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Exhaust gas recirculation technology is one of the main methods to reduce engine emissions. The pressure of the intake pipe of turbocharged direct-injection diesel engine is high, and it is difficult to realize EGR technology. The application of Venturi tube can easily solve this problem. In this paper, the working principle of guide-injection Venturi tube is introduced, the EGR system and structure of a turbocharged diesel engine using the guide-injection Venturi tube are studied. According to the working principle of EGR system of turbocharged diesel engine, the model of guide-injection Venturi tube is established, the calculation grid is divided, and it is carried out by using Computational Fluid Dynamics method that the three-dimensional numerical simulation of the internal flow of Venturi tube under different EGR rates injection.

The design of engine intake system affects the intake uniformity of each cylinder of the engine, which in turn has an important impact on the engine performance, the uniform distribution of EGR exhaust gas and the combustion process of each cylinder. In this paper, the constant-pressure supercharged diesel engine intake pipe is used as the research model to study the intake air flow unevenness of the intake pipe of the supercharged diesel engine. The pressure boundary condition at the outlet of each intake manifold is set as the dynamic pressure change condition. The three-dimensional numerical simulation of the transient flow process in the intake manifold of diesel engine is simulated and analyzed by using numerical method, and the change of the Intake air flow field in the intake manifold under different working conditions during the intake overlapping period is discussed.

This paper proposed a collision avoidance strategy that take over the control of ego vehicle when faced with urgent collision risk. To improve the applicability of collision avoidance strategy in complex scenarios, the theory of ICS (Inevitable Collision State) is introduced to evaluate the collision risk and compute the trigger flag of the system, and vehicle dynamic is taken into account when modeling ego vehicle to predict ego vehicle’s following moving. Vehicle specific characteristics including reaction time of the braking system and the braking force increasing process are taken into account. In order to reduce injury caused by collision accidents and minimize disruption to drivers, slight steering is added on top of emergency braking. The direction of the steering angle is determined according to IM (Imitating Maneuvers) The flow chart of the strategy is presented in the paper.

Low-pressure cooled EGR (LP-cEGR) systems can provide significant improvements in spark-ignition engine efficiency and knock resistance. However, open-loop control of these systems is challenging due to low pressure differentials and the presence of pulsating flow at the EGR valve. This research describes a control structure for Low-pressure cooled EGR systems using closed loop feedback control along with internal model control. A Smith Predictor based PID controller is utilized in combination with an intake oxygen sensor for feedback control of EGR fraction. Gas transport delays are considered as dead-time delays and a Smith Predictor is one of the conventional methods to address stability concerns of such systems. However, this approach requires a plant model of the air-path from the EGR valve to the sensor.

The ever-growing concern to reduce the impact of transportation systems on environment has pushed automotive industry towards fuel-efficient and sustainable solutions. While several approaches have been used to improve fuel efficiency, the light-weighting of automobile components has proven broadly effective. A substantial effort is devoted to lightweighting body-in-white which contributes ~35% of total weight of vehicle. Closure systems, however, have been often overlooked. Closure systems are extremely important as they account for ~ 50% of structural mass and have a very diverse range of requirements, including crash safety, durability, strength, fit, finish, NVH, and weather sealing. To this end, a carbon fiber-reinforced thermoplastic composite door is being designed for an OEM’s mid-size SUV, that enables 42.5% weight reduction. In this work, several novel composite door assembly designs were developed by using an integrated design, analysis and optimization approach.

Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

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.

Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

The conventional radar modeling methods for automotive applications were either function-based or physics-based. The former approach was mainly abstracted as a solution of the intersection between geometric representations of radar beam and targets, while the latter one took radar detection mechanism into consideration by means of “ray tracing”. Although they each has its unique advantages, they were often unrealistic or time-consuming to meet actual simulation requirements. This paper presents a combined geometric and physical modeling method on millimeter-wave radar systems for Frequency Modulated Continuous Wave (FMCW) modulation format under a 3D simulation environment. With the geometric approach, a link between the virtual radar and 3D environment is established. With the physical approach, on the other hand, the ideal target detection and measurement are contaminated with noise and clutters aimed to produce the signals as close to the real ones as possible.

Tracked vehicles are widely used for agriculture, construction and many other areas. Due to high emissions, hybrid electric driveline has been applied to tracked vehicles. The hybrid powertrain design for the tracked vehicle has been researched for years. Different from wheeled vehicles, the tracked vehicle not only requires high mobility while straight driving, but also pursues strong steering performance. The paper takes the hybrid track-type dozers (TTDs) as an example and proposes an optimal design of a novel power-split powertrain for TTDs. The commercial hybrid TTD usually adopts the series hybrid powertrain, and sometimes with an extra steering mechanism, which has led to low efficiency and made the structure more complicated. The proposed three-planetary-gear power-split hybrid powertrain can overcome the problems above by utilizing the characteristics of planetary gear sets.

Lane detection is one of the most important part in ADAS because various modules (i.e., LKAS, LDWS, etc.) need robust and precise lane position for ego vehicle and traffic participants localization to plan an optimal routine or make proper driving decisions. While most of the lane detection approaches heavily depend on tedious pre-processing and great amount of assumptions to get reasonable result, the robustness and efficiency are deteriorated. To address this problem, a novel framework is proposed in this paper to realize robust and real-time lane detection. This framework consists of two branches, where canny edge detection and Progressive Probabilistic Hough Transform (PPHT) are introduced in the first branch for efficient detection.

