Tire-Pressure Identification Using Intelligent Tire with Three-Axis Accelerometer - MDPI

sensors
Article
Tire-Pressure Identification Using Intelligent Tire
with Three-Axis Accelerometer
Bing Zhu , Jiayi Han and Jian Zhao *
 State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China;
 zhubing@jlu.edu.cn (B.Z.); jyhan18@mails.jlu.edu.cn (J.H.)
 * Correspondence: zhaojian@jlu.edu.cn
 



 Received: 27 April 2019; Accepted: 4 June 2019; Published: 5 June 2019 


 Abstract: An intelligent tire uses sensors to dynamically acquire or monitor its state. It plays a critical
 role in safety and maneuverability. Tire pressure is one of the most important status parameters
 of a tire; it influences vehicle performance in several important ways. In this paper, we propose a
 tire-pressure identification scheme using an intelligent tire with 3-axis accelerometers. As the primary
 sensing system, the accelerometers can continuously and accurately detect tire pressure with less
 electronic equipment mounted in the tire. To identify tire pressure in real time during routine driving,
 we first developed a prototype for the intelligent tire with three 3-axis accelerometers, and carried out
 data-acquisition tests under different tire pressures. Then we filtered the data and concentrated on
 the vibration acceleration of the rim in the circumferential direction. After analysis, we established
 the relationship between tire pressure and characteristic frequency of the rim. Finally, we verified our
 identification scheme with actual vehicle data at different tire pressures. The results confirm that the
 identified tire pressure is very close to the actual value.

 Keywords: intelligent tires; tire pressure; accelerometer; wheel vibration

1. Introduction
 Interactions between a tire and the road exert forces on a vehicle and alter its motion state.
The ultimate aim of most vehicle active safety systems, such as the anti-lock braking system (ABS) or
the electronic stability program (ESP), is to make the tires controllable [1–3]. The status information
of the tire is especially important for all kinds of active safety systems and especially for self-driving
vehicles [4,5].
 At present, tire information, such as tire force and coefficient of road adhesion, is estimated
by modules and filters [6–10]. During estimation, there is much simplification and linearization in
the models of the vehicle, wheel, and tires, and the kinematic state of the vehicle is indeterminate.
Therefore, application of estimated tire information is always restricted.
 To acquire more accurate and appropriate tire information, the concept of an intelligent tire
system is being researched extensively [11,12]. The intelligent tire, which is equipped with sensors
and other electronic equipment, is meant to measure the tire pressure, deformation, force, and other
information. Its most important part is the sensing system [13,14]. Intelligent tires can be classified
into five major types, based on their sensing methods, which are accelerometers, optical sensors,
strain sensors, polyvinylidene fluoride (PVDF) sensors, and other sensors.
 Yi Xiong et al. used an optical tire sensor-based laser to measure rolling tire deformation [15].
Matsuzaki et al. calculated the in-plane strain and out-of-plane displacement with a single CCD camera
fixed on the wheel rim [16]. Optical sensors can achieve noncontact measurement, hence they have
no adverse effect on the tire rubber, but these sensors are expensive and consume too much power,
and wheel vibration can influence their accuracy.

Sensors 2019, 19, 2560; doi:10.3390/s19112560 www.mdpi.com/journal/sensors

Sensors 2019, 19, 2560 2 of 13

 Strain sensors and PVDF sensors have gained more attention in tire sensing [17–21]. Zhang et al.
embedded a pressure-sensitive, electric conductive rubber sensor inside the tire rubber layer to extract
the 3-D forces on the contact patch [17]. Armstrong et al. applied three piezoelectric film sensors,
which are a kind of strain sensor, on the inner surface of a tire to detect normal pressure, deflection,
and longitudinal strain [18]. Erdogan et al. proposed a method to estimate the tire-road friction
coefficient using a sensor consisting of PVDF film attached to the surface of a cantilever beam [19].
However, these sensors occupy a large area of the inner surface of the tire, which will alter the elastic
property of the rubber and cause abnormal measurements. In addition, the huge temperature variation
of a tire has a strong impact on strain sensors and PVDF sensors.
 Accelerometers are now used widely for intelligent tires [22–25]. Savaresi et al. presented a
direct approach to estimate the instantaneous vertical and longitudinal forces by in-tire acceleration
measurements and standard wheel-speed sensors [22]. Matsuzaki et al. proposed an identification
scheme for the friction coefficient involving the slip angle, and applied force estimation through the
use of a 3-axis accelerometer glued on the inner surface of a tire [23]. Niskanen et al. studied the
distorted contact area of a tire in wet conditions using three accelerometers fixed on its inner liner [24].
Accelerometers are compact, small, and weigh just a few grams. They hardly influence a tire when
glued to its inner surface. Accelerometers also consume less energy than optical or other sensors.
This makes it possible to achieve a passive intelligent tire. In addition, their output signals are easy to
read and contain more information compared to PVDF or strain sensors. With all of these attractive
features, accelerometers are the most suitable sensors for intelligent tires.
 Intelligent tires equipped with accelerometers, as described above, are mostly researched to
identify and estimate tire deformation, slip angle, vertical or longitudinal force, and tire-road friction
coefficients. While this information is vital, there has been little research on tire pressure using
intelligent tires equipped with accelerometers. Tire pressure influences several important aspects of
vehicle performance [26]. Rolling resistance increases when a tire loses pressure, which reduces fuel
economy. Pressure that is too low results in stationary wear, which can cause a tire to burst. When the
tire pressure is high, the tire and road contact patch will be non-uniform, which reduces adhesive force.
In addition, the tire will harden, which leads to an uncomfortable ride for passengers. The tire pressure
also makes sense to vehicle active safety or control applications. The cornering stiffness is influenced
by the tire pressure greatly. The cornering stiffness of the front and rear tires determines the steering
behavior directly [27,28]. In this consideration, the tire pressure has something to do with the active
safety systems, such as ESP.
 The tire pressure monitoring in real time becomes more and more important for the present
and the future. The vehicle control algorithm is more and more complexity, and more variables
are taken into account during tire modelling. The influence of the tire pressure can be and must
be taken into consideration. For example, Janulevicius et.al. investigated how driving wheels of a
front-loaded vehicle interact with the terrain depending on tire pressure [29]. Toma et al. studied the
influence of tire pressure on the results of diagnosing brakes and suspension [30]. With the real time
tire pressure, the control strategy under different tire pressure can be optimized and enhances the
vehicle performance [31]. Therefore, Tire-pressure monitoring should be the fundamental function of
an intelligent tire. Tire-pressure monitor systems (TPMSs) are widely used all over the world [32,33].
 There are two types of TPMS. One is called indirect TPMS and the other is direct TPMS. The indirect
TPMS can work with no need for installing additional sensors. It uses the signal of the wheel speed
sensors or other existing sensors [34]. This kind of TPMS is only able to judge whether the tire is lacking
of pressure. It cannot provide the precise tire pressure in real time. Therefore, the use of the indirect
TPMS is limited. It can only help to acquaint the drivers with the tire situation and it can do nothing
with the advanced active safety systems [35]. The direct TPMS is based on a pressure sensor mounted
at the tire nozzle. It is practical and durable for the present, but it is a separate system. More electronic
circuits or equipment are acquired to combine this kind of TPMS with intelligent tire or advanced
active safety systems. This will reduce the reliability and add complexity. If we can estimate the tire

