Development and Validation of a Vehicle Corner Test Rig Designed

for Hardware-in-the-Loop Testing

Paulius Kojis

, Viktor Skrickij

, Valentin Ivanov

, Eldar Šabanovič

Marijonas Bogdevičius

and Tomas Grikenis

Department of Mobile Machinery and Railway Transport, Faculty of Transport Engineering, Vilnius Gediminas Technical

University, 10105 Vilnius, Lithuania

Transport and Logistics Competence Centre, Faculty of Transport Engineering, Vilnius Gediminas Technical University,

10223 Vilnius, Lithuania

Smart Vehicle Systems—Working Group, Technische Universität Ilmenau, Ehrenbergstr, 29, 98693 Ilmenau, Germany

Keywords: Vehicle Corner, Test Rig, Hardware-in-the-Loop, Electric Vehicle, Tyre Forces.

Abstract: Many original equipment manufacturers and Tier 1 suppliers have recently proposed various vehicle corner

concepts. This technology offers numerous benefits for enhancing vehicle dynamics but presents several

challenges. Field testing of the corners installed on a vehicle demonstrator could be an ideal solution to address

these challenges. Nevertheless, the proof-of-concept development phase also requires studies on the

component and system levels. In this regard, a vehicle corner test rig designed for hardware-in-the-loop testing

provides a balanced alternative that combines the accuracy, complexity, and accessibility of experimental

works. Real suspension components, including a rotating wheel with tyre, provide realistic suspension

kinematics and compliance that are very similar to those experienced in real driving scenarios. However, this

approach has limitations because the tyre contact forces and loading conditions cannot be fully replicated in

a laboratory environment. This paper explores these aspects and describes a developed comprehensive

methodology for eliminating inaccuracies, with results validated accordingly.

1 INTRODUCTION

Modern electric vehicle (EV) architectures with

individual corner actuators enable new motion

control technologies (Armengaud et al., 2021; Cheng

et al., 2024). Coupled with an integrated chassis

control (ICC) approach, the full potential of multi-

actuated vehicles can be exploited, with the benefits

of vehicle safety, comfort, fail-safe operation, and

redundancy (Skrickij et al., 2024). However, this

complex integration requires advanced components

and thorough testing. For this reason, automotive

researchers and engineers apply various hardware-in-

https://orcid.org/0000-0003-0566-559X

https://orcid.org/0000-0002-8080-875X

https://orcid.org/0000-0001-7252-7184

https://orcid.org/0000-0002-4724-0787

https://orcid.org/0000-0003-4947-7638

https://orcid.org/0009-0002-5400-9150

the-loop (HIL) setups to facilitate testing in real-time

(Heydrich et al., 2022; Ivanov et al., 2019).

A corner-based chassis architecture is a potential

pathway for further development of EVs. Such

architecture typically incorporates in-wheel motors

(IWM), brake-by-wire, and steer-by-wire systems;

some corners may also feature toe and camber

actuators. While the first prototypes of this

technology emerged over two decades ago, research

and development in this area have gained significant

momentum in recent years. Notable contributions

come from several Horizon projects, including

OWHEEL, SmartCorners, and industrial companies

358

Kojis, P., Skrickij, V., Ivanov, V., Šabanovi

c, E., Bogdevi

cius, M. and Grikenis, T.

Development and Validation of a Vehicle Corner Test Rig Designed for Hardware-in-the-Loop Testing.

DOI: 10.5220/0013208900003941

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2025), pages 358-365

ISBN: 978-989-758-745-0; ISSN: 2184-495X

such as Continental and Hyundai Mobis (Nguyen and

Trovão 2024; Kojis et al., 2022).

Novel corner architecture offers numerous

advantages. First, it enables torque control at each

vehicle corner and torque vectoring that enhances

manoeuvrability and stability (Guo et al. 2019; Hori

2004; Jneid and Harth 2024). Additionally, the

modular design of vehicle corners facilitates the

development of flexible vehicle platforms.

