CN-122009175-A - Differential lock control method, system, equipment and storage medium for commercial vehicle

Abstract

The invention discloses a method, a system, equipment and a storage medium for controlling a differential lock of a commercial vehicle, wherein the method comprises the steps of collecting images of road environments in front of the commercial vehicle and point cloud data in real time and processing the images and the point cloud data through a deep learning algorithm, and generating a digital pavement adhesion map comprising potential slip risk areas and predicted adhesion coefficients of the potential slip risk areas; the method comprises the steps of predicting a future running path of a commercial vehicle according to a road surface adhesion map, a current vehicle speed and a steering wheel rotation angle, automatically generating a pre-locking instruction of a differential lock before the commercial vehicle enters a potential slip risk area when the predicted running path intersects the potential slip risk area, executing complete locking of the differential lock when the commercial vehicle enters the potential slip risk area and the slip rate of a driving wheel is detected to exceed a set threshold value, calculating safe unlocking confidence coefficient based on a multi-factor decision model after the differential lock is locked, and executing unlocking of the differential lock when the safe unlocking confidence coefficient exceeds the set threshold value. The invention realizes the intelligent control of the differential lock of the commercial vehicle.

Inventors

• ZHOU FENG
• ZHANG CHI
• JIANG HUI
• ZHANG MINGWEI
• QIU BINGSHAN
• WANG XIANG
• QIN XIN
• ZHUANG MINGXING
• WEI TAO
• WANG KAI
• ZHANG YUFEI
• LI ZHENYU
• ZHANG YULU
• LIU HANHAN
• YANG XUAN

Assignees

• 徐州徐工新能源汽车有限公司

Dates

Publication Date

20260512

Application Date

20260112

Claims (9)

1. 1. A differential lock control method for a commercial vehicle, comprising: Acquiring images and point cloud data of a road environment in front of a commercial vehicle in real time, processing the acquired images and point cloud data through a deep learning algorithm, and generating a digital pavement adhesion map containing potential slip risk areas and predicted adhesion coefficients of the potential slip risk areas; predicting a running path of the commercial vehicle in a future set time according to the road surface adhesion map, the current vehicle speed and the steering wheel rotation angle, and automatically generating a pre-locking or preparation instruction of the differential lock before the commercial vehicle enters the potential slip risk area when the predicted running path intersects the potential slip risk area; when the commercial vehicle enters a potential slip risk area and the slip rate of the driving wheel is detected to exceed a set threshold value, executing the differential lock to be completely locked; After the differential lock is locked, the safety unlocking confidence coefficient is calculated based on the multi-factor decision model, and when the safety unlocking confidence coefficient exceeds a set threshold value, the differential lock is unlocked.
2. 2. The method for controlling the differential lock of the commercial vehicle according to claim 1, wherein the real-time acquisition of the image of the road environment in front of the commercial vehicle and the point cloud data is realized by a vehicle-mounted camera and a millimeter wave radar.
3. 3. The method for controlling a differential lock of a commercial vehicle according to claim 1, wherein the processing of the acquired image and the point cloud data by a deep learning algorithm comprises: and carrying out pixel-level semantic segmentation on the acquired image and the point cloud data through a deep learning algorithm, and identifying the pavement type and the local risk characteristics.
4. 4. The differential lock control method for a commercial vehicle according to claim 1, wherein automatically generating a pre-lock or ready instruction for the differential lock comprises: and sending a pre-activation signal of incomplete locking to the differential lock actuating mechanism to enable the differential lock actuating mechanism to enter a low-power-consumption standby state.
5. 5. The method of claim 1, wherein the multi-factor decision model is based on a multi-factor including road condition confidence, wheel speed differential stability, and steering wheel angle.
6. 6. The differential lock control method for a commercial vehicle according to claim 1, further comprising: When the differential lock is fully locked and the commercial vehicle is detected to fall into a severe slip state, peak output torque of the drive motor is limited and intermittent braking force is applied to the road wheels.
7. 7. An intelligent differential lock predictive control system for a commercial vehicle, comprising: The multi-source information sensing module is used for acquiring images and point cloud data of a road environment in front of the commercial vehicle in real time, processing the acquired images and the point cloud data through a deep learning algorithm, and generating a digital pavement adhesion map containing potential slip risk areas and predicted adhesion coefficients of the potential slip risk areas; The path prediction and pre-locking decision module is used for predicting the running path of the commercial vehicle in the future set time according to the road surface adhesion map, the current vehicle speed and the steering wheel rotation angle, and automatically generating a pre-locking or preparation instruction of the differential lock before the commercial vehicle enters the potential slip risk area when the predicted running path intersects the potential slip risk area; The self-adaptive accurate locking module is used for executing the differential lock to completely lock when the commercial vehicle enters a potential slip risk area and the slip rate of the driving wheel is detected to exceed a set threshold value; And the multi-factor intelligent unlocking module is used for calculating the safety unlocking confidence coefficient based on the multi-factor decision model after the differential lock is locked, and executing the differential lock unlocking when the safety unlocking confidence coefficient exceeds a set threshold value.
8. 8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the commercial vehicle differential lock control method as claimed in any one of claims 1 to 6 when the program is executed by the processor.
9. 9. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, realizes the steps of the differential lock control method for a commercial vehicle as claimed in any one of claims 1 to 6.