Sensors 2019, 19, x 3 of 14

function is developed by equipping accelerometer or other vibration sensors, the tire pressure
identification method
 Sensors 2019, 19, 2560 proposed in this paper will help to take the place of the present TPMS. 3 of 13 The
major motivation to install accelerometers in the tire may be to estimate tire deformation, slip angle
or frictional
 pressure force. In this
 precisely condition,
 in real time usingifthe
 wesensors
 can identify
 installedthe tireintelligent
 in the pressuretire,bya the accelerometers
 seamless tire pressure which
is already installed
 monitoring in the tire,
 function then
 will be pressure
 achieved. In sensors
 the futurein when
 the tirethewill be unnecessary.
 intelligent The less electronic
 tire with multi-function
 is developed
equipment we mount by equipping accelerometer
 in the tire, the moreorpractical
 other vibration sensors,an
 and reliable theintelligent
 tire pressuretire
 identification
 system we can
 method
achieve [5,36]. proposed in this paper will help to take the place of the present TPMS. The major motivation
 Intothis
 install accelerometers in the tire may be to estimate tire deformation, slip angle or frictional force.
 paper, we have tried to identify the tire pressure using 3-axis accelerometers in the tire.
 In this condition, if we can identify the tire pressure by the accelerometers which is already installed in
We designed a development platform for an intelligent tire, and conducted road tests under different
 the tire, then pressure sensors in the tire will be unnecessary. The less electronic equipment we mount
tire pressures formore
 in the tire, the data acquisition
 practical using
 and reliable the platform.
 an intelligent tire system We mathematically
 we can achieve [5,36]. processed the
accelerometer data and developed an identification scheme for tire
 In this paper, we have tried to identify the tire pressure using 3-axis accelerometers pressure by inanalyzing
 the tire. the
characteristics
 We designed in athe frequency
 development domain.
 platform for anTo verify tire,
 intelligent our and tire-pressure
 conducted road identification scheme, we
 tests under different
 tire pressures
conducted anotherfor data
 road acquisition
 test usingout
 was carried the with
 platform. We mathematically
 different tire pressures processed the accelerometer
 and compared the identified
 data and developed
and actual tire pressures. an identification scheme for tire pressure by analyzing the characteristics in the
 frequency domain. To verify our tire-pressure identification scheme, we conducted another road test
 was carried out with different tire pressures and compared the identified and actual tire pressures.
2. Intelligent Tire with 3-Axis Accelerometers and Data-Acquisition Test
 2. Intelligent Tire with 3-Axis Accelerometers and Data-Acquisition Test
2.1. Test Equipment and Method
 2.1. Test Equipment and Method
 To discover the pattern of wheel- and tire-vibration acceleration, we made a prototype for an
 To discover the pattern of wheel- and tire-vibration acceleration, we made a prototype for an
intelligent tire with 3-axis accelerometers; the overall structure is shown in Figure 1. The wheel was
 intelligent tire with 3-axis accelerometers; the overall structure is shown in Figure 1. The wheel was
16 × 6.516 ×J 6.5
 and the
 J and thetire wasa 205/55R16
 tire was a 205/55R16 (Michelin,
 (Michelin, Clermont-Ferrand,
 Clermont-Ferrand, France). TheFrance).
 wheel partThe wheel part
 contained
contained a wheel
 a wheel with with connector
 connector and accelerometers,
 and accelerometers, and a slipand
 ringamodule
 slip ring
 to module to conduct
 conduct an an electrical
 electrical signal.
signal.The
 The cabin
 cabin partpart contained
 contained a sensoraadapter,
 sensordata-acquisition
 adapter, data-acquisition system,
 system, host computer, andhost computer,
 inertial system and
 and global navigation satellite system (GNSS).
inertial system and global navigation satellite system (GNSS).

 Figure
 Figure 1. 1. Overallstructure
 Overall structure of
 ofintelligent
 intelligenttire.
 tire.

