Vehicle integrated electric drive axle cooperative control method, device and equipment and storage medium

Abstract

The invention discloses a vehicle integrated electric drive axle cooperative control method, device and equipment and a storage medium. The method comprises the steps that the real-time vehicle load, the real-time road slope, the real-time demand power and map prediction information of a current vehicle are collected in real time; inputting the real-time vehicle load, the real-time road gradient, the real-time required power and the map prediction information into a multi-condition fusion decision model to obtain a ground clearance instruction or a landing instruction; the current vehicle is subjected to off-ground control according to the off-ground instruction, or the current vehicle is subjected to landing control according to the landing instruction, so that the accuracy of off-ground or landing decision can be improved, intelligent control of the lifting bridge is realized, and through accurate torque control in the off-ground and landing processes, the ground contact impact stress is reduced, and the driving comfort and safety are improved; vehicle global energy closed-loop management is achieved, the comprehensive energy efficiency of the vehicle is improved, and the speed and efficiency of vehicle integrated electric drive axle cooperative control are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent chassis control technology for pure electric commercial vehicles, and in particular to a vehicle-integrated electric drive axle collaborative control method, device, equipment and storage medium. Background Art

[0002] Lifting axle technology for commercial vehicles achieves no-load energy savings and regulatory compliance by dynamically adjusting the axle grounding state. However, traditional solutions have the following drawbacks: Static decision-making: Liftoff / landing is determined solely based on current vehicle speed and axle load, without integrating road slope and future operating condition predictions. Energy efficiency loss: The energy-saving potential of the lifting bridge is not fully utilized during long downhill slopes or cruising on flat roads, the inertial kinetic energy of the wheels is not recovered during the lift-off phase, and the impact energy of the ground contact is dissipated in the form of heat energy, resulting in low system energy efficiency.

[0003] Traction risk: Inadvertent triggering of the ground lift logic on steep slopes or under high load conditions may result in insufficient power.

[0004] It is difficult to achieve coordinated optimization of traction continuity, impact suppression and energy recovery during the lift-off / landing phase, which restricts the high-end development of new energy commercial vehicles.

[0005] Existing solutions, such as CN114801823A and US20230256789A1, are unable to achieve multi-condition fusion predictive control, resulting in a balance between energy efficiency and stability. Among them, CN114801823A has the problem of not integrating map slope data, and the energy consumption in the measured no-load mountain road scenario increases. US20230256789A1 lacks dynamic judgment of required power, and the problem of torque imbalance caused by lifting off the ground during sudden acceleration. Summary of the Invention

[0006] The main purpose of the present invention is to provide a vehicle-integrated electric drive axle coordinated control method, device, equipment and storage medium, aiming to solve the technical problems in the prior art such as poor dynamic stability of the vehicle during liftoff and landing, energy efficiency loss during energy recovery, traction risks on steep slopes or under high-load conditions, and inability to achieve coordinated optimization of traction continuity, impact suppression and energy recovery during liftoff / landing.

[0007] In a first aspect, the present invention provides a vehicle integrated electric drive axle coordinated control method, the vehicle integrated electric drive axle coordinated control method comprising the following steps: Real-time collection of the current vehicle's real-time vehicle load, real-time road slope, real-time required power and map prediction information; Inputting the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; The current vehicle is controlled to take off according to the lift-off instruction, or the current vehicle is controlled to land according to the landing instruction.

[0008] Optionally, the real-time collection of the current vehicle's real-time vehicle load, real-time road slope, real-time required power, and map prediction information includes: Activate the vehicle's mid- and rear electric drive axles, lifting axle electric actuators, six-axis force sensors, inertial sensor IMU, lidar, and map module; When the vehicle control system self-check of the current vehicle is completed and the communication link is normal, real-time data collection is allowed; The six-dimensional force sensor is used to collect the axle load of the lifting bridge of the current vehicle in real time, and the axle load of the lifting bridge is used as the real-time vehicle load; Acquire GPS elevation data, fuse IMU data with the GPS elevation data, and obtain real-time road slope; reading the real-time power demand of the current driver from the vehicle controller of the current vehicle; Map data is loaded through the map module to obtain map prediction information of a preset distance ahead of the current vehicle.

[0009] Optionally, inputting the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction includes: Inputting the real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information into a multi-condition fusion decision model to obtain a decision value; Comparing the decision value with a preset decision threshold to generate a comparison result; When the comparison result shows that the decision value is less than the preset decision threshold, generating a lift-off instruction; When the comparison result shows that the decision value is not less than the preset decision threshold, a landing instruction is generated.

[0010] Optionally, inputting the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a decision value includes: The real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information are input into the multi-condition fusion decision model to obtain the decision value through the following formula:

[0011] in, is the decision value, is the axle load weight coefficient, is the real-time vehicle load, is the maximum load of the vehicle, is the real-time slope weight coefficient, is the real-time road slope, is the required power weight coefficient, For real-time power demand, is the maximum power of the vehicle, Predict the slope weight coefficient for the map, is the distance weight coefficient, For the Slope value of the road segment, is the total number of road slope values.

[0012] Optionally, the performing lift-off control on the current vehicle according to the lift-off instruction, or performing landing control on the current vehicle according to the landing instruction, includes: performing torque unloading and energy recovery on the current vehicle according to the lift-off command; or, Speed ​​synchronization and torque control are performed on the current vehicle according to the landing instruction.

[0013] Optionally, performing torque unloading and energy recovery on the current vehicle according to the lift-off instruction includes: The torque adjustment amount is obtained according to the lift-off command by the following formula:

[0014] in, is the torque adjustment, is the slope sensitivity coefficient, is the road slope value for the next stage, is the current road slope value, is the current axle load, is the tire radius of the current vehicle; performing torque transfer on the mid- and rear-axle motors of the current vehicle according to the torque adjustment amount and a preset load ratio; The recovery power is obtained according to the lift-off command by the following formula:

[0015] in, To recover power, is the overall efficiency of the motor, is the equivalent moment of inertia of the transmission system, is the angular velocity, is the shell mass, is the acceleration due to gravity, is the height above the ground; A charging current is calculated according to the recovered power, and energy is recovered from the current vehicle according to the charging current.

