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The Opposed Piston Two-Stroke (OPTS) engine has many advantages on power density, fuel tolerance, fuel flexibility and package space. A type of self-balanced opposed-piston folded-crank train two-stroke engine for Unmanned Aerial Vehicle (UAV) was studied in this paper. AVL BOOST was used for the thermodynamic simulation. It was a quasi-steady, filling-and-emptying flow analysis -- no intake or exhaust dynamics were simulated. The results were validated against experimental data. The effects of high altitude environment on engine performance have been investigated. Moreover, the matching between the engine and turbocharger was designed and optimized for different altitude levels. The results indicated that, while the altitude is above 6000m, a multi-stage turbocharged engine system need to be considered and optimized for the UAV.

Considering the randomness and instability of the oil pressure in the lubrication system, a new approach for fault detection and diagnosis of diesel engine lubrication system based on support vector machine optimized by particle swarm optimization (PSO-SVM) model and centroid location algorithm has been proposed. Firstly, PSO algorithm is chosen to determine the optimum parameters of SVM, to avoid the blindness of choosing parameters. It can improve the prediction accuracy of the model. The results show that the classify accuracy of PSO-SVM is improved compared with SVM in which parameters are set according to experience. Then, the support vector machine classification interface is fitted to a curve, and the boundary conditions of fault diagnosis are obtained. Finally, diagnose algorithm is achieved through analyzing the centroid movement of features. According to Performance degradation data, degenerate trajectory model is established based on centroid location.

In this paper, a novel driver model is proposed to track vehicle speed in MIL (Model-in-the-Loop) test system, which has structural consistency with HIL (Hardware-in-the-Loop) test system. First, the MIL test system which contains models of driver, vehicle and test bench is established. Second, according to the connections of the established models in Matlab/Simulink environment, the vehicle speed is calculated in vehicle model. Emphatically, through the deviation between driving cycle speed and calculated vehicle speed, PI controller in driver model adjusts the vehicle speed to ideal point through sending the torque command to drive motor, the ILC (Iterative Learning Control) controller modifies and stores P value of PI controller. Then, in order to obtain the better modification of PI controller, iterative learning control algorithm is deeply researched in term of types and parameters.

Voice Recognition (VR) systems have become an integral part of the infotainment systems in the current automotive industry. However, its recognition rate is impacted by external factors such as vehicle cabin noise, road noise, and internal factors which are a function of the voice engine in the system itself. This paper analyzes the VR performance under the effect of two external factors, vehicle cabin noise and the speakers’ speech patterns based on gender. It also compares performance of mid-level sedans from different manufacturers.

A vehicle simulation model is developed, validated and integrated into a closed-loop virtual driving environment using a state-of-the-art hexapod driving simulator. Thirty variant states are implemented and evaluated subjectively on steering and handling performance quality and quantity. Standard open-loop objective testing manoeuvres are simulated and performance metrics are calculated, allowing for a systematic cross-correlation process. Graphical analysis of the correlation metrics proves that chassis changes may accurately be felt through the simulator interface. It is proposed how obtained correlation models may serve for driver-feel optimizing target setting in early vehicle development stages, frontloading a great deal of costly prototype testing. System requirements are established and benefits and limitations are portrayed.

A new approach to achieve better customer perception of overall vehicle quietness is the sound balance improvement of vehicle interior sound during driving. Interior sound is classified into 3 primary sound source shares such as engine sound relative to revolution speed, tire road noise and wind noise relative to vehicle speed. Each interior sound shares are classified using the synchronous time-domain averaging method. The sound related to revolution order of engine and auxiliaries is considered as engine sound share, tire road noise and wind noise shares are extracted by multiple coherent output power analysis. Sound balance analysis focuses on improving the relative difference in interior sound share level between the 3 primary sound sources. Virtual sound simulator which is able to represent various driving conditions and able to adjust imaginary sound share is built for several vehicles in same compact segment.

This work studied the control technique for the engine clutch engagement at launch for the TMED parallel HEV for the improved drivability and dynamic performance. Analysis are done on the speed synchronization of the clutch plates, the speed control using the starter motor (ISG), and the fluid pressure control for the clutch. Possible external factors such as changes in the friction coefficient of transmission fluid, temperature variation, auxiliary power and pressure losses are identified and their effects on the targeted dynamic performance are examined. The targeted system performance was achieved with a learning control technique using fluid pressure as the only control input. This involves the compensation for the effect of external factors on the fluid pressure profile and this effect is memorized for the subsequent slip-launch application.

This paper proposes an effective nonlinear bicycle model including longitudinal, lateral, and yaw motions of a vehicle. This bicycle model uses a simplified piece-wise linear tire model and tire force tuning algorithm to produce closely matching vehicle trajectory compared to real vehicle for wide vehicle operation ranges. A simplified piece-wise tire model that well represents nonlinear tire forces was developed. The key parameters of this model can be chosen from measured tire forces. For the effects of dynamic load transfer due to sharp vehicle maneuvers, a tire force tuning algorithm that dynamically adjusts tire forces of the bicycle model based on measured vehicle lateral acceleration is proposed. Responses of the proposed bicycle model have been compared with commercial vehicle dynamics model (CarSim) through simulation in various vehicle maneuvers (ramp steer, sine-with-dwell).

