A braking strategy control system for electric tractors and electric trailers
By collecting comprehensive information on people, vehicles, and roads and using a deep deterministic strategy gradient reinforcement learning algorithm, combined with a feature voting decision method, the braking strategy of electric tractors and electric trailers is optimized. This solves the balance problem between braking safety and energy recovery, and improves the braking safety and energy recovery efficiency of electric vehicles.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JILIN UNIVERSITY
- Filing Date
- 2026-04-23
- Publication Date
- 2026-06-02
AI Technical Summary
Existing braking control technologies for electric tractors and electric trailers struggle to achieve a dynamic optimal balance between braking safety and maximizing energy recovery, and they do not fully integrate multi-dimensional information from people, vehicles, and roads for comprehensive decision-making.
A braking strategy control system for electric tractors and electric trailers was designed. The system uses a human-vehicle-road integrated information acquisition module, a characterization factor calculation module, and a deep deterministic strategy gradient reinforcement learning algorithm to calculate the required braking torque of the whole vehicle in real time, and uses a feature voting decision method to select the optimal braking mode.
It improves the driving safety and energy recovery efficiency of electric tractor and electric trailer combination vehicles during braking, and achieves a dynamic optimal balance between braking safety and energy recovery.
Smart Images
Figure CN122126094A_ABST