Vehicle Control Systems Dissertation
1. Introduction
1.1 Background on Cruise Control
Cruise
control sustains the velocity of a car, retards with no outside command and
also controls the torque in electric autos (EVs) hence efficient utilization of
energy. EV systems compensate for factors such as incline, decline as well as
wind drag, something that internal combustion engine vehicles cannot do.
Advanced versions like the adaptive cruise control enhance efficiency, safety
and the comfort level of driving. It is especially suitable for EVs because the
accurate control of torque improves the energy efficiency and range of the
vehicle. Cruise control is also built into more advanced systems for motor
vehicle control such as the automatic braking system and lane control, putting
it at the heart of semi-autonomous vehicles.
1.2 Importance of EVs for the Study
The
area of this research is electric vehicles (EVs) because it is energy-saving,
eco-friendly and equipped with sophisticated control systems. Thus, the
application of torque as well as regenerative braking make EVs optimal when it
comes to analyzing cruise control. It will contribute to enhancing the EV
cruise control performance under the impacts of the real-world disturbances and
ensures the driving comforts and efficiency. These factors, such as
regenerative brakes and the precise management of torque within the car, make
them perfect for research on cruise control. In this regard, the contribution
of this research will be to broaden the understanding of the factors
determining the utilisation of EVs to help improve other similar systems
towards fostering more efficient and clean transportation systems to the world.
1.3 Objectives of the Study
● To simulate an electric
vehicle with a cruise control system using simulink.
● To provide output for the
evaluation of ON/OFF and PID-based cruise control approaches.
● To analyze effects of
disturbance factors such as the inclined surface of the road and the force of
wind.
● To assess the ability of PID
tuning in cruising speed stability regulation.
2. Methodology
2.1. System Dynamics
In
MATLAB/Simulink, the dynamics of the EV are driven by Newton’s Second Law where
the forces that are considered includes traction force, air drag, rolling
resistance force, and gravitational force. Cruise control of a car’s speed is
maintained by a PID controller for motor torque and MATLAB data scripts for
external inputs such as road gradients, wind speed for changes in throttle
which consistently calculates the optimum of speed, fuel consumption and fuel
efficiency. The simulation in MATLAB by incorporating the proposed kinematic
and dynamic model also includes other parameters apart from Newton’s Second Law
in the simulation of the EV. One of the force factors, which in this model are
quantified and modeled include traction force, air drag, the rolling
resistance, and gravitational force to estimate the movements of a vehicle.
Further, these forces can be constantly refined depending on the dynamic inputs
like changing road gradient, wind speed and the auxiliary loads among others
(Shi and Li, 2021). The role of the PID controller varying the torque of the
motor as per the time period which overcome the external disturbances. The
control logic focuses on power consumption and increase the generation of fuel,
thus making the model more practical and versatile in real world conditions.
2.2. Trip Profile
A
model built in MATLAB/Simulink is EV trip profile where it imitates real
driving conditions with detail to various characteristics of the trip such as
speed, road gradient or inclination and disturbances. Data of elevation, wind
speed and auxiliary loads that affect the dynamics of the vehicle is obtained
through MATLAB scripts (Wang et al.2021).
The PID-based cruise control changes the torque control of the motor to
maintain, control the speed and efficiency with the help of output graphs. The
real-time environment which affects the vehicle’s performance such as gradient,
wind speed and traffic loads are incorporated when designing the trip profile
in MATLAB/Simulink models to simulate real-world driving scenario. These
disturbances are applied in a real time by feeding the model with real time
data for elevation, wind conditions and auxiliary loads using MATLAB scripts.
The trip features are the values of the automobile characteristics during the
trip, particularly in relation to acceleration and deceleration. These
variations dictate that there must be changes to the adaptive cruise control
(De Klerk and Saha, 2021). The speed control using the PID is enhanced to allow
the most suitable torque to manage the motor hence give the car the right speed
to maintain optimum efficiency in aspects such as climbing, headwind etc.
