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The energy management strategies (EMS) for hybrid electric vehicles (HEV) have a great impact on the fuel economy (FE). The Pontryagin's minimum principle (PMP) has been proved to be a viable control strategy for HEV. The optimal costate of the PMP control can be determined by the given information of the driving conditions. Since the full knowledge of future driving conditions is not available, this paper proposed a dynamic optimization method for PMP costate without the prediction of the driving cycle. It is known that the lower fuel consumption the method yields, the more efficiently the engine works. The selection of costate is designed to make the engine work in the high efficiency range. Compared with the rule-based control, the proposed method by the principle of Hamiltonian, can make engine working points have more opportunities locating in the middle of high efficiency range, instead of on the boundary of high efficiency range.

Accuracy of heat flux models is critical to clutch design in case of excessive temperatures due to large amounts of friction heat generated in the narrow space. Pressure distribution on the clutch friction interface is an important factor affecting heat flux distribution, thus affecting temperature distribution. In this paper, an experiment is conducted to obtain the pressure distribution for one typical dry clutch equipped with a set of diaphragm spring. Considering that the frictional interface is in contact, this study makes use of pressure sensitive film and acquires data based on image processing techniques. Then a polynomial mathematical model with dimensionless parameters is developed to fit the pressure distribution on the friction disc. After that, the proposed pressure model is applied to a thermal model based on finite element method. In addition, two conventional thermal models (i.e., uniform heat flux model and uniform pressure model), are implemented for comparison.

The vehicle chassis integrated control system can improve the stability of vehicles under extreme conditions using tire force allocation algorithm, in which, the nonlinearity and uncertainty of tire-road contact condition need to be taken into consideration. Thus, An MPC (Model Predictive Control) controller is designed to obtain the additional steering angle and the additional yaw moment. By using a robust optimal allocation algorithm, the additional yaw moment is allocated to the slip ratios of four wheels. An SMC (Sliding-Mode Control) controller is designed to maintain the desired slip ratio of each wheel. Finally, the control performance is verified in MATLAB-CarSim co-simulation environment with open-loop manoeuvers.

The internal combustion engine (ICE) is a typical complex multidisciplinary system which requires the support of precision design and manufacturing. To achieve a better performance of ICEs, tolerance assignment, or tolerance design, plays an important role. A novel multi-disciplinary tolerance design optimization problem considering two important disciplines of ICEs, the compression ratio and friction loss, is proposed and solved in this work, which provides a systematic procedure for the optimal determination of tolerances and overcomes the disadvantages of the traditional experience-based tolerance design. A bi-disciplinary analysis model is developed in this work to assist the problem solving, within which a model between the friction loss and tolerance is built based on the Gaussian Process using the corresponding simulation and experimental data.

Because of rising complexity during the automotive product development process, the number of disciplines to be concerned has been significantly increased. Multidisciplinary design optimization (MDO) methodology, which provides an opportunity to integrate each discipline and conduct compromise searching process, is investigated and introduced to achieve the best compromise solution for the automotive industry. To make a better application of MDO, the suitable coupling strategy of different disciplines and efficient optimization techniques for automotive design are studied in this article. Firstly, considering the characteristics of automotive load cases which include many shared variables but rare coupling variables, a multilevel MDO coupling strategy based on enhanced collaborative optimization (ECO) is studied to improve the computational efficiency of MDO problems.

Sampling-based methods are general but time consuming for solving a Reliability-Based Design Optimization (RBDO) problem. In order to alleviate the computation burden, score function together with the Monte Carlo method was used to compute the stochastic sensitivities of reliability functions. In literature, re-weighting schemes were shown to converge faster than the regular Monte Carlo method. In this paper, a reweighting scheme together with score function is employed to perform sampling-based stochastic sensitivity analysis to improve the computational efficiency and accuracy. An analytical example is used to show the advantages of the proposed method. Comparisons to the conventional methods are made and discussed. Two RBDO problems are solved to demonstrate the use of the proposed method.

Using highly efficient powertrain is one of the most important and effective approaches to increase the driving distance of electric vehicles (EVs). In this paper, a novel two-speed dual-clutch transmission (DCT) is proposed. The transmission is comprised of two traditional friction clutches and two-stage planetary gear sets. One clutch connects the input sun gear and the other connects the input carrier. The Simulink models including an electric motor and two-speed DCT are established. Gearshift schedule based on fuzzy logic which reflects the driver’s intensions is adopted to improve the dynamic and economic performance of the novel transmission. The simulation model is built using MATLAB/Simulink® to validate the effectiveness of the proposed gearshift schedule compared with the conventional two-parameter gearshift schedule. Simulation results show that both the dynamic and economic performance of the novel DCT for EVs are improved with the proposed fuzzy logic gearshift schedule.

