Intelligent driving control method and device for heavy electric engineering equipment

Abstract

The invention discloses an intelligent driving control method and device for heavy electric engineering equipment, and the method comprises the steps: obtaining the road surface information and road condition information in front of the equipment in real time through a road condition collection device; constructing a self-learning driving torque control model, carrying out comprehensive analysis according to the operation data and the road surface and road condition information, selecting a driving control mode suitable for the current driving working condition to analyze the power and speed feedback of a driving system, carrying out optimization decision on the output torque, and calculating to obtain a safety torque limit boundary value; meanwhile, energy scheduling is carried out on the energy consumption equipment to realize energy consumption management; and according to the safety torque limiting boundary, the maximum available torque of the vehicle, the vehicle-mounted traction motor driver and the information of the battery management system, a torque output value which is constructed and continuously corrected is transmitted to the traction motor driver through the vehicle control unit to control the permanent magnet synchronous motor to output corresponding torque. According to the invention, accurate torque output is realized, the energy efficiency, adaptability and driving safety of the whole machine are improved, and the operation difficulty of personnel is reduced.

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 electric engineering equipment. Background Art

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

[0003] At present, the driving control strategies of heavy-duty electric engineering equipment mostly follow the torque control system of passenger cars, lack the ability to coordinate high loads, and cannot effectively identify the road environment. In addition, existing heavy-duty electric engineering equipment has many practical problems such as poor torque control ability under complex working conditions and lack of accurate control of slip rate. At the same time, the equipment has unstable speed and excessive power consumption when driving on slippery roads, uphill at large angles, and downhill for long distances.

[0004] Therefore, it is necessary to develop an intelligent driving control method to improve the energy utilization and driving safety of heavy 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 comprises the following steps:

[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] Construct a self-learning driving torque control model, which performs 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 driving system to make an optimal decision on the output torque, calculates the safe torque limit boundary value, and performs energy scheduling on energy-consuming equipment during driving to achieve energy consumption management of the equipment under all working conditions;

[0010] A torque limit boundary is constructed based on the safety torque limit boundary, the vehicle's maximum available torque, the vehicle's 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.

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

[0012] The various sensors and measuring units of the 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 abnormal conditions are 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 operating status of the equipment, set driving parameters, and receive safety prompts through local display devices or mobile terminals.

[0015] Furthermore, the pavement information includes asphalt, hardened, wetland, ice and snow, and the road condition information is a combination of pavement conditions and road conditions. The pavement conditions include hardened, slippery, sandy, and 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 the 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 driving system to make an optimal decision on the output torque, and calculates the safe torque limit boundary value, which specifically 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 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 drive 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, which are all 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 condition, slope and slip ratio;

[0022] in: is the driving force, F f 、F w are rolling resistance and air resistance, respectively, i = mgsinθ, F j =ma are the 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 feedback torque and speed of the traction motor driver. 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 construct the vehicle's driving torque T under the current road conditions d The corresponding relationship between and acceleration a;

[0025] Similarly, based on the 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 according to the identification and solution information to build a multi-dimensional control model between the 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 value at 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] Slippery road: preset slip rate is 30%;

[0034] Ice and snow road: the preset slip rate is 60%;

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

[0036] The preset speed limits are:

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

[0038] Wet and slippery road: 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 according to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system to obtain a torque request limit value, a torque output value of the electric drive system is generated according to the torque request limit value, and the torque output value is continuously corrected, and then the torque output value is transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque, specifically including the steps of:

[0042] The torque request limit value is obtained by constructing the torque limit boundary according to 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 according to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system to obtain a torque request limit value, a torque output value of the electric drive system is generated according to the torque request limit value, and the torque output value is continuously corrected, and then the torque output value is transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque, which specifically also includes the steps of:

[0045] When the sensor feeds back the vehicle's tilt angle value and determines that the vehicle is climbing a large slope, the vehicle controller automatically reduces the power demand of the auxiliary system, which includes the air conditioning system, and increases the heat dissipation capacity of the drive system, including: increasing the flow 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 descent, 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 axle brake pressure, and avoid axle brake overheating and failure.