2.3. Motor Selection
In
MATLAB/Simulink for motor selection of EV, requirements such as power, torque
and efficiency available in the motor can be considered. The motor must work
against many forces and keep the required cruising velocity. The motor control
system simulates with the help of Simulink models, whereas cruise control for
the motor is achieved with the help of the PID controller for controlling the
speed, power, and efficiency of the motor during the simulation. The EV
simulation motor selection has to ensure that the required power from the
vehicle is matched with the torque and power of the motor. A detailed motor
performance map which can illustrate the motor behavior under a wide range of
loads such as low speed torque and the peak power is constructed. Dynamic
efficiency refers to the comparison of the motor efficiency with regards to
vehicle energy demand of every stage of a trip including cruise, acceleration,
and deceleration phases. In Simulink model, motor efficiency is labeled as one
of the most important parameters within a system to be controlled since if
optimal speeds are to be achieved minimal energy is expected to be consumed (Ko
et al., 2022). The performance map plays the significant role to keep the
motors to meet the maximum power and torque when the vehicle is demanded in
different driving conditions while providing consistent supply in terms of
loads and speeds.
2.4 Cruise Control
Implementation
Cruise
control in MATLAB/Simulink is implemented using both ON/OFF and the PID
controller in order to control the car speed. The ON/OFF controller makes the
motor torque be either totally turned on or off thus causing variations in
speed while the PID controller increments the throttle inputs gradually to
control the speed (Chen et al.2022).
Simulink simulations compare both methods under disturbances like road
elevation and wind resistance. The ON/OFF controller and PID controller are the
two controllers involved in the cruise control system in Simulink, and
consequently, the two have different ways of keeping the speed of the vehicle
constant. The ON/OFF controller often entails frequent changes in speed because
of the sudden variation of the motor torque, hence high energy wastage. On the
other hand, the PID controller acts more aptly as it changes the throttle input
or command of the motor and it is more suitable for this type of control
because its changes are smooth and gradual. This helps in canceling out or
limiting undesirable oscillatory movements of the vehicle speed, saving energy
in the process (Quan et al., 2021). The scenario also provides a comparison of
PID control in cases with and without The influence of various disturbances for
instance, road incline, wind resistance, and mechanical load, which
demonstrates that PID controller delivers better performance and stability in
comparison with the conventional controller.
2.5 Simulink Model Development
Figure 1:
Simulink Model Development
(Source: Self-Created in Matlab)
The
Simulink comprises blocks that represent the EV system such as motor dynamics, cruise
control and disturbances. Road gradient, wind speed and auxiliary load are
taken as data inputs and input data and interfaced through MATLAB scripts.
These disturbances are handled by the model whereby the motor torque is
regulated to respond to the disturbances in order to achieve the required speed
and energy efficiency stability. The desired speed input is then passed through
a gain block and then it is passed to the PID controller and which controls the
power supplied to the Motor to achieve steady speed. Regulation feedback loops
are also used to maintain the speed of the vehicle and would be making constant
adjustments to the control signals. Random variables which are implemented
through MATLAB scripts provide a representation of the external interferences
such as road slope and wind velocity.
Data Input for Disturbances
Road
elevation: Pertaining to changes in the
geographical altitude in relation to the height of the vehicle and the driving
speed as well as the energy expenditure. Wind Speed: Wind force that influences the vehicle
kinematics and needs a change in the torque from the motor (Kakouche et al.,
2022). Auxiliary Load: Other loads that affect the responsiveness of the
vehicle and its energy utilization. These disturbances are incorporated into
its Simulink model to mimic actual environment on the road as possible.