Stringent particulate emission regulations are applied to spark-ignition direct-injection (SIDI) engines, calling for a significant in-cylinder reduction of soot particles. To enhance fundamental knowledge of the soot formation and oxidation process inside the cylinder of the engine, a new in-flame particle sampling system has been developed and implemented in a working optical SIDI engine with a side-mounted, wall-guided injection system. Using the sampling probes installed on the piston top, the soot particles are directly sampled from the petrol flame for detailed analysis of particle size distribution, structure, and shape. At the probe tip, a transmission electron microscope (TEM) grid is stored for the soot collection via thermophoresis, which is imaged and post-processed for statistical analysis. Simultaneously, the flame development was recorded using two high-speed cameras to evidence the direct exposure of the sampling grids to the soot-laden diffusion flames and pool fires.

With the rapid development of series-parallel hybrid electric vehicles (SPHEVs), mode transition from pure electrical drive to hybrid drive has attracted considerable attention. The presence of time delay due to response capacity of actuators and signal transmission of communication may cause decrease of speed tracking accuracy, even instable dynamics. Consequently, drivability of the SPHEV is unacceptable, and durability of the components is reduced. So far, plenty of control strategies have been proposed for mode transition, however, no previous research has been reported to deal with the time delay during mode transition. In this paper, a dynamic model with time delay of hybrid electric system is established. Next, a mode transition time-delay controller is proposed based on a two degree of freedom internal model controller (2-DOF-IMC).

Vehicle lateral states such as lateral distance at a preview point and heading angle are indispensable for lane keeping control systems, and such states are normally estimated by fusing signals from an onboard vision system and inertial sensors. However, the sampling rates and measurement delays are different between the two kinds of sensing devices. Most of the conventional methods simply neglect measurement delay and reduce sampling rate of the estimator to adapt to the slow sensors/devices. However, the estimation accuracy is deteriorated, especially considering the delay of visual signals may not be constant. In case of electric vehicles, the actuators for steering and traction are motors that have high control frequency. Therefore, the frequency of vehicle state feedback may not match the control frequency if the estimator is infrequently updated. In this paper, a multi-rate estimation algorithm based on Kalman filter is proposed to provide lateral states with high frequency.

Three-phase sequential turbocharging system with two unequal-size turbochargers is developed to improve fuel economy performance and reduce emission of the automotive diesel engine, which satisfies wide range of intake flow demand. However, it results in complicated transient control strategies under frequently changing operating conditions. The present work aims to optimize the control scheme of boost system and fuel injection and evaluate their contributions to the improvement of transient performance. A mean value model for diesel engine was built up in SIMULINK environment and verified by experiment for transient study. Then a mathematical model of optimization issue was established. Strategies of control valves and fuel injection for typical acceleration and loading processes are obtained by coupled calculating of the simulation model and optimization algorithm.

Lift-off length is defined as the distance from injector hole to the location where flame stabilized on a high injection pressure direct injection (DI) diesel spray. In this paper we used the high-speed (40 kHz) Schlieren and time-averaged OH chemiluminescence imaging technique to simultaneously measure the flame lift-off locations on a DI diesel spray in an optically accessible and constant-volume combustion vessel. The time-resolved development of the diesel spray acquired from the high-speed Schlieren imaging system enabled us to observe the instantaneous spray structure details of the spray flames. The OH chemiluminescence image obtained from a gated, intensified CCD video camera with different delay and width settings was used to determine the quiescent lift-off length. Experiments were conducted under various ambient temperatures, ambient gas densities, injection pressures and oxygen concentrations.

Conventionally, the engines are calibrated under the assumption that engines will be made exactly to the prints, and all the engines from the same batch will be identical. However, engine-to-engine variations do exist which will affect the engine performances, and part-to-part variations, i.e., the tolerance, is an important factor leading to engine-to-engine variations. There are researches conducted on the influence of dimensional tolerances on engine performance, however, the impact of straightness, which is an important geometric tolerance, on lubrication is an unsolved issue. This study presents a systematic method to model the straightness and to analyze its effects on the friction loss. The bearing model is built based on elastohydrodynamic (EHD) theory. Meanwhile a novel modeling method to represent any form of straightness in three-dimensional space is proposed.

