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This paper introduces the characteristics of the 4 wheel independent driving/4 wheel independent steering (4WID/4WIS) electric vehicle (EV). Models of Subsystems and the vehicle are constructed based on Matlab/simulink. The vehicle model allows the inputs of different drive torques and steer angles of four wheels. The dynamic characteristics of drive motors and steer motors are considered, and also it can reflect the vehicle longitudinal dynamics change due to the increase of the mass and inertia of the four wheels. Besides, drive mode selection function that is unique to this type vehicle is involved. Simulations and analyses of crab, oblique driving and zero radius turning which are the special conditions of 4WID/4WIS EV are conducted. The results show that the model can reflect the dynamic response characteristics. The model can be used to the simulation analyses of handling, stability, energy saving and control strategies verification of 4WID/4WIS EVs.

Inerters have become a hot topic in recent years, especially in vehicle, train, and building suspension systems. The performance of a passive inerter and a semi-active inerter was analyzed and compared with each other and it showed that the semi-active inerter has much better performance than the passive inerter, especially with the Hybrid control method. Eight different layouts of suspensions were analyzed with a quarter car model in this paper. The adaptation of dimensionless parameters was considered for a semi-active suspension and the semi-active inerters. The performance of the semi-active inerter suspensions with different layouts was compared with a semi-active suspension with a conventional parallel spring-damper arrangement. It shows a semi-active suspension, with more simple configuration and lower cost, has similar or better compromise between ride and handling than a semi-active inerter with the Hybrid control.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

The purpose of present numerical study was to extend the operating range of alcohol (methanol and ethanol) fueled Homogeneous Charge Compression Ignition (HCCI) engine under low load conditions. Ignition of pure methanol and ethanol under HCCI mode of operation requires high intake temperatures and misfires at low loads are common in HCCI engines. Three methods have been adapted to optimize the use of methanol and ethanol for HCCI operation without increasing the intake temperature. First, blending methanol and ethanol with ignition improver, namely di-methyl ether (DME) and di-ethyl ether (DEE), was used to increase the cetane number and ignitability of premixed charge. Second, based on the blended fuels, the spark assistance was used to reduce required intake temperature for auto-ignition. Third, DME and DEE were directly injected to methanol and ethanol operated HCCI engine, in the form of Reactivity Controlled Compression Ignition (RCCI) combustion.

The combustion of dual-fuel engines usually uses a pilot flame to burn out a background fuel inside a cylinder under high compression. The background fuel can be either a gaseous fuel or a volatile liquid fuel, commonly with low reactivity to prevent premature combustion and engine knocking; whereas the pilot flame is normally set off with the direct injection of a liquid fuel with adequate reactivity that is suitable for deterministic auto-ignition with a high compression ratio. In this work, directly injected butanol is used to generate the pilot flame, while intake port injected ethanol or butanol is employed as the background fuel. Compared with the conventional diesel-only combustion, dual-fuel operations not only broaden the fuel applicability, but also enhance the potential for clean combustion, in high efficiency engines. The amount of background fuel and the scheduling of pilot flame are investigated through extensive laboratory experiments.

In recent years, with the increase of traffic accidents and traffic jams, lane change, as one of the most important and commonly automatic driving operations for autonomous vehicles, is receiving attention in academia. It is considered to be one of the important solutions that play an important role in improving road traffic safety and efficiency. However, most existing lane-changing models are rule-based lane-changing models. These models only assume a one-direction impact of surrounding vehicles on the lane-changing vehicle. In fact, lane change is a process of mutual interaction between vehicles due to the complexity and uncertainty of the traffic environment. Moreover, the safety and efficiency of existing lane-changing decision algorithms need to be improved. In this paper, we proposed a multivehicle cooperative control approach with a distributed control structure to control the model.

Percussive riveting is a widely used way of fastening in the field of aircraft assembly, which used to be done manually. Nowadays, replacing the traditional percussive riveting with automated percussive riveting becomes a trend worldwide, which improves the quality of riveting significantly. For the automated riveting system used in aircraft assembly, reliability is of great importance, deserving to be deeply researched and fully enhanced. In this paper, a high reliable automated percussive riveting system integrated into a dual robot drilling and riveting system is proposed. The riveting system consists of the hammer part and the bucking bar part. And both parts have been optimized to enhance the reliability. In the hammer side, proximity switches are fully used to detect the state of rivet insertion.

