GMU The Evaluation & Development of Improved Braking Model Discussion: Java Answers 2021

GMU The Evaluation & Development of Improved Braking Model Discussion: Java Answers 2021

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GMU The Evaluation & Development of Improved Braking Model Discussion

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Journal of Mechanical Science and Technology 29 (7) (2015) 2747~2754
www.springerlink.com/content/1738-494x(Print)/1976-3824(Online)
DOI 10.1007/s12206-015-0603-2
Evaluation and development of improved braking model for a motor-assisted
vehicle using MATLAB/simulink?
Sangmyeong Kim1, Jinsu Kim1, Gisu Sung1 and Jinwook Lee2,*
1
Department of Mechanical Engineering, Graduate School, Soongsil University, Seoul, 156-743, Korea
2
Department of Mechanical Engineering, Soongsil University, Seoul, 156-743, Korea
(Manuscript Received June 28, 2014; Revised January 22, 2015; Accepted March 24, 2015)
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Abstract
In recent years, R&D trends in the automobile industry have involved developing vehicles that are eco-friendly and have good fuel
economy in order to meet increasingly stringent vehicle emission and fuel economy regulations. Among various environmentallyfriendly vehicles, recently-developed hybrid electric vehicles have been considered a successful technology. To improve the hybrid electric vehicle?s efficiency, a regenerative braking system is applied that can save waste braking energy and fuel consumption. To date,
much research has been conducted that is related to regenerative brake systems for motor-assisted vehicles. In this study, a vehicle brake
model and DC motor model have been developed to predict the braking and electric characteristics of vehicle and motor by using
MATLAB/Simulink code and compared with brake performance reference data. Since generating current is a key factor for regenerative
braking in a hybrid electric vehicle (HEV) system, electric characteristics are the focus of this study. Therefore, in order to investigate the
electric characteristics, analysis is also performed using the DC motor model. The main results obtained by this study are that the vehicle
braking dynamics calculated by MATLAB/Simulink simulation were in reasonable agreement with reference values. Using the vehicle
brake model, analysis is carried out for investigating the numerical model?s characteristics and performances at initial velocity, slip ratio
and road conditions. Additionally, experimentation is carried out using a chassis dynamometer and a hybrid electric vehicle, and the data
are analyzed.
Keywords: Vehicle brake model; DC motor model; MATLAB/SIMULINK; Regenerative brake system; Current balance; Hybrid electric vehicle
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1. Introduction
Recently, R&D trends in the automobile industry have been
concentrated on developing vehicles that are eco-friendly and
have good fuel economy. These design goals are due to unstable oil costs and more strict regulations on CO2 and exhaust
emission gas. As a result, many developed countries have
been focused on designing various automotive powertrains for
environmentally-friendly vehicles, including hybrid electric
vehicles (HEVs) and alternative fuel vehicles [1, 2]. Among
these cars, the hybrid electric vehicle has been considered a
successful technology and development of these designs has
accelerated in order to address the abovementioned recent
issues.
There are many state of the art techniques that improve
automotive efficiency, such as regenerative break systems in
the case of motor-assisted hybrid electric vehicles. From the
perspective of usage of energy, chemical energy from fuel is
*
Corresponding author. Tel.: +82 2 820 0929, Fax.: +82 2 820 0650
E-mail address: immanuel@ssu.ac.kr
?
Recommended by Associate Editor Eung-Soo Shin
? KSME & Springer 2015
partially converted to the mechanical energy utilized for vehicles, but the remaining energy is wasted by friction and heat
energy when the vehicle slows down. According to some research [3-5], during city driving, approximately 30% of a typical car?s engine output is lost to the braking process [3].
Other research has investigated the issue that the conventional
spark ignition engine uses only 30% of the fuel energy generated from combustion, while 70% of the fuel energy is dissipated as heat [4]. Other research is related to analyzing the
thermal energy dissipated by disc brakes using a simulation
process for enhancing the brake efficiency [5]. In motordriven vehicles, the regenerative braking system is a technology that converts wasted kinetic energy from braking to
chemical energy through a battery. When a regenerative braking system operates, the motor serves in the role of generator
to collect wasted energy and to reduce energy consumption
for the hydraulic brake system. Presently, many studies [6-8]
on regenerative braking systems have shown the high potential possibility of these systems in commercial automobiles in
order to meet future regulations on emissions and to improve
fuel economy. One group investigated a new brake system
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S. Kim et al. / Journal of Mechanical Science and Technology 29 (7) (2015) 2747~2754
using two motors to control the regenerative brake force and
the friction brake force [6]. Interesting research is related to
the electrically-controlled regenerative brake system developed for electric passenger vehicles, and vehicles applied the
new brake system can reduce energy consumption [7]. Another group investigated the effect of regenerative braking
efficiency; fuel consumption was able to be reduced by up to
25% in short-route driving cycles [8].