[0016] Optionally, performing speed synchronization and torque control on the current vehicle according to the landing instruction includes: According to the landing instruction, the predicted touchdown time is obtained by the following formula:

[0017] in, For the predicted time of touchdown, is the remaining height above the ground, To increase speed; The target tire linear speed of the current vehicle is obtained by the following formula:

[0018] in, is the target tire linear speed, is the current vehicle speed, is the slope sensitivity coefficient, is the road slope value, is the acceleration due to gravity, For the predicted time of touchdown, System response delay; Calculate the target wheel speed using the following formula:

[0019] in, is the target wheel speed, is the target tire linear speed, is the tire radius of the current vehicle; The feedforward torque is calculated by the following formula:

[0020] in, is the feedforward torque, is the equivalent moment of inertia of the transmission system, Based on The target angular acceleration calculated by differentiation, is the damping system calibration value, is the angular velocity; The feedback torque is calculated by the following formula:

[0021] in, is the feedback torque, 、 、 are the PID control parameters, is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the speed deviation integral, is the speed deviation differential; The total output torque is obtained by the following formula:

[0022] in, is the total output torque, is the feedforward torque, is the feedback torque; Torque control is performed on the current vehicle according to the total output torque.

[0023] In a second aspect, to achieve the above-mentioned objectives, the present invention further proposes a vehicle-integrated electric drive axle coordinated control device, the vehicle-integrated electric drive axle coordinated control device comprising: Data acquisition module, used to collect the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle; a fusion decision module, configured to input the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; A control module is configured to control the current vehicle to lift off according to the lift-off instruction, or to control the current vehicle to land according to the landing instruction.

[0024] On the third aspect, in order to achieve the above-mentioned purpose, the present invention also proposes a vehicle-integrated electric drive axle cooperative control device, which includes: a memory, a processor, and a vehicle-integrated electric drive axle cooperative control program stored on the memory and runnable on the processor, and the vehicle-integrated electric drive axle cooperative control program is configured to implement the steps of the vehicle-integrated electric drive axle cooperative control method as described above.

[0025] Fourthly, in order to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a vehicle integrated electric drive axle cooperative control program is stored. When the vehicle integrated electric drive axle cooperative control program is executed by the processor, the steps of the vehicle integrated electric drive axle cooperative control method as described above are implemented.

[0026] The vehicle integrated electric drive axle collaborative control method proposed in the present invention collects the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle in real time; inputs the real-time vehicle load, real-time road slope, real-time required power and map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; performs lift-off control on the current vehicle according to the lift-off instruction, or performs landing control on the current vehicle according to the landing instruction, which can improve the accuracy of lift-off or landing decisions, realize intelligent control of the lifting bridge, and greatly improve the vehicle's cruising range under no-load and light-load conditions. At the same time, automatic lifting control reduces the driver's operating difficulty, and through precise control of torque during the lift-off and landing processes, reduces the ground impact stress, improves driving comfort and safety, realizes global energy closed-loop management of the vehicle, improves the comprehensive energy efficiency of the vehicle, and improves the speed and efficiency of the vehicle integrated electric drive axle collaborative control. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention; Figure 2 This is a flow chart of a first embodiment of a coordinated control method for an integrated electric drive axle in a vehicle according to the present invention; Figure 3 This is a flow chart of a second embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention; Figure 4 This is a flow chart of a third embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention; Figure 5 This is a flow chart of a fourth embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention; Figure 6 This is a functional module diagram of the first embodiment of the vehicle integrated electric drive axle cooperative control device of the present invention.

[0028] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0029] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0030] The solution of the embodiment of the present invention is mainly: through real-time collection of the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle; inputting the real-time vehicle load, real-time road slope, real-time required power and map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; performing lift-off control on the current vehicle according to the lift-off instruction, or performing landing control on the current vehicle according to the landing instruction, which can improve the accuracy of lift-off or landing decisions, realize intelligent control of the lifting bridge, and greatly improve the vehicle's mileage under no-load and light-load conditions. The automatic lift control reduces the difficulty of operation for the driver, reduces the ground contact impact stress through precise control of torque during lift-off and landing, improves driving comfort and safety, realizes global energy closed-loop management of the vehicle, improves the comprehensive energy efficiency of the vehicle, and improves the speed and efficiency of the coordinated control of the vehicle's integrated electric drive axle. It solves the technical problems in the existing technology of poor dynamic stability of the vehicle during lift-off and landing, energy efficiency loss during energy recovery, traction risk on steep slopes or under high load conditions, and inability to achieve coordinated optimization of traction continuity, impact suppression and energy recovery during lift-off / landing.

[0031] Reference Figure 1 , Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0032] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement communication between these components. The user interface 1003 may include a display and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as a disk storage. The memory 1005 may also be a storage device independent of the processor 1001.

[0033] Those skilled in the art will understand that Figure 1 The device structure shown in the figure does not constitute a limitation of the device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0034] like Figure 1As shown, the memory 1005 as a storage medium may include an operating device, a network communication module, a user interface module, and a vehicle integrated electric drive axle cooperative control program.

[0035] The device of the present invention calls the vehicle integrated electric drive axle cooperative control program stored in the memory 1005 through the processor 1001 and performs the following operations: Real-time collection of the current vehicle's real-time vehicle load, real-time road slope, real-time required power and map prediction information; Inputting the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; The current vehicle is controlled to take off according to the lift-off instruction, or the current vehicle is controlled to land according to the landing instruction.

[0036] The device of the present invention calls the vehicle integrated electric drive axle cooperative control program stored in the memory 1005 through the processor 1001, and further performs the following operations: Activate the vehicle's mid- and rear electric drive axles, lifting axle electric actuators, six-axis force sensors, inertial sensor IMU, lidar, and map module; When the vehicle control system self-check of the current vehicle is completed and the communication link is normal, real-time data collection is allowed; The six-dimensional force sensor is used to collect the axle load of the lifting bridge of the current vehicle in real time, and the axle load of the lifting bridge is used as the real-time vehicle load; Acquire GPS elevation data, fuse IMU data with the GPS elevation data, and obtain real-time road slope; reading the real-time power demand of the current driver from the vehicle controller of the current vehicle; Map data is loaded through the map module to obtain map prediction information of a preset distance ahead of the current vehicle.

