A computer-implemented method is provided for tire state estimation using a physics-based model rather than empirical models. During a calibration process, data is collected in data storage as correlating a first set of tire acceleration values for a given tire model to respective known values for each of a plurality of tire state variables. A physics-based tire model is generated corresponding to the given tire model and comprising one or more tire model parameters determined upon calibration and which remain constant under different conditions. During operation of the tire, measurements are collected for a second set of tire acceleration values and certain of the tire state variables (e.g., speed and inflation) via one or more tire-mounted sensors. At least one of the unmeasured tire state variables (e.g., load and/or tread depth) is estimated based on the second set of tire acceleration values and using the physics-based tire model.

Tires including a tire bodies formed of one or more tire plies are disclosed. In some implementations, tire plies may include a temperature sensor that may detect a temperature of a respective tire ply. The temperature sensor may include a ceramic material organized as a matrix and one or more split-ring resonators (SRRs). Each of the SRRs may have a natural resonance frequency configured to shift in response to one or more of a change in an elastomeric property or a change in the temperature of a respective one or more tire plies. The temperature sensor may include an electrically-conductive layer dielectrically separated from a respective one or more SRRs. A thickness each of the SRRs may be approximately between 0.1 micrometers (μm) and 100 μm.

A monitoring of a tire is performed by using a monitoring unit operated at low frequency and with low power needs, without the need of providing complex hardware and software adapted for reconstructing a signal descriptive of the tire deformations and/or for recognizing the start and the end of peaks or valleys or other significant points of such signal. The monitoring uses a statistical approach for the estimation of the length of the contact area, or of other parameters related to it, based on an estimation of a probability of finding the monitoring unit in correspondence of the contact area at a certain time during rolling.

A method for estimating a condition of a tire is provided. The tire supports a vehicle and is mounted on a wheel. The wheel is rotatably mounted on an axle. A sensor is mounted on at least one of the tire, the wheel, the axle, and a component of the brake system. Vibrational data is measured with the sensor. The data from the sensor is transmitted to a processor, and the data is processed. The processed data is normalized and at least one of the normalized data and pre-processed data is input into a machine learning model. A condition estimation for the tire is generated, which includes at least one of a tread depth of the tire, a pressure of the tire, and a dual tire mismatch.

A tire state detection device that includes: a temperature sensor that detects a temperature in a tire air chamber; an acceleration sensor that detects acceleration associated with rotation of a tire; a pressure sensor that detects pressure in the tire air chamber; a storage unit that stores a plurality of maps for calculating a fatigue state of the tire on the basis of the detected temperature, the detected acceleration and the detected pressure; and a tire fatigue index calculation unit that calculates, by referring to each of the plurality of maps, a tire fatigue index from the detected acceleration, the detected temperature, and the detected pressure.

Described herein are systems and methods for determination of rolling resistance from a sensor or sensors in a tire or tires for application in smart cars to provide feedback to interested parties, such as Departments of Transportation or tire manufacturers.

A method for determining a wheel load acting on a vehicle wheel having a rim, a tire mounted on the rim and a sensor unit mounted at the wheel, includes determining a tire pressure of the tire using the sensor unit, determining a tire footprint of the tire using the sensor unit when the vehicle is driving, determining the wheel load based on a predetermined relationship between the wheel load, the tire pressure and the tire footprint, analyzing temporal variations of the tire pressure during a standstill of the vehicle for determining one or more parameters indicating temporal variations of the tire pressure. A change of the wheel load during the standstill is estimated based on the determined parameters. A device for determining a wheel load and a method and a device for determining a weight of a vehicle are also provided.

A method, device and apparatus for dynamically monitoring the working state of a vehicle, and a storage medium are disclosed. The method includes: waking up a monitoring unit according to a vehicle starting signal, acquiring the load of the vehicle, the tire pressure of a wheel and the temperature of a wheel hub through the monitoring unit, and determining the working state of the vehicle according to the load of the vehicle, the tire pressure of the wheel and the temperature of the wheel hub.

A tire assembly includes a tire and a shape measurement unit utilizing magnetism. The shape measurement unit is provided with one device of a magnetic generator or a magnetic sensor configured to detect a distance of separation from the magnetic generator based on a magnetic field generated by the magnetic generator to acquire a deformation state of a sidewall, in a first region that is any one of a tread-portion-corresponding region and bead-portion-corresponding regions that are on an inner surface of the tire, the tread-portion-corresponding region corresponding to a tread portion and the bead-portion-corresponding regions corresponding to bead portions, and is provided with another of the magnetic generator or the magnetic sensor in a second region.

A road surface condition estimation apparatus which accurately estimates a road surface condition even under changes of external environment such as weather, etc., and a road surface condition estimation method using the same is described. The road surface condition estimation apparatus includes: a sensor module which is mounted on a tire; a receiver module which receives sensing information measured by the sensor module; a processing module which extracts a parameter for estimating a road surface condition by analyzing the sensing information received by the receiver module; and an estimation module which estimates the road surface condition by using the parameter extracted by the processing module. The sensing information includes an acceleration of the tire.