There are two ways to conduct research on regenerative
braking systems. Firstly, using a chassis dynamometer, regenerative braking efficiency can be measured from a test vehicle.
This method has an advantage in that practical results are acquired from experimentation with a vehicle. However, the
drawback of this method is that an experimental study is timeconsuming and requires high initial costs for setup. The other
method to conduct the studies on regenerative braking systems
is to use numerical simulation code consisting of two designs,
for the regenerative braking model and for a vehicle dynamic
model. The latter method involves some differences from the
practical results obtained from physical experimentation. On
the other hand, the low initial costs and ability to adjust various parameters bring huge advantages.
From this perspective, a simulation of a regenerative braking system for a hybrid electric vehicle should be designed to
meet the precise prediction of generated current and regenerative energy efficiency. Some interesting research [9-11], is
related to simulation of the regenerative braking systems. The
projects investigate HEV modeling and simulation for the
optimization of various hybrid vehicle configurations and
control strategies [9]. Some studies are performed for regenerative braking systems to enhance the energy efficiency and
braking performance of the vehicle with simple design and
low cost [10]. Other studies have researched the validation of
regenerative braking system using hardware in the loop simulation (HILS) and enhancement of the regenerative torque
control strategy against booster delay compensation [11].
In this study, numerical simulation with MATLAB/ Simulink was carried out to design a control model of a vehicle
braking system to address the most critical part of a HEV
model. Then, the model credibility was compared with other
models [12]. An analytic model has been applied with the
purpose of confirming the electric current generation of a DC
motor model driven by a vehicle braking model in order to
predict the electric characteristics of the DC motor by using
the MATLB/Simulink environment.
2. Numerical procedures
2.1 Vehicle brake model description
Some considerations are specified for simplification purposes. The first condition is that the vehicle moves in a longitudinal direction and force is applied in the horizontal direction. Secondly, the initial instant is considered with null acceleration, resistance of the air, and constant vehicle speed. Finally, the vehicle model is simplified by adapting single-wheel
system to investigate the possibility of regenerative braking.
The passive brake type vehicle has been applied with the
purpose to develop an analytic model of the vehicle braking
system to predict the braking characteristics of the mechanical
components by using the MATLAB/Simulink environment, as
a unified approach to mechatronic modeling. In order to take
into account the braking characteristics of the vehicle in this
study, we developed the related analytic model for a vehicle
brake model. It consists of numerical simulation code in which
each component is represented by an appropriate block and is
associated to a mathematical component. The vehicle braking
system model was developed to solve the variable equations.
From Newton?s second law, we can derive the vehicle dynamic equation, and the vehicle?s state at any given time can
be determined by solving the equations [12]. In the case of the
applied longitudinal force, the force equation of the vehicle
can be calculated by the following Eq. (1).
M
d 2x
+ Fx = 0
dt 2
(1)
where M : Mass of the vehicle
Fx : Reactive force of friction
In the case of the normal force, it can be calculated by the
following Eq. (2).
Fz = Mg .
(2)
Rolling resistance is calculated by normal force. Rolling resistance is simplified and can be expressed as equivalent force
acting at the center of the wheel, given by Eq. (3).
Fx = m x Fz
(3)
where m x : Rolling resistance coefficient
The m x value is a function of many parameters including
environmental conditions, vertical loads, surface types, translation speeds and internal pressures of the tires [13]. The
Pacejk magic formula is the equation for the longitudinal friction coefficient and the rolling resistance coefficient can be
calculated by the following Eq. (4) [14].
m x = a(1 ? e ? bl ? cl )
(4)
where ?
: Wheel slip ratio
a, b, c : Road condition coefficient
The variables a, b and c can be changed depending on the
road surface conditions. We assume that the vehicle is being
driven on dry road and wet road conditions.
