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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.

In partially automated and conditionally automated vehicles, a part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality. Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks.

An EV prototype, with all the wheels respectively driven by 4 inwheel motors, is developed, and undergoes a series of practical measurements and road tests. Based on the obtained vehicle parameters, a multi-body dynamics model is built by using SolidWorks and Adams/Car, and then validated by track test data. The virtual prototype is served as the control plant in simulation. An adaptive fractional order PID (A-FO-PID) controller is designed to enhance the handling and stability performance of the EV. Considering the model uncertainties, e.g. the variation in body mass distribution and the consequent change in yaw moment of inertial, a Parameter Self-Adjusting Differential Evolution (PSA-DE) algorithm is adopted for tuning the controller parameters, i.e. KP, KI, KD, λ and μ. As a modification of traditional DE algorithm, the so-called Variance of Population’s Fitness is utilized to evaluate the diversity of the population.

High fuel injection pressure has been regarded as a key controlling factor for internal combustion engines to achieve good combustion performance with reduced emissions and improved fuel efficiency. For common-rail injection system (CRS) used in advanced diesel engines, fuel injection pressure can often be raised to beyond 200 MPa. Although characteristics of diesel spray has been thoroughly studied, little work has been done at ultra-high injection pressures. In this work, the characteristics of CRS diesel spray under ultra-high injection pressure up to 250 MPa was investigated. The experiments were conducted in an optically accessible high-pressure and high-temperature constant volume chamber. The injection pressure varied from 50 MPa to up to 250 MPa. Both non-evaporating condition and evaporating condition were studied. A single-hole injector was specially designed for this investigation.

Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data.

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.

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.