 We used three 3-axis accelerometers to measure the vibration acceleration of different parts. The 3-axis
 We used three 3-axis accelerometers to measure the vibration acceleration of different parts. The
 accelerometers (SAE30050Q, Yutian, Wuxi, China) were small and light (9.5 mm × 9.5 mm × 9.5 mm, 6 g),
3-axis with
 accelerometers
 measurement ranges(SAE30050Q, Yutian,
 ±500 g and 2~5000 Wuxi, China)
 Hz. Accelerometer were
 C was fixed to the small
 well of theand
 rim, light
(9.5 mmand× accelerometers
 9.5 mm × 9.5 A mm,
 and 6B g),
 werewith measurement
 respectively ranges
 fixed to the ±500
 internal g and
 surface 2~5000
 of the treadHz. Accelerometer
 and the sidewall C
was fixed totire.
 of the theThe
 well of the
 setup rim,
 of the and accelerometers
 accelerometers A and B of
 and the coordinates were respectively
 the wheel are shown fixed to the
 in Figure 2. internal
surface of the tread and the sidewall of the tire. The setup of the accelerometers and the coordinates
of the wheel are shown in Figure 2.

Sensors 2019, 19, 2560 4 of 13
Sensors 2019, 19, x 4 of 14

 Figure 2. Accelerometer setup and tire coordinates.

 To export the signal from inside the tire, we drilled on the well and installed a sealed connection
socket, as shown in Figure 3a. The maximum sealing pressure of the socket was 60 bars, which
exceeded the maximum tire pressure of 3.5 bars. The socket introduced no possibility of leaks from
the tire and transmitted electrical signals without loss.
 Because a wheel constantly spins during driving, we designed a slip ring module to transmit the
signal from the wheel to the car body, as shown in Figure 3b. The slip ring was assembled in a circular
plate, which was attached to the wheel hub by five joint cylinders, each with a claw at one end to
clamp the nut of the hub bolt and a threaded hole at the other end to attach the circular plate.
 We used integral electronic piezoelectric (IEPE) accelerometers, so a sensor adapter (CT5210,
CHENGTEC, Shanghai, China) was needed to supply power and a condition signal, as shown in
Figure 3e.
 An RT3000 (OxTS, Middleton Stoney, United Kingdom) combined inertial and GNSS system
was installed in the vehicle, as shown in Figure 3f, to acquire vehicle dynamic states, such as velocity,
acceleration, and yaw rate. The velocity accuracy was 0.05 km/h, and the data frequency was 100 Hz.
 We used a MicroAutoBox II (dSPACE, Paderborn, Germany) data-acquisition system to record
the vibration acceleration, vehicle speed, and other
 Accelerometer data, as
 setup shown in Figure 3d. This device has 32
 Figure
 Figure 2.
 2. Accelerometer setup and
 and tire
 tire coordinates.
 coordinates.
16-bit analog input channels with a 200 Ksps conversion rate and −10~10V input voltage range, eight
16-bitTo
 To
 exportoutput
 analog the signal
 export the signal
 from inside
 channels, and six
 from inside
 thecontroller
 tire, we drilled on the well
 area network
 the tire, we drilled
 and channels.
 (CAN)
 on the well
 installed a sealed
 and installed aThe
 connection
 outputs
 sealed of the
 connection
socket, as shown
accelerometers in
 were Figure 3a. The maximum sealing pressure of the socket was 60 bars, which exceeded
socket, as shown in conditioned
 Figure 3a. The by the adapter sealing
 maximum and then input toofthe
 pressure theMicroAutoBox
 socket was 60II bars,
 through
 whichan
the maximum tireconverter.
analog-to-digital pressure ofThe3.5 RT3000
 bars. The socket
 and introduced no
 MicroAutoBox II possibility of leaks
 communicated by from the
 CAN tire and
 (Controller
exceeded the maximum tire pressure of 3.5 bars. The socket introduced no possibility of leaks from
transmitted
Area electrical
 Network) bus. Thesignals
 datawithout loss. through a CAN channel.
 weresignals
 acquired
the tire and transmitted electrical without loss.
 Because a wheel constantly spins during driving, we designed a slip ring module to transmit the
signal from the wheel to the car body, as shown in Figure 3b. The slip ring was assembled in a circular
plate, which was attached to the wheel hub by five joint cylinders, each with a claw at one end to
clamp the nut of the hub bolt and a threaded hole at the other end to attach the circular plate.
 We used integral electronic piezoelectric (IEPE) accelerometers, so a sensor adapter (CT5210,
CHENGTEC, Shanghai, China) was needed to supply power and a condition signal, as shown in
Figure 3e.
 An RT3000 (OxTS, Middleton Stoney, United Kingdom) combined inertial and GNSS system
was installed
Sensors 2019, 19, xin the vehicle, as shown in Figure 3f, to acquire vehicle dynamic states, such as velocity,
 5 of 14
 (a) (b) (c)
acceleration, and yaw rate. The velocity accuracy was 0.05 km/h, and the data frequency was 100 Hz.
SensorsWe used
 2019, 19, x a MicroAutoBox II (dSPACE, Paderborn, Germany) data-acquisition system to record
 www.mdpi.com/journal/sensors
the vibration acceleration, vehicle speed, and other data, as shown in Figure 3d. This device has 32
16-bit analog input channels with a 200 Ksps conversion rate and −10~10V input voltage range, eight
16-bit analog output channels, and six controller area network (CAN) channels. The outputs of the
accelerometers were conditioned by the adapter and then input to the MicroAutoBox II through an
analog-to-digital converter. The RT3000 and MicroAutoBox II communicated by CAN (Controller
Area Network) bus. The data were acquired through a CAN channel.

 (d) (e) (f)
 Test setup and equipment: (a) Connector
 Figure 3. Test Connector on the rim well; (b) slip ring
 ring module; (c) connector
 on the
 the vehicle
 vehiclebody;
 body;(d) data-collection
 (d) system;
 data-collection (e) battery
 system; and and
 (e) battery adapter; (f) combined
 adapter; inertialinertial
 (f) combined and global
 and
 navigation
 global satellitesatellite
 navigation systemsystem
 (GNSS)(GNSS)
 system.system.