[0037] The device of the present invention calls the vehicle integrated electric drive axle cooperative control program stored in the memory 1005 through the processor 1001, and further performs the following operations: Inputting the real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information into a multi-condition fusion decision model to obtain a decision value; Comparing the decision value with a preset decision threshold to generate a comparison result; When the comparison result shows that the decision value is less than the preset decision threshold, generating a lift-off instruction; When the comparison result shows that the decision value is not less than the preset decision threshold, a landing instruction is generated.

[0038] The device of the present invention calls the vehicle integrated electric drive axle cooperative control program stored in the memory 1005 through the processor 1001, and further performs the following operations: The real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information are input into the multi-condition fusion decision model to obtain the decision value through the following formula:

[0039] in, is the decision value, is the axle load weight coefficient, is the real-time vehicle load, is the maximum load of the vehicle, is the real-time slope weight coefficient, is the real-time road slope, is the required power weight coefficient, For real-time power demand, is the maximum power of the vehicle, Predict the slope weight coefficient for the map, is the distance weight coefficient, For the Slope value of the road segment, is the total number of road slope values.

[0040] The device of the present invention calls the vehicle integrated electric drive axle cooperative control program stored in the memory 1005 through the processor 1001, and further performs the following operations: performing torque unloading and energy recovery on the current vehicle according to the lift-off command; or, Speed ​​synchronization and torque control are performed on the current vehicle according to the landing instruction.

[0041] The device of the present invention calls the vehicle integrated electric drive axle cooperative control program stored in the memory 1005 through the processor 1001, and further performs the following operations: The torque adjustment amount is obtained according to the lift-off command by the following formula:

[0042] in, is the torque adjustment, is the slope sensitivity coefficient, is the road slope value for the next stage, is the current road slope value, is the current axle load, is the tire radius of the current vehicle; performing torque transfer on the mid- and rear-axle motors of the current vehicle according to the torque adjustment amount and a preset load ratio; The recovery power is obtained according to the lift-off command by the following formula:

[0043] in, To recover power, is the overall efficiency of the motor, is the equivalent moment of inertia of the transmission system, is the angular velocity, is the shell mass, is the acceleration due to gravity, is the height above the ground; A charging current is calculated according to the recovered power, and energy is recovered from the current vehicle according to the charging current.

[0044] The device of the present invention calls the vehicle integrated electric drive axle cooperative control program stored in the memory 1005 through the processor 1001, and further performs the following operations: According to the landing instruction, the predicted touchdown time is obtained by the following formula:

[0045] in, For the predicted time of touchdown, is the remaining height above the ground, To increase speed; The target tire linear speed of the current vehicle is obtained by the following formula:

[0046] in, is the target tire linear speed, is the current vehicle speed, is the slope sensitivity coefficient, is the road slope value, is the acceleration due to gravity, For the predicted time of touchdown, System response delay; Calculate the target wheel speed using the following formula:

[0047] in, is the target wheel speed, is the target tire linear speed, is the tire radius of the current vehicle; The feedforward torque is calculated by the following formula:

[0048] in, is the feedforward torque, is the equivalent moment of inertia of the transmission system, Based on The target angular acceleration calculated by differentiation, Damping system calibration value, is the angular velocity; The feedback torque is calculated by the following formula:

[0049] in, is the feedback torque, 、 、 are the PID control parameters, is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the speed deviation integral, is the speed deviation differential; The total output torque is obtained by the following formula:

[0050] in, is the total output torque, is the feedforward torque, is the feedback torque; Torque control is performed on the current vehicle according to the total output torque.

[0051] Through the above-mentioned scheme, this embodiment collects the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle in real time; inputs the real-time vehicle load, real-time road slope, real-time required power and map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; and controls the lift-off of the current vehicle according to the lift-off instruction, or controls the landing of the current vehicle according to the landing instruction. This can improve the accuracy of the lift-off or landing decision, realize intelligent control of the lifting axle, and greatly improve the vehicle's cruising range under no-load and light-load conditions. At the same time, automatic lifting control reduces the driver's operating difficulty. Through precise control of torque during the lift-off and landing processes, the impact stress of touching the ground is reduced, and driving comfort and safety are improved. This realizes global energy closed-loop management of the vehicle, improves the overall energy efficiency of the vehicle, and improves the speed and efficiency of the coordinated control of the vehicle's integrated electric drive axle.

[0052] Based on the above hardware structure, an embodiment of the vehicle integrated electric drive axle collaborative control method of the present invention is proposed.

[0053] Reference Figure 2 , Figure 2 This is a flow chart of the first embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention.

[0054] In a first embodiment, the vehicle integrated electric drive axle coordinated control method includes the following steps: Step S10: collecting the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle in real time.

[0055] It should be noted that before making a multi-condition fusion decision, it is necessary to collect the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle.

[0056] Step S20: Input the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction.

[0057] It should be understood that after the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information are input into the multi-condition fusion decision model, a lift-off instruction corresponding to the lift-off decision and a landing instruction corresponding to the landing decision are obtained.

[0058] Step S30: Control the current vehicle to take off according to the lift-off instruction, or control the current vehicle to land according to the landing instruction.

[0059] It can be understood that the current vehicle can be controlled to take off according to the lift-off instruction, and correspondingly, the current vehicle can be controlled to land according to the landing instruction.

[0060] Through the above-mentioned scheme, this embodiment collects the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle in real time; inputs the real-time vehicle load, real-time road slope, real-time required power and map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; and controls the lift-off of the current vehicle according to the lift-off instruction, or controls the landing of the current vehicle according to the landing instruction. This can improve the accuracy of the lift-off or landing decision, realize intelligent control of the lifting axle, and greatly improve the vehicle's cruising range under no-load and light-load conditions. At the same time, automatic lifting control reduces the driver's operating difficulty. Through precise control of torque during the lift-off and landing processes, the impact stress of touching the ground is reduced, and driving comfort and safety are improved. This realizes global energy closed-loop management of the vehicle, improves the overall energy efficiency of the vehicle, and improves the speed and efficiency of the coordinated control of the vehicle's integrated electric drive axle.