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.

The prediction of agents' future trajectory is a crucial task in supporting advanced driver-assistance systems (ADAS) and plays a vital role in ensuring safe decisions for autonomous driving (AD). Currently, prevailing trajectory prediction methods heavily rely on high-definition maps (HD maps) as a source of prior knowledge. While HD maps enhance the accuracy of trajectory prediction by providing information about the surrounding environment, their widespread use is limited due to their high cost and legal restrictions. Furthermore, due to object occlusion, limited field of view, and other factors, the historical trajectory of the target agent is often incomplete This limitation significantly reduces the accuracy of trajectory prediction. Therefore, this paper proposes ETSA-Pred, a mapless trajectory prediction model that incorporates enhanced temporal modeling and spatial self-attention.

An autonomous vehicle is a comprehensive intelligent system that includes environment sensing, vehicle localization, path planning and decision-making control, of which environment sensing technology is a prerequisite for realizing autonomous driving. In the early days, vehicles sensed the surrounding environment through sensors such as cameras, radar, and lidar. With the development of 5G technology and the Vehicle-to-everything (V2X), other information from the roadside can also be received by vehicles. Such as traffic jam ahead, construction road occupation, school area, current traffic density, crowd density, etc. Such information can help the autonomous driving system understand the current driving environment more clearly. Vehicles are no longer limited to areas that can be sensed by sensors. Vehicles with different autonomous driving levels have different adaptability to the environment.

Taking a good longitudinal braking performance on flat and level road of tractor and semi-trailer combination as a target, in order to achieve an ideal braking force distribution among axles, while the vehicle deceleration is just depend on the driver's intention, not affected by the variation of semi-trailer mass, the paper proposes a model based vehicle mass identification and braking force distribution strategy. The strategy identifies the driver's braking intention via braking pedal, estimates semi-trailer's mass during the building process of braking pressure in brake chamber, distributes braking force among axles by using the estimated mass. And a double closed-loop regulation of the vehicle deceleration and utilization adhesion coefficient of each axle is presented, in order to eliminate the bad effect of mass estimation error, and enhance the robustness of the whole algorithm. A simulation is conducted by utilizing MATLAB/Simulink and TruckSim.

Precise trajectory prediction of surrounding vehicles is critical for decision-making of autonomous vehicles, and learning-based approaches are well recognized for the robustness. However, state-of-the-art learning-based methods ignore (1) the feasibility of the vehicle’s multimodal state information for prediction and (2) the mutually exclusive relationship between the global traffic scene receptive fields and the local position resolution when modeling vehicles’ interactions, which may influence prediction accuracy. Therefore, we propose a “vehicle descriptor”-based long short-term memory (LSTM) model with the dilated convolutional social pooling (VD+DCS-LSTM) to cope with the above issues.

Dry dual clutch transmission (DCT) has played an important role in the high performance applications as well as low-cost market sectors in Asia, with a potential as the future mainstream transmission technology due to its high mechanical efficiency and driving comfort. Control system simplification and cost reduction has been critical in making dry DCT more competitive against other transmission technologies. Specifically, DCT clutch actuation system is a key component with a great potential for cost-saving as well as performance improvement. In this paper, a new motor driven clutch actuator with a force-aid lever has been proposed. A spring is added to assist clutch apply that can effectively reduce the motor size and energy consumption. The goal of this paper is to investigate the feasibility of this new clutch actuator, and the force-aid lever actuator's principle, physical structure design, and validation results are discussed in details.

