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With the rapid advancement in intelligent vehicle technologies, comprehensive environmental perception has become crucial for achieving higher levels of autonomous driving. Among various perception tasks, monitoring road types and evenness is particularly significant. Different road categories imply varied surface adhesion coefficients, and the evenness of the road reflects distinct physical properties of the road surface. This paper introduces a two-stage road perception framework. Initially, the framework undergoes pre-training on a large annotated drivable area dataset, acquiring a set of pre-trained parameters with robust generalization capabilities, thereby endowing the model with the ability to locate road areas in complex regions.

In hybrid vehicles, the drive motor is directly connected to the drive train and the inherent drive train damping is low. When subjected to external disturbance, the low damping characteristics of the transmission system may cause torsional vibration, which will continue to oscillate the transmission system and affect the driving performance of the vehicle. In this paper, we propose a harmonic injection wheel control method based on motor speed to suppress oscillations and improve the driving performance of hybrid electric vehicles. The harmonic injection control method based on motor speed is based on Fourier transform to decompose sinusoidal harmonics based on specific order of motor speed. RLS algorithm is used to estimate the amplitude and phase, and PI control is used to calculate the compensation torque for the actual amplitude and target amplitude. Simulation and test results show that the proposed control strategy is effective in suppressing oscillations.

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.

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.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.

A multiple-injection combustion strategy has been developed for heated gasoline direct injection compression ignition (HGCI). Gasoline was injected into a 0.4L single cylinder engine at a fuel pressure of 300bar. Fuel temperature was increased from 25degC to a temperature of 280degC by means of electric injector heater. This approach has the potential of improving fuel efficiency, reducing harmful CO and UHC as well as particulate emissions, and reducing pressure rise rates. Moreover, the approach has the potential of reducing fuel system cost compared to high pressure (>500bar) gasoline direct injection fuel systems available in the market for GDI SI engines that are used to reduce particulate matter. In this study, a multiple injection strategy was developed using electric heating of the fuel prior to direct fuel injection at engine speed of 1500rpm and load of 12.3bar IMEP.

The aim of this study is to develop a pathway towards Hydrogen combustoin on an opposed-piston four stroke engine (OP4S) by using 1D simulation code from Gamma Technologies. By its configuration, the OP4S engine has significant thermal efficiency benefits versus conventional ICE. The benefit of the OP4S is reduced heat losses due to elimination of the cylinder head, which increase the brake thermal efficiency. A hydrogen-fueled (H2) opposed-piston four stroke (OP4S) engine was modeled using GTPower to determine the potential on performance, thermal efficiency and emissions targets. The 1D model was first validated on E10 gasoline using experimental data and was used to explore changes to fuel type in NG and H2, fueling location (TPI and DI), fuel mixture strength (stoichiometric and lean), for an optimized plenum volume and turbocharger selection.

China's plug-in electric vehicle (PEV) market with stocks at 7.8 million is the world's largest in 2021, and it accounts for half of the global PEV growth in 2021. The PEV market in China has dramatically evolved since the pandemic in 2020: over 20% of all new PEV sales are from China by mid-2022. Recent features of PEV market dynamics, consumer acceptance, policies, and infrastructure have important implications for both the global energy market and manufacturing stakeholders. From the perspective of demand pull-supply push, this study analyzes China's PEV industry with a market dynamics framework by reviewing sales, product and brand, infrastructure, and government policies from the last few years and outlooking the development of the new government’s 14th Five-Year Plan (2021-2025).

To tackle the over-actuated and highly nonlinear characteristics that four-wheel-independent-steering and four-wheel-independent -driving (4WIS/4WID) vehicles exhibit when tracking aggressive trajectory, a hierarchical controller with layers of computation-intensive modules is commonly adopted. The high-level linear motion controller commands the desired state derivatives of the vehicle to meet the overall trajectory tracking objectives. Then the system dynamic is inversed by the mid-level control allocation layer and the low-level wheel control layer to map the target state derivatives to steering angle and motor torque commands. However, this type of controller is difficult to implement on the embedded hardware onboard since the nonlinear dynamic inversion is typically solved by nonlinear programming.

An intelligent connected vehicle (ICV) swarm system that includes N vehicles is considered. Based on the special properties of potential functions, a kinematic model describing the swarm performances is proposed, which allows all vehicles to enclose the tracking target and show both tracking and formation characteristics. Treating the performances as the desired constraints, the analytical form of constraint forces can be obtained inspired by the Udwadia-Kalaba approaches. A special approach of uncertainty decomposition to deal with uncertain interferences is proposed, and a switching-type robust control method is addressed for each vehicle agent in the swarm system. The features and validity of the addressed control are demonstrated in the numerical simulations.

The intelligent unmanned ground vehicle (UGV) motorcade system consisting of one leader and n − 1 followers is considered. The safety distance between the front and rear UGVs is treated as the control target. Since the safety distance constraint is a unilateral constraint, the state transformation is needed. Hence, a piecewise type conversion function is formulated to serve for the transformation of the original inequality constraint. The system equation is further expressed by the new state. We assume that the input of the leading UGV is known. Combined with the uncertainty evaluation, a class of collaborative controls for the following UGVs is proposed to deal with the uncertainty with unknown bound. The effectiveness of the designed control is verified by both Lyapunov stability theory and simulations. Both theoretical and simulation results illustrate that the longitudinal safety, stability and global behavior of the intelligent motorcade system are guaranteed.

