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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.

Hat-sections, single and double, made of steel are frequently encountered in automotive body structural components. These components play a significant role in terms of impact energy absorption during vehicle crashes thereby protecting occupants of vehicles from severe injury. However, with the need for higher fuel economy and for compliance to stringent emission norms, auto manufacturers are looking for means to continually reduce vehicle body weight either by employing lighter materials like aluminum and fiber-reinforced plastics, or by using higher strength steel with reduced gages, or by combinations of these approaches. Unlike steel hat-sections which have been extensively reported in published literature, the axial crushing behavior of hat-sections made of fiber-reinforced composites may not have been adequately probed.

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks.

An experimental fuel surrogate validation approach is proposed for a compression ignition application, and applied to validate a Jet-A POSF 4658 fuel surrogate. The approach examines the agreement of both physical and chemical properties of surrogate and target fuels during validation within a real compression-ignition engine environment during four sequential but distinct combustion phases. In-cylinder Mie Scattering measurements are applied to evaporating sprays to compare the behavior of the surrogate, its target fuel, and for reference, n-heptane. Early mixture formation and low temperature reaction behavior were investigated using 2-D broadband chemiluminescence imaging, while high temperature ignition and combustion chemistry were studied using OH chemiluminescence imaging. The optical measurements were combined with cylinder pressure-based combustion analysis, including ignition delay and premixed burn duration, to validate the global behavior of the surrogate.

Driving style and skill classification are of great significance in human-oriented advanced driver-assistance system (ADAS) development. In this paper, we propose Fully-Connected Deep Neural Networks (FC-DNN) to classify drivers’ styles and skills with naturalistic driving data. Followed by the data collection and pre-processing, FC-DNN with a series of deep learning optimization algorithms are applied. In the experimental part, the proposed model is validated and compared with other commonly used supervised learning methods including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and multilayer perceptron (MLP). The results show that the proposed model has a higher Macro F1 score than other methods. In addition, we discussed the effect of different time window sizes on experimental results. The results show that the driving information of 1s can improve the final evaluation score of the model.

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.

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.

A new friction testing system has been designed and built to simulate the actual engine conditions in friction and wear test of piston-ring and cylinder liner assembly. Experimental data has been developed as Friction Coefficient / Crank Angle Degree diagrams including the effects of running speed (500 and 700 rpm) and ring normal load. Surface roughness profilocorder traces were obtained for tested samples. Mixed lubrication regime observed in the most part of the test range. New cylinder bore materials and lubricants can be screened easily and more reliable simulated engine friction data can be collected using this technique.

The RON(Research Octane Number) is the most important indicator of motor petrol, and the petrol refining process is one of the important links in petrol production. However, RON is often lost during petrol refining and RON Loss means the value of RON lost during petrol refining. The prediction of the RON loss of petrol during the refining process is helpful to the improvement of petrol refining process and the processing of petrol. The traditional RON prediction method relied on physical and chemical properties, and did not fully consider the high nonlinearity and strong coupling relationship of the petrol refining process. There is a lack of data-driven RON loss models. This paper studies the construction of the RON loss model in the petrol refining process.

The current extensive revisitation of the application of gasoline direct-injection to automotive, four-stroke, spark-ignition engines has been prompted by the availability of technological capabilities that did not exist in the late 1970s, and that can now be utilized in the engine development process. The availability of new engine hardware that permits an enhanced level of computer control and dynamic optimization has alleviated many of the system limitations that were encountered in the time period from 1976 to 1984, when the capabilities of direct-injection, stratified-charge, spark-ignition engines were thoroughly researched. This paper incorporates a critical review of the current worldwide research and development activities in the gasoline direct-injection field, and provides insight into new areas of technology that are being applied to the development of both production and prototype engines.

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.

Significant A/F ratio excursion may occur during some engine transient operations, especially for transient periods of throttle tip in or tip out. A/F ratio excursion results in excessive emissions, extra fuel consumption, driveability deterioration and three-way-catalyst (TWC) efficiency drop. Simple two-parameter (X, τ) wall wetting models have traditionally been used to describe this transient A/F ratio excursion phenomenon. The transient fuel control techniques are utilized for this model to be applicable across vehicles, engines, fuel types and ambient conditions, so as to compensate for the A/F ratio excursion with the extra compensation fuel. More complicated model structures must be further expanded and model dependence on various environment conditions must be established to achieve a precise model. In this paper, a simple linear approach is proposed for transient fuel control, using least squares estimation.

This paper presents a study of LPG lean burn in a motorcycle SI engine. The lean burn limits are compared by several ways. The relations of lean burn limit with the parameters, such as engine speed, compression ratio and advanced spark ignition etc. are tested. The experimental results show that larger throttle opening, lower engine speed, earlier spark ignition timing, larger electrode gap and higher compression ratio will extend the lean burn limit of LPG. The emission of a LPG engine, especially on NOx emission, can be significantly reduced by means of the lean burn technology.