Eliminating components such as driveshafts and

differentials reduces energy losses and improves

vehicle packaging (Deepak et al., 2023). However,

there are drawbacks: increased unsprung mass (UM)

due to IWM and other components result in comfort

and handling issues if passive suspension components

are used; larger vehicle inertia around the vertical axis

impairs with vehicle stability; increased system

complexity, number of parts and price (Kojis et al.,

2022).

In order to fully exploit vehicle corners, advanced

components and thorough testing are inevitable.

Testing can typically be carried out in three main

ways: simulation environments, laboratories, and

proving grounds, each with its advantages and

disadvantages.

Simulations provide high repeatability and rapid

evaluation, making them the most cost-effective

method for investigating complex systems. However,

they can be limited by assumptions that may reduce

the accuracy and fidelity of system replication. In

addition, the high nonlinearity of certain system

elements can make mathematical modelling complex

and computationally intensive.

Vehicle field testing on a proving ground can be

considered the best option despite being subject to

various uncertainties and issues with repeatability. To

ensure consistent replicability of tests, vehicle control

inputs must remain stable and free from human error.

Laboratory testing serves as a primary alternative

to the field testing, offering high repeatability and

accelerating the execution of tests. Experiments in the

laboratory conditions can be conducted at the whole

vehicle level for tasks such as suspension testing,

kinematics and compliance (KnC), component

durability assessments, ride comfort evaluations, and

noise, vibration, and harshness (NVH) testing

(Chindamo et al., 2017; Gräbe et al., 2020).

The second group of testing equipment consists of

various quarter-car test rigs, categorised into three

types according to the literature (Maher and Young,

2010). The first category includes setups that consist

solely of a sprung mass (SM), UM, damper, and

spring. This configuration is primarily utilised for

developing and validating active and semi-active

suspension control systems (Omar et al., 2017; Gysen

et al., 2010). The second type incorporates complete

suspension components, a wheel and a tyre. Including

these elements ensures a more realistic suspension

response to vertical excitations induced by the shaker

system beneath the tyre (Yu et al., 2019; Lauwerys et

al., 2005). This setup can be employed for suspension

KnC testing, examining the influence of the tyre on

vertical vehicle dynamics, conducting frequency

analyses, and prototyping suspension components.

The final group features a rolling road, which can be

implemented using flat belts or drums. Such

construction enables the consideration of brake- and

acceleration-induced longitudinal forces, steering-

induced lateral forces and associated suspension

deflections. Vertical excitation is provided by cleats

attached to the rotating surface or by shaking the

entire rotating assembly using an eccentric and

various actuators (Li and Lee, 2019; Gießler et al.,

2022).

Each of the described quarter-car test rigs has

inherent flaws and simplifications that can affect the

accuracy of the measured quantities and their

correlation to field tests. These setups typically allow

the SM to move only vertically, meaning they do not

account for weight transfer due to braking or

acceleration nor capture roll or pitch angles. Flat belt

systems tend to produce lower rolling resistance

values than observed during field tests, as the belt

heats up more and generates higher temperatures on

the tyre surface. According to the SAE J 1106

standard, flat belts can induce lateral tyre forces up to

10% greater than those observed with drum-type

systems.

On the other hand, drum-type rolling roads can

distort the tyre/road contact patch, reducing tyre

lateral force and aligning torque while increasing tyre

rolling resistance. It is therefore necessary to properly

recalibrate the values recorded on corner test rigs to

allow comparison with field test results. To address

these challenges, automotive researchers and

engineers utilise HIL setups (Heydrich et al., 2022;

Ivanov et al., 2019; Stolte et al., 2023). In a HIL setup,

a mathematical model is connected to a physical

system, enabling the investigation of a subsystem

under development by incorporating its physical

components into a closed-loop mathematical

simulation of the overall system (Fathy et al., 2006).

In this context, recalculation of physical outputs from

the corner test rig is essential to ensure that they align

with the expected values from the real system.

