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Tyre behavior variations. (a) Compound temperature influence on the characteristic interaction shape. (b) Wear effect on available grip.
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In recent years the increasing needs of reducing the costs of car development expressed by the automotive market have determined a rapid development of virtual driver prototyping tools that aims at reproducing vehicle behaviors. Nevertheless, these advanced tools are still not designed to exploit the entire vehicle dynamics potential, preferring to...
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Context 1
... the Figure 12a it is possible to observe how the vehicle maneuver characterized by the highest friction coefficient (dry pavement) performs the DLC with a largest trajectory and the highest velocity Figure 12b in minimum amount of time Figure 13c and Table 3. Since the global NMPC set is limited by the most critical dynamic condition (worn cold tyre in contact with the icy road), the Figure13a shows higher values in terms of side slip angle for snowy and icy road surfaces, foreseeing the possibility to perform the maneuver in more aggressive way for dry and wet road conditions. ...
Context 2
... the Figure 12a it is possible to observe how the vehicle maneuver characterized by the highest friction coefficient (dry pavement) performs the DLC with a largest trajectory and the highest velocity Figure 12b in minimum amount of time Figure 13c and Table 3. Since the global NMPC set is limited by the most critical dynamic condition (worn cold tyre in contact with the icy road), the Figure13a shows higher values in terms of side slip angle for snowy and icy road surfaces, foreseeing the possibility to perform the maneuver in more aggressive way for dry and wet road conditions. ...
Citations
... Moreover, the ability to evaluate system parameters using real-time signals enables necessary improvements in control systems and advances future smart mobility, considering not only vehicle performance and safety but also overall grid efficiency and environmental impact [4,5]. In this scenario, autonomous vehicles should become increasingly able to preserve their maneuverability in a wider range of driving conditions and environmental adversities, assuring that the system's state and run-time parameters are estimated with good accuracy [6]- [8]. ...
Tyres show a strong non-linear dependence on vertical force, road roughness, wear level, temperature gradient, and slip resulting in an additional challenge in calibration, whose parameters may vary significantly with the tyre’s condition. An additional challenge to identifying and modeling the multi-dimensional tyre variability lies in the low accuracy level of tyre-road interaction data presenting physical inconsistencies and outliers, thus affecting outdoor testing scenarios. Indeed, outliers, gaps, or errors in the data can compromise calibration performance, potentially leading to incorrect model identification and rendering it unsuitable for further offline and online applications. In this paper, the authors aim to optimize the process of identifying tyre parameters by applying machine learning techniques to the dataset’s pre-processing with particular attention to clustering and anomaly detection algorithms. The process is split into two phases: first, different clustering algorithms are applied to the tyre data to group similar operating conditions; then, anomaly detection algorithms are applied to clustered data to recognize and remove inconsistencies. Additionally, to objectively compare the proposed data processing results, the preprocessed specifically acquired experimental data have been employed for the calibration of the reference mathematical tyre formulation, comparing the deviations of the fundamental tyre-related quantities to the previously identified tyre model, already validated in both offline and online scenarios. For the grip coefficient evaluation versus both lateral and longitudinal slip variables, the Elliptic Envelope algorithm shows to be the best anomaly detection algorithm while the One-Class Support Vector Machine technique demonstrates lower deviations for the stiffness evaluation in both longitudinal and lateral directions.
... Based on the research by Sakhnevych, et al., (2021), environmental conditions are one of the issues in EV range estimation. When it rains, it has been observed that the rolling resistance of a tire on a wet road is increased by up to 10% when compared to that on a dry road surface. ...
The Malaysian government has initiated a collaboration with private agencies to expand the green transport ecosystem by introducing electric buses. Despite these efforts, the adoption of electric buses in urban areas remains minimal. This paper addresses two research objectives: (a) to identify the challenges in adopting electric buses, and (b) to recommend improvements for adopting electric buses in Malaysia. Utilizing a qualitative methodology, this study aims to capture the experiences and reflections of interviewees through targeted population or place studies. This approach allows for the collection of detailed information and the development of new concepts and theories. The interviews revealed three main barriers to adopting electric buses in Melaka: battery reliability and durability, a lack of charging infrastructure, and insufficient operational knowledge. The paper suggests that the government and policymakers should take proactive measures to promote green technology and increase the acceptance of electric buses in urban areas through awareness campaigns.
... For the purposes of vehicle safety and dynamic performance, the next-generation vehicle control systems take into consideration the tires-road characteristics. The impact of tire dynamics and the interaction with different road surfaces have been studied [25]. Song et al. developed a filter to evaluate lateral tire-road forces and the vehicle sideslip angle by utilizing real-time measurements [26]. ...