2.2. Vehicle Test under Different Tire Pressures
 (a) (b) (c)
 To determine how to identify the tire pressure in an intelligent tire, we conducted an experiment
in which
Sensors 2019,the test vehicle traveled on an urban road with different tire pressures.
 19, x The tire pressure
 www.mdpi.com/journal/sensors
was set at 150 kPa, 250 kPa, and 350 kPa. To include a variety of road surfaces and to develop a
practical and universal method for tire pressure identification, we chose a road of about

Sensors 2019, 19, 2560 5 of 13

 Because a wheel constantly spins during driving, we designed a slip ring module to transmit the
Sensors
signal 2019,
 from19,the
 x
 wheel to the car body, as shown in Figure 3b. The slip ring was assembled in a circular 5 of 14
plate, which was attached to the wheel hub by five joint cylinders, each with a claw at one end to clamp
the nut of the hub bolt and a threaded hole at the other end to attach the circular plate.
 We used integral electronic piezoelectric (IEPE) accelerometers, so a sensor adapter (CT5210,
CHENGTEC, Shanghai, China) was needed to supply power and a condition signal, as shown in
Figure 3e.
 An RT3000 (OxTS, Middleton Stoney, United Kingdom) combined inertial and GNSS system
was installed in the vehicle, as shown in Figure 3f, to acquire vehicle dynamic states, such as velocity,
acceleration, and yaw rate. The velocity accuracy was 0.05 km/h, and the data frequency was 100 Hz.
 We used a MicroAutoBox II (dSPACE, Paderborn, Germany) data-acquisition system to record
the vibration acceleration, vehicle speed, and other data, as shown in Figure 3d. This device has
32 16-bit analog input channels with a 200 Ksps conversion rate and −10~10V input voltage range,
 (d) (e) (f)
eight 16-bit analog output channels, and six controller area network (CAN) channels. The outputs of
the accelerometers were
 Figure 3. Test setup andconditioned
 equipment: by the adapter
 (a) Connector on and then
 the rim input
 well; to the
 (b) slip ringMicroAutoBox II through
 module; (c) connector
an analog-to-digital converter.
 on the vehicle body; The RT3000system;
 (d) data-collection and MicroAutoBox
 (e) battery andIIadapter;
 communicated by CAN
 (f) combined (Controller
 inertial and
Areaglobal navigation
 Network) satellite
 bus. The datasystem (GNSS) system.
 were acquired through a CAN channel.

 Vehicle Test
2.2. Vehicle Test under Different Tire
 Tire Pressures
 Pressures
 To determine how to identify the tire pressure in an intelligent tire, we conducted an experiment experiment
in which the
 the test
 testvehicle
 vehicletraveled
 traveledononananurban
 urbanroadroadwith different
 with tiretire
 different pressures. TheThe
 pressures. tire pressure was
 tire pressure
set at 150 kPa, 250 kPa, and 350 kPa. To include a variety of road surfaces and
was set at 150 kPa, 250 kPa, and 350 kPa. To include a variety of road surfaces and to develop a to develop a practical
and universal
practical and method for tire
 universal pressure
 method foridentification,
 tire pressure weidentification,
 chose a road of we about 2 km,aincluding
 chose road ofasphalt,
 about
cement, damaged, and snow-covered road surfaces, and the test vehicle traveled
2 km, including asphalt, cement, damaged, and snow-covered road surfaces, and the test vehicle at speeds from 0 to
50 km/h at
traveled in speeds
 a natural fashion
 from 0 to 50atkm/h
 each in
 tire pressure.
 a natural The weather
 fashion waspressure.
 at each tire sunny and Thethe temperature
 weather was
 was sunny
about −20 ◦ C.
and the temperature was about −20 °C.
 A truncated
 truncated section
 sectionof ofthe
 theresultant
 resultantdata
 dataisisshown
 shownininFigure
 Figure4.4.The
 The data
 data include
 include nine
 nine channels
 channels of
of vibration
vibration acceleration
 acceleration fromfrom
 threethree 3-axis
 3-axis accelerometers,
 accelerometers, and and the velocity,
 the velocity, acceleration,
 acceleration, yaw yaw
 rate, rate,
 and
and other
other parameters
 parameters werewere acquired
 acquired synchronously.
 synchronously.

 Figure
 Figure 4.
 4. Vibration
 Vibration acceleration
 acceleration of
 of rim
 rim well,
 well, tire
 tire tread, and sidewall in three directions.

Sensors 2019, 19, 2560 6 of 13

3. Identification Scheme for Tire Pressure
 The wheel, which is the combination of the rim and tire, is a kind of spring-damp system.
That means a change of stiffness will alter the characteristic frequency of the wheel. According to the
spring-loaded ring tire model in the vibration model [37,38], the relationship between torsion vibration
natural frequency and tire stiffness can be derived as:
 s
 1 Kθ
 ftor = , (1)
 2π ρbl

where ftor is the torsion natural frequency, which can reflect the characteristics of the circumferential
vibration; Kθ is the rotational spring constant of the sidewall portion per unit length, representing the
tire and wheel stiffness; ρ is the density; and b and l are the thickness and half-length, respectively,
of the tread ring.
 From Equation (1), we can see that the natural frequency increases with the stiffness. Furthermore,
the stiffness of the wheel can be influenced by the tire pressure. Another tire model from Akasaka
illustrates the relationship between stiffness and tire pressure [39]. The theoretical formula of the
rotational spring constant about p is [38]:

 Kt = g(rB , rc , rD , ϕD , αD , θ) · p + h(h, rB , rD , ϕD , Vr , Gr ), (2)

where Kt is the rotational spring constant, which is nearly the same as Kθ in the spring-loaded ring tire
mode; p is the tire pressure; rB , rC , and rD are the tire radius parameters related to special tire positions;
ϕD , and αD are the angle parameters; θ is the rotational displacement; Vr is content rate; Gr is the
rigidity modulus; and h is the thickness. Equation (2) is used to illustrate the relationship between the
stiffness and tire pressure, so there is no need to derive it in detail.
 Generally, the stiffness of the wheel increases with the tire pressure and when the tire pressure
continues to increase, the stiffness will remain unchanged or will decrease slightly.
 According to this principle, we can identify the tire pressure by mining the data provided by
the intelligent tire, and we can determine the characteristic frequency. Above all, it is important to
determine how the tire pressure influences the stiffness and establish an accurate relationship between
characteristic frequency and tire pressure.

