.

[0133] Example 7

[0134] In another preferred embodiment of the present application, the preset speed limit is:

[0135] Hardened road surface: operating speed is 100% of the maximum speed;

[0136] On wet and slippery roads: the operating speed is 70% to 80% of the maximum speed;

[0137] On icy and snowy roads: the operating speed is 40% to 50% of the maximum speed;

[0138] Gravel road surface: operating speed is 20% to 30% of the maximum speed.

[0139] This embodiment initializes and sets preset speed limits suitable for different road surfaces. The basic principle is: when the road slip rate is high, the maximum speed limit is limited to a smaller value, thereby improving the equipment's adhesion level, ensuring that the speed limit of the self-learning drive torque control model in the initial control stage can adapt to the needs of the current road surface, and providing optimal raw data for subsequent comprehensive perception calculations to obtain safe torque limit boundary values ​​between slip rate and output torque under different road surface information, road condition information, and different vehicle speeds, thereby avoiding excessive data fluctuations and ensuring smooth torque output during the control process.

[0140] Example 8

[0141] like Figure 6 As shown, in another preferred embodiment of the present application, a torque limit boundary is established based on the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and information from the battery management system to obtain a torque request limit value. A torque output value of the electric drive system is generated based on the torque request limit value, and the torque output value is continuously corrected. The torque output value is then transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque. Specifically, the steps include:

[0142] S31. Establishing a torque limit boundary based on a safe torque limit boundary, a maximum available torque of the vehicle, an onboard traction motor driver, a real-time acquired power battery SOC, and an allowable charge and discharge power of the power battery to obtain a torque request limit value;

[0143] S32. Based on the torque request limit value and model predictive control, the torque output value for several future cycles is generated, and the torque output value is continuously corrected. The vehicle controller transmits the torque output value to the traction motor driver via CAN to control the permanent magnet synchronous motor to output the corresponding torque.

[0144] On the one hand, this embodiment constructs a torque limit boundary based on the safe torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, the real-time acquired power battery SOC, and the allowable charge and discharge power of the power battery to obtain a torque request limit value, so that the torque output conforms to the limitations of the current actual hardware configuration, such as the power battery, and the torque output is within the capacity, avoiding exceeding the range of the current actual hardware configuration. While ensuring safety, the torque output can also match the actual capabilities provided by the current actual hardware configuration to avoid over-limit. On the other hand, this application generates torque output values ​​for several future cycles based on the torque request limit value and model predictive control (MPC), and continuously corrects the torque output value as a corresponding parameter to control the permanent magnet synchronous motor to output the corresponding torque, thereby obtaining better dynamic control performance based on rolling optimization.

[0145] Example 9

[0146] like Figure 7 As shown, in another preferred embodiment of the present application, a torque limit boundary is established based on the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and information from the battery management system to obtain a torque request limit value. A torque output value of the electric drive system is generated based on the torque request limit value, and the torque output value is continuously corrected. The torque output value is then transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque. Specifically, the steps include:

[0147] S33. When the sensor feeds back the vehicle's tilt angle value and determines that the vehicle is climbing a steep slope, the vehicle controller automatically reduces the power demand of auxiliary systems, including the air conditioning system, and simultaneously increases the heat dissipation capacity of the drive system, including increasing the flow rate of the cooling water pump and turning on the cooling fan.

[0148] S34. When the sensor feeds back the vehicle's tilt angle value and the vehicle is in a long-term regenerative braking state, determining that the vehicle is in a long-distance downhill slope, or the driver actively enters the steep slope descent mode, the vehicle controller sends a preset speed limit to the traction motor driver to maintain a relatively stable vehicle speed, reduce the axle brake pressure, and avoid axle brake overheating and failure.

[0149] This embodiment has the ability to distribute and control energy consumption. When the power demand is large, the energy consumption of the auxiliary system is reduced, and the driving capacity and regenerative braking capacity of the vehicle are maximized. On the one hand, when it is determined that the vehicle is climbing a large slope, the power demand of the auxiliary system is reduced and the heat dissipation capacity of the drive system is increased, ensuring that the driving capacity of the vehicle is maximized when climbing a large slope to ensure the vehicle's climbing performance; on the other hand, when the vehicle is on a long downhill descent, or the driver actively enters the steep slope descent mode, the regenerative braking capacity of the vehicle is fully utilized, so that the traction motor driver sends a preset speed limit, maintains a relatively stable vehicle speed, reduces the axle brake pressure, avoids overheating and failure of the axle brake, and provides long-distance or steep slope braking safety for the vehicle.

