Methods and systems for estimating road surface friction
Methods and systems are provided for determining a road surface friction coefficient and controlling a feature of the vehicle based thereon. In one embodiment, a method includes: receiving signals from an electronic power steering system and an inertial measurement unit; estimating parameters associated with an electronic power steering system model using an iterative optimization method; calculating an electronic power steering system variable using the electronic power steering system model, the estimated parameters and one or more of the received signals; determining whether the calculated electronic power steering system variable satisfies a fitness criterion; and when the calculated electronic power steering system variable does satisfy the fitness criterion, determining a road surface friction coefficient based on at least one of the estimated parameters.
1. A method, comprising:
receiving signals from an electric power steering system and an inertial measurement unit;
iteratively estimating parameters including a moment of inertia, a friction co-efficient, and a self-aligning torque coefficient associated with an electric power steering system model using a particle swarm optimization method and the signals;
determining whether the parameters satisfy a fitness criterion; and
when the estimated parameters satisfy the fitness criterion, determining a road surface friction coefficient based on at least one of the estimated parameters.
2. The method of claim 1 , further comprising detecting a slip angle and determining the road surface friction coefficient when the slip angle is less than a pre-determined value.
3. The method of claim 1 , wherein the estimated parameter chosen to base the determination of the road surface friction coefficient on is the self-aligning torque coefficient.
4. The method of claim 1 , further comprising calculating an electronic power steering system torque value using the electronic power steering system model, the estimated parameters, and one or more of the received signals; and comparing the calculated electric power steering system torque value with a measured torque value to determine the fitness criterion.
5. The method of claim 1 , determining an iteration count and wherein the fitness criterion is based on the iteration count.
6. The method of claim 1 , wherein the signals from the electric power steering system comprise a torsion bar angle and a total electric power steering delivered torque.
7. The method of claim 1 , wherein the signals from the inertial measurement unit comprise a yaw rate, a lateral speed, and a longitudinal speed.
8. The method of claim 1 , further comprising determining that the road surface friction coefficient indicates a wet surface and at least one of:
determining an autonomous actuating vehicle braking strategy;
communicating the road surface friction coefficient to a wireless communication system for alerting other vehicle drivers of the identified wet surface of low friction;
alerting a driver of a potential reduced traction between vehicle tires and the surface as a result of the wet surface;
alerting a driver to not use a driver assistance system; and
providing a notification of the wet surface to a vehicle controller, and the vehicle controller autonomously modifying a control setting of an automated control feature in response to the notification.
9. A system, comprising:
a non-transitory computer readable medium, comprising:
a first module configured to receive sensor signals from an electric power steering system and an inertial measurement unit, and to iteratively estimate parameters including a moment of inertia, a friction co-efficient, and a self-aligning torque coefficient associated with an electric power steering system model using a particle swarm optimization method;
a third module configured to determine whether the parameters satisfy a fitness criterion, and when the parameters do satisfy the fitness criterion, the third module is further configured to determine a road surface friction coefficient based on at least one of the estimated parameters; and
a fourth module configured to control one or more vehicle features based on the road surface friction coefficient.
10. The system of claim 9 , further comprising a slip angle sensor configured to detect a slip angle, wherein the third module is configured to determine the road surface friction coefficient when the slip angle is less than a pre-determined value.
11. The system of claim 9 , wherein the estimated value chosen to base the determination of the road surface friction coefficient on is the self-aligning torque coefficient.
12. The system of claim 9 , further comprising a second module configured to calculate an electronic power steering system torque value using the electronic power steering system model, the estimated values, and one or more of the received signals; and wherein the third module is configured to compare the calculated electric power steering system torque value with a measured torque value to determine the fitness criterion.
13. The system of claim 9 , wherein the third module is further configured to determine an iteration count and wherein the fitness criterion is based on the iteration count.
14. The system of claim 9 , wherein the third module determines the road surface coefficient by selecting the road surface coefficient from a trained database.
15. The system of claim 9 , wherein the signals from the electric power steering system comprise a torsion bar angle and a total electric power steering delivered torque.
16. The system of claim 9 , wherein the signals from the inertial measurement unit comprise a yaw rate, a lateral speed, and a longitudinal speed.