3.1. Vibration Analysis of the Tire
 Previous research has shown that one of the characteristic frequencies of the wheel is between 35 Hz
and 80 Hz [40,41]. Therefore, we concentrated on analyzing the amplitude-frequency characteristic in
this range.
 We first analyzed the vibration acceleration data under different tire pressures from accelerometers
A and B. A piece of data under a steady speed was selected to show the frequency component of the
tire vibration.
 We used the fast Fourier transform (FFT) algorithm to achieve a time-frequency transformation.
FFT is an improved algorithm for the discrete Fourier transform (DFT). The main formulas are
as follows:
 k
 X(k) = X1 (k) + WN X2 (k) , (3)
 N k X (k )
 X k + 2 = X1 ( k ) − W N 2

where: PN/2−1
 X1 ( k ) = kr
 x1 (r)WN/2
 r=0
 PN/2−1 , (4)
 X2 ( k ) = kr
 x2 (r)WN/2
 r=0

where:
 kn 2π
 WN = e− j N kn , (5)

Sensors 2019, 19, 2560 7 of 13
Sensors 2019,
Sensors 2019, 19,
 19, xx 77 of
 of 14
 14

where
where
where WW is
 W is the
 is the twiddle
 thetwiddle factor,
 factor,xxx111(r)
 twiddlefactor, (r) and
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 The The results
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 5 and 6.5 Theand 6. The semitransparent
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 6. The semitransparent spectrums spectrums
 spectrums are
 are the are the original
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 results byresults
 Gaussian by Gaussian
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 smoothing smoothing
 smoothing filter.
 filter. Thefilter. The vibrations
 The
 vibrations vibrations
 of the tireofoftread
 the tire
 the tire
 were
tread were
tread were almost
 almost alike
 alike in
 in three
 three directions
 directions under under different
 different tire
 tire pressures,
 pressures, and and the
 the tire
 tire side
 side wall
 wall was
 was
almost alike in three directions under different tire pressures, and the tire side wall was the same. It is
the same.
the same. ItIt is
 is difficult
 difficult to
 to distinguish
 distinguish between tire tire pressures
 pressures through
 through spectral
 spectral analysis.
 analysis. We We speculated
 speculated
difficult to distinguish between tirebetween pressures through spectral analysis. We speculated that the tire
that
that the tire had filtered the vibration through its elastic property. The changes in tire pressure did
had filtered the vibration through its elastic property. The changes in tire pressure did not lead did
 the tire had filtered the vibration through its elastic property. The changes in tire pressure to the
not lead
not lead to thethe obvious
 obvious differences
 differences of of the
 the vibration
 vibration characteristic
 characteristic of of the
 the tire.
 tire.
obvious todifferences of the vibration characteristic of the tire.

 (a)
 (a) (b)
 (b) (c)
 (c)
 Figure
 Figure 5.
 Figure 5. Amplitude-frequency
 5. Amplitude-frequency
 Amplitude-frequency curve
 curve
 curveof ofaccelerometer
 of accelerometer
 accelerometer A A
 A (tire
 (tire tread)
 (tiretread) in inthree
 tread)in three directions:
 threedirections:
 directions:
 (a)
 (a) Circumferential
 (a) Circumferential direction;
 Circumferentialdirection; (b)
 direction;(b) axial
 (b)axial direction;
 axialdirection;
 direction;(c) radial
 (c)(c)
 radial direction.
 direction.
 radial direction.

 (a)
 (a) (b)
 (b) (c)
 (c)
 Figure 6.
 Figure
 Figure Amplitude-frequencycurve
 6. Amplitude-frequency
 Amplitude-frequency curveof
 curve of ofaccelerometer
 accelerometer
 accelerometer B B(tire
 B (tiresidewall)
 (tire sidewall)
 sidewall) in inthree
 in threedirections:
 three directions:
 directions:
 (a)
 (a) Circumferential
 Circumferential direction;
 direction; (b)
 (b) axial
 axial direction;
 direction; (c)
 (c) radial
 radial direction.
 direction.
 (a) Circumferential direction; (b) axial direction; (c) radial direction.
3.2. Vibration
3.2. VibrationAnalysis
 Analysisofofthe
 theRim
 Rim
3.2. Vibration Analysis of the Rim
 We analyzed
 We analyzed the thedata
 datafrom
 fromaccelerometer
 accelerometerC, C,which
 whichreflected
 reflectedthe thevibration
 vibrationofofthe the rim.
 rim. AsAs a rigid
 We analyzed the data from accelerometer C, which reflected the vibration of the rim. As aa rigid
 rigid
body,
body, thethe rim
 the rim will
 rim will vibrate
 will vibrate with
 vibrate with a regular
 with aa regular pattern
 regular pattern when
 pattern when excited
 when excited
 excited by by
 by thethe road
 the road surface.
 road surface.
 surface. The The
 The data data
 data of of of
body,
accelerometer A
accelerometer Awere
 wereprocessed
 processedin inthe
 thesame
 sameway.
 way.TheTheresult
 resultisisshown
 shownininFigure
 Figure7.7.It It
 is is observed
 observed that
 that
accelerometer A were processed in the same way. The result is shown in Figure 7. It is observed that
the axial
the axial and
 axial and radial
 and radial vibration
 radial vibration amplitude-frequency
 vibration amplitude-frequency
 amplitude-frequency curves curves remain
 curves remain
 remain thethe same
 the same
 same forfor accelerometers
 for accelerometers
 accelerometers A A and B,
 and
 A and
the
reflecting
B, reflectingno
 reflecting no distinction.
 no distinction. However,
 distinction. However, the
 However, the result in
 the result the
 result in circumferential
 in the
 the circumferential direction
 circumferential direction reveals
 direction reveals a difference
 reveals aa difference in the
 difference
B,
peak
in thefrequency at different
 peak frequency
 frequency tire pressures.
 at different
 different The peak
 tire pressures.
 pressures. Thefrequency rises with
 peak frequency
 frequency risesthe tirethe
 with pressure. The changes
 tire pressure.
 pressure. The
in the peak at tire The peak rises with the tire The
in tire
changes inpressure
 in tire are reflected
 tire pressure
 pressure are in the
 are reflected peak
 reflected in frequency
 in the
 the peak of the
 peak frequency amplitude-
 frequency of of the frequency
 the amplitude- curve.
 amplitude- frequency Therefore,
 frequency curve.
 curve.
changes