[0150] Example 10

[0151] like Figure 8 and 9 As shown, in another preferred embodiment of the present application, when the sensor feeds back the vehicle tilt angle value, and the vehicle is in a long-term regenerative braking state, it is determined that the vehicle is in a long-distance downhill slope, or the driver actively enters the steep slope descent mode, the vehicle controller sends a preset speed limit to the traction motor driver to maintain a relatively stable vehicle speed, reduce the axle brake pressure, and avoid axle brake overheating and failure. Specifically, the steps include:

[0152] S341: The traction motor driver performs regenerative braking according to the received preset speed limit to fully feed back energy;

[0153] S342: The vehicle controller obtains information from the electromagnetic management system. If the power battery SOC is higher than the set threshold of 85%, it issues an energy consumption request and sends a start signal to the auxiliary traction motor driver. The 30kW oil pump motor consumes the regenerative energy to prevent the SOC from being too high to provide regenerative braking torque.

[0154] S343, the oil pump motor converts the regenerative energy into heat energy of the hydraulic system, causing the hydraulic oil temperature to rise. When the hydraulic oil temperature rises, the cooling device is activated to dissipate heat, accelerating the consumption of the regenerative energy;

[0155] S344. When the consumed energy is greater than the regenerative energy, the preset speed limit is increased; when the consumed energy is less than the regenerative energy, the preset speed limit is decreased.

[0156] This embodiment actively controls the auxiliary system to consume excess power during regenerative braking. Specifically, when controlling the traction motor driver to perform regenerative braking according to a preset speed limit, it fully regenerates energy. When the power battery SOC exceeds a set threshold of 85%, the pump motor and other components convert the regenerative energy into heat energy for the hydraulic system, raising the hydraulic oil temperature. When the hydraulic oil temperature rises, the cooling device activates to dissipate heat, accelerating the consumption of regenerative energy and thus consuming excess power. Furthermore, this embodiment adaptively adjusts the preset speed limit received by the traction motor driver to regulate the amount of regenerative energy based on the relationship between consumed and regenerative energy, thereby maintaining a balance between regenerative energy and consumed energy, preventing energy accumulation and ensuring the normal, stable operation and service life of the power battery and auxiliary energy-consuming devices. In addition to the oil pump motor and cooling device, other modules including the hydraulic system, booster pump, and heat dissipation system can also be used for energy consumption.

[0157] Example 11

[0158] like Figure 10 As shown, another preferred embodiment of the present application further provides an intelligent driving control device for heavy-duty electric engineering equipment, comprising:

[0159] The environmental perception module is used to obtain real-time road and road condition information ahead of the equipment through a road condition acquisition device installed on the vehicle. The road condition acquisition device, such as a camera, performs image denoising and enhancement processing during acquisition, and uses intelligent algorithms to obtain road and road condition information. It can also include sensor equipment such as an inertial measurement unit and a gyroscope to improve positioning accuracy and stability. It can also use lidar and ultrasonic waves to obtain road information and expand the warning function of the intelligent driving control system.

[0160] A decision-making and planning module is used to build a self-learning drive torque control model. The self-learning drive torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to optimize the output torque and calculate the safe torque limit boundary value. At the same time, energy consumption of energy-consuming equipment is dispatched during driving to achieve energy consumption management for all equipment operating conditions.

[0161] The control execution module is used to establish a torque limit boundary based on the safe torque limit boundary, the vehicle's maximum available torque, the on-board traction motor driver, and information from the battery management system to obtain a torque request limit value, generate a torque output value of the electric drive system based on the torque request limit value, continuously correct the torque output value, and then transmit the torque output value to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque.

[0162] Example 12

[0163] like Figure 11 As shown, in another preferred embodiment of the present application, the intelligent driving control device for heavy electric engineering equipment further includes:

[0164] The status detection and fault diagnosis module is used to integrate various sensors and measurement units of the vehicle to build status monitoring and fault diagnosis functions, monitor the operating status of the equipment in real time, and perform fault diagnosis and prompt corresponding treatment measures when abnormal conditions are detected, including:

[0165] Drive system overload, overspeed, overtemperature;

[0166] The vehicle has excessive slippage and stalls;

[0167] Hydraulic system overpressure, overtemperature and other fault detection, if any abnormal situation is detected, the fault diagnosis can be carried out and the corresponding treatment measures can be prompted.

[0168] Example 13

[0169] like Figure 12 As shown, in another preferred embodiment of the present application, the intelligent driving control device for heavy electric engineering equipment further includes:

[0170] The human-computer interaction module is used to provide an interactive interface between the driver and the equipment, allowing the user to view the equipment's operating status, set driving parameters, and receive safety prompts through a local display device or mobile terminal.