The development of HIL systems for vehicle

corner testing offers several key benefits (Mihalič et

al., 2022): real-time feedback, cost-effectiveness,

Development and Validation of a Vehicle Corner Test Rig Designed for Hardware-in-the-Loop Testing

359

scalability and reproducibility. Traditional HIL

applications typically involve a single test rig

implementation (e.g., brakes, suspension, steering,

etc.). However, multiple test rigs in the same

laboratory can be interconnected to create a Test-rig-

in-the-Loop (TRIL) system (Augsburg et al., 2011).

When test setups are distributed between different

locations, shared and connected X-in-the-Loop (XIL)

testing procedures can be employed (Ivanov et al.,

2019).

The literature review indicates that the HIL

approach is effective for investigating vehicle corner

proof of concept when field testing is not feasible. A

new vehicle corner test rig concept has been

developed in this regard. The primary contribution of

this paper is a detailed methodology for validating

developed test rig, aiming to address the key

limitations associated with such equipment.

The paper is structured as follows: Section 2

outlines the HIL design for vehicle corner testing.

Section 3 identifies the main sources of inaccuracies

and discusses strategies for their mitigation, along

with the presentation of validation results. Section 4

includes a case study related to vehicle corner HIL

testing. Finally, Section 5 summarises the findings

and offers an outlook on potential next steps.

2 HIL FOR VEHICLE CORNER

TESTING

An experimentally validated, sport utility vehicle’s

(SUV) mathematical model which is running on the

real-time target machine and connected with the

corner test rig is used for the HIL application. Vehicle

model features 14 degrees of freedom (DoF) and is

implemented via the IPG CarMaker and Simulink

conjunction. More information regarding the

mathematical model can be found in an article by

Šabanovič et al., 2021. Physical nonlinear corner

components, which are hard to model, are used on the

corner test rig. The whole system architecture is

presented in Figure 1, and the main technical data is

presented in Table 1.

Host PC containing the MATLAB and IPG

CarMaker HIL software packages is directly

connected to the dSPACE real-time target machine.

Once the real-time simulation is initialised, the

dSPACE ControlDesk is utilised to exchange data

between software and hardware components installed

on the test rig.

The dSPACE inputs are employed to measure the

characteristics and critical values induced by the

experiments on the quarter-car test rig. The wheel

transducer measures tyre forces ( 𝐹



, 𝐹



, 𝐹



) and

torques (𝑀



, 𝑀



, 𝑀



). It also provides the angular

velocity of the wheel (𝜔



). This velocity is then

compared to the reading of the encoder attached to the

steel drum ( 𝜔



) axis of rotation, and the

longitudinal slip is calculated. Displacement of the

actuators is measured by linear potentiometers and

supplied to the real-time target machine (𝛼



Additionally, to implement suspension control, SM

and UM vertical accelerations ( 𝐴

_

, 𝐴

_

suspension stroke (𝑧



− 𝑧



) and actual current at

the damper valve ( 𝐼

_

) are measured. The

dSPACE outputs are mainly used to send inputs to the

low-level controllers of electric toe actuators (𝛼, 𝑣



suspension damper (𝐼

_

) and electric motor (𝑉)

controllers. Since hardware components are installed

on the test rig, this HIL setup allows the test of control

algorithms, element activation delays, actuators

dynamics and overall system operation.

Figure 1: HIL set up.

Such a system has two main drawbacks. First,

these setups do not consider vehicle’s roll and pitch

angles. As a result, the vertical load does not fully

correspond to real-world system dynamics during

cornering and braking. For this reason, weight

transfer from the simulation environment is applied to

recalculate vertical loading in different driving

scenarios. Second, contact forces between the tyre

and the drum do not represent the actual ones

generated in the contact between the road and the tyre

in real conditions. Hence, tyre forces and moments

generated on the test rig are registered using a wheel

force transducer and further recalculated using

provided methodology.