Explanation of External Factors
Road
elevation: Full details involving sloping or tilting of the road affect the
speed of the car or any other vehicle, and in responding to these adjustments,
torque from the engine is needed to offer stability. Wind Speed: As it affects
the dynamics of the vehicle, constant changes should be made to the control
parameters to enable it to achieve its momentum. Auxiliary Loads: Usually, extra
loads including occupants’ weights, cargo affect the performance and fuel
efficiency of the vehicle, and therefore, change the manner in which the cruise
control mechanism works.
2.6 Simulation Scenarios
The
simulation models in Simulink assess the performance of cruise control with
regard to two factors in the absence of disturbances and with disturbances. In
the initial case, the EV is set where there is a leveled ground with no
interference from external forces to the EV. In the second, road elevation,
wind speed and other auxiliary loads influence speed and thus need change of
control parameters for stability.
Without
Disturbances
In
this case the road is flat and there is no motion disturbance in the general
operation of the EV. Increased regularity of large cruise provides the
necessary conditions for density to reveal the effect ache i.e the efficiency
and response of the PID controller (Onyando and Naikuru, 2021). It is used as a
reference for evaluation to consider the performance of the project in its
purest form without any deviations.
With
Disturbances
Several
challenges are incorporated such as road surfaces of different heights;
different wind speeds as well as different loads to replicate real life
conditions. These disturbance scenarios entail dynamic control change so that
the stability and the optimal rate are achieved as presented in the following
figure depicting the PID control.
3. Results & Discussion
3.1 Graph 1: Vehicle Speed vs.
Time
Figure 2:
Vehicle Speed vs. Time
(Source: Self-Created in Matlab)
The
Vehicle Speed Vs. Time graph illustrates how the cruise control system works on
the car through the Computer Controlled Cruise Control Subsystem. Only when
there are no external impulses, the identified speed stays the same, which
means that it fluctuates in reaction to various stimuli. PID controller also
helps in controlling the oscillation which is very effective while accelerating
and decelerating (Yang et al.2021).
This graph permits evaluating the capability of the controller in sustaining a
constant cruising speed even with the change in occurrences. It also shows that
how much the system is capable to dampen the oscillation during acceleration
and deceleration which is important factor in terms of comfort of passengers as
well as fuel efficiency (Wang et al., 2021). Through the response time as well
as stability of speed control, engineers are able to improve the PID
continually in order to gain the optimized results for different road
conditions to provide the smoother ride and higher efficiency when driving.
3.2 Graph 2: Throttle Response
vs. Time
Figure 3: Throttle Response vs. Time
(Source: Self-Created in Matlab)
The
Control System Dynamic Response is shown on the Throttle Response vs. Time
graph whereby motor torque is adjusted to help in maintaining speed. Here in
ON/OFF control, the throttle changes throughout the operation are wide, thus
leading to oscillations, while in the case of PID control, changes are gradual.
This graph is useful for the purpose of comparing efficiency of energy response
and the effectiveness of response in relation to the given driving conditions
and interference. Here, the PID controller presents a more sophisticated
approach towards the use of the throttle control. One can single out gradual
and continuous changes, which enable to keep constant speed on the route. This
affords a smoother regulation thus reducing on the unnecessary fluctuations
that are undesirable in terms of energy consumptions and ride comfort (Li and
Du, 2021). Since it constitutes an incremental control system, the PID
controller can reverse external interferences including changes in gradient or
wind resistance to maintain a standard performance regardless of the prevailing
conditions.
3.3 Graph 3: Elevation Profile
vs. Distance
Figure 4: Elevation Profile vs. Distance
(Source: Self-Created in Matlab)
The
Elevation Profile vs. Distance graph defines the alterations in height of the
road taken during the journey. It is made in MATLAB coding and deployed into
Simulink to do actual incline and declining movements (Tang et al.2023). These depressions and elevations
alter the speed and energy used by the vehicle hence calls for constant use of
cruise control to enhance the best drivetrain. The graph is valuable in the
sense that it will give an indication of how effective the cruise control
system is when faced challenges in the road. It assists engineers to determine
how the system behaves under elevation changes and facilitates the distribution
of torque that enhance efficiency and smooth running of the system. Thus, the
further optimization of the control algorithms can be obtained in accordance
with the results of comparative data analysis, allowing to provide a high level
of the vehicle control in various types of terrain. In summary, the Elevation
Profile vs. Distance representation is extremely useful for improving cruise
control systems, so that they are best suited for real road conditions.