[0171] Example 14

[0172] like Figure 13 As shown, a preferred embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the intelligent driving control method for heavy electric engineering equipment in the above-mentioned embodiment when executing the computer program.

[0173] Example 15

[0174] like Figure 14 As shown, the preferred embodiment of the present application further provides a computer device, which can be a terminal or a liveness detection server, and its internal structure diagram can be as shown in FIG. Figure 14As shown in the figure. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is used to communicate with other computer devices outside through network connection. The computer program is executed by the processor to implement the steps of the heavy electric engineering equipment intelligent driving control method described above.

[0175] Those skilled in the art can understand that, Figure 14 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0176] Embodiment 16

[0177] The preferred embodiments of the present application also provide a storage medium including a stored program, which controls the device where the storage medium is located to execute the steps of the heavy electric engineering equipment intelligent driving control method in the above-mentioned embodiments when the program runs.

[0178] In summary, the above-mentioned embodiments of the present application have the ability to learn the driving torque control model for different working conditions, realize the accurate output of torque, and improve the output capability of the driving system. When driving on a slope, it has the ability to allocate and control energy consumption. When the power demand is high, reduce the energy consumption of the auxiliary system. When regenerative braking, you can actively control the auxiliary system to consume excess power.

[0179] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0180] If the functions described in the method of this embodiment are implemented in the form of a software functional unit and sold or used as an independent product, they can be stored in a storage medium readable by one or more computing devices. Based on this understanding, the part of the embodiment of the present application that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computing device (which can be a personal computer, server, mobile computing device or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0181] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0182] The present application 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 application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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.

[0183] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0185] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic accurate concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0186] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for intelligent driving control of heavy electric engineering equipment, characterized in that: Including steps: Through the road condition collection device, the road surface information and road condition information in front of the equipment can be obtained in real time; A self-learning drive torque control model is constructed. The self-learning drive torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to optimize the output torque and calculate the safe torque limit boundary value. At the same time, energy consumption of energy-consuming equipment is dispatched during driving to achieve energy consumption management for all operating conditions of the equipment. A torque limit boundary is constructed based on the safe torque limit boundary, the maximum available torque of the vehicle, the onboard traction motor driver, and information from the battery management system to obtain a torque request limit value. A torque output value of the electric drive system is generated based on the torque request limit value, and the torque output value is continuously corrected. The torque output value is then transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque; A self-learning driving torque control model is constructed. The self-learning driving torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to make an optimized decision on the output torque and calculates a safe torque limit boundary value. Specifically, the model includes the following steps: Constructing a self-learning driving torque control model, and setting preset speed limits and preset slip ratios for different road surfaces as preset parameters of the self-learning driving torque control model based on the acquired road surface information and road condition information; When the vehicle first solves and outputs the requested torque, the self-learning driving torque control model uses the preset parameters, rolling resistance F f , air resistance F w is the theoretical calculated value, obtained by looking up the table; The vehicle controller first obtains the current parameters of the target vehicle, including vehicle speed, pedal opening, slope angle, vehicle mass, total reduction ratio, transmission system mechanical efficiency, wheel rolling radius, and specific rolling resistance, all of which are preset values. Based on the obtained parameters, real-time calculations are performed according to the following formula: F t =F f +F w +F i +F j Calculate the torque request value under the current road conditions, slope and slip ratio; in: is the driving force, F f 、F w are rolling resistance and air resistance, F i =mgsinθ、F j =ma are slope resistance and acceleration resistance respectively; T d is the driving torque, i T is the total reduction ratio, η T is the mechanical efficiency of the transmission system, r d is the wheel rolling radius; m is the vehicle mass, g is the acceleration of gravity, θ is the road slope angle, and a is the acceleration; When the vehicle speed is stable and the slip rate is less than the preset slip rate, the vehicle controller calculates the comprehensive resistance F based on the current traction motor driver feedback torque and speed. r = rolling resistance F f + Air resistance F w ; Obtain the required key information, including real-time road slope angle, slip rate, road surface information and road condition information, and build the vehicle's driving torque T under the current road conditions d The corresponding relationship between and acceleration a; Similarly, based on diverse road slope angles, slip rates, road surface information, and road condition information, the self-learning drive torque control model is updated in real time based on the identified and solved information, building a multi-dimensional control model between expected acceleration and drive torque: a=f(T d ,θ,F f ,F w ); When road condition recognition fails, the self-learning drive torque control model uses the preset vehicle speed limit for control; If the vehicle speed change is greater than the set value or the slip rate is greater than the preset slip rate, the vehicle controller reduces the torque output value at a constant rate until the slip rate meets the preset slip rate, and records the acceleration limit under the current road slope angle, slip rate, road surface information and road condition information; Repeat the above steps to obtain the acceleration limit under different vehicle speeds n Constructing acceleration limit boundaries at full speed According to T d The linear relationship between the multi-dimensional control model and a is used to calculate the safe torque limit boundary value As the upper limit of the torque request value of the vehicle controller under any working condition, it ensures that the motor output torque is stable and controllable under various road conditions; The preset slip rate is: Hardened road surface: preset slip rate is 15%; wet road surface: preset slip rate is 30%; icy road surface: preset slip rate is 60%; gravel road surface: preset slip rate is 85%; The preset speed limits are: Hardened road surface: operating speed is 100% of the maximum speed; slippery road surface: operating speed is 70% to 80% of the maximum speed; icy and snowy road surface: operating speed is 40% to 50% of the maximum speed; gravel road surface: operating speed is 20% to 30% of the maximum speed.