[0061] Furthermore, Figure 3 This is a flow chart of the second embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention. Figure 3As shown, a second embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention is proposed based on the first embodiment. In this embodiment, step S10 specifically includes the following steps: Step S11: Activate the mid-rear electric drive axle, lifting axle electric actuator, six-dimensional force sensor, inertial sensor IMU, laser radar and map module of the current vehicle.

[0062] It should be noted that before data collection, the hardware system must be initialized, that is, the vehicle's mid- and rear electric drive axles, lifting axle electric actuators, six-dimensional force sensors, inertial sensors (Inertial Measurement Unit, IMU), lidar and map module must be activated. The map module is a high-precision map module.

[0063] Step S12: When the vehicle control system self-check of the current vehicle is completed and the communication link is normal, real-time data collection is allowed.

[0064] It is understandable that real-time data collection is allowed when the vehicle control system (eg 800V / 400kWh) of the current vehicle completes self-test and confirms that the communication links of the current vehicle are normal.

[0065] Step S13: The lifting axle load of the current vehicle is collected in real time by the six-dimensional force sensor, and the lifting axle load is used as the real-time vehicle load.

[0066] It should be understood that the lifting axle is an axle equipped on vehicles with three axles or more that can be lifted and lowered and has a load-bearing function. The lifting axle axle load refers to the load borne by the lifting axle. The lifting axle axle load of the current vehicle can be collected in real time through the six-dimensional force sensor, and then the lifting axle load can be used as the real-time vehicle load of the current vehicle.

[0067] In the specific implementation, the axle load detection can obtain the lifting bridge axle load in real time through the six-dimensional force sensor, that is, (Unit: kN), the sampling frequency can be set to 200Hz, with an accuracy of ±0.5%.

[0068] Step S14: Obtain GPS elevation data, fuse IMU data and the GPS elevation data, and obtain real-time road slope.

[0069] It is understandable that after obtaining the Global Positioning System (GPS) elevation data, the real-time road slope can be obtained by fusing the IMU data and the GPS elevation data.

[0070] It should be noted that IMU can collect vehicle acceleration, angular velocity and other motion status data at high frequency through the built-in accelerometer and gyroscope, indirectly reflecting the changes in road slope, but long-term use can easily lead to a decrease in accuracy due to accumulated errors; GPS can provide absolute elevation information and calculate the elevation change rate through continuous positioning, which can calibrate the drift error of IMU, but is greatly affected by signal obstruction (such as tunnels and high-rise buildings); after the two are integrated, they can complement each other's shortcomings, and the preset algorithm can process and correct the data in real time, and ultimately output accurate and stable road slope values.

[0071] In the specific implementation, the slope detection process is to fuse IMU and GPS elevation data to calculate the real-time road slope (Unit: °), error <0.5°.

[0072] Step S15: Read the real-time power requirement of the current driver from the vehicle controller of the current vehicle.

[0073] It should be understood that corresponding information can be read from the vehicle controller of the current vehicle to obtain the real-time power demand of the current driver.

[0074] In a specific implementation, the power demand detection process is to read the driver's power demand from the vehicle controller. (Unit: kW).

[0075] Step S16: loading map data through the map module to obtain map prediction information of a preset distance ahead of the current vehicle.

[0076] It is understandable that by loading map data through the map module, map prediction information of a preset distance ahead of the current vehicle can be obtained.

[0077] In the specific implementation, the map data loading process is to obtain the road slope sequence of the 5km ahead through the high-precision map application programming interface (API) , each 1km is a section.

[0078] For example: , the actual slope , power demand , the map shows that the slope is -3° 1km ahead.

[0079] This embodiment uses the above scheme to activate the mid-rear electric drive axles, lifting axle electric actuators, six-dimensional force sensors, inertial sensors IMU, lidar and map modules of the current vehicle; when the vehicle control system self-check of the current vehicle is completed and the communication link is normal, real-time data collection is allowed; the lifting axle axle load of the current vehicle is collected in real time through the six-dimensional force sensor, and the lifting axle load is used as the real-time vehicle load; GPS elevation data is obtained, and the IMU data and the GPS elevation data are integrated to obtain the real-time road slope; the real-time power demand of the current driver is read from the vehicle controller of the current vehicle; map data is loaded through the map module to obtain map prediction information of a preset distance in front of the current vehicle; vehicle data can be collected in real time, the accuracy of take-off or landing decisions is improved, and the speed and efficiency of the coordinated control of the vehicle integrated electric drive axle are improved.

[0080] Furthermore, Figure 4 This is a flow chart of the third embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention. Figure 4 As shown, a third embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention is proposed based on the first embodiment. In this embodiment, step S20 specifically includes the following steps: Step S21: input the real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information into a multi-condition fusion decision model to obtain a decision value.

[0081] It should be noted that the decision value can be obtained by inputting the real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information into the multi-condition fusion decision model through calculation.

[0082] Furthermore, the step S21 specifically includes the following steps: The real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information are input into the multi-condition fusion decision model to obtain the decision value through the following formula:

[0083] in, is the decision value, is the axle load weight coefficient, is the real-time vehicle load, is the maximum load of the vehicle, is the real-time slope weight coefficient, is the real-time road slope, is the required power weight coefficient, For real-time power demand, is the maximum power of the vehicle, Predict the slope weight coefficient for the map, is the distance weight coefficient, For the Slope value of the road segment, is the total number of road slope values.

[0084] It should be understood that the Vehicle Control Unit (VCU) makes decisions through a multi-condition fusion decision model. Calculation, the decision model is shown in the above formula, is the distance weight coefficient (i.e., near end to far end).