3.4 Graph 4: Wind Speed
Profile vs. Distance
Figure 5: Wind Speed Profile vs.
Distance
(Source: Self-Created in Matlab)
One
of the changes observed from the Wind Speed Profile vs. Distance graph is wind
resistance that impacts vehicle dynamics. Developed also with the help of
MATLAB codes, it is embedded in Simulink to investigate the effect of different
wind conditions on speed and power. Such effects are counteracted by the cruise
control system to give stability and efficient use of energy in the trip.
3.5
Comparison of ON/OFF vs. PID Control
Figure 6: ON/OFF
vs. PID Control
(Source: Self-Created in Matlab)
The
cruise control is analysed using the ON/OFF and PID control such that the
differences in performance can be depicted. ON/OFF control is not very
efficient since the speed changes in steps, and this is caused by the change in
throttle signal abruptly (Punzo et al.2021).
While PID control brings continuous changes, small variations and fluctuations
in its operation, thus reducing waste, in energy consumption. PID is also shown
to outperform the other controllers in simulation by keeping the car stable,
besides other observations, especially under different driving conditions.
Figure 7: ON/OFF
vs. PID Control
(Source: Self-Created in Matlab)
The
PID control can be described as a far more complex and efficient method. The
PID control can be used to smoothly regulate vehicle speed through adjusting
the throttle input in terms of, proportional, integral and derivative thereof.
It helps in avoiding vibrational motion and energy consumption fluctuations
thus giving a more confortable drive. This feature of strong control makes it
possible for the PID control to counteract minor disturbances such as change in
gradient of the road or wind restrain. However, from the above analysis, it is
perceived that PID control provides a much stable performance than ON OFF
control type for the steady operation of the vehicles (Gao et al., 2021). As
claimed earlier, the presence of such content may be preferred by most present
day cruise control systems because it provides a satisfactory drive torque
level and response time for various driving situations and is also physically
flexible.
3.6 Discussion of Controller
Performance
The
discussion on the operation of the controller deals with aspects such as PID
tuning, stability and response issues. Correct tuning results in low overshoot
and steady-state errors hence achieving the optimal energy consumption. Some
benefits consist of better provision of smooth speed regulation while the
disadvantages include the following: Sensitivity to parameter selection.
Potential refinements for future development consists of adaptive control or
implementation of AI that will make the system perform better under varying
driving conditions in real time.
4. Conclusion
4.1 Summary of key findings
It
states that regarding the efficiency of PID over ON/OFF controllers for stable
speed and energy efficiency of the total system, the study provides adequate
evidence. Some of the external interferences include road gradient and wind
forces that affect the behavior of the car, and through PID control, these are
catered for. There is also the possibility to incorporate adaptive or even AI
methods in an attempt to achieve higher control performance.
4.2 Discussion of future work
Discrete
research could be done in the future to incorporate the regenerative braking
functions into cruise control systems to enhance power values could be done in
the future to extend the regenerative braking to cover cruise controls. Some
strategies that concern the improvement of traffic conditions can be discussed,
for instance adaptive cruise control or AI-based optimization to control
real-time response to the traffic condition to adjust vehicle speed and
performance in real-time with the ultimate goal to optimize vehicle behavior to
suit drivers and passengers, and enhance fuel efficiency and comfort in different
traffic situations. Further works may be designs to incorporate machine
learning models to predict or self-adjust the road and traffic conditions
appropriately. This would require creation of models that could take the
historical and contemporary information and then, make the control parameters
change in real time. Thus, expanding research to other environmental factors,
for instance temperatures and humidity levels in the environment that the
vehicle is affected by, would offer more understanding on the required
adjustments on the cruise control system.