For the sake of enhancing the handling and stability of distributed drive electric vehicles (DDEVs) under four-wheel steering (4WS) conditions, this study proposes a novel hierarchical control strategy based on a phase plane analysis. This approach involves a meticulous comparison of the stable region in the phase plane to thoroughly analyze the intricate influence of the front wheel angle, rear wheel angle, road adhesion coefficient, and longitudinal speed on the complex dynamic performances of DDEVs and to accurately determine the critical stable-state parameter. Subsequently, a hierarchical control strategy is presented as an integrated solution to achieve the coordinated control of maneuverability and stability. On the upper control level, a model predictive control (MPC) motion controller is developed, wherein the real-time adjustment of the control weight matrix is ingeniously achieved by incorporating the crucial vehicle stable-state parameter. The lower control level is responsible for the optimal torque allocation among the four wheel motors to minimize the tire load rate, thereby ensuring a sufficient tire grip margin. The optimal torque distribution for the four wheel motors is achieved using a sophisticated two-level allocation algorithm, wherein the friction ellipse is employed as a judgement condition. Finally, this developed control strategy is thoroughly validated through co-simulation utilizing the CarSim 2019 and Simulink 2020b commercial software, demonstrating the validity of the developed control strategy. The comparative results indicate that the presented controller ensures a better tracking capability to the desired vehicle state while exhibiting improved handling stability under both the double lane shifting condition and the serpentine working condition.
... In addition, a physical model-based control method is proposed in the literature [20] for the problem that environmental and road conditions, as well as tire conditions, can significantly affect vehicle traction, and a model predictive control algorithm is tested under different road and tire conditions to enhance the reliability of the virtual driver concerning the dynamic limits of the tires. Aiming at solving the challenging problem of measuring kinematic parameters such as tire-road forces and vehicle sideslip angle in the literature [21], a method based on traceless Kalman filtering is proposed for the real-time estimation of these parameters, and the stability of the optimized controller is confirmed by simulation experiments. ...
The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved model predictive control (MPC) method based on fuzzy control and an online update algorithm. First, a vehicle dynamics model is constructed and a feedforward MPC controller is designed; second, a real-time updating method of the time domain parameters is proposed to replace the previous method of empirically selecting the time domain parameters; lastly, a fuzzy controller is proposed for the real-time adjustment of the weight coefficient matrix of the model predictive controller according to the lateral and heading errors of the vehicle, and a state matrix-based cosine similarity updating mechanism is developed for determining the updating nodes of the state matrix to reduce the controller computation caused by the continuous updating of the state matrix when the longitudinal vehicle speed changes. Finally, the controller is compared with the traditional model prediction controller through the co-simulation of CARSIM and MATLAB/Simulink, and the results show that the controller has great improvement in terms of tracking accuracy and controller computational load.
... Z. Liang et al. proposed an adaptive sliding mode fault-tolerant control (ASM-FTC) strategy to stabilize the error by taking into account the tire force saturation in the vehicle motion [8]. S. Aleksandr and Y. Song et al. comprehensively analyzed the influence of tires on the stability of the vehicle under different working conditions by taking into full consideration tires and road characteristics, which represent the next generation of control systems [9,10]. Currently, the most common control algorithms are still based on the control algorithm of the sway stability control methods, including traditional PID-based control [11], model predictive control (MPC) [12], fuzzy logic control [13], neural network control [14], sliding mode control (SMC) [15], and so on. ...
In order to improve the yaw stability of a front-wheel dual-motor-driven driverless vehicle, a yaw stability control strategy is proposed for a front-wheel dual-motor-driven formula student driverless racing car. A hierarchical control structure is adopted to design the upper torque distributor based on the integral sliding mode theory, which establishes a linear two-degree-of-freedom model of the racing car to calculate the expected yaw angular velocity and the expected side slip angle and calculates the additional yaw moments of the two front wheels. The lower layer is the torque distributor, which optimally distributes the additional moments to the motors of the two front wheels based on torque optimization objectives and torque distribution rules. Two typical test conditions were selected to carry out simulation experiments. The results show that the driverless formula racing car can track the expected yaw angular velocity and the expected side slip angle better after adding the yaw stability controller designed in this paper, effectively improving driving stability.
... Advanced safety systems, such as the anti-lock braking system (ABS), traction control system (TCS), and stability control systems, have become common features in modern vehicles [5,6]. To guarantee the optimal performance of these vehicle control systems, it is crucial that all the data collected from on-board instruments, combined with insights from predictive models, maintain a high level of quality [7][8][9][10][11][12][13]. ...