[0085] In the specific implementation, the hardware configuration and calibration of the decision model are as follows: (1) Calibration of multi-condition weight coefficients In actual engineering applications, the genetic algorithm can be used to optimize theoretical calculations and combine them with actual calibration to determine the value range. For example, parameter calibration can be performed using calibration equipment such as a six-axis chassis dynamometer, a high-precision slope simulation platform, and a data acquisition system. By simulating 10 groups of typical operating conditions (flat road, climbing, descending, and rapid acceleration), the weight coefficient can be optimized using a genetic algorithm (objective function: energy efficiency maximization + pitch angle <1.5°) to obtain the optimal solution. The parameters of this embodiment are exemplified as follows. Of course, other parameters may be used, and this embodiment is not limited to these parameters:

[0086] (2) Map data interface Data source: Real-time acquisition of slope data 5km ahead through the AutoNavi / Baidu high-precision map API; Data processing: Slope values ​​are stored in segments of 1 km each , through weight distribution .

[0087] Step S22: Compare the decision value with a preset decision threshold to generate a comparison result.

[0088] It can be understood that after comparing the decision value with the preset decision threshold, a corresponding comparison result is generated.

[0089] Step S23: When the comparison result shows that the decision value is less than the preset decision threshold, a lift-off instruction is generated.

[0090] It should be understood that when the comparison result is that the decision value is less than the preset decision threshold, a lift-off command for torque unloading and energy recovery may be generated.

[0091] Step S24: When the comparison result shows that the decision value is not less than the preset decision threshold, a landing instruction is generated.

[0092] It can be understood that when the comparison result is that the decision value is not less than the preset decision threshold, a landing instruction with speed synchronization and torque limitation can be generated.

[0093] In a specific implementation, the preset decision threshold is 0.6 as an example. Of course, it can also be other values. This embodiment does not limit this. The decision execution logic is: like : Trigger the lift-off mode to perform torque unloading and energy recovery; like : Trigger landing mode, execute speed synchronization and torque limit.

[0094] Through the above scheme, this embodiment obtains a decision value by inputting the real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information into a multi-condition fusion decision model; compares the decision value with a preset decision threshold to generate a comparison result; generates a lift-off instruction when the comparison result shows that the decision value is less than the preset decision threshold; and generates a landing instruction when the comparison result shows that the decision value is not less than the preset decision threshold. This can improve the accuracy of lift-off or landing decisions, realize intelligent control of the lifting axle, and greatly improve the vehicle's cruising range under no-load and light-load conditions. At the same time, automatic lifting control reduces the driver's operating difficulty, reduces ground impact stress through precise control of torque during lift-off and landing, and improves driving comfort and safety.

[0095] Furthermore, Figure 5 This is a flow chart of the fourth embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention. Figure 5 As shown, a fourth embodiment of the vehicle integrated electric drive axle cooperative control method of the present invention is proposed based on the first embodiment. In this embodiment, step S30 specifically includes the following steps: Step S31: performing torque unloading and energy recovery on the current vehicle according to the lift-off instruction.

[0096] It should be noted that, according to the lift-off instruction, torque unloading and energy recovery can be performed on the current vehicle, thereby completing the lift-off operation.

[0097] Furthermore, the step S31 specifically includes the following steps: The torque adjustment amount is obtained according to the lift-off command by the following formula:

[0098] in, is the torque adjustment, is the slope sensitivity coefficient, is the road slope value for the next stage, is the current road slope value, is the current axle load, is the tire radius of the current vehicle; performing torque transfer on the mid- and rear-axle motors of the current vehicle according to the torque adjustment amount and a preset load ratio; The recovery power is obtained according to the lift-off command by the following formula:

[0099] in, To recover power, is the overall efficiency of the motor, is the equivalent moment of inertia of the transmission system, is the angular velocity, is the shell mass, is the acceleration due to gravity, is the height above the ground; A charging current is calculated according to the recovered power, and energy is recovered from the current vehicle according to the charging current.

[0100] It should be understood that the process of torque unloading, namely feedforward unloading, is to dynamically adjust the torque based on the map preview slope, and the torque adjustment amount is obtained by the above formula.

[0101] In the specific implementation, For example, The unit is KN.

[0102] According to the slope change of 1km ahead , calculate the torque adjustment:

[0103] The vehicle controller VCU transfers torque to the middle and rear axle motors according to the load ratio based on the torque calculated by feedforward unloading, e.g., the torque of the middle axle is reduced , rear axle torque increases .

[0104] It can be understood that the energy recovery control process is that when the middle bridge is lifted off the ground, the motor switches to the power generation mode and the recovery power is calculated in real time using the above formula.

[0105] In the specific implementation, For example, the energy recovery control power can be calculated to be 75.66Kw, then the charging current can be calculated as , control the maximum recovery current of the mid-bridge until the speed of the mid-bridge motor is 0.

[0106] Step S32: Perform speed synchronization and torque control on the current vehicle according to the landing instruction.

[0107] It can be understood that, according to the landing instruction, speed synchronization and torque control can be performed on the current vehicle to complete the landing operation.

[0108] Furthermore, the step S32 specifically includes the following steps: According to the landing instruction, the predicted touchdown time is obtained by the following formula:

[0109] in, For the predicted time of touchdown, is the remaining height above the ground, To increase speed; The target tire linear speed of the current vehicle is obtained by the following formula:

[0110] in, is the target tire linear speed, is the current vehicle speed, is the slope sensitivity coefficient, is the road slope value, is the acceleration due to gravity, For the predicted time of touchdown, System response delay; Calculate the target wheel speed using the following formula:

[0111] in, is the target wheel speed, is the target tire linear speed, is the tire radius of the current vehicle; The feedforward torque is calculated by the following formula:

[0112] in, is the feedforward torque, is the equivalent moment of inertia of the transmission system, Based on The target angular acceleration calculated by differentiation, is the damping system calibration value, is the angular velocity; The feedback torque is calculated by the following formula:

[0113] in, is the feedback torque, 、 、 are the PID control parameters, is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the speed deviation integral, is the speed deviation differential; The total output torque is obtained by the following formula:

[0114] in, is the total output torque, is the feedforward torque, is the feedback torque; performing torque control on the current vehicle according to the total output torque; Torque control is performed on the current vehicle according to the total output torque.