[0047] Furthermore, 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, 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 overheating and failure of the axle brake. 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 the electromagnetic management system information. If the power battery SOC is higher than the set threshold of 85%, an energy consumption request is issued. The vehicle controller sends a start signal to the auxiliary traction motor driver. The 30kW oil pump motor will consume the feedback energy to avoid the SOC being too high to perform regenerative braking torque.

[0050] The oil pump motor converts the regenerative energy into the heat energy of the hydraulic system to increase the temperature of the hydraulic oil. When the temperature of the hydraulic oil 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 regenerated energy, the preset speed limit is increased; when the consumed energy is less than the regenerated 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 surface information and road condition information in front of the equipment through a road condition collection device;

[0054] A decision-making and planning module is used to construct a self-learning driving torque control model. The self-learning driving torque control model performs a comprehensive analysis based on the 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 driving system to make an optimal decision on the output torque, calculates the safe torque limit boundary value, and performs energy scheduling on energy-consuming equipment during driving to achieve energy consumption management of the equipment under all working conditions;

[0055] The control execution module is used to construct a torque limit boundary according to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system to obtain a torque request limit value, generate a torque output value of the electric drive system according to the torque request limit value, and 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 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.

[0058] Furthermore, it also includes:

[0059] The human-computer interaction module is used to 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 prompt information 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 further provides a storage medium, which includes a stored program, and when the program is run, 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 prior art, this application has the following beneficial effects:

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

[0064] 1) Improve the energy efficiency of the whole machine: By building a self-learning drive torque control model, based on the comprehensive perception of the electric drive system on the load conditions, the torque can be accurately output and the energy utilization efficiency can be improved;

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

[0066] 3) Improve driving safety: Optimize the output torque by comprehensively analyzing the power and speed feedback of the drive system, calculate the safe torque limit boundary value to obtain the control boundary of the slip rate and output torque, optimize the ramp driving control logic, improve the feedback braking intervention capability, and reduce the risk of axle brake overheating 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, and reduce 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 with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present 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 schematic flow chart of the sub-steps of step S2 of another preferred embodiment of the present application.

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

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

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

[0078] Fig. 9 This is a schematic flow chart of the sub-steps of step S34 of another preferred embodiment of the present application.

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

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

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

[0082] Fig.13 It is a schematic block diagram of an electronic device entity of a preferred embodiment of the present application.

[0083] Fig.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 in conjunction with 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. Obtain the road surface information and road condition information in front of the equipment in real time through the road condition collection device installed on the vehicle;

[0088] S2. Construct a self-learning driving torque control model, wherein the self-learning driving torque control model performs a comprehensive analysis based on the 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 driving system to optimize the output torque, calculates the safe torque limit boundary value, and performs energy scheduling on energy-consuming equipment during driving to achieve energy consumption management of the equipment under all working conditions;

[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 the information of 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, and 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 principle of heavy-duty electric engineering equipment. The drive device includes a power battery pack, a permanent magnet synchronous motor, a traction motor driver (Motor Control Unit, MCU), a reducer, front and rear drive axles, and a vehicle control unit (VCU). Heavy-duty electric engineering equipment also includes an auxiliary motor (30kW oil pump motor), an on-board charger (On-Board Charger), a cooling water pump, and a cooling fan; external charging uses a DC charging pile.

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

[0092] 1) Improve the energy efficiency of the whole machine: By building a self-learning drive torque control model, based on the comprehensive perception of the electric drive system on the load conditions, the torque can be accurately output and the energy utilization efficiency can be improved;

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

[0094] 3) Improve driving safety: Optimize the output torque by comprehensively analyzing the power and speed feedback of the drive system, calculate the safe torque limit boundary value to obtain the control boundary of the slip rate and output torque, optimize the ramp driving control logic, improve the feedback braking intervention capability, and reduce the risk of axle brake overheating 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, and reduce 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 processing 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 a 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 enabling 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 pavement information includes asphalt, hardened, wetland, ice and snow, and the road condition information is a combination of pavement conditions and road conditions. The pavement conditions include hardened, slippery, sandy, and 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, and the self-learning driving torque control model performs a comprehensive analysis based on the 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 driving system to optimize the output torque and calculate the safe torque limit boundary value, which specifically includes the steps of:

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

[0110] S22, when the vehicle first solves and outputs the requested torque, the self-learning drive 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, which are all preset values, and performs real-time calculation based on the obtained parameters 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 condition, slope and slip ratio;

[0114] in: is the driving force, F f 、F w are rolling resistance and air resistance, respectively, i = mgsinθ, F j =ma are the 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 feedback torque and speed of the traction motor driver. 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 driving torque T under the current road condition d The corresponding relationship between and acceleration a;

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

[0121] S29, repeat the above steps to obtain the acceleration limit value 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 can control torque output more accurately and improve system performance and efficiency through self-learning and optimization. The model is based on input information such as current slip rate, slope angle, vehicle weight, and identifies the current equipment load based on real-time feedback information such as requested torque and acceleration. It automatically calculates the required torque required to overcome resistance, improves the accuracy of torque control and the overall performance of the system, and can adapt to the influence of factors such as mechanical wear, hardware deviation, and road condition changes, and automatically corrects.

[0124] The self-learning drive torque control model is not limited to a specific application scenario. It 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 under any working condition of the vehicle controller. 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 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 accurate 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] Slippery road: preset slip rate is 30%;

[0130] Ice and snow road: the preset slip rate is 60%;

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

[0132] This embodiment initializes and sets a preset slip rate suitable for different road surfaces, thereby ensuring that the slip rate of the self-learning drive torque control model in the initial control stage can adapt to the needs of the current road surface, and provides preferred original data for subsequent comprehensive perception calculations to obtain the safe torque limit boundary value between the slip rate and the 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 vehicle speed limit is:

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

[0136] Wet and slippery road: 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 adhesion level of the equipment, 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 preferred original data for subsequent comprehensive perception calculations to obtain safe torque limit boundary values ​​between slip rates and output torques 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 constructed according to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system to obtain a torque request limit value, a torque output value of the electric drive system is generated according to the torque request limit value, and the torque output value is continuously corrected, and then the torque output value is transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque, specifically including the steps of:

[0142] S31, constructing a torque limit boundary according to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, the power battery SOC acquired in real time, and the 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 according to 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 the torque request limit value, so that the torque output meets the current actual hardware configuration such as the power battery and other hardware configuration limitations, and the torque output is done within the capacity to avoid exceeding the current actual hardware configuration range, so that the torque output meets the safety requirements and 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 constructed according to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system to obtain a torque request limit value, and a torque output value of the electric drive system is generated according to the torque request limit value, and the torque output value is continuously corrected, and then the torque output value is transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque, which specifically also includes the steps of:

[0147] S33, when the sensor feeds back the vehicle tilt angle value and determines that the vehicle is climbing a large slope, the vehicle controller automatically reduces the power demand of the auxiliary system, the auxiliary system including the air conditioning system, and increases the heat dissipation capacity of the drive system, including: increasing the flow 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 descent, 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 ability and regenerative braking ability of the whole vehicle are maximized. On the one hand, when it is determined that the vehicle is in a large-angle climbing state, the power demand of the auxiliary system is reduced and the heat dissipation capacity of the drive system is increased, ensuring that the driving ability of the whole vehicle is maximized when climbing a large angle to ensure the climbing performance of the vehicle; on the other hand, when the vehicle is in a long-distance downhill state, or the driver actively enters the steep slope descent mode, the regenerative braking ability 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, 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 overheating and failure of the axle brake, which specifically also includes the steps of:

[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 the electromagnetic management system information. If the power battery SOC is higher than the set threshold of 85%, an energy consumption request is issued, and the vehicle controller sends a start signal to the auxiliary traction motor driver. The 30kW oil pump motor will consume the feedback energy to avoid the SOC being too high to perform regenerative braking torque;

[0154] S343, the oil pump motor converts the regenerative energy into the heat energy of the hydraulic system to increase the temperature of the hydraulic oil. When the temperature of the hydraulic oil increases, the cooling device is started to dissipate heat, thereby accelerating the consumption of the feedback energy;