VEHITS 2025 - 11th International Conference on Vehicle Technology and Intelligent Transport Systems

360

Table 1: Vehicle corner test rig specifications.

Parameter Value

Overall dimensions of

test rig (L x W x H)

2.7 x 1.7 x 1.8 m

Minimal sprung mass 200 kg

Maximal sprung mass 900 kg

Electric motor-rated

power on drum

11 kW (400 V)

Maximum wheel’s linear

velocity

170 km/h

Diameter of the drum 0.22 m

Single-toe actuator-rated

force

1000 N (12 V 4.6 A)

Rear toe actual range

±3°

Controller for the toe

actuators

Cytron MDDS30

Vertical actuator

Custom-made semi-active

damper with continuously

controlled valve

Controller for the vertical

actuator

Custom-made low-level

damper controller

Wheel force transducer

Michigan Scientific

Corporation LW12.8-50

Real-time target machine

dSPACE ds1006 total 64

input and output slots

Software

MATLAB, Simulink, IPG

CarMaker HIL, dSPACE

ControlDesk

Vertical excitation Sinusoidal – Amplitude

up to 16 mm

Other

Possibility to run drum

and belt setups, for IWM

consideration, an

additional 35 kg mass can

be added to UM

3 TYRE FORCES

RECALCULATION

In typical test rigs with a rolling drum, the maximal

longitudinal tyre force is achieved at slip values of

around 0.1. However, in such test rigs, the drum

diameter is much higher compared to the tyre

diameter. In the case under investigation, such

construction was not feasible; the drum diameter was

only 0.22 m. The registered values must be

recalculated since the rolling drum surface has

friction and geometrical properties different from the

actual road surface. Using such an approach, various

road conditions can be simulated (for example,

dry/wet asphalt, icy roads, etc.) (Arosio et al., 2005).

A series of braking tests using the vehicle corner

test rig have been performed for longitudinal contact

force estimation. The wheel was rolling on the steel

drum in all of the tests. Different vertical loading

scenarios need to be investigated. Mathematical

modelling showed that vertical load may change in a

wide range from 2000 N to 9000 N. The magnitude

of the vertical load during the experiments was solely

based on the additional SM. At the beginning of each

test run, static load, tyre surface temperature and

vehicle suspension settings were registered.

Afterwards, the wheel is excited to reach predefined

linear velocities of 50 km/h, 75 km/h and 100 km/h.

Once the speed has settled in, the braking is applied

till the full stop of the wheel. With each load and

velocity setting, five tests are performed. The average

of the results from 5 braking tests, captured on the

same F

and wheel linear velocities, are taken to form

longitudinal tyre force curves versus slip angle,

considering different road types and tyre wear. To

omit outliers, data from the force transducer was

filtered using median filters. Experimental data

showed that maximal longitudinal force is achieved

when the slip exceeds 0.2. As described above, the

resulting forces must be validated since the tyre

contact properties are distorted. For this reason, the

actual values of the tyre forces in the longitudinal

direction are used, which were generated with the

experimentally validated MF 6.1 tyre model at the

same slip angles and vertical loads.

These target curves are then divided by the curves

gathered in experiments on the test rig with a steel

drum. The result of curve division produces

longitudinal tyre force recalculation coefficients, and

the polynomial curve fit is done to capture the trend

of the recalculation coefficients (Figure 2).

In Figure 2, each curve is marked for the specific

vertical load and vehicle travel speed, which is 100

km/h. Due to repetition, this paper does not provide

results achieved at other loading conditions and

velocities.

During the investigation, it was found that

a polynomial curve of the fifth order can be used for

force recalculation. For all the cases, the value of

the determination coefficient was higher than 0.98.

Once the database containing polynomial curves is

completed for the most common loading scenarios on

the test rig, the longitudinal slip, vertical load, and

measured longitudinal force proceed to the algorithm,

which contains polynomials, and the recalculation is

performed. The force recalculation for dry/wet and

new/worn tyres using the algorithm proposed above

is shown in Figure 3.