[0115] In a specific implementation, the touchdown time prediction is based on , For example, the ground contact time can be calculated .

[0116] It is understandable that the target tire linear speed calculation process is to compensate for the preview slope. According to the forward slope, the vehicle acceleration demand is predicted and the tire linear speed is compensated in advance to offset the speed difference caused by the slope. Due to the response delay of the system , the compensation action cannot take effect immediately and needs to be started t time in advance, so the effective compensation time is .

[0117] For system response delay, set to 0.1s, , k is the slope sensitivity coefficient, ranging from 0.8 to 1.2. For example, when

[0118]

[0119] According to the above parameters, we can calculate .

[0120] It should be understood that the calculation process of the feedforward torque is to perform feedforward-feedback proportional-integral-derivative (PID) control according to the target wheel speed of the landing bridge.

[0121] is the equivalent moment of inertia of the transmission system, such as , is the target angular acceleration, Differential calculus, For the damping system, set it to the calibration value, such as , the calculation example is as follows:

[0122] Feedback torque correction (PID control), the execution logic is to measure the actual speed of the motor in real time, calculate the speed deviation and output the feedback torque through the PID controller , is the speed deviation, , are PID control parameters respectively; for example, .

[0123] The dynamic torque constraint condition is

[0124] in is the maximum allowable torque, which can be calculated based on the road adhesion coefficient and axle load Dynamic calculation. ; as an example For example, , if the real-time slip rate , reduced proportionally .

[0125] Implementation case reference: Example 1: No-load energy recovery Scene parameters: No load , real-time slope 0°, map shows slope -5° 3km ahead, power requirement 30kW; Decision results: , allowed to leave the ground Example 2: Climbing a hill in a heavy-loaded mountainous area Scene Parameters: Overload , the real-time slope is 8°, the map shows the slope is 10° 3km ahead, and the required power is 160kW; Decision results: , forced landing.

[0126] Through the above-mentioned solution, this embodiment performs torque unloading and energy recovery on the current vehicle according to the lift-off command; or performs speed synchronization and torque control on the current vehicle according to the landing command. This can improve the accuracy of lift-off or landing decisions, realize intelligent control of the lifting axle, and greatly improve the vehicle's cruising range under no-load and light-load conditions. At the same time, automatic lifting control reduces the driver's operating difficulty. Through precise torque control during the lift-off and landing processes, the impact stress of contact with the ground is reduced, and driving comfort and safety are improved. This realizes global energy closed-loop management of the vehicle, improves the vehicle's overall energy efficiency, and improves the speed and efficiency of the coordinated control of the vehicle's integrated electric drive axle.

[0127] Accordingly, the present invention further provides a vehicle integrated electric drive axle cooperative control device.

[0128] Reference Figure 6 , Figure 6 This is a functional module diagram of the first embodiment of the vehicle integrated electric drive axle cooperative control device of the present invention.

[0129] In a first embodiment of the vehicle integrated electric drive axle cooperative control device of the present invention, the vehicle integrated electric drive axle cooperative control device includes: The data acquisition module 10 is used to collect the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle in real time.

[0130] The fusion decision module 20 is used to input the real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information into a multi-condition fusion decision model to obtain a take-off instruction or a landing instruction.

[0131] The control module 30 is configured to control the current vehicle to take off according to the lift-off instruction, or to control the current vehicle to land according to the landing instruction.

[0132] The data acquisition module 10 is also used to activate the mid- and rear electric drive axles, lifting axle electric actuators, six-dimensional force sensors, inertial sensors IMU, lidar and map modules of the current vehicle; real-time data acquisition is allowed when the vehicle control system self-test of the current vehicle is completed and the communication link is normal; the lifting axle load of the current vehicle is collected in real time through the six-dimensional force sensor, and the lifting axle load is used as the real-time vehicle load; GPS elevation data is obtained, and the IMU data and the GPS elevation data are integrated to obtain the real-time road slope; the real-time power demand of the current driver is read from the vehicle controller of the current vehicle; and map data is loaded through the map module to obtain map prediction information of a preset distance in front of the current vehicle.

[0133] The fusion decision module 20 is further configured to input the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a decision value; compare the decision value with a preset decision threshold to generate a comparison result; generate a lift-off instruction when the comparison result shows that the decision value is less than the preset decision threshold; and generate a landing instruction when the comparison result shows that the decision value is not less than the preset decision threshold.

[0134] The fusion decision module 20 is further configured to input the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a decision value through the following formula:

[0135] in, is the decision value, is the axle load weight coefficient, is the real-time vehicle load, is the maximum load of the vehicle, is the real-time slope weight coefficient, is the real-time road slope, is the required power weight coefficient, For real-time power demand, is the maximum power of the vehicle, Predict the slope weight coefficient for the map, is the distance weight coefficient, For the Slope value of the road segment, is the total number of road slope values.

[0136] The control module 30 is further configured to perform torque unloading and energy recovery on the current vehicle according to the lift-off instruction; or perform speed synchronization and torque control on the current vehicle according to the landing instruction.

[0137] The control module 30 is further configured to obtain a torque adjustment value according to the lift-off command by using the following formula:

[0138] in, is the torque adjustment, is the slope sensitivity coefficient, is the road slope value for the next stage, is the current road slope value, is the current axle load, is the tire radius of the current vehicle; performing torque transfer on the mid- and rear-axle motors of the current vehicle according to the torque adjustment amount and a preset load ratio; The recovery power is obtained according to the lift-off command by the following formula:

[0139] in, To recover power, is the overall efficiency of the motor, is the equivalent moment of inertia of the transmission system, is the angular velocity, is the shell mass, is the acceleration due to gravity, is the height above the ground; A charging current is calculated according to the recovered power, and energy is recovered from the current vehicle according to the charging current.