Part 2
1. Introduction
Systems like the anti-lock braking system and
the vehicle suspension systems, which are commonly referred to as the modern
car systems form the category of ADAS that are very important in automotive
safety and comfort. ABS avoids locking of the wheels during braking and on the
other hand suspension systems improves the ride quality and handling of the Tesla
Model 3. This type of analysis is carried out on both the systems using
MATLAB Simulink and the dynamic response of each system is predicted under
various conditions. The front wheel ABS was chosen as the student
identification number was odd, and the velocity for the braking test was 55 mph
as stated in the direction of the given project. For the suspension model, the
constant speed of the vehicle was taken to be 20 mph.
2. Method and Approach
D2.1 ABS Modelling
Figure 8: ABS modelling
(Source:
Self-created using Matlab)
The ABS model applied to this site for this
project is the front wheel model as applicable if the student numbers end at an
odd digit. The model was developed in the MATLAB environment with the help of
Simulink blocks for organizational purposes and to run the model in a
programmatic way. The dynamic model of the vehicle was developed based on
certain physical parameters which includes: mass was taken as 1500 kg , wheel
inertia as 2 kg·m² and wheel radius as 0.3 m. A constant block has been used to
model the maximum brake torque that is 3000 Nm it is the maximum braking force
the brake system can apply in an emergency. The following formula was used to
determine the calculated friction force at the interface of the tyre and road
F= μ.Fz The friction coefficient, μ was assumed to be 0.8 while the normal
load, Fz was estimated to be 60% of the weight of the car approximating a
front-wheel drive car (Settey et al., 2021). These were derived from Newton's
second law while integration blocks depicted the tendency of angular velocity
after a specific interval of time.
Figure 9: Wheel speed scope of the ABS
(Source:
Self-created using Matlab)
To control the slip and to avoid wheel lock
the bang-bang controller was used as a result. One of the control logics used
in this work is a proportional control that compares slip at a particular
instance of time to the optimum threshold that is at 0.2 in this case. When the
slip reaches its optimal level, the entire brake torque is applied in the case
of the slip being lesser than the set threshold it is zero. This on-off
mechanism leads to the appearance of a torque rapidly varying between the zero
and its maximum levels (Pardhı et al., 2021). The simulation loop was run with
respect to a time of five seconds with control over vehicle speed and state of
rotation of wheels in each time step in order to study the effects of braking
dynamics. Furthermore real-world conditions such as
gradient and tyre condition play an active role in the effectiveness of the ABS
in emergency situations. Climbing a steep downhill slope is a problem for the
ABS since it escalates the velocity of the car and can hardly stop the wheels
from locking. Statistically, worn tyres decrease grip, increase the slip ratios
and thus, decrease the braking ability affecting the accuracy of an ABS system.
Figure 10: ABS control logic
Flowchart
(Source: Self-created using Matlab)
This flow chart represents a
basic procedure of how the ABS or the Anti-lock Braking System works. More
specifically, it measures the wheel speed, estimates the slip and its value
compared with a predefined value. This is because if the slip is high, braking
torque is reduced otherwise, it is maintained. The dynamics are updated until
the simulation reaches the end.
Figure 11: Vehicle Speed - Different
Road Conditions
(Source: Self-created using Matlab)
This
plot describes vehicle speed in dry, wet and icy surfaces. The speed of the
vehicle slows down with time, but more so on icy roads because the friction
produced in icy roads is comparatively low to that of wet and dry roads.
Wheel
Slip - Different Road Conditions
This
plot shows the level of wheel slip ratio with respect to time at dry, wet, and
icy road surfaces. Slip rises as ABS is activated (slip > optimal) to
demonstrate that ABS reduces the amount of slip or expand it to ideal thus
enhancing control in places like icy tracks.