Multi-material design is one of the trending methods for automakers to achieve lightweighting cost-efficiently and meet stringent regulations and fuel efficiency concerns. Motivated by this trend, the hybrid single-shot (HSS) process has been recently introduced to manufacture thermoset-thermoplastic composites in one single integrated operation. Although this integration is beneficial in terms of reducing the cycle time, production cost, and manufacturing limitations associated with such hybrid structures, it increases the process complexity due to the simultaneous filling, forming, curing, and bonding actions occurring during the process. To overcome this complexity and have a better understanding on the interaction of these physical events, a quick yet accurate simulation of the HSS process based on an experimentally calibrated numerical approach is presented here to elucidate the effect of different process settings on the final geometry of the hybrid part.

With the increasing number of urban cars, parking has become the primary problem that people face in daily life. Therefore, many scholars have studied the automatic parking system. In the existing research, most of the path planning methods use the combined path of arc and straight line. In this method, the path curvature is not continuous, which indirectly leads to the low accuracy of path tracking. The parking path designed using the fifth-order polynomial is continuous, but its curvature is too large to meet the steering constraints in some cases. In this paper, a continuous-curvature parking path is proposed. The parking path tracker based on Model Predictive Control (MPC) algorithm is designed under the constraints of the control accuracy and vehicle steering. Firstly, in order to make the curvature of the parking path continuous, this paper superimposes the fifth-order polynomial with the sigmoid function, and the curve obtained has the continuous and relatively small curvature.

As engines are equipped with an increased number of control actuators to meet fuel economy targets they become more difficult to control and calibrate. The large number of control actuators encourages the investigation of physics-based control strategies to reduce calibration time and complexity. Of particular interest is spark timing control and calibration since it has a significant influence on engine efficiency, emissions, vibration and durability. Spark timing determination to achieve a desired combustion phasing is currently an empirical process that occurs during the calibration phase of engine development. This process utilizes a large number of stored surfaces and corrections to account for the wide range of operating environments and conditions that a given engine will experience. An obstacle to realizing feedforward physics-based combustion phasing control is the requirement for an accurate and fast combustion model.

This paper seeks to identify key input parameters needed to achieve a production-viable control strategy for spark-assisted compression ignition (SACI) engines. SACI is a combustion strategy that uses a spark plug to initiate a deflagration flame that generates sufficient ignition energy to trigger autoignition in the remaining charge. The flame propagation phase limits the rate of cylinder pressure rise, while autoignition rapidly completes combustion. High dilution within the autoignited charge is generally required to maintain reaction rates feasible for production. However, this high dilution may not be reliably ignited by the spark plug. These competing constraints demand novel mixture preparation strategies for SACI to be feasible in production. SACI with charge stratification has demonstrated sufficiently stable flame propagation to reliably trigger autoignition across much of the engine operating map.

Accurate in-cylinder air charge estimation is important for engine torque determination, controlling air-to-fuel ratio, and ensuring high after-treatment efficiency. Spark ignition (SI) engine technologies like variable valve timing (VVT) and exhaust gas recirculation (EGR) are applied to improve fuel economy and reduce pollutant emissions, but they increase the complexity of air charge estimation. Increased air-path complexity drives the need for cost effective solutions that produce high air mass prediction accuracy while minimizing sensor cost, computational effort, and calibration time. A large number of air charge estimation techniques have been developed using a range of sensors sets combined with empirical and/or physics-based models. This paper provides a technical review of research in this area, focused on SI engines.

Part V of this five-part paper investigates the flow field and the total pressure loss mechanisms for three valve lifts in the exhaust region of a V8 racecar engine using the robust, systematic computational methodology described in Part I. The replica of the engine geometry includes a cylinder, detailed combustion chamber, exhaust valve, valve seat, port, and “exhaust pipe”. A set of fully-converged and grid-independent solutions for the steady, time-averaged (or RANS), non-linear Navier-Stokes equations are obtained using dense and high quality grids, involving 2.1∼3.0 finite volumes, and unusually strict convergence criteria. Turbulence closure is attained via the realizable k-ε (RKE) model used in conjunction with the non-equilibrium wall function near-wall treatment. The validation presented in Part I showed that flow rate results from the “blind simulations” agree well with the experimental measurements.

An energy management strategy is needed to optimally allocate the driver's power demands to different power sources in the fuel cell hybrid vehicles. The driver's power demand is modelled as a Markov process in which the transition probabilities are estimated on the basis of the observed sample paths. The Markov Decision Process (MDP) theory is applied to design a stochastic energy management strategy for fuel cell hybrid vehicles. This obtained control strategy was then tested on a real time simulation platform of the fuel cell hybrid vehicles. In comparison to the other 3 strategies, the constant bus voltage strategy, the static optimization strategy and the dynamic programming strategy, simulations in the Beijing bus driving cycle demonstrate that the obtained stochastic energy management strategy can achieve better performance in fuel economy in the same demand of dynamic.

Fuel efficiency improvement in automobiles has been a topic of great interest over the past few years, especially with the introduction of the new CAFE 2025 standards. Although there are multiple ways of improving the fuel efficiency of an automobile, lightweighting is one of the most common approaches taken by many automotive manufacturers. Lightweighting is even more significant in electric vehicles as it directly affects the range of the vehicle. Amidst this context of lightweighting, the use of composite materials as alternatives to metals has been proven in the past to help achieve substantial weight reduction. The focus of using composites for weight reduction has however been typically limited to major structural components, such as BiW and closures, due to high material costs. Secondary structural components which contribute approximately 30% of the vehicle weight are usually neglected by these weight reduction studies.