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Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

Vehicle yaw stability control (YSC) can actively adjust the working state of the chassis actuator to generate a certain additional yaw moment for the vehicle, which effectively helps the vehicle maintain good driving quality under strong transient conditions such as high-speed turning and continuous lane change. However, the traditional YSC pursues too much driving stability after activation, ignoring the difference of multi-objective requirements of yaw maneuverability, actuator energy consumption and other requirements in different vehicle stability states, resulting in the decline of vehicle driving quality. Therefore, a vehicle yaw stability model predictive control strategy for dynamic and multi-objective requirements is proposed in this paper. Firstly, the unstable characteristics of vehicle motion are analyzed, and the nonlinear two-degree-of-freedom vehicle dynamics models are established respectively.

Human driving behavior's inherent variability, randomness, individual differences, and dynamic vehicle-road situations give human-machine cooperative (HMC) driving considerable uncertainty, which affects the applicability and effectiveness of HMC control in complex scenes. To overcome this challenge, we present a novel data-enabled game output regulation approach for HMC driving. Firstly, a global human-vehicle-road (HVR) model is established considering the varied driver's steering characteristic parameters, such as delay time, preview time, and steering gain, as well as the uncertainty of tire cornering stiffness and variable road curvature disturbance. The robust output regulation theory has been employed to ensure the global DVR system's closed-loop stability, asymptotic tracking, and disturbance rejection, even with an unknown driver's internal state. Secondly, an interactive shared steering controller has been designed to provide personalized driving assistance.

Under complex and extreme operating conditions, the road adhesion coefficient emerges as a critical state parameter for tire force analysis and vehicle dynamics control. In contrast to model-based estimation methods, intelligent tire technology enables the real-time feedback of tire-road interaction information to the vehicle control system. This paper proposes an approach that integrates intelligent tire systems with machine learning to acquire precise road adhesion coefficients for vehicles. Firstly, taking into account the driving conditions, sensor selection is conducted to develop an intelligent tire hardware acquisition system based on MEMS (Micro-Electro-Mechanical Systems) three-axis acceleration sensors, utilizing a simplified hardware structure and wireless transmission mode. Secondly, through the collection of real vehicle experiment data on different road surfaces, a dataset is gathered for machine learning training.

The installation of the Electronic Braking System (EBS) could effectively improve braking response speed, shorten braking distance, and ensure driving safety of commercial vehicles. However, during longitudinal deceleration control process, the commercial vehicles face not only challenges such as large inertia mass and random road gradient resistance of the vehicle layer, but also non-linear characteristics of the EBS actuator layer. In order to solve these problems, this paper proposes a commercial vehicle’s longitudinal deceleration precise control strategy considering vehicle-actuator dynamic characteristics. First, longitudinal dynamics of commercial vehicle is analyzed, and so is the EBS’ non-linear response hysteresis characteristics. Then, we design the dual layer deceleration control strategy. In vehicle layer, the recursive least squares with forgetting factor and Kalman filtering are comprehensively applied to dynamically estimate the vehicle mass and driving road slope.

Ambient temperature is a very sensitive use condition for electric vehicles (EVs), so it is imperative to ensure the maintenance of suitable temperature. This is particularly important in regions characterized by prolonged exposure to unfavorable temperature conditions. In such cases, it becomes necessary to implement insulation measures within parking facilities and allocate energy resources to sustain a desired temperature level. Solar energy is a renewable and environmentally friendly source of energy that is widely available. However, the effectiveness of utilizing solar energy is influenced by various factors, such as the time of day and weather conditions. The use of phase change material (PCM) in a latent heat energy storage (LHES) system has gained significant attention in this field. In contrast to single-phase energy storage materials, PCM offer a more effective heat storage capacity.

Currently, the rapid expansion of the global road transport industry and the imperative to reduce carbon emissions are propelling the advancement of electrified highways (EH). In order to conduct a comprehensive economic analysis of EH, it is crucial to develop a detailed /8.and comprehensive economic model that takes into account various transportation modes and factors that influence the economy. However, the existing economic models for EH lack comprehensiveness in terms of considering different transportation modes and economic factors. This study aims to fill this gap by designing an economic model for an EH-based Online DC-driven system (ODS) for long distance heavy-duty transport vehicle incorporating multi-factor sensitivities. Firstly, the performance parameters of the key components of the system are calculated using vehicle dynamics equations which involves selecting and matching the relevant components and determining the fundamental cost of vehicle transformation.