2. The intelligent driving control method for heavy electric engineering equipment according to claim 1 is characterized in that: Also includes the steps: The various sensors and measurement units of the entire vehicle are integrated to build status monitoring and fault diagnosis functions, monitor the operating status of the equipment in real time, and perform fault diagnosis and prompt corresponding treatment measures when an abnormal situation is detected.

3. The intelligent driving control method for heavy electric engineering equipment according to claim 1 or 2, characterized in that: Also includes the steps: Provides an interactive interface between the driver and the equipment, allowing users to view the equipment's operating status, set driving parameters, and receive safety reminders through local display devices or mobile terminals.

4. The intelligent driving control method for heavy electric engineering equipment according to claim 1 is characterized in that: The road surface information includes asphalt, hardened, wet, ice and snow, and the road condition information is a combination of road surface conditions and road conditions. The road surface conditions include hardened, slippery, sandy, ice and snow, and the road conditions include flat roads, potholes, ramps, and obstacles.

5. The intelligent driving control method for heavy electric engineering equipment according to claim 1 is characterized in that: A torque limit boundary is constructed based on the safe torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and information from the battery management system to obtain a torque request limit value. A torque output value of the electric drive system is generated based on the torque request limit value, and the torque output value is continuously corrected. The torque output value is then transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output a corresponding torque. The specific steps include: The torque request limit value is obtained by constructing a torque limit boundary based on the safe torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, the real-time acquired power battery SOC, and the allowable charge and discharge power of the power battery; Based on the torque request limit value and model predictive control, the torque output value for several future cycles is generated and the torque output value is continuously corrected. The vehicle controller transmits the torque output value to the traction motor driver via CAN to control the permanent magnet synchronous motor to output the corresponding torque.

6. The intelligent driving control method for heavy electric engineering equipment according to claim 5 is characterized in that: A torque limit boundary is constructed based on the safe torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and information from the battery management system to obtain a torque request limit value; a torque output value of the electric drive system is generated based on the torque request limit value, and the torque output value is continuously corrected. The torque output value is then transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output a corresponding torque. Specifically, the steps include: When the sensor reports the vehicle's tilt angle value and determines that the vehicle is climbing a steep slope, the vehicle controller automatically reduces the power demand of auxiliary systems, including the air conditioning system, and increases the drive system's heat dissipation capacity by increasing the flow rate of the cooling water pump and turning on the cooling fan. When the sensor feeds back the vehicle's tilt angle value and the vehicle is in a long-term regenerative braking state, determining that the vehicle is in a long-distance downhill slope, or the driver actively enters the steep slope descent mode, the vehicle controller sends a preset speed limit to the traction motor driver to maintain a relatively stable vehicle speed, reduce the axle brake pressure, and avoid axle brake overheating and failure.

7. The intelligent driving control method for heavy electric engineering equipment according to claim 6 is characterized in that: When the sensor feeds back the vehicle's tilt angle value and the vehicle is in a long-term regenerative braking state, determining that the vehicle is in a long-distance downhill slope, or the driver actively enters the steep slope descent mode, the vehicle controller sends a preset speed limit to the traction motor driver to maintain a relatively stable vehicle speed, reduce the axle brake pressure, and avoid axle brake overheating and failure. The specific steps include: The traction motor driver performs regenerative braking according to the received preset speed limit to fully feed back energy; The vehicle controller obtains information from the battery management system. If the power battery SOC exceeds the set threshold of 85%, it issues an energy consumption request and sends a start signal to the auxiliary traction motor driver. The 30kW oil pump motor consumes the regenerative energy to prevent the SOC from being too high and unable to provide regenerative braking torque. The oil pump motor converts the regenerative energy into heat energy of the hydraulic system, which increases the temperature of the hydraulic oil. When the hydraulic oil temperature increases, the cooling device is activated to dissipate heat, accelerating the consumption of the feedback energy. When the consumed energy is greater than the regenerative energy, the preset speed limit is increased; when the consumed energy is less than the regenerative energy, the preset speed limit is decreased.