Hydraulic retarder is important auxiliary brake device which widely used in commercial vehicles for its economy, safety and driving comfort, however cavitation will occur and reduce the braking performance when hydraulic retarder operates at high speed. In this paper, a model of hydraulic retarder considering cavitation effect was established, and the reliability of the model was verified by comparing with the external characteristics of the product which was obtained from Voith’s official discloses data. Then the cavitation of hydraulic retarder under high-speed working condition was studied by the establishing simulation model. The simulation model can describe and analyze the internal flow field in the hydraulic retarder, and can be used as an important tool for the development and optimization of hydraulic retarder in the future. When hydraulic retarder’s rotational speed is about 1500rpm, the cavitation will be observed in the working chamber.

This work presents a multi-objective adaptive cruise control (ACC) system via deep reinforcement learning (DRL). During the control period, it quantitatively considers three indexes: tracking accuracy, riding comfort, and fuel economy. The system balances contradictions between different indexes to achieve the best overall control results. First, a hierarchical control architecture is utilized, where the upper level controller is synthesized under DRL framework to give out the vehicle desired acceleration. The lower level controller executes the command and compensates vehicle dynamics. Then, four state variables that can comprehensively determine the car-following states are selected for better convergence. Multi-objective reward function is quantitatively designed referring to the evaluation indexes, in which safety constraints are considered by adding violation penalty. Thereafter, the training environment which excludes the disturbance of preceding car acceleration is built.

The accuracy of road input identifiaction for autonomous vehicles (AVs) system, especially in state-based AVs control for improving road handling and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely and effectively acquire road rough information and chose the reasonable road-based control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for AVs system, e.g., varying center of gravity (C.G.) of sprung mass, controllable suspension damping force or variable spring stiffness. To tackle the above mentioned, this paper develops a novel observer approach, which combines unscented Kalman filter (UKF) and Minimum Model Error (MME) theory, to optimize the estimation accuracy of the road rough for AVs system. A full-car nonlinear model and road profile model are first established.

Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate Ground Truth (GT) to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system.

The driving power system can be combined with lasers, lights, etc., and applied to automobiles to achieve various functions. Under the general trend of the development of intelligent vehicles, people have higher and higher requirements for the accuracy and power of various equipment. However, as power increases, how to ensure the stability of factors such as current is a challenging problem. Therefore, it is extremely important to study and design a high-power drive system in this paper, so as to ensure a stable output of the current. The system is composed of power supply, load, secondary power supply and control chip. The choice of power supply and load is conventional model. The secondary power supply adopts step-down circuit, with synchronous rectifier chip, which can effectively reduce energy consumption, and with temperature protection device, which can ensure the safe and reliable operation of equipment.

In automotive body manufacturing the dies for blanking/trimming/piercing are under most severe loading condition involving high contact stress at high impact loading and large number of cycles. With continuous increase in sheet metal strength, the trim die service life becomes a great concern for industries. In this study, competing trim die manufacturing routes were compared, including die raw materials produced by hot-working (wrought) vs. casting, edge-welding (as repaired condition) vs. bulk base metals (representing new tools), and the heat treatment method by induction hardening vs. furnace through-heating. CaldieTM, a Uddeholm trademarked grade was used as trim die material. The mechanical tests are performed using a WSU developed trimming simulator, with fatigue loading applied at cubic die specimen’s cutting edges through a tungsten carbide rod to accelerate the trim edge damage. The tests are periodically interrupted at specified cycles for measurement of die edge damage.

High speed on-off valves (HSVs) are widely used in advanced hydraulic braking actuators, including regenerative braking systems and active safety systems, which take crucial part in improving the energy efficiency and safety performance of vehicles. As a component involving multiple physical fields, the HSV is affected by the interaction of the fields-fluid, electromagnetic, and mechanical. Since the opening of the HSV is small and the flow speed is high, cavitation and vortex are inevitably brought out so that increase the valve’s noise and instability. However, it is costly and complex to observe the flow status by visual fluid experiments. Hence, in this article a visual multi-physics system simulation model of the HSV is explored, in which the flow field model of the HSV built by computational fluid dynamic (CFD) is co-simulated with the model of hydraulic actuator established by AMESim.

According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction.

Fuel quality has a significant influence on the combustion engine operation. In recent years the increasing concerns about environmental protection, energy saving, energy security and the requirements of protecting fuel injection and aftertreatment systems have been major driving forces for the Chinese fuel specification evolution. The major property changes in the evolution of Chinese national gasoline and diesel standards are introduced and the reasons behind these changes are analyzed in this paper. The gasoline fuel development from State I to State VI-B involved a decrease of sulfur, manganese, olefins, aromatics and benzene content. The diesel fuel quality improvement from State I to State VI included achieving low sulfur fuels and a cetane number (CN) increase. Provincial fuel standards, stricter than corresponding national standards, were implemented in economically developed areas in the past.