Vehicle Friction Estimation Using Dynamic Envelopes and Bins
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle control systems lack accurate and efficient friction estimation, leading to potential vehicle instability and accidents, especially on varying road surfaces, as they rely on limited or inaccurate heuristic representations of tire-road friction.
Innovation Solution
The system employs advanced friction estimation techniques using multiple bins and dynamic envelopes, integrating sensor data to continuously adjust friction estimates and reshape stability envelopes, ensuring the vehicle operates within safe limits by comparing actual behavior with predicted boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional heuristic representations of tire-road friction are used, then the system is simple to implement, but friction estimation accuracy deteriorates leading to vehicle instability
Solution Approach 1:
The friction estimation is divided into multiple discrete bins representing different friction levels. Each bin corresponds to a specific friction coefficient range, allowing the system to segment the continuous friction estimation problem into manageable discrete categories that can be evaluated and updated independently based on vehicle sensor data.
Solution Approach 2:
The friction bins are dynamically updated based on real-time vehicle behavior and sensor data. The system continuously adjusts the friction estimation by comparing actual vehicle responses with predicted responses, allowing the friction bins to adapt to changing road conditions rather than using static heuristic values.
2Measurement precision
If friction estimation is updated continuously using sensor data, then friction estimation accuracy improves, but computational load increases
Solution Approach 1:
The system performs friction estimation updates only when necessary, based on changes in vehicle state or road conditions. Rather than continuously updating at every control cycle, the system selectively updates friction bins when sensor data indicates a change in friction conditions, reducing unnecessary computational energy consumption while maintaining accurate friction estimation.
Solution Approach 2:
The system uses feedback from vehicle sensors (accelerometers, gyroscopes, wheel speed sensors) to continuously monitor vehicle behavior and compare it with predicted behavior based on current friction bin estimates. This feedback mechanism allows the system to detect when friction conditions have changed and trigger updates only when needed, optimizing the balance between accuracy and computational energy usage.
3Productivity
If the vehicle operates close to friction limits to maximize performance, then productivity increases, but vehicle stability deteriorates risking accidents
Solution Approach 1:
The system performs preliminary friction estimation and updates before the vehicle reaches critical friction limits. By continuously monitoring vehicle behavior and updating friction bins in advance, the system can predict when the vehicle is approaching unsafe operating conditions and take preventive control actions to maintain stability while still maximizing performance within safe boundaries.
Solution Approach 2:
The system applies preliminary anti-action by detecting trends toward friction limit violations and counteracting them before they occur. When sensor data indicates the vehicle is approaching unsafe friction conditions, the control system preemptively adjusts control inputs to prevent loss of stability, thereby protecting against accidents while allowing the vehicle to operate near but not beyond safe performance limits.
Data Source
AI summary
Systems and methods for controlling a vehicle using operational constraints, including friction estimates are disclosed. Friction estimation include estimating a tire-road coefficient of friction using bins and envelopes. Bounding envelopes are configured to ensure that stability of the vehicle is maintained. The friction estimate is used to define the bounding envelopes. Further, the bounding envelopes are received as feedback into the friction estimation, itself. Based on the bounding envelope, the friction estimation can be adjusted. Then, the adjusted friction estimation can be fed back to reshape the bounding envelopes. Multiple bins can be used to evaluate an operating range of friction. Each bin can be used to compare actual vehicle dynamics with expected dynamics based on the estimation using the range assigned to that bin. Multiple bins and multiple controllers can run in parallel to re-estimate friction considering the vehicle dynamics over time.