Fig. 1 shows the correlation between slip ratio and rolling
resistance coefficient of dry and wet road conditions. The
wheel slip ratio is defined by Eq. (5).
l=
v ? rw
v
(5)
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S. Kim et al. / Journal of Mechanical Science and Technology 29 (7) (2015) 2747~2754
Table 1. Specifications of the vehicle brake model used in this study.
Table 2. Specifications of DC motor model used in this study.
Vehicle mass (kg)
450
Motor constant B (Nms)
0.1
Gravity acceleration (m/s2)
9.81
Motor Kt (Nm/A)
0.41
Dry road coefficient a, b, c
0.6, 18, 0.52
Back EMF Kv (Vs)
0.25
Wet road coefficient a, b, c
0.15, 40, 0.52
Inductance L (H)
1.9E-5
Moment of inertia of tire Jt (kgm2)
1
Resistance R (ohm)
0.16
Fig. 3. DC motor model driven by equations used in this study.
Fig. 1. Correlation between slip ratio and rolling resistance coefficient.
Fig. 2. Vehicle brake model driven by equations used in this study.
where v : Tangent velocity
r : Radius of the wheel
w : Angular velocity of the wheel
The wheel slip is calculated by receiving tangent and angular velocity data from Eq. (1). Additionally, constant slip ratio
is controlled by using a PI controller. The moment of the
wheel can be calculated by the following Eq. (6).
-Jt
d 2q
? T f + Fx r = 0
dt 2
(6)
where Jt : Moment of inertia of tire
Tf : Torque of the brake
Fig. 2 shows the vehicle brake model using the composition
of the block diagram elaborated for MATLAB/Simulink by
applying Eqs. (1)-(6).
2.2 DC motor description
Many electric motors used in hybrid electric vehicles can be
categorized as DC (Direct current) motors or AC (Alternating
current) motors. Direct current motors have advantages related
to smaller volume, the higher speed and power compared AC
motor. However, variation in the motoring can be made brush
short-lived because it has drawbacks. AC motors offer high
efficiency, high power, eases of speed change, and the advantages of long life and low price. However, they involve a dis-
advantage that the rotation speed is slow [15]. For a variety of
reasons, assortments of vehicles are equipped with brushless
direct current motors (BLDC), permanent magnet synchronous motors (PMSM) or other forms of motors. In this study,
we have researched the DC motor type, including BLDC and
PMSM types.
The DC motor has been applied with the purpose to predict
the electric characteristics of the motor component. The DC
motor is modeled as a rotating mass damper system coupled to
an electric circuit. In the case of applied rotational torque, the
torque equation of the motor can be calculated by the following Eqs. (7) and (8) [16, 17].
di
+ kvw + Ri = Vin
dt
Tmotor = k ? i
L
(7)
(8)
where L : Inductance
Kv : Back EMP constant
R : Resistance
Vin : Applied voltage constant
k : Generator constant
We can solve for the current applied to the motor?s rotor by
rearranging to solve the equations and combining Tmotor.
Fig. 3 shows the DC motor system using the composition of
the block diagram, applying Eqs. (7) and (8). ?From block?,
labeled [w], shown in Fig. 3, is an angular velocity input signal from the vehicle braking model. When operating the vehicle brake model and the DC motor model, the vehicle angular
speed is multiplied by back EMP constant.
Table 2 shows the specifications of the DC motor model.
The parameters of the model were selected by Ref. [17]. Although verifying the data of the DC motor model, the reference did not describe the numerical result data of the generator
model. In order to study basic regenerative braking, we have
focused on measuring the current produced by the numerical
model. In order to confirm the effect on generating current, it
was analyzed in terms of the slip ratio and initial vehicle ve-
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S. Kim et al. / Journal of Mechanical Science and Technology 29 (7) (2015) 2747~2754
Table 3. Numerical conditions of the final model used in this study.
Condition
Value
Road condition
Dry road
Slip ratio
0.05, 0.1
Initial velocity (m/s)
16.66 (60 km/h),
25 (90 km/h)
Fig. 5. Vehicle brake model validation for velocity profile.
Fig. 4. Main block SIMULINK diagram of vehicle braking model with
DC motor model developed by this study.
locity.