The objective of this work is to develop a strategy to reduce the penalties in the diesel engine performance, fuel economy and HC and CO emissions, associated with the operation in the low temperature combustion regime. Experiments were conducted on a research high speed, single cylinder, 4-valve, small-bore direct injection diesel engine equipped with a common rail injection system under simulated turbocharged conditions, at IMEP = 3 bar and engine speed = 1500 rpm. EGR rates were varied over a wide range to cover engine operation from the conventional to the LTC regime, up to the misfiring point. The injection pressure was varied from 600 bar to 1200 bar. Injection timing was adjusted to cover three different LPPCs (Location of the Peak rate of heat release due to the Premixed Combustion fraction) at 10.5° aTDC, 5 aTDC and 2 aTDC. The swirl ratio was varied from 1.44 to 7.12. Four steps are taken to move from LTC to ALTC.

The objective of the research into modeling and simulation was to provide an improvement to the Wayne State EcoCAR 2 team’s math-based modeling and simulation tools for hybrid electric vehicle powertrain analysis, with a goal of improving the simulation results to be less than 10% error to experimental data. The team used the modeling and simulation tools for evaluating different outcomes based on hybrid powertrain architecture changes (hardware), and controls code development and testing (software). The first step was model validation to experimental data, as the plant models had not yet been validated. This paper includes the results of the team’s work in the U.S. Department of Energy’s EcoCAR 2 Advanced vehicle Technical Competition for university student teams to create and test a plug-in hybrid electric vehicle for reducing petroleum oil consumption, pollutant emissions, and Green House Gas (GHG) emissions.

Vehicle lane keeping system is becoming a new research focus of drive assistant system except adaptive cruise control system. As we all known, vehicle lateral dynamics show strong nonlinear and time-varying with the variety of longitudinal velocity, especially tire’s mechanics characteristic will change from linear characteristic under low speed to strong nonlinear under high speed. For this reason, the traditional PID controller and even self-tuning PID controller, which need to know a precise vehicle lateral dynamics model to adjust the control parameter, are too difficult to get enough accuracy and the ideal control quality. Based on neural network’s ability of self-learning, adaptive and approximate to any nonlinear function, an adaptive PID control algorithm with BP neural network self-tuning online was proposed for vehicle lane keeping.

Rigid polyurethane (PU) foam finds wide applications as a lightweight material in impact safety design such as improving occupant safety in vehicle crashes. The two principal reacting compounds for formulating such foam are variants of polyol and isocyanate. In the present study, an alternative mechanical engineering-based approach for determining, with confidence, the desirable ratio of reacting compounds for formulation of a rigid/crushable PU foam for mechanical applications is demonstrated. According to the present approach, PU foam samples are prepared by varying the mixing ratio over a wide range. The desirable mixing ratio is shown to be the one that optimizes key mechanical properties under compression such as total absorbed energy, specific absorbed energy and energy absorption efficiency.

Several mechanisms are discussed to understand the formation of both regulated and unregulated emissions in a high speed, direct injection, single cylinder diesel engine using low sulphur diesel fuel. Experiments were conducted over a wide range of injection pressures, EGR rates, injection timings and swirl ratios. The regulated emissions were measured by the standard emission equipment. Unregulated emissions such as aldehydes and ketones were measured by high pressure liquid chromatography and hydrocarbon speciation by gas chromatography. Particulate mass was measured with a Tapered Element Oscillating Microbalance (TEOM). Analysis was made of the sources of different emission species and their relationship with the combustion process under the different operating conditions. Special attention is given to the low temperature combustion (LTC) regime which is known to reduce both NOx and soot. However the HC, CO and unregulated emissions increased at a higher rate.

Simultaneous measurements of the radial and the tangential components of velocity are obtained in a high-speed, direct-injection diesel engine typical of automotive applications. Results are presented for engine operation with fuel injection, but without combustion, for three different swirl ratios and four injection pressures. With the mean and fluctuating velocities, the r-θ plane shear stress and the mean flow gradients are obtained. Longitudinal and transverse length scales are also estimated via Taylor's hypothesis. The flow is shown to be sufficiently homogeneous and stationary to obtain meaningful length scale estimates. Concurrently, the flow and injection processes are simulated with KIVA-3V employing a RNG k-ε turbulence model. The measured turbulent kinetic energy k, r-θ plane mean strain rates ( 〈Srθ〉, 〈Srr〉, and 〈Sθθ〉 ), deviatoric turbulent stresses , and the r-θ plane turbulence production terms are compared directly to the simulated results.

Acetone-Butanol-Ethanol (ABE), an intermediate product in the ABE fermentation process for producing bio-butanol, is considered a promising alternative fuel because it not only preserves the advantages of oxygenated fuel which typically emit less pollutants compared to conventional diesel, but also lowers the cost of fuel recovery for each individual component during the fermentation. With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. In this respect, it is desirable to estimate the performance of different ABE blends to determine the best blend and optimize the production process accordingly. ABE fuels with different component ratio, (A: B: E: 6:3:1; 3:6:1; 0:10:0, vol. %), were blended with diesel and tested in a constant volume chamber.