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Sensors 2019, 19, 2560 8 of 13
Sensors 2019, 19, x 8 of 14

Therefore, the circumferential
 the circumferential vibration vibration
 of the rimofbecomes
 the rim becomes thetire-pressure
 the key for key for tire-pressure identification.
 identification. To prove
To prove
 that that the
 the change of change
 the peakoffrequency
 the peak isfrequency is not we
 not occasional, occasional,
 analyzedwetheanalyzed the between
 relationship relationship
 peak
between
 frequencypeak
 andfrequency and over
 tire pressure tire pressure
 the wholeover the whole
 vehicle test. vehicle test.

 (a) (b) (c)
 Figure
 Figure 7.7. Amplitude-frequency
 Amplitude-frequency curve
 curveof ofaccelerometer
 accelerometer C C(rim
 (rimwell)
 well)in inthree
 threedirections:
 directions:
 (a)
 (a)Circumferential
 Circumferentialdirection;
 direction;(b)
 (b)axial
 axialdirection;
 direction;(c)
 (c)radial
 radialdirection.
 direction.

 3.3.Relationship
3.3. RelationshipBetween
 BetweenCharacteristic
 CharacteristicFrequency
 Frequencyand
 andTire
 TirePressure
 Pressure
 The circumferential
 The circumferential acceleration
 acceleration data
 dataofofaccelerometer
 accelerometer C under
 C undertire pressures of 150ofkPa,
 tire pressures 150250 kPa,
 kPa,
 andkPa,
250 350and
 kPa350were
 kPasegmented every every
 were segmented five seconds, and each
 five seconds, segment
 and each waswas
 segment processed
 processedthrough
 through FFT.
 We truncated
FFT. the amplitude-frequency
 We truncated the amplitude-frequencycurvescurves
 from 35from
 Hz to3580Hz
 Hz,tothen smoothed
 80 Hz, and normalized
 then smoothed and
 each curve each
normalized and plotted
 curve andthem in chronological
 plotted order. Theorder.
 them in chronological resultThe
 is shown
 result isinshown
 Figurein8.Figure
 The vibration
 8. The
 acceleration
vibration intensityintensity
 acceleration at different frequency
 at different is represented
 frequency by colors.
 is represented The intensity
 by colors. increases
 The intensity from
 increases
 blue blue
from to yellow. TheThe
 to yellow. redred
 weighted
 weightedstraight
 straightlines
 linesrepresent
 represent the average peak
 the average peakpositions
 positionsatatdifferent
 different
 tirepressures.
tire pressures.

 (a) (b) (c)
 Segmentedamplitude-frequency
 Figure8.8.Segmented
 Figure amplitude-frequencycurve curveofofaccelerometer
 accelerometer
 A A(rim
 (rimwell)
 well)inincircumferential
 circumferential
 direction:(a)
 direction: (a)150
 150kPa;
 kPa;(b)
 (b)250
 250kPa;
 kPa;(c)(c)350
 350kPa.
 kPa.

 Figure88shows
 Figure showsthethepeak
 peakvalues
 valuesaround
 around4545Hz Hzatata atire
 tirepressure
 pressureofof150150kPa.
 kPa.AsAsthethe tire
 tire pressure
 pressure
 increased,the
increased, thepeak
 peakvalues
 valueswent
 wenttotoabout
 about5050HzHzwhenwhenthe thetire
 tirepressure
 pressurewas was250 250kPa.
 kPa.WhenWhen the
 the tire
 tire
 pressure increased to 350 kPa, the peak values gathered around 53 Hz. This trend
pressure increased to 350 kPa, the peak values gathered around 53 Hz. This trend is consistent with is consistent with
 theanalysis
the analysis presented atat the
 thebeginning
 beginningofofthis
 thissection.
 section.ThatThatis to
 is say, the the
 to say, characteristic
 characteristicfrequency about
 frequency
 whichwhich
about the peak
 the values gathered
 peak values is an inherent
 gathered attribute
 is an inherent of the wheel.
 attribute It will It
 of the wheel. not be not
 will affected by external
 be affected by
 factors such as vehicle speed, but increases with tire pressure within a certain range.
external factors such as vehicle speed, but increases with tire pressure within a certain range. Therefore, we next
 sought to establish
Therefore, we next an accurate
 sought to rule or relationship
 establish an accurate between
 rule or characteristic
 relationship frequency
 betweenand tire pressure.
 characteristic
frequency and tire pressure.
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Sensors2019,
Sensors 19,x 2560
 2019,19, 9 9ofof1413