Development and Validation of a Vehicle Corner Test Rig Designed for Hardware-in-the-Loop Testing

361

Figure 2: Examples of longitudinal tyre force recalculation

coefficients for a few loading cases and velocity 100 km/h.

Figure 3: Longitudinal tyre force recalculation for medium

vertical loading scenario (4600 N).

Similar conditions were maintained in dedicated

experiments for lateral tyre force measurements.

Sideslip angle input was generated in the form of an

active toe. Due to the used vehicle rear suspension

and overall corner construction application, toe

amplitude of 6° (±3°) was used. All the required

variables for validation were gathered in real-time

and saved in the database. The polynomial curves of

the fifth order were used for recalculation (Figure 4);

for all the cases, the value of the determination

coefficient was higher than 0.98.

Since the wheel was cambered due to high vertical

load, different polynomial curves were required to

capture the asymmetry of the lateral tyre force (Figure

4). Using the same methodology, actual lateral force

values were achieved and compared to measurement

ones on the test rig. An example of a few scenarios is

shown in Figure 5.

For all cases under investigation, the created

recalculation methodology matches both longitudinal

and lateral forces well.

Figure 4: Examples of lateral tyre force recalculation

coefficients for few loading cases.

Figure 5: Lateral tyre force recalculation for medium

vertical loading scenario (4600 N).

3.1 Process of Interpolation

Since vertical load on the test rig is not affected by

the roll and pitch dynamics, it may only be changed

manually in a range from 2000 N to 9000 N.

Therefore, during the measurements, applying the

tyre force recalculation algorithm described above,

longitudinal and lateral forces at discrete values of

VEHITS 2025 - 11th International Conference on Vehicle Technology and Intelligent Transport Systems

362

vertical load, 𝐹



can be performed and stored as a

lookup table (LUT) (Table 2). An algorithm to

recalculate longitudinal and lateral forces for every F

is needed. This is where interpolation becomes

necessary.

Table 2: LUT for 𝐹



and 𝐹



estimation.

zmin

... F

zmax

Slip, λ F

(λ

zmin

) ... F

(λ

zmax

)

Toe

le, α

(α

zmin

) ... F

(α

zmax

)

Interpolation operates on two surrounding points

from the LUT. For example, consider the following

two points in Table 2 (for 𝐹



estimation): 𝐹



𝜆, 𝐹



,

𝐹



𝜆, 𝐹



. Here: 𝜆 is the current slip value, 𝐹



and

𝐹



are the lower and upper values of the vertical

load. The goal is to interpolate the value of F

at a

desired slip λ and vertical load 𝐹



. To calculate the

interpolated value 𝐹





𝜆, 𝐹





, we linearly interpolate

along the 𝐹



-axis as follows:

𝐹





𝜆, 𝐹





F





𝜆, 𝐹













,











,



















𝐹



−𝐹





)

A similar approach is used to calculate F

based

on toe angle and vertical load:

𝐹





𝛼, 𝐹





𝐹





𝛼, 𝐹













,











,



















𝐹



−𝐹





)

This method ensures smooth transitions between the

points in the LUT. To end with, overall recalculation

sequence can be summarised by these steps: 1)

generation of the target curves using MF 6.1; 2)

extraction of the actual tyre force curves from the

experiments; 3) curve division to obtain recalculation

coefficients; 4) polynomial curve fit over the

coefficients; 5) force recalculation using the

appropriate polynomial; 6) estimation of 𝐹



and 𝐹



for any given slip, toe and vertical load combination.

4 CASE STUDY

After developing a methodology for longitudinal and

lateral force recalculation and considering the vertical

load changes, the HIL setup was launched, and initial

tests were performed.