Electric vehicles (EVs) have become a hot research topic due to the petroleum crisis and air pollution issues, and Hybrid EVs (HEVs) equipped with engines and motors are popular nowadays due to their advantage over Pure EVs. The energy distribution between the engine and the motor is the major task of the control strategy or energy management for HEVs. Rule-based and optimization-based approaches are developed in this area, but not much work has been done for large-size super-capacitor (SC) equipped HEVs, like Hybrid buses. In this paper, a new optimization-based control strategy for a hybrid bus equipped with SCs as the energy regeneration system is presented. Considering the driving patterns of a bus that is of frequent accelerations and decelerations, it is proposed to characterize each time instant by its speed and acceleration, and the energy distribution is optimized based on these two state variables.

Rotating flow inside an internal combustion engine cylinder is deliberately engineered for improved fuel-air mixing and combustion. The details of the rotating flow structure vary temporally over an engine cycle as well as cyclically at the same engine phase. Algorithms in the literature to identify these structural details of the rotating flow invariably focus on locating its center and, on occasion, measuring its rotational strength and spatial extent. In this paper, these flow structure parameters are evaluated by means of complex moments, which have been adapted from image (scalar field) recognition applications to two-dimensional flow pattern (vector field) analysis. Several additional detailed characteristics of the rotating flow pattern - the type and extent of its deviation from the ideal circular pattern, its rotational and reflectional symmetry (if exists), and thus its orientation - are also shown to be related to the first few low-order complex moments of the flow pattern.

For the purposes of on-line control, e.g., in an automatic driving system, or of closed-loop directional control simulation, an optimal preview artificial neural network (ANN) driver model based on error elimination algorithm(EEA) is built. Then the optimal preview times are discussed in high frequency range in this system. The simulation results of optimal preview ANN driver model and Error Elimination Algorithm driver model are compared under the condition of different vehicle speeds and paths, which shows that the proposed approach is efficient and reliable enough, particularly for driver-vehicle closed-loop system.

The present work aims at the reduction of transmission delay at the level of AFDX ES (Avionics Full Duplex Switched Ethernet End-Systems). To this end, two approaches, namely Network Calculus and response time analysis (RTA), are employed in the computation of upper bound delay. To evaluate the delay regarding different scheduling policies, the arrival curve of the flow on output of ES is established for given traffic shaping algorithm and service mode. Computational analysis shows that Bandwidth Allocation Gap (BAG) based scheduling is the optimal policy at the level of AFDX ES, which leads to the tightest output arrival curve among all possible scheduling policies. BAG-based scheduling consists in assigning higher priority to virtual links with smaller BAG thus corresponding to the well known Rate-Monotonic Algorithm. Furthermore, schedulability criterion are established based on RTA.

Manufacturing of the internal combustion engines (ICEs) has very critical requirements on the precision and tolerance of engine parts in order to guarantee the engine performance. As a typical complex nonlinear system, small changes in dimensions of ICE components may have great impact on the performance and cost of the manufacturing of ICES. In this regard, it is still necessary to discuss the optimization of the tolerance and manufacturing precision of the critical components of ICEs even though the tolerance optimization in general has been reported in the literature. A systematic process for determining optimal tolerances will overcome the disadvantages of the traditional experience-based tolerance design and therefore improve the system performance.

A new design method is proposed for a semi-tracked air-cushion vehicle for soft terrain by using a flexible bind, which offers more flexibility in designing. This paper describes the design principle focusing on optimizing the total power consumption of the vehicle. The relationships of load distribution and power consumption are analyzed. The prototype experiments showed that the proposed design can meet the demand of tractive and transport efficiency with its optimal state of using minimum total power consumption and meanwhile maintaining ride comfort.

Owing to the stochastic nature of engine knock, determination of the knock limited spark angle (KLSA) is difficult in engine cycle simulation. Therefore, the state-of-the-art knock modeling is mostly limited to either merely predicting knock onset (i.e. auto-ignition of end gas) or combining a simple unburned mass fraction (UMF) model representative of knock intensity (KI). In this study, a newly developed KLSA model, which takes both predictions of knock onset and intensity into account, is firstly introduced. Multiple variables including the excess air ratio, EGR ratio, cylinder pressure and the end gas temperature are included in the knock onset model. Based on the auto-ignition theory of hot spots in end gas, both the energy density and heat release rate in hot spots are taken into consideration in the KI model.