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

[0156] This embodiment can actively control the auxiliary system to consume excess power during regenerative braking, that is, when controlling the traction motor driver to perform regenerative braking according to the received preset speed limit, fully feed back energy, and when the power battery SOC is higher than the set threshold of 85%, the pump motor and the like are used to convert the regenerative energy into the heat energy of the hydraulic system to increase the hydraulic oil temperature, and when the hydraulic oil temperature increases, the cooling device is started to dissipate heat, accelerate the consumption of the feedback energy, and thus consume excess power. In addition, this embodiment will also adaptively adjust the preset speed limit received by the traction motor driver according to the relationship between the consumed energy and the fed-back energy, adjust the size of the fed-back energy, thereby maintaining a balance between the fed-back energy and the consumed energy, avoiding energy accumulation, and ensuring the normal, stable operation and service life of the power battery and the auxiliary energy-consuming device. In addition to the oil pump motor and the cooling device, modules including the hydraulic system, the boost pump, the heat dissipation system, etc. can also be used for energy consumption.

[0157] Embodiment 11

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

[0159] The environmental perception module is used to obtain the road surface information and road condition information in front of the equipment in real time through the road condition collection device installed on the vehicle. The road condition collection device, such as a camera, performs denoising and enhancement processing on the image during collection, and uses intelligent algorithms to obtain road surface information and road condition information. It can also include sensor equipment such as inertial measurement units and gyroscopes to improve positioning accuracy and stability. It can also use laser radar and ultrasound to obtain road information and expand the early warning function of the intelligent driving control system.

[0160] A decision-making and planning module is used to construct a self-learning driving torque control model. The self-learning driving torque control model performs a comprehensive analysis based on the 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 driving system to make an optimal decision on the output torque, calculates the safe torque limit boundary value, and performs energy scheduling on energy-consuming equipment during driving to achieve energy consumption management of the equipment under all working conditions;

[0161] The control execution module is used to construct a torque limit boundary according to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system to obtain a torque request limit value, generate a torque output value of the electric drive system according to the torque request limit value, and 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 Fig.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 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, 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 an abnormal situation is detected, the fault diagnosis can be carried out and the corresponding treatment measures can be prompted.

[0168] Embodiment 13

[0169] like Fig.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, so that the user can view the operating status of the equipment, set driving parameters, and receive safety prompt information through a local display device or mobile terminal.

[0171] Embodiment 14

[0172] like Fig.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] Embodiment 15

[0174] like Fig.14 As shown, the preferred embodiment of the present application also 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.14As shown. The computer device includes a processor, a memory and a network interface connected via 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 and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned intelligent driving control method for heavy electric engineering equipment are implemented.

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

[0176] Example 16

[0177] A preferred embodiment of 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 in the above embodiment.

[0178] In summary, the above embodiments of the present application have the ability to self-learn the driving torque control model for different working conditions, realize accurate torque output, and improve the output capacity of the drive system; have the ability to distribute and control energy consumption when driving on a slope, reduce the energy consumption of the auxiliary system when the power demand is large, and actively control the auxiliary system to consume excess power during regenerative braking.

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

[0180] If the functions described in the method of this embodiment are implemented in the form of software functional units and sold or used as independent products, they can be stored in one or more computing devices readable storage media. 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 several instructions to enable a computing device (which can be a personal computer, server, mobile computing device or network device, etc.) to perform 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 present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present 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 embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0183] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 1A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions 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 falling within the scope of the present application.

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

Claims

1. A method for intelligent driving control of heavy electric engineering equipment, characterized in that: Includes 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; Construct a self-learning driving torque control model, which performs 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 driving system to make an optimal decision on the output torque, calculates the safe torque limit boundary value, and performs energy scheduling on energy-consuming equipment during driving to achieve energy consumption management of the equipment under all working conditions; A torque limit boundary is constructed based on the safety torque limit boundary, the vehicle's maximum available torque, the vehicle's 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.