Many high risks of failure in developing and applying new technologies exist in the recent automotive industry because of big volume of selling cars in a global market. Several recalls cost companies more than $ 100 million per problem. New technologies always have uncertainty in performing intended functions at various given conditions despite the fact that engineers do their best to develop technologies to meet all the requirements. Uncertainty of new technologies put companies into danger of failing in their business. Therefore, many companies tend to take interest in reducing risks from the uncertainty in technologies, but the increasing complexity of modern automotive technologies make it difficult to develop complete technologies. A new engineering methodology called SPEED Engineering was introduced to reduce the risks of new technology applications and to facilitate engineers to conceive innovative ideas dominating the market in the future.

Supervisory control strategy of a hybrid electric vehicle (HEV) provides target powers and operating points of an internal combustion engine and an electric motor. To promise efficient driving of the HEV, it is needed to find the proper values of control parameters which are used in the strategy. However, it is very difficult to find the optimal values of the parameters by doing experimental tests, since there are plural parameters which have dependent relationship between each other. Furthermore variation of the test results makes it difficult to extract the effect of a specific parameter change. In this study, a model based parameter optimization method is introduced. A vehicle simulation model having the most of dynamics related to fuel consumption was developed and validated with various experimental data from real vehicles. And then, the supervisory control logic including the control parameters was connected to the vehicle model.

The series-parallel hybrid electric vehicle(HEV), which employs a planetary gear set to combine one internal combustion engine(ICE) and two electric motors(EMs), can take advantages of both series and parallel hybrid system. The efficient powertrain operating point of the system can be obtained by the instantaneous optimization of equivalent fuel consumption. However, heavy computational requirements and variable constraints of the optimization process make it difficult to build real-time control strategy. To overcome the difficulty, this study suggests the control strategy which divides the optimization process into 2 stages. In the first stage, a target of charge/discharge power is determined based on equivalent fuel consumption, then in the second stage, an engine operating point is determined taking power transfer efficiency into account.

As CO2 emissions from vehicles is gaining a global attention the low fuel consuming power-train is in much greater demand than before. Some alternatives are suggested but the HSDI diesel engine would be the most realistic solution. Vehicle simulation shows that low fuel consuming car can be realized by applying 1∼1.2L HSDI diesel engine in vehicles weighing about 750kg. While the direct injection diesel engine has been researched for a long time enhancement of mixing between air and fuel in a limited space makes it challenging area to develop a small swept volume HSDI diesel engine. We are investigating small HSDI diesel engine combustion technologies as an effort to realize low fuel consuming vehicle. Our main objective in this study is to have a better understanding of the combustion related parameters from such a small size HSDI diesel engine in order to improve engine performance.

The electrical power system is the vital lifeline to most of the control systems on modern vehicles. The demands on the system are highly complex, and a detailed understanding of the system behavior is necessary both to the process of systems integration and to the economic design of a specific control system or actuator. The vehicle electric power system, which consists of two major components: a generator and a battery, has to provide numerous electrical and electronic systems with enough electrical energy. A detailed understanding of the characteristics of the electric power system, electrical load demands, and the driving environment such as road, season, and vehicle weight are required when the capacities of the generator and the battery are to be determined for a vehicle. An easy-to-use and inexpensive simulation program may be needed to avoid the over/under design problem of the electric power system. A vehicle electric power simulator is developed in this study.

In order to investigate the characteristics of top ring groove deposit formation in gasoline and diesel engine, engine test and simulation test were performed. From component analysis of used oils sampled from actual running engines, oxidation and nitration for gasoline engine and soot content for diesel engine were selected as main parameters for evaluating oil degradation. In gasoline engine, deposit formation increases linearly with oxidation and nitration, and especially, oil oxidation is a dominant factor on the deposit formation rather than nitration. And, deposit formation increases gradually in low temperature ranges below 260°C even if oils are highly oxidized, but it increases rapidly if piston top ring groove temperature is above 260°C. In diesel engine, deposit formation is highly related to soot content in lubricating oils.

This paper discusses the effects of lubricant viscosity on the friction characteristics of engine bearing and cam/tappet which are the typical moving parts of an engine and operate in different lubrication regimes. Based on the measured crankshaft temperatures, we calculated the friction coefficient of the engine bearing according to Sommerfeld number by a simple heat equilibrium equation. The oil film thicknesses between cam and tappet were measured in a motored cylinder head which had a direct acting type overhead camshaft. The boundary and viscous friction components were estimated separately according to a parameter defined as the ratio of the central oil film thickness to the composite surface roughness. These two friction components were added to calculate the friction coefficient. Finally, the motoring friction torque was measured and compared with the estimated friction coefficient.