Brake
Torque - Different Road Conditions
This
plot represents the brake torque for all the road conditions test conducted on
the car. This occurs in the following manner, in which ABS increases brake
torque to compensate for greater levels of slip in comparison to the optimal
level of slip of the vehicle. The plot indicates the manner in which ABS
operates in managing torque most especially on different terrains.
Figure 12: Braking Distance
Comparison (ABS vs. Non-ABS)
(Source: Self-created using Matlab)
This
bar chart compares the braking distance with and without ABS across dry, wet,
and icy roads. ABS in general reduces the braking distance, mainly on wet and
icy surfaces since the wheels do not lock up and braking is much more
effective.
Figure 13: Brake Pressure (Normalized
Brake Torque)
(Source: Self-created using Matlab)
This
plot shows the patterns of the normalized brake pressure with respect to each
type of road condition. This illustrates how ABS controls brake pressure, the
higher the value the more pressure used to brake. It changes depending on the
road conditions and hence the ABS system aims at varying the applied pressure
to achieve the best braking result.
D 2.2 Suspension Modelling
A quarter-car suspension model was developed
to quantify the vibration response of front suspension system. The model
provides a basic two d.o.f. system which incorporates the sprung mass of 450 kg
and the unsprung mass of 50 kg representing the vehicle and the wheel
respectively. This is accompanied by a spring-damper pair for the suspension
with the stiffness of 15,000 N/m and the damping of 1000 Ns/m, and an
additional spring-damper pair for the tire having the stiffness 180,000 N/m and
the damping of 1500 Ns/m as the stiffness and the damping of the tire are less
than those of the suspension and the tire responds quicker.
Figure 14: Suspension modelling and
performance analysis
(Source: Self-created using Matlab)
It takes its input from the road surface
profile which is given within the interval of 6 seconds. This profile was
interpolated to give a continuous form of road displacement which was developed
to mimic normal fluctuations in the urban roads. This rate is however constant
and uniformly equal to 20 mph, turning spatial Form for roads into time based
motion excitation. The specific way of structuring the system mathematically
was chosen to be a state-space representation (Quan et al., 2023). The dynamics
was described by the matrices A and B with the state vector containing the body
and the wheel displacements and velocities. Thus, here u(t) denotes the road
displacement which influences the unsprung mass. The simulations had been
carried out in MATLAB by using ode45 solver for the system where the suspension
was consistent in the first 10s and then numerically integrated. Furthermore the enhancement entails an addition of an active
suspension system employing state feedback to achieve comfortable ride. It is
done to compare vehicle’s behaviour under light loads and high loads. Others
such as suspension travel and body acceleration are also analyzed in the
additional outputs. Comfort is measured by RMS of acceleration that shows the
effectiveness of an active control to the result of passive suspension.
D 2.3 Literature Review –
Hybrid Vehicle Architecture and Vehicle Electrification
Vehicle electrification has instigated major
changes in the design more so in the hybrid vehicles that integrate ICE with
electrical systems. These systems are generally classified into series,
parallel and power-split systems that differ on the basis of efficiency, total
weight, and drive system management benefits (Lukoševičius et al., 2021).
Electrification raises questions in weight control and structures, and these
mainly come about as a result of the inclusion of batteries and electric drive
components. As per Settey et al. (2021), the changes in the energy consumption
of electric light commercial vehicles were analyzed under different loads and
road conditions and the authors have stated that while load weight does
influence the outcome by a small margin, it is difficult to determine its
cause. More potent was the dynamic effect over power demands high gradient and
fast cyclic vehicle consumption proved limited the range that observed the
effect of car dynamics and topo-graphy on electric driving range (Zhao et al.,
2024). While the study is limited to pure EVs only, it can be extended to
hybrids because minimizing regenerative braking and managing loads where the
system might be used are significant in the hybrid vehicle applications. It is
thus apparent that, due to the addition of an extra system and its introduction
of unsprung and total weight it’s also necessary to take into consideration the
suspension ,braking, and handling (Desai et al., 2021). The function of car
application is the balanced utilization of power, thus energy efficiency,
dynamic performance, and body weight.