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 measuring units of the 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 abnormal conditions are 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 operating status of the equipment, set driving parameters, and receive safety prompts 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, 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, and 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 self-learning driving torque control model is constructed. The self-learning driving torque control model performs a comprehensive analysis based on the 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 driving system to make an optimal decision on the output torque, and calculates the safe torque limit boundary value, which specifically includes the following steps: 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 road surface information and road condition information as preset parameters of the self-learning driving torque control model; When the vehicle first solves and outputs the requested torque, the self-learning drive 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, which are all 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 condition, slope and slip ratio; in: is the driving force, F f 、F w are rolling resistance and air resistance, respectively, i = mgsinθ, F j =ma are the 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 feedback torque and speed of the traction motor driver. 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 construct the vehicle's driving torque T under the current road conditions d The corresponding relationship between and acceleration a; Similarly, based on the 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 according to the identification and solution information to build a multi-dimensional control model between the 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 value at 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 under any working condition of the vehicle controller, it ensures that the motor output torque is stable and controllable under various road conditions.

6. The intelligent driving control method for heavy electric engineering equipment according to claim 5 is characterized in that: 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: the operating speed is 100% of the maximum speed; slippery road surface: the operating speed is 70% to 80% of the maximum speed; icy and snowy road surface: the operating speed is 40% to 50% of the maximum speed; gravel road surface: the operating speed is 20% to 30% of the maximum speed.

7. The intelligent driving control method for heavy electric engineering equipment according to claim 6 is characterized in that: According to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system, a torque limit boundary is constructed to obtain a torque request limit value, a torque output value of the electric drive system is generated according to the torque request limit value, and the torque output value is continuously corrected, and then the torque output value is transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque, specifically including the steps of: The torque request limit value is obtained by constructing the torque limit boundary according to 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.

8. The intelligent driving control method for heavy electric engineering equipment according to claim 7 is characterized in that: According to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system, a torque limit boundary is constructed to obtain a torque request limit value, a torque output value of the electric drive system is generated according to the torque request limit value, and the torque output value is continuously corrected, and then the torque output value is transmitted to the traction motor driver through the vehicle controller to control the permanent magnet synchronous motor to output the corresponding torque, which specifically also includes the steps of: When the sensor feeds back the vehicle's tilt angle value and determines that the vehicle is climbing a large slope, the vehicle controller automatically reduces the power demand of the auxiliary system, which includes the air conditioning system, and increases the heat dissipation capacity of the drive system, including: increasing the flow 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 descent, 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 axle brake pressure, and avoid axle brake overheating and failure.

9. The intelligent driving control method for heavy electric engineering equipment according to any one of claim 8, 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, it is determined that the vehicle is in a long-distance downhill, 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 failure, which specifically includes the following steps: The traction motor driver performs regenerative braking according to the received preset speed limit to fully feed back energy; The vehicle controller obtains the electromagnetic management system information. If the power battery SOC is higher than the set threshold of 85%, an energy consumption request is issued. The vehicle controller sends a start signal to the auxiliary traction motor driver. The 30kW oil pump motor will consume the feedback energy to avoid the SOC being too high to perform regenerative braking torque. The oil pump motor converts the regenerative energy into the heat energy of the hydraulic system to increase the temperature of the hydraulic oil. When the temperature of the hydraulic oil increases, the cooling device is activated to dissipate heat, accelerating the consumption of the feedback energy. When the consumed energy is greater than the regenerated energy, the preset speed limit is increased; when the consumed energy is less than the regenerated energy, the preset speed limit is decreased.

10. An intelligent driving control device for heavy electric engineering equipment, characterized in that: include: Environmental perception module, used to obtain real-time road surface information and road condition information in front of the equipment through a road condition collection device; A decision-making and planning module is used to construct a self-learning driving torque control model. The self-learning driving torque control model performs a comprehensive analysis based on the 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 driving system to make an optimal decision on the output torque, calculates the safe torque limit boundary value, and performs energy scheduling on energy-consuming equipment during driving to achieve energy consumption management of the equipment under all working conditions; The control execution module is used to construct a torque limit boundary according to the safety torque limit boundary, the maximum available torque of the vehicle, the on-board traction motor driver, and the information of the battery management system to obtain a torque request limit value, generate a torque output value of the electric drive system according to the torque request limit value, and 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.

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