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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.

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.

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.

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.

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.

In the internal combustion engine, about 25%-40% of the energy released by burned fuel is taken away by the exhaust gas. The part of the usable energy in the exhaust can be used in the turbocharged engine. So, at present, turbocharged diesel engine hasn't made full use of exhaust gas energy. The authors propose a model of the 4-stroke turbocharged diesel engine of split exhausting system. Adding a rapidly on-and-off exhaust control valve between exhaust passage and manifold in the 4-stroke turbocharged diesel engine can improve the utilization rate of the usable energy in the exhaust. By utilizing the mean effective pressure (MEP), this paper is to calculate the maximum usable energy, the energy provided by exhaust and the energy required by intake. The results gets that the new type of exhausting system can help engine to increase usage rate of the exhaust gas energy to around 20% at the rated condition compared to the existing vehicle diesel engines with VNT.

With the development of economic globalization, the speed of development of container terminals is also very rapid. Under the pressure brought by the surge in throughput, the unmanned and intelligent terminals will become the future development direction of terminals. As the cornerstone of the unmanned terminal, the positioning technology provides the most basic position information for system scheduling, path planning, real-time correction, and loading and unloading. Therefore, this paper is aimed to design a low-cost, high-precision, and easy-to-maintain unmanned dock positioning system in order to better solve the problem of unmanned dock positioning. The main research content of this paper is to design a positioning algorithm for unmanned terminal Automated Guided Vehicle (AGV) based on single-line lidar, including point cloud data acquisition, background filtering, point cloud clustering, vehicle position extraction, and result optimization.