[0140] The control module 30 is further configured to obtain the predicted touchdown time according to the landing instruction using the following formula:

[0141] in, For the predicted time of touchdown, is the remaining height above the ground, To increase speed; The target tire linear speed of the current vehicle is obtained by the following formula:

[0142] in, is the target tire linear speed, is the current vehicle speed, is the slope sensitivity coefficient, is the road slope value, is the acceleration due to gravity, For the predicted time of touchdown, System response delay; Calculate the target wheel speed using the following formula:

[0143] in, is the target wheel speed, is the target tire linear speed, is the tire radius of the current vehicle; The feedforward torque is calculated by the following formula:

[0144] in, is the feedforward torque, is the equivalent moment of inertia of the transmission system, Based on The target angular acceleration calculated by differentiation, is the damping system calibration value, is the angular velocity; The feedback torque is calculated by the following formula:

[0145] in, is the feedback torque, 、 、 are the PID control parameters, is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the speed deviation integral, is the speed deviation differential; The total output torque is obtained by the following formula:

[0146] in, is the total output torque, is the feedforward torque, is the feedback torque; Torque control is performed on the current vehicle according to the total output torque.

[0147] Among them, the steps for implementing each functional module of the vehicle integrated electric drive axle cooperative control device can refer to the various embodiments of the vehicle integrated electric drive axle cooperative control method of the present invention, and will not be repeated here.

[0148] In addition, an embodiment of the present invention further provides a storage medium, on which a vehicle integrated electric drive axle cooperative control program is stored. When the vehicle integrated electric drive axle cooperative control program is executed by a processor, the following operations are implemented: Real-time collection of the current vehicle's real-time vehicle load, real-time road slope, real-time required power and map prediction information; Inputting the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; The current vehicle is controlled to take off according to the lift-off instruction, or the current vehicle is controlled to land according to the landing instruction.

[0149] Furthermore, when the vehicle integrated electric drive axle cooperative control program is executed by the processor, the following operations are also implemented: Activate the vehicle's mid- and rear electric drive axles, lifting axle electric actuators, six-axis force sensors, inertial sensor IMU, lidar, and map module; When the vehicle control system self-check of the current vehicle is completed and the communication link is normal, real-time data collection is allowed; The six-dimensional force sensor is used to collect the axle load of the lifting bridge of the current vehicle in real time, and the axle load of the lifting bridge is used as the real-time vehicle load; Acquire GPS elevation data, fuse IMU data with the GPS elevation data, and obtain real-time road slope; reading the real-time power demand of the current driver from the vehicle controller of the current vehicle; Map data is loaded through the map module to obtain map prediction information of a preset distance ahead of the current vehicle.

[0150] Furthermore, when the vehicle integrated electric drive axle cooperative control program is executed by the processor, the following operations are also implemented: Inputting the real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information into a multi-condition fusion decision model to obtain a decision value; Comparing the decision value with a preset decision threshold to generate a comparison result; When the comparison result shows that the decision value is less than the preset decision threshold, generating a lift-off instruction; When the comparison result shows that the decision value is not less than the preset decision threshold, a landing instruction is generated.

[0151] Furthermore, when the vehicle integrated electric drive axle cooperative control program is executed by the processor, the following operations are also implemented: The real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information are input into the multi-condition fusion decision model to obtain the decision value through the following formula:

[0152] in, is the decision value, is the axle load weight coefficient, is the real-time vehicle load, is the maximum load of the vehicle, is the real-time slope weight coefficient, is the real-time road slope, is the required power weight coefficient, For real-time power demand, is the maximum power of the vehicle, Predict the slope weight coefficient for the map, is the distance weight coefficient, For the Slope value of the road segment, is the total number of road slope values.

[0153] Furthermore, when the vehicle integrated electric drive axle cooperative control program is executed by the processor, the following operations are also implemented: performing torque unloading and energy recovery on the current vehicle according to the lift-off command; or, Speed ​​synchronization and torque control are performed on the current vehicle according to the landing instruction.

[0154] Furthermore, when the vehicle integrated electric drive axle cooperative control program is executed by the processor, the following operations are also implemented: The torque adjustment amount is obtained according to the lift-off command by the following formula:

[0155] in, is the torque adjustment, is the slope sensitivity coefficient, is the road slope value for the next stage, is the current road slope value, is the current axle load, is the tire radius of the current vehicle; performing torque transfer on the mid- and rear-axle motors of the current vehicle according to the torque adjustment amount and a preset load ratio; The recovery power is obtained according to the lift-off command by the following formula:

[0156] in, To recover power, is the overall efficiency of the motor, is the equivalent moment of inertia of the transmission system, is the angular velocity, is the shell mass, is the acceleration due to gravity, is the height above the ground; A charging current is calculated according to the recovered power, and energy is recovered from the current vehicle according to the charging current.

[0157] Furthermore, when the vehicle integrated electric drive axle cooperative control program is executed by the processor, the following operations are also implemented: According to the landing instruction, the predicted touchdown time is obtained by the following formula:

[0158] in, For the predicted time of touchdown, is the remaining height above the ground, To increase speed; The target tire linear speed of the current vehicle is obtained by the following formula:

[0159] in, is the target tire linear speed, is the current vehicle speed, is the slope sensitivity coefficient, is the road slope value, is the acceleration due to gravity, For the predicted time of touchdown, System response delay; Calculate the target wheel speed using the following formula:

[0160] in, is the target wheel speed, is the target tire linear speed, is the tire radius of the current vehicle; The feedforward torque is calculated by the following formula:

[0161] in, is the feedforward torque, is the equivalent moment of inertia of the transmission system, Based on The target angular acceleration calculated by differentiation, is the damping system calibration value, is the angular velocity; The feedback torque is calculated by the following formula:

[0162] in, is the feedback torque, 、 、 are the PID control parameters, is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the speed deviation integral, is the speed deviation differential; The total output torque is obtained by the following formula:

[0163] in, is the total output torque, is the feedforward torque, is the feedback torque; Torque control is performed on the current vehicle according to the total output torque.

[0164] Those skilled in the art will understand that all or part of the steps in the above-mentioned implementation methods can be implemented by instructing related hardware through a program. The program is stored in a storage medium and includes a number of instructions for enabling a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application; and the aforementioned storage medium is a computer-readable storage medium, including: 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 codes.