 To determine the characteristic frequency at different tire pressures, it is practical and effective
 To determine the characteristic frequency at different tire pressures, it is practical and effective to
to combine all segments of amplitude-frequency curves and locate the peak value. However, we first
 combine all segments of amplitude-frequency curves and locate the peak value. However, we first must
must consider how the vehicle speed affects the amplitude of the amplitude-frequency curves, or the
 consider how the vehicle speed affects the amplitude of the amplitude-frequency curves, or the features
features of the amplitude-frequency curves at low vehicle speed will be neglected. Because the
 of the amplitude-frequency curves at low vehicle speed will be neglected. Because the vibration
vibration acceleration intensity is influenced greatly by the speed. The wheel and tire will be excited
 acceleration intensity is influenced greatly by the speed. The wheel and tire will be excited more
more greatly when the speed goes higher [42]. We will use the segmented amplitude-frequency data
 greatly when the speed goes higher [42]. We will use the segmented amplitude-frequency data at
at 350 kPa to illustrate the effect of vehicle speed on peak amplitude. We found that the peak
 350 kPa to illustrate the effect of vehicle speed on peak amplitude. We found that the peak amplitude
amplitude is directly proportional to the square of the vehicle speed, as shown in Figure 9.
 is directly proportional to the square of the vehicle speed, as shown in Figure 9.

 Relationshipbetween
 Figure9.9.Relationship
 Figure betweenpeak
 peakamplitude
 amplitudeand
 andvehicle
 vehiclespeed.
 speed.

 Accordingto to
 According thisthis relationship,
 relationship, we developed
 we developed a function
 a function to the
 to calculate calculate theamplitude-
 weighted weighted
 amplitude-frequency curves at different
frequency curves at different tire pressures:tire pressures:

 = ∑ 1 , ,
 X
 Gsum,tp, = Gtp,i , (6)
 (6)
 v2i
where Gsum,tp is the weighted amplitude-frequency curve (tp = 150 kPa, 250 kPa, 350 kPa); Gtp,i is the
 where Gsum,tp
segmented is the weighted amplitude-frequency
 amplitude-frequency curve (i = 1,2,3,…); 1/v curve (tp =
 i2 is the 150 kPa,
 weight 250 kPa,
 function to eliminate Gtp,ieffect
 350 kPa); the is the
ofsegmented amplitude-frequency
 vehicle speed; (i = 1,2,3,
 curvevehicle
 and vi is the average . . . during
 speed 2
 ); 1/vi isthe thecurrent
 weightsegment.
 function to eliminate the effect
 of vehicle speed; and
 The weighted vi is the average vehicle
 amplitude-frequency speed
 curves at during
 different thetire
 current segment.
 pressures were smoothed and
 The weighted
normalized amplitude-frequency
 for convenient observation. The curves
 results at are
 different
 showntire in pressures
 Figure 10.wereThe smoothed
 characteristic and
 normalizedatfor
frequencies 150convenient
 kPa, 250 kPa, observation.
 and 350 kPaThe resultsobserved
 are easily are shown in45.4
 to be Figure
 Hz, 10.
 50.2The characteristic
 Hz, and 53.2 Hz,
 frequencies at 150 kPa, 250 kPa, and 350 kPa are easily observed to be 45.4 Hz, 50.2 Hz,
respectively.
 Sensors 2019, 19, x 10 of 14
 and 53.2 Hz, respectively.

 Figure 10. Weighted
 Figure amplitude-frequency
 10. Weighted amplitude-frequency curves atdifferent
 curves at differenttire
 tire pressures.
 pressures.

 We used frequency as the independent variable and tire pressure as the dependent variable, and
 used19,the
Sensors 2019, x least square method to fit the relationship between characteristic frequency and tire
 www.mdpi.com/journal/sensors
 pressure. The curve is shown in Figure 11, and the relation function is shown as Equation (7).

Sensors 2019, 19, 2560 10 of 13

 We used frequency as the independent variable and tire pressure as the dependent variable,
and used the least square method to fit the relationship between characteristic frequency and tire
 Figure 10. Weighted amplitude-frequency curves at different tire pressures.
pressure. The curve is shown in Figure 11, and the relation function is shown as Equation (7).
 We used frequency as the independent variable and tire pressure as the dependent variable, and
 ptire = 1.60 · f 2 − 132.37 · fpeak + 2856.54, (7)
 used the least square method to fit thepeakrelationship between characteristic frequency and tire
 pressure.
where The
 ptire is thecurve is shown
 identified in Figureand
 tire pressure 11, and
 fpeak the relation
 is the functionfrequency.
 characteristic is shown as Equation (7).

 .
 Figure 11. Relationship between characteristic frequency and tire pressure.
 Figure 11. Relationship between characteristic frequency and tire pressure.
4. Verification and Discussion

 To verify the scheme developed = in
 1.60 ⋅ 3,−we
 Section 132.37 ⋅ 
 conducted + 2856.54,
 another experiment, which was the (7)
same as the experiment in Section 2, except that the tire pressures were set at 200 kPa and 300 kPa.
 where ptire is the identified tire pressure and fpeak is the characteristic frequency.
 We first broke the circumferential vibration acceleration data into small segments of five-seconds
duration. We transformed each segment from the time to frequency domain by FFT, smoothed the
 4. Verification
Sensors 2019, 19, x and Discussion 11 of 14
amplitude-frequency curve, and extracted the peak frequency. Figure 12 shows the result at 200 kPa.
 To verify the scheme developed in Section 3, we conducted another experiment, which was the
 same as the experiment in Section 2, except that the tire pressures were set at 200 kPa and 300 kPa.
 We first broke the circumferential vibration acceleration data into small segments of five-seconds
 duration. We transformed each segment from the time to frequency domain by FFT, smoothed the
 amplitude-frequency curve, and extracted the peak frequency. Figure 12 shows the result at 200 kPa.