The Sin with Dwell manoeuvre was selected for

the case study to test rear-wheel toe control. Vehicle

velocities of 80 and 100 km/h were used. Three cases

were analysed: 1) Passive – a vehicle with corners,

without toe actuators; 2) Model – a vehicle with

corners and toe actuators. Notation "Model" refers to

the fact that the request of the mathematical model is

fed into the HIL test rig, but the actual position and

the response of the HIL is not fed-back into the

mathematical model. This means that "Model" is an

idealised system running in open loop; 3) HIL – a

vehicle with corners and toe actuators realised in

closed loop HIL application. Results are presented in

Figures 6 and 7.

Figure 6: Sin with Dwell at 80 km/h.

The toe actuators installed on HIL at lower vehicle

velocities could follow the target angle perfectly.

Feeding the actuator's position to the vehicle model in

real-time produced no distortions to the yaw rate and

overall vehicle response. Figure 6 shows no

difference between the Model and HIL results; the

RMSE value for this case under investigation is only

0.00438 rad/s. It needs to be mentioned that the

passive system could not perform manoeuvres.

Figure 7: Sin with Dwell at 100 km/h.

However, the difference between the Model and

HIL results appears at the higher vehicle speed (100

km/h, Figure 7). After analysing the results, it was

found that the toe actuator does not repeat the

Development and Validation of a Vehicle Corner Test Rig Designed for Hardware-in-the-Loop Testing

363

reference value so well as in a previous case (Figure

8); RMSE is 0.55° even though the manoeuvre was

performed successfully.

Due to the rule-based control strategy used for

active toe system, actuator nonlinearity, delay, and

fluctuations, the differences are more pronounced.

Since the mathematical model does not include any

actuator dynamics, it presents an idealised system.

Using the HIL simulation with real actuators allows

us to fully evaluate the effectiveness of the control

system and its influence on the vehicle dynamics.

Figure 8: Reference toe and actual toe value at velocity 100

km/h.

HIL experiments show that the actual yaw rate

differs from the one in the idealised system. Hence,

these outcomes from the HIL application can help

further develop the control system, mitigate control

errors, and ensure proper vehicle response. However,

it will be presented in future works.

5 CONCLUSIONS

With the completed investigation, there are several

conclusions:

1. A test rig for vehicle corner testing has been

manufactured, and a HIL system has been

developed. A real-time, high-fidelity

mathematical model of the vehicle is utilised

within the HIL, with the rear axle corner swapped

for physical components.

2. Since the test rig incorporates a vehicle quarter, it

cannot accurately account for roll and pitch

angles; consequently, the vertical loading does not

reflect reality. Additionally, the contact between

the tyre and the drum does not adequately

represent the actual contact between the tyre and

the road surface. Various road types, such as dry

and wet asphalt, ice, and other surfaces, cannot be

represented effectively. Therefore, a methodology

for recalculating longitudinal and lateral forces

has been developed to consider different loading

conditions, and the obtained results have been

compared with ground truth.

3. A case study demonstrating toe actuator testing is

presented. At up to 80 km/h velocities for the

selected manoeuvre, the results achieved with

HIL and the mathematical model are very similar

(with a RMSE of 0.0048 rad/s). However, as the

velocity increases, discrepancies arise due to the

nonlinearity of actuator and noise from the

sensors, which is not addressed within the

mathematical model.

4. Using the HIL approach, it was found that active

toe control of a vehicle equipped with corners

improves its dynamics. A vehicle with passive

corners, which includes IWM but does not contain

an active toe control system, is not able to perform

Sin with Dwell manoeuvre even at 80 km/h.

5. Finally, further modifications to the control

algorithm are necessary. The case study

demonstrated the advantages of employing HIL

for investigations. However, control algorithms

are not the primary focus of this paper and will be

explored in future work.

ACKNOWLEDGEMENTS

This work was supported by the Research Council of

Lithuania project no. S-MIP-23-120.

REFERENCES

Armengaud, E., Eitzinger, S., Pirker, H., Buh, J., Gramstat,

S., Heimann, S., Chen, C., Ivanov, V., Heydrich, M.,

Sorniotti, A., Gruber, P., and Tavernini, D. (2021). E-

Mobility-Opportunities and Challenges of Integrated

corner Solutions. SAE International Journal of

Advances and Current Practices in Mobility, 3(5),

2462–2472. https://doi.org/10.4271/2021-01-0984

Arosio, D., Braghin, F., Cheli, F., and Sabbioni, E. (2005).