In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network configurations. Then, the proposed approach involves using two separate neural networks in a dual-network architecture. The first network is dedicated to estimating the longitudinal velocity, while the second network predicts the sideslip angle and takes the longitudinal velocity estimate from the first network as input. This enables the creation of a virtual sensor to replace the real one. To obtain a reliable training dataset, several test sessions were conducted on different tracks with various layouts and characteristics, using the same reference instrumented vehicle. Starting from the acquired channels, such as lateral and longitudinal acceleration, steering angle, yaw rate, and angular wheel speeds, it has been possible to estimate the sideslip angle through different neural network architectures and training strategies. The goodness of the approach was assessed by comparing the estimations with the measurements obtained from an optical sensor able to provide accurate values of the target variable. The obtained results show a robust alignment with the reference values in a great number of tested conditions. This confirms that the adoption of artificial neural networks represents a reliable strategy to develop real-time virtual sensors for onboard solutions, expanding the information available for controls.
... Brakes are a critical component in the automotive industry, serving the fundamental role of slowing down or stopping the motion of vehicles and equipment. Their significance lies in enhancing safety and control, preventing accidents, and allowing for smooth deceleration [9]. The fundamental concept behind a braking system revolves around the idea of decelerating or halting a vehicle by transforming its kinetic energy into heat through frictional contact between the brake pad and the disc [10]. ...
In the world of motorsports engineering, improving brake performance is a crucial goal. One significant factor that affects this performance is the increase in brake disc temperature due to reduced cooling airflow, a phenomenon called “blanking”. This temperature increase also impacts the rim and the air inside the tire, causing changes in tire temperature and pressure, which affects the vehicle’s performance. Properly adjusting the brake blanking can be essential to keep the tire running at the right temperature, resulting in maximization of the performance on track. To address this complex problem, this study describes the problem of cooling brake discs, and this problem is then used as an opportunity to introduce a new variable in order to optimize the performance of the vehicle. By changing the thermal evolution of the brake disc, through the blanking, it can change a large percentage of heat that heats the tire. When combining an existing brake model in the literature with a tire thermal model in a co-platform simulation, it was seen that it is possible to work these two models together with the aim of being able to obtain the prediction of the optimal blanking value to be adopted before proceeding on track, thus saving time and costs.
... Amongst the external stimuli that may excite nonstationary behaviours in the tyre-wheel assembly there are, for example, time-varying slip and spin inputs [4,5], unsteady effects due to the compliance of the tyre tread and carcass, and even abrupt discontinuities in the available friction at the tyre-road interface. In order to synthesise and implement ad-hoc algorithms and strategies for vehicle state estimation and control [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21], it is crucial to rely on simple and plausible physical models, capable of explaining at least qualitatively all the above-mentioned processes, and their influence upon the transient generation of tyre forces and moments [2,22,23]. ...
... (21) consists of two linear, uncoupled transport PDEs involving only two partial derivatives: one taken with respect to the longitudinal coordinate ξ and one with respect to the travelled distance s. Enforcing the BC (4a) and IC (4b), in turn, provides two different solutions to the PDE (21). These solutions are uniquely defined on P, and may be sought using the method of the characteristic lines [80][81][82][83], which yields [34] ...
... As a result, varying heat transfer flows at different tire radial positions contribute to the variation in tire spatial temperature distribution. Previous studies recognize the non-linear dependency of tire rubber mechanical characteristics on tire temperatures [9][10][11]. Due to this temperature dependency of tire mechanical characteristics, in return, the intensity of tire-road friction also varies with different tire temperature levels [12,13]. ...
... Assuming both the thermal boundary conditions T a and T r are independent of T t , Newton's law of cooling is applied to calculate the coefficients h lc and h ta as expressed by Eqs. (10) and (11). ...
... An agent that combines both speed and steering control may find better solutions, which is our future research direction. Furthermore, control strategies that take into account both the path-following and tyre management in contact with various terrains [37,38], or more importantly include the pollution due to particles of worn rubber [39,40], is a practical aspect we aim to investigate in our future work. ...
The potential of autonomous driving technology to revolutionize the transportation industry has attracted significant attention. Path following, a fundamental task in autonomous driving, involves accurately and safely guiding a vehicle along a specified path. Conventional path-following methods often rely on rule-based or parameter-tuning aspects, which may not be adaptable to complex and dynamic scenarios. Reinforcement learning (RL) has emerged as a promising approach that can learn effective control policies from experience without prior knowledge of system dynamics. This paper investigates the effectiveness of the Deep Deterministic Policy Gradient (DDPG) algorithm for steering control in ground vehicle path following. The algorithm quickly converges and the trained agent achieves stable and fast path following, outperforming three baseline methods. Additionally, the agent achieves smooth control without excessive actions. These results validate the proposed approach’s effectiveness, which could contribute to the development of autonomous driving technology.