Also, the current is multiplied by the motor resistance to determine the drop of voltage. The DC motor model operates
with only the input signal of angular velocity from the vehicle
brake model. Therefore, the impact on the motor model of Vin
is negligible. The quantity of voltage is divided by motor inductance. The model can be synchronized to the electrical
circuit by introducing a relational expression that generates a
current. When the motor rotates, current can be generated by
the DC motor model. We have studied the possibility of braking generated by electric current through the braking system.
The final model, which is a combination of the vehicle
brake model and the DC motor model, was developed to solve
the braking and electric characteristics, as shown in Fig. 4.
The step function represents a driver?s reaction speed of activating brake when driver want to stop the vehicle setting the
response time by 0.2 s. The signals calculated by numerical
model can observe a scope that is shown left side in Fig. 4.
2.3 System simulation conditions
Table 3 shows the numerical conditions of the final model
used in this study. We select the slip ratio by considering dry
and wet road conditions, shown in Fig. 1. As shown, the dry
condition?s rolling resistance coefficient is the highest value,
with a slip ratio of 0.1. Also, as shown, the wet condition?s
rolling resistance coefficient is the highest value, with a the
slip ratio of the 0.05.
3. Result and discussions
3.1 Model verification
Figs. 5 and 8 show the numerical results of the vehicle
brake model at the initial velocity of 16.66 m/s, including the
comparison of the results with reference data. The vehicle
Fig. 6. Vehicle brake model validation for angular velocity profile.
brake model has reported the velocity, angular velocity, acceleration and reaction force. The simulation data is compared
with reference data with same figures.
Fig. 5 shows the comparison of velocity between the vehicle brake model and reference data at the initial velocity of
16.66 m/s using the dry road condition. The velocity profile is
similar to that of the reference data, even though the vehicle
brake model tended to show a late response due to initial
brake perception behavior and vehicle?s weight.
Fig. 6 shows the verification of angular velocity, and there
is a difference between brake model and reference data. The
vehicle brake model showed that variation occurs in the first
part of the model. The difference was caused that the brake
model used the PI controller. Because of the different vehicle
weights, the initial angular velocity had a difference value.
Figs. 7 and 8 show the comparisons of acceleration and
force between the vehicle brake model and the reference data.
The vehicle brake model and reference data display differences among the slope, maximum power and acceleration.
The differences are determined by the influence of the difference vehicle weights of the models, and the acceleration and
force are exerted relatively rapidly using the control model.
The acceleration and force graphs show a similar tendency,
despite the differences of the vehicle masses and the controller
operating the vehicle brake model.
Fig. 9 shows the effect of the different slip ratios at an initial
velocity of 16.66 m/s and using the dry road condition. It is
found that the slip ratio conditions have an effect on the brak-
S. Kim et al. / Journal of Mechanical Science and Technology 29 (7) (2015) 2747~2754
Fig. 7. Vehicle brake model validation for acceleration profile.
2751
Fig. 10. Effect of different velocities 16.66 m/s and 25 m/s at 0.1 slip
ratio (Dry road condition).
Fig. 8. Vehicle brake model validation for reaction force profile.
Fig. 11. Electrical effect of different slip ratio at 16.66 m/s (Dry road
condition).
velocity has an effect on the distance and braking time.
3.2 Predicted electric energy solutions
Fig. 9. Effect of different slip ratio at 16.66 m/s (Dry road condition).
ing time, distance and acting force. When the slip ratio is high,
braking time and distance are shorter, and braking force is
higher than for the low slip ratio. As mentioned above, since
the vehicle brake model is controlled by a PI controller whose
input is slip ratio, there is an initial difference in the force between 0.1 and 0.05 slip ratio. Because of the different acting
forces, the high slip ratio condition has a shorter braking time
and distance.
Fig. 10 shows the effect of the different velocities at 0.1 slip
ratio and dry road condition. Regardless of the different velocities, it was found that the shapes of the force, distance and
velocity trend lines are similar, even though the high initial
Fig. 11 shows the electrical effect of the different slip ratios
at 16.66 m/s and using the dry road condition calculated by
the developed model. Comparing on the 0.05 and 0.1 slip
ratios, it is found that the increase of braking time has effects
on the longer current generation and motor operation at the
0.05 slip condition. Also, it is not affected by the slip ratio to
generate the amount of the maximum current, but it is influenced by the initial speed. Since the developed model receives
the input signal of the vehicle angular velocity, the?

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