This work represents an advanced engineering research project partially funded by the U.S. Department of Energy (DOE). Ford Motor Company, FEV North America, and Oak Ridge National Laboratory collaborated to develop a next generation boosted spark ignited engine concept. The project goals, specified by the DOE, were 23% improved fuel economy and 15% reduced weight relative to a 2015 or newer light-duty vehicle. The fuel economy goal was achieved by designing an engine incorporating high geometric compression ratio, high dilution tolerance, low pumping work, and low friction. The increased tendency for knock with high compression ratio was addressed using early intake valve closing (EIVC), cooled exhaust gas recirculation (EGR), an active pre-chamber ignition system, and careful management of the fresh charge temperature.

This work aims to develop a PA6 nanocomposite with glass fiber (GF) and graphene nanoplatelets (GNPs) focusing on automotive parts application. Polyamide 6 is a semi-crystalline polymer that exhibits high fatigue and flexural strength, making it viable for rigorous applications. Along with the improved electrical, mechanical, thermal, and optical performance achieved in PA6 and GF-based nanocomposites, they can fill complex geometries, have great durability, and are widely utilized due to their capacity of reducing the weight of the vehicle besides a cost reduction potential. The glass fiber is a filamentary composite, usually aggregated in polymeric matrices, which aims to amplify the mechanical properties of polymers, mainly the tensile strength in the case of PA6.

Manufacturing processes impact many factors on a product. Depending on the selected method, development time, part performance and cost are affected. In the automotive sector, there is a growing demand for weight reduction due to the advent of electrification and the greenhouse gas emission regulations. In addition, geometric complexity is a challenging factor for the feasibility of mass production of parts. In this scenario, plastic materials are a very interesting option for application in various vehicle parts, since these materials can be molded by injection, vacuum forming, among others, while maintaining good mechanical properties. Almost a third of a vehicle’s parts are polymeric, making the development of these materials strategic for car manufacturers. This article investigates the impact of the presence of fiberglass in a thermoplastic automotive body part.

Polyurethane (PU) foams are versatile in automotive applications for sound absorption, due to their superior acoustic-absorbing properties, vibration damping and robustness, and seat cushioning products due to their easiness of manufacturing process and cost-effectiveness. In recent studies, micro- and nano-particles were used to improve sound absorption efficiency, these fillers help to form interconnected pore structures in the foam matrix, and this interconnection of pores is advantageous in dissipating heat generated from wave friction with the air. Some of the micro- and nano-particles used are natural fibers (like cellulose, fir, palm), silica, clay, graphene and derivatives, zeolite, and others. This review is an overview of recent advances in the incorporation of fillers in PU foams and the influence they have on the sound absorption capacity of the foams.

The rise of greenhouse gas emissions has reached historic levels, with 37 billion tons of CO2 released into the atmosphere in 2018 alone. In the European Union, 32% of these emissions come from transportation, with 73.3% of that percentage coming from vehicles. To address this problem, solutions such as cleaner fuels and more efficient engines are necessary. Artificial Intelligence can also play a crucial role in climate analysis and verification to move towards a more sustainable future. By utilizing connected vehicle data, automakers can analyze real-time vehicle performance data to identify opportunities for improvement and reduce carbon emissions. This approach benefits the environment, improves vehicle quality, and reduces engineering work time, making it a win-win solution. Connected vehicle data offers a wealth of information on vehicle performance, such as fuel consumption and carbon emissions.

Steel represents more than 50% of weight in vehicles, being more susceptible to corrosion processes. Corrosion studies in these components are of great industrial and economic interest, and anticorrosive coatings with efficiency of superior protection is still a relevant area in materials research. Paintings from inorganic and organic hybrid compounds have been used to produce more effective and efficient coatings. Among polymeric coatings, epoxy resin is considered one of the most used anticorrosion coatings, mainly due its excellent protective properties. High barrier level is reached by reinforcing the coatings with inorganic fillers such heavy metal, nanoparticles, silica, and now more recently, carbon-based materials, like graphene and its derivatives.

Rubber is one of the most used materials currently selected to produce automotive parts, but, for specific applications, some improvement is required in its properties through the addition of some components to the rubber compound formulation. Because of that, mechanical, thermal, and chemical properties are enhanced in order to meet strict requirements of the vast range of application of the rubber compounds. In addition to improving material properties, the combination of different substances, also aims to improve processability and reduce the costs of the final product. Recently, the use of nanofillers has been very explored because of their distinctive properties and characteristics. Among the nanofillers under study, graphene is known for its high-barrier property, thermal and electrical conductivities, and good mechanical properties.