Identification of Pacejka's scaling factors from full-

scale experimental tests. Vehicle System Dynamics,

43(sup1), 457–474. https://doi.org/10.1080/00423110

500229683

Augsburg, K., Gramstat, S., Horn, R., Ivanov, V., Sachse,

H., and Shyrokau, B. (2011). Investigation of brake

control using Test Rig-in-the-Loop technique. SAE

Technical Papers on CD-ROM/SAE Technical Paper

Series. https://doi.org/10.4271/2011-01-2372

Cheng, S., Peng, H.-N., Yang, C., Wang, W.-D., and Li, L.

(2024). Chassis Global Dynamics Optimization for

Automated Vehicles: A Multiactuator Integrated

Control Method. In IEEE Transactions on Systems,

VEHITS 2025 - 11th International Conference on Vehicle Technology and Intelligent Transport Systems

364

Man, and Cybernetics: Systems, 54(1), pp. 578–587.

https://doi.org/10.1109/tsmc.2023.3311446

Chindamo, D., Gadola, M., and Marchesin, F. P. (2017).

Reproduction of real-world road profiles on a four-

poster rig for indoor vehicle chassis and suspension

durability testing. Advances in Mechanical

Engineering/Advances in Mechanical Engineering,

9(8), 168781401772600. https://doi.org/10.1177/16878

14017726004

Deepak, K., Frikha, M. A., Benômar, Y., Baghdadi, M. E.,

and Hegazy, O. (2023). In-Wheel motor drive systems

for electric vehicles: state of the art, challenges, and

future trends. Energies, 16(7), 3121.

https://doi.org/10.3390/en16073121

Fathy, H. K., Filipi, Z. S., Hagena, J., and Stein, J. L.

(2006). Review of hardware-in-the-loop simulation and

its prospects in the automotive area. SPIE Proceedings.

https://doi.org/10.1117/12.667794

Gießler, Martin, Gauterin, F., & and Unrau, H. J. (2022).

New inner drum test bench for dynamic tests of

PLT and truck tires. KITopen-ID:1000156885.

https://doi.org/10.5445/IR/1000156885

Gräbe, R. P., Kat, C., Van Staden, P. J., and& Els, P. S.

(2020). Difference thresholds for a vehicle on a 4-poster

test rig. Applied Ergonomics/Applied Ergonomics, 87,

103115. https://doi.org/10.1016/j.apergo.2020.103115

Guo, L., Ge, P., and& Sun, D. (2019). Torque Distribution

Algorithm for stability control of electric vehicle driven

by four In-Wheel motors under emergency conditions.

IEEE Access, 7, 104737–104748. https://doi.org/10.11

09/access.2019.2931505

Gysen, B., Paulides, J., Janssen, J., and& Lomonova, E.

(2010). Active electromagnetic suspension system for

improved vehicle dynamics. IEEE Transactions on

Vehicular Technology, 59(3), 1156–1163.

https://doi.org/10.1109/tvt.2009.2038706

Heydrich, M., Ivanov, V., Bertagna, A., Rossi, A.,

Mazzoni, M., and& Bücner, F. (2022). Hardware-in-

the-Loop testing of a hybrid Brake-by-Wire system for

electric vehicles. SAE International Journal of Vehicle

Dynamics, Stability, and NVH, 6(4).

https://doi.org/10.4271/10-06-04-0031

Hori, Y. (2004). Future Vehicle Driven by Electricity and

Control—Research on Four-Wheel-Motored ""UOT

Electric March II."." IEEE Transactions on Industrial

Electronics, 51(5), 954–962.

https://doi.org/10.1109/tie.2004.834944

Ivanov, V., Augsburg, K., Bernad, C., Dhaens, M., Dutré,

M., Gramstat, S., Magnin, P., Schreiber, V., Skrt, U.,

and& Van Kelecom, N. (2019). Connected and shared

X-in-the-Loop technologies for electric vehicle design.