[0165] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0166] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0167] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A vehicle integrated electric drive axle coordinated control method, characterized in that: The vehicle integrated electric drive axle coordinated control method includes: Real-time collection of the current vehicle's real-time vehicle load, real-time road slope, real-time required power and map prediction information; Inputting the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; The current vehicle is controlled to take off according to the lift-off instruction, or the current vehicle is controlled to land according to the landing instruction.

2. The vehicle integrated electric drive axle coordinated control method according to claim 1, characterized in that: The real-time collection of the current vehicle's real-time vehicle load, real-time road slope, real-time required power and map prediction information includes: Activate the vehicle's mid- and rear electric drive axles, lifting axle electric actuators, six-axis force sensors, inertial sensor IMU, lidar, and map module; When the vehicle control system self-check of the current vehicle is completed and the communication link is normal, real-time data collection is allowed; The six-dimensional force sensor is used to collect the axle load of the lifting bridge of the current vehicle in real time, and the axle load of the lifting bridge is used as the real-time vehicle load; Acquire GPS elevation data, fuse IMU data with the GPS elevation data, and obtain real-time road slope; reading the real-time power demand of the current driver from the vehicle controller of the current vehicle; Map data is loaded through the map module to obtain map prediction information of a preset distance ahead of the current vehicle.

3. The vehicle integrated electric drive axle coordinated control method according to claim 1, characterized in that: The step of inputting the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction includes: Inputting the real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information into a multi-condition fusion decision model to obtain a decision value; Comparing the decision value with a preset decision threshold to generate a comparison result; When the comparison result shows that the decision value is less than the preset decision threshold, generating a lift-off instruction; When the comparison result shows that the decision value is not less than the preset decision threshold, a landing instruction is generated.

4. The vehicle integrated electric drive axle coordinated control method according to claim 3, characterized in that: The step of inputting the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a decision value includes: The real-time vehicle load, the real-time road slope, the real-time required power and the map prediction information are input into the multi-condition fusion decision model to obtain the decision value through the following formula: in, is the decision value, is the axle load weight coefficient, is the real-time vehicle load, is the maximum load of the vehicle, is the real-time slope weight coefficient, is the real-time road slope, is the required power weight coefficient, For real-time power demand, is the maximum power of the vehicle, Predict the slope weight coefficient for the map, is the distance weight coefficient, For the Slope value of the road segment, is the total number of road slope values.

5. The vehicle integrated electric drive axle coordinated control method according to claim 1, characterized in that: The performing lift-off control on the current vehicle according to the lift-off instruction, or performing landing control on the current vehicle according to the landing instruction, includes: performing torque unloading and energy recovery on the current vehicle according to the lift-off command; or, Speed ​​synchronization and torque control are performed on the current vehicle according to the landing instruction.

6. The vehicle integrated electric drive axle coordinated control method according to claim 5, characterized in that: The performing torque unloading and energy recovery on the current vehicle according to the lift-off instruction includes: The torque adjustment amount is obtained according to the lift-off command by the following formula: in, is the torque adjustment, is the slope sensitivity coefficient, is the road slope value for the next stage, is the current road slope value, is the current axle load, is the tire radius of the current vehicle; performing torque transfer on the mid- and rear-axle motors of the current vehicle according to the torque adjustment amount and a preset load ratio; The recovery power is obtained according to the lift-off command by the following formula: in, To recover power, is the overall efficiency of the motor, is the equivalent moment of inertia of the transmission system, is the angular velocity, is the shell mass, is the acceleration due to gravity, is the height above the ground; A charging current is calculated according to the recovered power, and energy is recovered from the current vehicle according to the charging current.

7. The vehicle integrated electric drive axle coordinated control method according to claim 5, characterized in that: The performing speed synchronization and torque control on the current vehicle according to the landing instruction includes: According to the landing instruction, the predicted touchdown time is obtained by the following formula: in, For the predicted time of touchdown, is the remaining height above the ground, To increase speed; The target tire linear speed of the current vehicle is obtained by the following formula: in, is the target tire linear speed, is the current vehicle speed, is the slope sensitivity coefficient, is the road slope value, is the acceleration due to gravity, For the predicted time of touchdown, System response delay; Calculate the target wheel speed using the following formula: in, is the target wheel speed, is the target tire linear speed, is the tire radius of the current vehicle; The feedforward torque is calculated by the following formula: in, is the feedforward torque, is the equivalent moment of inertia of the transmission system, Based on The target angular acceleration calculated by differentiation, is the damping system calibration value, is the angular velocity; The feedback torque is calculated by the following formula: in, is the feedback torque, 、 、 are the PID control parameters, is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the speed deviation integral, is the speed deviation differential; The total output torque is obtained by the following formula: in, is the total output torque, is the feedforward torque, is the feedback torque; Torque control is performed on the current vehicle according to the total output torque.

8. A vehicle integrated electric drive axle cooperative control device, characterized in that: The vehicle integrated electric drive axle coordinated control device includes: Data acquisition module, used to collect the real-time vehicle load, real-time road slope, real-time required power and map prediction information of the current vehicle; a fusion decision module, configured to input the real-time vehicle load, the real-time road slope, the real-time required power, and the map prediction information into a multi-condition fusion decision model to obtain a lift-off instruction or a landing instruction; A control module is configured to control the current vehicle to lift off according to the lift-off instruction, or to control the current vehicle to land according to the landing instruction.

9. A vehicle integrated electric drive axle cooperative control device, characterized in that: The vehicle integrated electric drive axle cooperative control device includes: a memory, a processor, and a vehicle integrated electric drive axle cooperative control program stored on the memory and executable on the processor. The vehicle integrated electric drive axle cooperative control program is configured to implement the steps of the vehicle integrated electric drive axle cooperative control method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a vehicle integrated electric drive axle cooperative control program, which, when executed by a processor, implements the steps of the vehicle integrated electric drive axle cooperative control method according to any one of claims 1 to 7.

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