Accurate and fast positioning of large aircraft component is of great importance for Automated Alignment System. The Ball joint is a widely-used mechanical device connecting the aircraft component and positioners. However, there are some shortcomings for the device in man-machine engineering, such as the entry state of the ball-head still needs to be confirmed by the workers and then switched to the locking state manually. To solve above problems, a new positioning mechanism is present in this paper, which consists of a ball-head and a ball-socket. The new device is equipped with a monocular vision system, in which a calibrated industrial camera is used to collect the images of the ball-head. And then, the 3-D coordinate of the ball-head center is calculated by a designed algorithm, guiding the positioner to capture the ball-head. Once the ball-head gets into the ball-socket, the pneumatic system will drive the pistons to move to the specified location.

Rotating clutches play an important role in automatic transmissions (AT), dual-clutch transmissions (DCT) and hybrid transmissions. It is very important to continually improve the transmission systems in the areas such as simplifying actuator designs, reducing cost and increasing controllability. A new concept of electrical motor driven actuation using a wedge mechanism, a wedge clutch, demonstrates potential benefits. This wedge clutch has the characteristics of good mechanical advantage, self-reinforcement, and faster and more precise controllability using electrical motor. In this paper, a new rotating wedge clutch is proposed. It presents a challenge since the motor actuator has to be stationary while the clutch piston is rotating. A new mechanism to connect the motor to the wedge piston, including dual-plane bearings and two mechanical ramp linkages, is studied. The design and verification of the physical structure of the actuator are discussed in detail in the paper.

Knowledge of the tire slip ratio can greatly improve vehicle longitudinal stability and its dynamic performance. Most conventional slip ratio observers were mainly designed based on input of non-driven wheel speed and estimated vehicle speed. However, they are not applicable for electric vehicles (EVs) with four in-wheel motors. Also conventional methods on speed estimation via integration of accelerometer signals can often lead to large offset by long-time integral calculation. Further, model uncertainties, including steady state error and unmodeled dynamics, are considered as additive disturbances, and may affect the stability of the system with estimated state error. This paper proposes a novel slip ratio observer based on input-to-state stability (ISS) method for electric vehicles with four-wheel independent driving motors.

To achieve higher efficiencies and lower emissions, dual-fuel strategies have arisen as advanced engine technologies. In order to fully utilize engine fuels, understanding the combustion chemistry is urgently required. However, due to computation limitations, detailed kinetic models cannot be used in numerical engine simulations. As an alternative, approaches for developing reduced reaction mechanisms have been proposed. Nevertheless, existing simplified methods neglecting the real engine combustion processes, which is the ultimate goal of reduced mechanism. In this study, we propose a novel simplified approach based on fuel reactivity. The high-reactivity fuel undergoes pyrolysis first, followed by the pyrolysis and oxidation of the low-reactivity fuel. Therefore, the simplified mechanism consists of highly lumped reactions of high-reactivity fuel, radical reactions of low-reactivity fuel and C0-C2 core mechanisms.

Effectively detecting road boundaries in real time is critical to the applications of autonomous vehicles, such as vehicle localization, path planning and environmental understanding. To precisely extract the road boundaries from the 3D-LiDAR data, a dedicated algorithm consisting of four steps is proposed in this paper. The steps are as follows: Firstly, the 3D-LiDAR data is pre-processed by employing the RANSAC method, the ground points are quickly separated from the original 3D-LiDAR point cloud to reduce the disturbance from the obstacles on the road, this greatly decreases the size of the point cloud to be processed. Secondly, based on the principle of 3D-LiDAR scanning, the ground points are divided into scan layers. And the road boundary points of each scan layer are detected by using three spatial features based on sliding window.

Sound quality of vehicle interior noise affects passenger comfort. In order to improve the sound quality of a micro commercial vehicle, the vehicle interior noise under different conditions such as idle, constant speeds and accelerating is recorded by using artificial head with dual microphones. The sound quality of recorded noise is evaluated in both objective and subjective ways. Physical parameters of interior noise are calculated objectively, and annoyance score is analyzed subjectively using paired-comparison method. According to the regression analyzing of the annoyance score and the physical parameters, an objective evaluation parameter of the sound quality is employed. To analyze the vehicle body panel contribution to interior noise sound quality, the location and spectrum characteristics of major panel emission noise sources are identified based on partial singular valued decomposition (PSVD) method.

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.