Description

Differential lock control method, system, equipment and storage medium for commercial vehicle Technical Field The invention belongs to the technical field of automobile electronic control, and particularly relates to a method, a system, equipment and a storage medium for controlling a differential lock of a commercial vehicle. Background The differential lock is key equipment for improving the passing performance of the commercial vehicle under the condition of low adhesive force. The access and release of the differential lock are mostly dependent on manual operation of a driver, and have the remarkable defects that 1, experience depends on and operation burden that the driver needs to accurately judge road conditions and slip states, the driver is distracted to operate in the tension escaping process, and the work burden and the safety risk are increased, 2, reaction is delayed, the manual operation is performed after the slip occurs, power interruption can cause inertia loss of the vehicle to aggravate the vehicle sinking risk, 3, the problem of forgetting to unlock is solved, and if the driver does not unlock the differential lock in time after the vehicle returns to a good road surface, abnormal abrasion and ablation of gears of the differential mechanism are caused, and huge internal stress is generated in a drive train, so that serious mechanical faults and economic losses are caused. At present, although an attempt is made to automatically control the differential lock by detecting the wheel speed difference, the scheme is simply reactive control of slipping-locking and non-slipping-unlocking, and has obvious defects that ① cannot cope with the upcoming risk and lacks predictability, ② is easy to generate frequent and rough locking/unlocking circulation on a road section with frequent adhesive force change (such as an intermittent ice-snow road surface), driving comfort and system service life are influenced, ③ cannot distinguish slight slipping from serious vehicle sinking, and the intelligent degree is low. Disclosure of Invention The invention aims to overcome the defects in the prior art, and provides a differential lock control method, a differential lock control system, differential lock control equipment and a storage medium for a commercial vehicle, In order to solve the technical problems, the invention is realized by adopting the following scheme: The invention provides a differential lock control method for a commercial vehicle, which comprises the following steps: Acquiring images and point cloud data of a road environment in front of a commercial vehicle in real time, processing the acquired images and point cloud data through a deep learning algorithm, and generating a digital pavement adhesion map containing potential slip risk areas and predicted adhesion coefficients of the potential slip risk areas; predicting a running path of the commercial vehicle in a future set time according to the road surface adhesion map, the current vehicle speed and the steering wheel rotation angle, and automatically generating a pre-locking or preparation instruction of the differential lock before the commercial vehicle enters the potential slip risk area when the predicted running path intersects the potential slip risk area; when the commercial vehicle enters a potential slip risk area and the slip rate of the driving wheel is detected to exceed a set threshold value, executing the differential lock to be completely locked; After the differential lock is locked, the safety unlocking confidence coefficient is calculated based on the multi-factor decision model, and when the safety unlocking confidence coefficient exceeds a set threshold value, the differential lock is unlocked. Further, the image of the road environment in front of the commercial vehicle and the point cloud data are acquired in real time through the vehicle-mounted camera and the millimeter wave radar. Further, the processing of the acquired image and the point cloud data by a deep learning algorithm includes: and carrying out pixel-level semantic segmentation on the acquired image and the point cloud data through a deep learning algorithm, and identifying the pavement type and the local risk characteristics. Further, automatically generating a pre-lock or ready instruction for the differential lock includes: and sending a pre-activation signal of incomplete locking to the differential lock actuating mechanism to enable the differential lock actuating mechanism to enter a low-power-consumption standby state. Further, the multi-factor decision model is based on multiple factors including road condition confidence, wheel speed differential stability, and steering wheel angle. Further, the method further comprises the following steps: When the differential lock is fully locked and the commercial vehicle is detected to fall into a severe slip state, peak output torque of the drive motor is limited and intermittent braking force is applie