8. An intelligent driving control device for heavy electric engineering equipment, characterized in that: include: Environmental perception module, used to obtain real-time road and road condition information in front of the equipment through the road condition collection device; A decision-making and planning module is used to build a self-learning drive torque control model. The self-learning drive torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to optimize the output torque and calculate the safe torque limit boundary value. At the same time, energy consumption of energy-consuming equipment is dispatched during driving to achieve energy consumption management for all equipment operating conditions. a control execution module, configured to construct a torque limit boundary based on the safe torque limit boundary, the maximum available torque of the vehicle, the onboard traction motor driver, and information from the battery management system to obtain a torque request limit value, generate a torque output value of the electric drive system based on the torque request limit value, continuously modify the torque output value, and then transmit the torque output value to the traction motor driver via the vehicle controller to control the permanent magnet synchronous motor to output a corresponding torque; A self-learning driving torque control model is constructed. The self-learning driving torque control model performs a comprehensive analysis based on operating data, road surface information, and road condition information to select a driving control mode suitable for the current driving condition. The driving control mode comprehensively analyzes the power and speed feedback of the drive system to make an optimized decision on the output torque and calculates a safe torque limit boundary value. Specifically, the model includes the following steps: Constructing a self-learning driving torque control model, and setting preset speed limits and preset slip ratios for different road surfaces as preset parameters of the self-learning driving torque control model based on the acquired road surface information and road condition information; When the vehicle first solves and outputs the requested torque, the self-learning driving torque control model uses the preset parameters, rolling resistance F f , air resistance F w is the theoretical calculated value, obtained by looking up the table; The vehicle controller first obtains the current parameters of the target vehicle, including vehicle speed, pedal opening, slope angle, vehicle mass, total reduction ratio, transmission system mechanical efficiency, wheel rolling radius, and specific rolling resistance, all of which are preset values. Based on the obtained parameters, real-time calculations are performed according to the following formula: F t =F f +F w +F i +F j Calculate the torque request value under the current road conditions, slope and slip ratio; in: is the driving force, F f 、F w are rolling resistance and air resistance, F i =mgsinθ、F j =ma are slope resistance and acceleration resistance respectively; T d is the driving torque, i T is the total reduction ratio, η T is the mechanical efficiency of the transmission system, r d is the wheel rolling radius; m is the vehicle mass, g is the acceleration of gravity, θ is the road slope angle, and a is the acceleration; When the vehicle speed is stable and the slip rate is less than the preset slip rate, the vehicle controller calculates the comprehensive resistance F based on the current traction motor driver feedback torque and speed. r = rolling resistance F f + Air resistance F w ; Obtain the required key information, including real-time road slope angle, slip rate, road surface information and road condition information, and build the vehicle's driving torque T under the current road conditions d The corresponding relationship between and acceleration a; Similarly, based on diverse road slope angles, slip rates, road surface information, and road condition information, the self-learning drive torque control model is updated in real time based on the identified and solved information, building a multi-dimensional control model between expected acceleration and drive torque: a=f(T d ,θ,F f ,F w ); When road condition recognition fails, the self-learning drive torque control model uses the preset vehicle speed limit for control; If the vehicle speed change is greater than the set value or the slip rate is greater than the preset slip rate, the vehicle controller reduces the torque output value at a constant rate until the slip rate meets the preset slip rate, and records the acceleration limit under the current road slope angle, slip rate, road surface information and road condition information; Repeat the above steps to obtain the acceleration limit under different vehicle speeds n Constructing acceleration limit boundaries at full speed According to T d The linear relationship between the multi-dimensional control model and a is used to calculate the safe torque limit boundary value As the upper limit of the torque request value of the vehicle controller under any working condition, it ensures that the motor output torque is stable and controllable under various road conditions; The preset slip rate is: Hardened road surface: preset slip rate is 15%; wet road surface: preset slip rate is 30%; icy and snowy road surface: The preset slip rate is 60%; for gravel roads: the preset slip rate is 85%; The preset speed limits are: Hardened road surface: operating speed is 100% of the maximum speed; slippery road surface: operating speed is 70% to 80% of the maximum speed; icy and snowy road surface: operating speed is 40% to 50% of the maximum speed; gravel road surface: operating speed is 20% to 30% of the maximum speed.

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