# US 10,245,906 B2 · Tire wear compensated load estimation system and method

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- Number: US 10,245,906 B2
- Document ID: US10245906B2
- Status: Granted
- Kind: B2
- Application number: 14/538,244
- Filed: 2014-11-11
- Published: 2016-05-12
- Granted: 2019-04-02
- Earliest claimed: 2014-11-11
- Assignees: The Goodyear Tire & Rubber Company
- Applicants: The Goodyear Tire & Rubber Company
- Inventors: Kanwar Bharat Singh; Anthony William Parsons; Marc Engel
- Web page: https://patentgrep.com/patent/US10245906B2

## CPC

- B60C 23/0488: {Movement sensor, e.g. for sensing angular speed, acceleration or centripetal force}
- B60C 11/246: {Tread wear monitoring systems}
- B60C 23/04: mounted on the wheel or tyre
- B60C 23/061: {by monitoring wheel speed (measuring distance traversed on the ground by vehicles G01C22/00)}
- B60C 23/064: {comprising tyre mounted deformation sensors, e.g. to determine road contact area}
- B60W 40/13: Load or weight
- G01G 19/086: {wherein the vehicle mass is dynamically estimated}
- G01M 17/02: Tyres
- B60C: VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- G01G: WEIGHING (sorting by weighing B07C5/16)
- G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR

## Abstract

A system and method of estimating a load bearing on a vehicle tire includes an inflation pressure measuring sensor for measuring an inflation pressure level within a tire cavity; a contact patch length sensor measuring the rolling contact patch length of the tire; a tire rolling speed sensor measuring a rolling speed of the tire; and a tire wear state estimation calculator estimating a tire wear state of a tread of the tire. An artificial neural network estimates a tire load from the contact patch length, the inflation pressure level, and the tire rolling speed by utilizing as a compensation factor input the estimated tire wear state.

## Claims

### Claim 1

A system of estimating a load bearing on a vehicle tire, comprising:
a vehicle tire having a tire cavity and supporting a load;
an inflation pressure measuring sensor attached to the tire for measuring an inflation pressure level within the tire cavity;
a contact patch length sensor attached to the tire for measuring the contact patch length of the tire rolling against a ground surface carrying a tire load;
a tire rolling speed sensor mounted to the vehicle for measuring a rolling speed of the tire;
a hub acceleration sensor for measuring a hub acceleration of a hub supporting the vehicle tire;
a tire wear state estimation calculator for deriving an estimated depth of a tread of the tire based on the hub acceleration;
a compensation factor based upon the estimated depth of the tread; and
a tire load calculator for estimating a tire load from the contact patch length, the compensation factor, the inflation pressure level, and the tire rolling speed.

### Claim 2 (depends on 1)

The system of claim 1, wherein the tire load calculator comprises an artificial neural network-based signal processor receiving as inputs the contact patch length, the inflation pressure level, the tire rolling speed, and the estimated tire tread depth.

### Claim 3

A method of estimating a load bearing on a vehicle tire having a tire cavity and supporting a load, comprising:
attaching an inflation pressure measuring sensor to the tire for measuring an inflation pressure level within the tire cavity;
attaching a contact patch length sensor to the tire for measuring the contact patch length of the tire rolling against a ground surface carrying the tire load;
mounting a tire rolling speed sensor to the vehicle for measuring a rolling speed of the tire;
measuring a hub acceleration of a hub supporting the vehicle tire;
deriving an estimated depth of a tread of the tire based on the hub acceleration;
basing a compensation factor upon the estimated depth of the tread; and
estimating a tire load from the contact patch length, the compensation factor, the inflation pressure level, and the tire rolling speed.

### Claim 4 (depends on 3)

The method of claim 3, further comprising employing an artificial neural network-based signal processor in estimating the tire load, the artificial neural network receiving as inputs the contact patch length, the inflation pressure level, the tire rolling speed, and the estimated tire tread depth.