World Electric Vehicle Journal, 10(4), 83.

https://doi.org/10.3390/wevj10040083

Jneid, M. S., and& Harth, P. (2024). Radial Flux In-Wheel-

Motors for vehicle electrification. Cognitive Sustaina-

bility, 3(3). https://doi.org/10.55343/cogsust.105

Kojis, P., Danilevičius, A., Šabanovič, E., and& Skrickij,

V. (2022). The second generation of electric vehicles:

Integrated Corner Solutions. In Lecture notes in

intelligent transportation and infrastructure (pp. 87–

100). https://doi.org/10.1007/978-3-030-94774-3_9

Lauwerys, C., Swevers, J., and& Sas, P. (2005). Robust

linear control of an active suspension on a quarter car

test-rig. Control Engineering Practice, 13(5), 577–586.

https://doi.org/10.1016/j.conengprac.2004.04.018

Li, I., and& Lee, L. (2019). Design and development of an

active suspension system using Pneumatic-Muscle

actuator and intelligent control. Applied Sciences,

9(20), 4453. https://doi.org/10.3390/app9204453

Maher, D., and& Young, P. (2010). An insight into linear

quarter car model accuracy. Vehicle System

Dynamics/Vehicle System Dynamics, 49(3), 463–480.

https://doi.org/10.1080/00423111003631946

Mihalič, F., Truntič, M., and& Hren, A. (2022). Hardware-

in-the-Loop Simulations: A Historical Overview of

Engineering Challenges. Electronics, 11(15), 2462.

https://doi.org/10.3390/electronics11152462

Nguyen, B.-M., and& Trovão, J. P. (2024). Advancing

Automotive Electronics: The Role of Collaborative

Education and Project Development [Automotive

Electronics]. IEEE Vehicular Technology Magazine,

19(3), pp. 116–127. https://doi.org/10.1109/mvt.2024.3

414068

Omar, M., El-Kassaby, M., and& Abdelghaffar, W. (2017).

A universal suspension test rig for electrohydraulic

active and passive automotive suspension system.

Alexandria Engineering Journal /Alexandria

Engineering Journal, 56(4), 359–370. https://doi.org/

10.1016/j.aej.2017.01.024

Šabanovič, E., Kojis, P., Šukevičius, Š., Shyrokau, B.,

Ivanov, V., Dhaens, M., and& Skrickij, V. (2021).

Feasibility of a neural Network-Based virtual sensor for

vehicle unsprung mass relative velocity estimation.

Sensors, 21(21), 7139. https://doi.org/10.3390/s2121

7139

Skrickij, V., Kojis, P., Šabanovič, E., Shyrokau, B., and&

Ivanov, V. (2024). Review of Integrated chassis control

techniques for Automated ground vehicles. Sensors,

24(2), 600. https://doi.org/10.3390/s24020600

Society of Automotive Engineers International. (2012).

Laboratory Testing Machines for Measuring the Steady

State Force And Moment Properties of Passenger Car

Tires (SAE J 1106). https://doi.org/10.4271/J1106_201

208

Stolte, T., Loba, M., Nee, M., Wu, L., and& Maurer, M.

(2023). Toward Fault-Tolerant Vehicle Motion Control

for Over-Actuated Automated Vehicles: A Non-Llinear

Model Predictive Approach. IEEE Access, 11, 10499–

10519. https://doi.org/10.1109/access.2023.3239518

Yu, M., Arana, C., Evangelou, S. A., and& Dini, D. (2019).

Quarter-Car Experimental Study for Series Active

Variable Geometry Suspension. IEEE Transactions on

Control Systems Technology

, 27(2), 743–759.

https://doi.org/10.1109/tcst.2017.2772912

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