 Sensors 2019, 19, x www.mdpi.com/journal/sensors

 (a) (b)
 Figure
 Figure 12.
 12. Result
 Resultat
 at200
 200kPa:
 kPa:(a)
 (a)Peak-frequency
 Peak-frequencyextraction;
 extraction;(b)
 (b)peak
 peakfrequency
 frequencydistribution
 distributioncurve.
 curve.

 We can
 We can infer
 infer that
 that there
 there is
 isno
 norelation
 relationbetween
 betweenpeak
 peakfrequency
 frequency and
 and vehicle
 vehicle speed.
 speed. Whatever
 Whateverspeed
 speed
 the vehicle
the vehicle travels,
 travels, the
 the peak
 peak frequency
 frequency isis steady
 steady and
 and easily
 easily determined.
 determined. This
 This isis consistent
 consistent with
 with the
 the
 analysis in
analysis in Section
 Section 3. 3. The
 Theprobability
 probabilityof ofan
 an identification
 identificationresult
 resultwhose
 whoseerror
 error is
 is below
 below 5%5% compared
 compared
 with the
with the actual
 actual tire
 tire pressure
 pressure 200 200 kPa is 85%. TheThe average
 average value
 value of
 of the
 the peak
 peak frequency
 frequency during
 during the
 the
experiments was 48.01 Hz. Using the result of Section 3, the identified tire pressure is determined as
195.39 kPa through Equation (4). The identification error is 2.31%.
 There is no denying that the instantaneous peak-frequency extraction is not accurate enough, so
we analyzed the errors, as shown in Figure 12b. The errors are normally distributed. So, we can
enhance the identification precision by averaging several continuous peak frequencies.

Figure 12. Result at 200 kPa: (a) Peak-frequency extraction; (b) peak frequency distribution curve.

 We can infer that there is no relation between peak frequency and vehicle speed. Whatever speed
the vehicle travels, the peak frequency is steady and easily determined. This is consistent with the
analysis in19,
Sensors 2019, Section
 2560 3. The probability of an identification result whose error is below 5% compared 11 of 13
with the actual tire pressure 200 kPa is 85%. The average value of the peak frequency during the
experiments was 48.01 Hz. Using the result of Section 3, the identified tire pressure is determined as
experiments
195.39 was 48.01
 kPa through Hz. Using
 Equation the identification
 (4). The result of Section 3, the
 error identified tire pressure is determined as
 is 2.31%.
195.39 kPa through Equation (4). The identification error is 2.31%.
 There is no denying that the instantaneous peak-frequency extraction is not accurate enough, so
 There is no
we analyzed the denying that
 errors, as the instantaneous
 shown in Figure 12b.peak-frequency
 The errors are extraction is not accurate
 normally distributed. So, enough,
 we can
enhance the identification precision by averaging several continuous peak frequencies. So, we can
so we analyzed the errors, as shown in Figure 12b. The errors are normally distributed.
enhance
 We thedid identification
 the same with precision
 the data byataveraging
 300 kPa.several
 Figurecontinuous
 13a showspeak frequencies. of the peak
 the identification
 We didThe
frequency. the same with the
 probability ofdata at 300 kPa. Figure
 an identification 13awhose
 result showserrorthe identification
 is below 5%ofcompared
 the peak frequency.
 with the
actual tire pressure of 300 kPa is 83%. The average peak frequency was 52.02 Hz, andthe
The probability of an identification result whose error is below 5% compared with the actual tire
 identified
pressure
tire of 300iskPa
 pressure is 83%. The
 computed average
 as 300.38 peak
 kPa frequency
 through was 52.02
 Equation (4). Hz,
 The and the identified
 identification tireispressure
 error 0.13%.
is computed as 300.38 kPa through Equation (4). The identification error
Figure 13b shows the error distribution of the peak-frequency identification. It is much theis 0.13%. Figure 13bsame
 showsas
the error distribution
the result at 200 kPa. of the peak-frequency identification. It is much the same as the result at 200 kPa.

 (a) (b)
 Figure 13. Result
 Result at
 at 300
 300 kPa:
 kPa: (a)
 (a) Peak-frequency
 Peak-frequency extraction;
 extraction; (b)
 (b) peak
 peak frequency
 frequency distribution curve.

 These results prove that the tire pressure identification scheme using vibration acceleration we
Sensors 2019, 19,
developed xto estimate the tire pressure accurately. The specific valuewww.mdpi.com/journal/sensors
 is able of the tire pressure can be
given in real time using this method, rather than a simple tire pressure level.

5. Conclusions
 To “intelligentize” a tire, which means that the tire itself is capable of sensing and communicating,
we designed a prototype of an intelligent tire with three 3-axis accelerometers. To develop a tire-pressure
identification scheme, we conducted a data-acquisition experiment using the prototype at different tire
pressures and analyzing the wheel vibration in the frequency domain. We discovered a relationship
between tire pressure and tire vibration. Generally, the peak frequency rises with the tire pressure.
The identification scheme was verified with actual vehicle data at different tire pressures. The results
confirm that the identified tire pressure is very close to the actual value. The probability of identification
error below 5% is above 80%. The processing complexity and period are reasonable and practical.

Author Contributions: In this article, the experiments, calculations, and analysis were done by B.Z. and J.H. with
advice and inspiration from J.Z. The detailed methodology was provided by J.H. The original draft was prepared
by J.H. The review and editing were done by B.Z and J.Z.
Funding: This work is partially supported by National Key R&D Program of China (2018YFB0105103).
National Natural Science Foundation of China (51575225, 51775235, 51475206).
Conflicts of Interest: The authors declare no conflict of interest.

Sensors 2019, 19, 2560 12 of 13

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 article distributed under the terms and conditions of the Creative Commons Attribution
 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).