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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.

Lane changing is an essential action in commercial vehicles to prevent collisions. However, steering system malfunctions significantly escalate the risk of head-on collisions. With the advancement of intelligent chassis control technologies, some autonomous commercial vehicles are now equipped with a four-wheel independent braking system. This article develops a lane-changing control strategy during steering failures using torque vectoring through brake allocation. The boundaries of lane-changing capabilities under different speeds via brake allocation are also investigated, offering valuable insights for driving safety during emergency evasions when the steering system fails. Firstly, a dual-track vehicle dynamics model is established, considering the non-linearity of the tires. A quintic polynomial approach is employed for lane-changing trajectory planning. Secondly, a hierarchical controller is designed.

The performance of suspension system has a direct impact on the riding comfort and smoothness. For the traditional suspension can not effectively alleviate the impact of road surface and the poor anti-vibration performance, The dynamics model of vehicle suspension system is established, and the control model of vehicle four-degree-of-freedom active suspension is designed with fuzzy control strategy. On this basis, a comprehensive simulation model of the control model of vehicle active suspension coupled with road excitation is established. and the ride comfort of vehicles under different types of suspension are tested through Simulink. The simulation results show that compared with the passive suspension, the reduction of vehicle acceleration and dynamic deformation of the active suspension controlled by fuzzy PID can reach 33.76% and 22.45%. and the reduction of pitch Angle speed and dynamic load of the active suspension controlled by fuzzy PID can reach 16.18% and 10.72%.

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.

Ammonia shows promise as an alternative fuel for internal combustion engines (ICEs) in reducing CO2 emissions due to its carbon-free nature and well-established infrastructure. However, certain drawbacks, such as the high ignition energy, the narrow flammability range, and the extremely low laminar flame speed, limit its widespread application. The dual fuel (DF) mode is an appealing approach to enhance ammonia combustion. The combustion characteristics of ammonia-diesel dual fuel mode and ammonia-PODE3 dual fuel mode were experimentally studied using a full-view optical engine and the high-speed photography method. The ammonia energy ratio (ERa) was varied from 40% to 60%, and the main injection energy ratio (ERInj1) and the main injection time (SOI1) were also varied in ammonia-PODE3 mode.

Compared with urban areas, the road surface in mountainous areas generally has a larger slope, larger curvature and narrower width, and the vehicle may roll over and other dangers on such a road. In the case of limited driver information, if the two cars on the mountain road approach fast, it is very likely to occur road blockage or even collision. Multi-vehicle cooperative control technology can integrate the driving data of nearby vehicles, expand the perception range of vehicles, assist driving through multi-objective optimization algorithm, and improve the driving safety and traffic system reliability. Most existing studies on cooperative control of multiple vehicles is mainly focused on urban areas with stable environment, while ignoring complex conditions in mountainous areas and the influence of driver status. In this study, a digital twin based multi-vehicle cooperative warning system was proposed to improve the safety of multiple vehicles on mountain roads.

The collision accidents of electric vehicles are gradually increasing, and the response of battery cell under mechanical abuse conditions has attracted more and more attention. In the real collision, the mechanical load on battery generally has the following characteristics, including multiple loading directions, dynamic impact and blunt intrusion. Therefore, it is necessary to study the mechanical response and deformation of battery under complex loading, especially in-plane dynamic loading condition. According to the actual accident, we designed the constrained blunt compression test of the battery in different speeds and directions. For out-of-plane loading, the structural stiffness of battery increases obviously and the fracture is advanced compared with the corresponding quasi-static tests. For in-plane constrained loading, the force response can be approximately divided into two linear segments, in which the structural stiffness increases abruptly after the inflection point.

Both ammonia and hydrogen, as zero-carbon fuels for internal combustion engines, are received growing attention. However, ammonia faces a challenge of low flame propagation velocity. Through injecting hydrogen into active pre-chamber, its jet flame ignition can accelerate the flame propagation velocity of ammonia. The influence of different pre-chamber structures on engine combustion characteristics is significant. In this paper, numerical studies were conducted to assess the impact of various pre-chamber structures and hydrogen injection strategy on the combustion characteristics of ammonia/hydrogen engines while maintaining the equivalent ratio of 1.0. The results indicate that the jet angle significantly affects the position of jet flame and the followed main combustion. The in-cylinder combustion pressure peaks at jet angle of 150°. Meanwhile, the combustion duration of 150° is shortened by 74.3% compared with that of 60°.