Velocity prediction on hilly road can be applied to the energy-saving predictive control of intelligent vehicles. However, the existing methods do not deeply analyze the difference and diversity of road slope driving characteristics, which affects prediction performance of some prediction method. To further improve the prediction performance on road slope, and different road slope driving features are fully exploited and integrated with the common prediction method. A rolling prediction-based multi-scale fusion prediction considering road slope transition driving characteristics is proposed in this study. Amounts of driving data in hilly sections were collected by the advanced technology and equipment. The Markov chain model was used to construct the velocity and acceleration joint state transition characteristics under each road slope transition pair, which expresses the obvious driving difference characteristics when the road slope changes.

Ammonia has attracted the attention of a growing number of researchers in recent years. However, some properties of ammonia (e.g., low laminar burning velocity, high ignition energy, etc.) inhibit its direct application in engines. Several routes have been proposed to overcome these problems, such as oxygen enrichment, partial fuel cracking strategy and co-combustion with more reactive fuels. Improving the reactivity of ammonia from the oxidizer side is also practical. Ozone is a highly reactive oxidizer which can be easily and rapidly generated through electrical plasma and is an effective promoter applicable for a variety of fuels. The dissociation reaction of ozone increases the concentration of reactive radicals and promotes chain-propagating reactions. Thus, obtaining accurate rate constants of reactions related to ozone is necessary, especially at elevated to high pressure range which is closer to engine-relevant conditions.

In order to meet increasingly stringent emission regulations, it is significance to establish a control- oriented transient NOx and PM emission prediction model and improve the accuracy and real-time performance. In this study, the prediction model of transient PM emissions based on Transformer is established. In terms of model accuracy and real-time performance, Transformer emission prediction model is compared with Multilayer perceptron (MLP) neural network and Long-Short Term Memory (LSTM) emission prediction model. The results show that the performance of Transformer transient emission prediction model is superior to other model structures, it can be used for real-time prediction.

In the present work, five surrogate components (n-Hexadecane, n-Tetradecane, Heptamethylnonane, Decalin, 1-Methylnaphthalene) are proposed to represent liquid phase of diesel fuel, and another different five surrogate components (n-Decane, n-Heptane, iso-Octane, MCH (methylcyclohexane), Toluene) are proposed to represent vapor phase of diesel fuel. For the vapor phase, a 5-component surrogate chemical kinetic mechanism has been developed and validated. In the mechanism, a recently updated H2/O2/CO/C1 detailed sub-mechanism is adopted for accurately predicting the laminar flame speeds over a wide range of operating conditions, also a recently updated C2-C3 detailed sub-mechanism is used due to its potential benefit on accurate flame propagation simulation. For each of the five diesel vapor surrogate components, a skeletal sub-mechanism, which determines the simulation of ignition delay times, is constructed for species C4-Cn.

The requirements of the automotive industry move along due to product competitiveness and this contributes to increase complexity in the requirements for evaluation. Simulation tools play a key role thanks to their versatility and multiple physical phenomena that can be represented. The axis of analysis for this paper is the problem of the interaction of airflow and water flow in the cowl/plenum/leaf screen components. Airflow is represented by HVAC system operating and water flow by the vehicle in torrential rain. Initially, one simulation is evaluated at a time, in one side, the airflow entering the HVAC system in which the amount of air entering is monitored and pressure drop, on the other, the water simulation on the vehicle, both using a Lagrangian CFD model (using with tools such as STAR CCM+® or Ansys Fluent®) Due to this, a CFD methodology was developed to evaluate the interaction of air and water flow.

The accuracy of tire forces directly affects the vehicle dynamics model precision and determines the ability of the model to develop the simulation platform or design the control strategy. In the high slip angle, due to the complex interactions at tire-road interfaces, the forces generated by the tires are high nonlinearity and uncertainty, which pose issues in calculating tire force accurately. This paper presents a hybrid physical and data-driven tire force calculation framework, which can satisfy the high nonlinearity and uncertainty condition, improve the model accuracy and effectively leverage prior knowledge of physical laws. The parameter identification for the physical tire model and the data-based compensation for the unknown errors between the physical tire model and actual tire force data are contained in this framework. First, the parameters in the selected combined-slip Burckhardt tire model are identified by the nonlinear least square method with tire test data.