ABS Results
Figure 15: Performance parameters of the ABS
(Source:
Self-created using Matlab)
The simulation of the ABS model resulted in
three outputs, these are speed response, slip ratio and brake torque as seen
below. As it was expected, the speed of the vehicle reduced consistently over
the time at each iteration of applying and releasing the brake torque. As can
be seen, except for some over and under shoot and slightly outside the optimal
range during the beginning and end of the phase the slip ratio was near the
optimal value of 0.2. Some of these deviations can be explained by the fact
that the bang-bang controller is quite basic and there is also delay in the
system. The brake torque profile was a bang-bang control similar to the brake
torque, oscillating between zero and 3000 Nm when slip ratio deviated from a
specific range. This sort of on-and-off switching effectively ensured that
wheel lockage was avoided and consequently, minor traction was retained to
ensure reduced stopping distances (Țoțu and Alexandru, 2021). However, the
desired performance attracted new limitations with the modified method torque
chattering, which results in mechanical wear or discomfort to passengers in the
real vehicle.
Suspension Results
The analysis of the quarter-car suspension
model provided the displacement-time and velocity-time response of the body,
wheel, and velocity of the system body. The body displacement graph indicates
how the sprung mass moved to the disturbance on the road with minor up and down
movement of about 0.3 meters. This was indicating the ability of the suspension
to take the shocks from the road surface plus the ability to isolate the
passengers, albeit the high peaks portraying the signs of some responsiveness
to large inputs. The wheel oscillation showed higher frequency response than
that of the body, because of the unsprung weight which responds quickly and in
a more oscillating manner to the input from the road (Abd El-Fatah et al.,
2021). This can be explained in the real-world observation since the wheels
follow the roads’ surfaces in a perfect fashion and the suspension system damps
other infinitesimal vibrations which would be felt in the cabin of the vehicle.
From the vehicle body velocity plot, it was apparent that the damping occurred
in the system to some extent. It indicated that the suspension was able to
eliminate high frequency vertical movement to a certain extent while low
frequency oscillatory movement occurred due to passive damping parameters.
These results clearly depict that even though the passive suspension system is
easier to implement and cheaper, the damper characteristics cannot be tuned to
suit the variation in load or road conditions. In other words, through the
quarter-car model the lore of basic response characteristics of a passive
suspension as well as the role of such a suspension in managing ride comfort
became clear (Zhang et al., 2022). Further development could consist of
simulating the loaded vehicle configuration with a system of active suspension
in order to compare its performance when subjected to various dynamic
conditions.
Discussion
It is easy to observe from the passive
suspension model that the high-frequency disturbances from the road decayed to
a large extent. It is therefore unlikely that stiffness and damping could
retain the same values for both low and high loads because fixed amounts would
yield to worst performance at high loads. An active suspension system on the
other hand would be more effective in view of the fact that it would allow for
control of big changes in dynamic damper force in real time (Fedotov and
Gromalova, 2021). This was done in order to mimic real road conditions, and
show that the presented model offers great potential in adapting to changing
conditions effectively.
Conclusion
It is evident that this report has adequately addressed and implemented the design and simulation of two of the vehicle control subsystems which are ABS and suspension. The ABS bang-bang controller is powerful but not very smooth so the improvement of a smoother profile for the car is deserving of further research in the future. The suspension model with realistic dimensions adopted in this paper is a linear ¼ car system and can be effective in studying body and wheel dynamics though it lacks complexity such as varying mass or nonlinearity. In all, the MATLAB Simulink implementation supports the use of numerical techniques in the analysis of the vehicle's systems.
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