In the context of carbon neutrality, ammonia is considered a zero-carbon fuel with potential applications in the transportation sector. However, its high ignition energy, low flame speed, and high natural temperature, indicative of low reactivity, make it challenging to be applied as a sole fuel in engines. In such a scenario, the use of another zero-carbon and highly reactive fuel, hydrogen, becomes necessary to enhance the combustion of ammonia. Furthermore, jet ignition, a method known for improving engine combustion performance, may also hold potential for enhancing the combustion performance of ammonia engines. To explore the applicability of jet ignition in engines, this study conducted experimental research on a single-cylinder engine. Two ignition methods were employed: passive jet ignition of premixed ammonia-hydrogen at a compression ratio of 11.5, and active jet ignition of pure ammonia using hydrogen jet flame at a compression ratio of 17.3.

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.

Autonomous driving technology represents a significant direction for future transportation, encompassing four key aspects: perception, planning, decision-making, and control. Among these aspects, vehicle trajectory planning and control are crucial for achieving safe and efficient autonomous driving. This paper introduces a Combined Model Predictive Control algorithm aimed at ensuring collision-free and comfortable driving while adhering to appropriate lane trajectories. Due to the algorithm is divided into two layers, it is also called the Bi-Level Model Predictive Control algorithm (BLMPC). The BLMPC algorithm comprises two layers. The upper-level trajectory planner, to reduce planning time, employs a point mass model that neglects the vehicle's physical dimensions as the planning model. Additionally, obstacle avoidance cost functions are integrated into the planning process.

Ammonia (NH3), a zero-carbon fuel, has great potential for internal combustion engine development. However, its high ignition energy, low laminar burning velocity, narrow range of flammability limits, and high latent heat of vaporization are not conducive for engine application. This paper numerically investigates the feasibility of utilizing ammonia in a heavy-duty diesel engine, specifically through low-pressure direct injection (LP-DI) of hydrogen to ignite ammonia combustion. Due to the lack of a well-corresponding mechanism for the operating conditions of ammonia-hydrogen engines, this study serves only as a trend-oriented prediction. The paper compares the engine's combustion and emission performance by optimizing four critical parameters: excess air ratio, hydrogen energy ratio, ignition timing, and hydrogen injection timing. The results reveal that excessively high hydrogen energy ratios lead to an advanced combustion phase, reducing indicated thermal efficiency.

Turbulent jet ignition (TJI) combustion using pre-chamber ignition can accelerate the combustion speed in the cylinder and has garnered growing interest in recent years. However, it is complicated for the optimization of the pre-chamber structure and combustion system. This study investigated the effects of the pre-chamber structure and the intake ports on the combustion characteristics of a gasoline engine through CFD simulation. Spark ignition (SI) combustion simulation was also conducted for comparison. The results showed that the design of the pre-chamber that causes the jet flame colliding with walls severely worsen the combustion, increasing the knocking intendency, and decrease the thermal efficiency. Compared with SI combustion mode, the TJI combustion mode has the higher heat transfer loss and lower unburned loss. The well-optimized pre-chamber can accelerate the flame propagation with knock suppression.

The active sound generation systems (ASGS) for electric vehicles (EVs) play an important role in improving sound perception and transmission in the car, and can meet the needs of different user groups for driving and riding experiences. The active sound synthesis algorithm is the core part of ASGS. This paper uses an efficient variable-range fast linear interpolation method to design a frequency-shifted and pitch-modified sound synthesis algorithm. By obtaining the operating parameters of EVs, such as vehicle speed, motor speed, pedal opening, etc., the original sound signal is interpolated to varying degrees to change the frequency of the sound signal, and then the amplitude of the sound signal is determined according to different driving states. This simulates an effect similar to the sound of a traditional car engine. Then, a dynamic superposition strategy is proposed based on the Hann window function.

The vehicle stability assessment system is an indispensable component to ensure driving safety and enhance vehicle motion control, whether for automated or human-driven vehicles, especially in extreme operating conditions. However, the existing stability assessment methods tend to be conservative and often ignore the coupled longitudinal and lateral dynamics, as well as the nonlinear characteristics of tires. To evaluate the vehicle stability accurately and quickly, an 8-degree-of-freedom (DOF) vehicle dynamic model is constructed first, considering the nonlinear characteristics of tires through a physics-based approach. Subsequently, the vehicle and environment parameters are auto-tuned using Bayesian optimization with field test data. Based on the adjusted vehicle model, a Lyapunov exponent (LE) based vehicle stability analysis method is proposed to quantitatively assess the stability of the vehicle state and determine the corresponding stability boundary.

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.

This paper defines a control method for shift torque exchange stage and a torque distribution control method for speed regulation stage. In the torque exchange stage, the torque distribution problem of active and passive clutches considers the injection of sine curve for local correction, which can solve the fish belly problem of hydraulic response (i.e. the hydraulic response is slow at the beginning and the hydraulic response is fast at the end). In the speed regulation stage, the target speed gradient profile is determined according to different shift types. The determination of the target speed gradient profile integrates different driving modes, throttle, P2 energy and clutch temperature.