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In China there is a strong trend in the application of vehicles equipped with automatic transmissions in considering the complexity of traffic and the convenience of automatic transmissions. As a type of automatic transmission, automated mechanical transmission (AMT) shows great potential to be developed as a main transmission because of its simple structures, easy upgrade from manual transmission (MT) and low price. Support Vector Machine (SVM) is a new statistic method which could make a good prediction with limited training instances. Compared with Artificial Neutral Network (ANN), SVM can provide better genetic ability. In order to verify the ability of the new method, the model trained by one set of AMT car data was applied on some other AMT vehicles, and the predicted results were compared with subjective rating results by expert drivers and analyzed to identify the potential of this new assessment system.

Gasoline Direct Injection (GDI) engine has a significant fuel economy improvement over the traditional port fuel injection engine. The tradeoff for this benefit is excessive exhaust emissions, especially NOx. Three-way-catalyst (TWC) is inefficient to treat NOx emission during lean operation. So Lean NOx Trap (LNT) is invented for NOx aftertreatment and it has both storage mode and purge mode. Research on modeling and control of LNT has been conducted, but it is still lack of the essential information on the temperature effect. This research focuses on the impact of trap temperature on LNT storage time, purge time and fuel economy. The mechanism of temperature effect on LNT is investigated at first. Then the temperature control strategy based on fuel economy improvement is proposed.

Many metrics have been used in an attempt to predict the effects of secondary tasks on driving behavior. Such metrics often give rise to seemingly paradoxical results, with one metric suggesting increased demand and another metric suggesting decreased demand for the same task. For example, for some tasks, drivers maintain their lane well yet detect events relatively poorly. For other tasks, drivers maintain their lane relatively poorly yet detect events relatively well. These seeming paradoxes are not time-accuracy trade-offs or experimental artifacts, because for other tasks, drivers do both well. The paradoxes are resolved if driver demand is modeled in two orthogonal dimensions rather than a single “driver workload” dimension. Principal components analysis (PCA) was applied to the published data from four simulator, track, and open road studies of visual-manual secondary task effects on driving.

Gasoline direct injection (GDI) engine technology is now widely used due to its high fuel efficiency and low CO2 emissions. However, particulate emissions pose one challenge to GDI technology, particularly in the presence of fuel injector deposits. In this paper, a 4-cylinder turbocharged GDI engine in the Chinese market was selected and operated at 2000rpm and 3bar BMEP condition for 55 hours to accumulate injector deposits. The engine spark timing, cylinder pressure, combustion duration, brake specific fuel consumption (BSFC), gaseous pollutants which include total hydro carbon (THC), NOx (NO and NO2) and carbon dioxide (CO), and particulate emissions were measured before and after the injector fouling test at eight different operating conditions. Test results indicated that mild injector fouling can result in an effect on engine combustion and emissions despite a small change in injector flow rate and pulse width.

Steer-by-wire System is a new conception for steering system, which eliminates those mechanical linkages between hand steering wheel and front wheels, and communicates among the driver and wheels by signals and controllers. All these facilities improve the safety and conformability of the vehicle system and get rid of the mechanical constricts. This paper proposed three vehicle stability control strategies, including front wheel control, yaw rate feedback control and yaw rate& acceleration feedback control. We compared these three control methods by simulation and simulator tests. We also studied the integrated control algorithm of Steer-by-Wire System and 4WS, and compared with 2WS for SBW and the classical 4WS.

During the engaging process of sleeve and teeth ring in mechanical transmissions, their rotational speed and position differences cause multiple engaging ways and trajectories, and casual impacts between them will delay the engaging process and cause a long power off time for a gear shift. In order to reveal the engaging mechanism of the sleeve and the teeth ring, it is essential to build a high-fidelity model to cover all of their engaging ways and capture their speed changes for an impact. In this work, our contribution is that their impact process is modeled as a precise, continuous and nonlinear damping model, and then a hybrid automaton model is built to connect the system dynamics in different mechanical coupling relationships.

Simulation has been considered as one of the key enablers on the development and testing for autonomous driving systems as in-vehicle and field testing can be very time-consuming, costly and often impossible due to safety concerns. Accurately modeling traffic, therefore, is critically important for autonomous driving simulation on threat assessment, trajectory planning, etc. Traditionally when modeling traffic, the motion of traffic vehicles is often considered to be deterministic and modeled based on its governing physics. However, the sensed or perceived motion of traffic vehicles can be full of errors or inaccuracy due to the inaccurate and/or incomplete sensing information. In addition, it is naturally true that any future trajectories are unknown. This paper proposes a novel modeling method on traffic considering its motion uncertainties, based on Gaussian process (GP).

Vehicle Longitudinal Control (VLC) algorithm is the basis function of automotive Cruise Control system. The main task of VLC is to achieve a longitudinal acceleration tracking controller, performance requirements of which include fast response and high tracking accuracy. At present, many control methods are used to implement vehicle longitudinal control. However, the existing methods are need to be improved because these methods need a high accurate vehicle dynamic model or a number of experiments to calibrate the parameters of controller, which are time consuming and costly. To overcome the difficulties of controller parameters calibration and accurate vehicle dynamic modeling, a vehicle longitudinal control algorithm based on iterative learning control (ILC) is proposed in this paper. The algorithm works based on the information of input and output of the system, so the method does not require a vehicle dynamics model.

In this paper, a computer model of a multi-purpose vehicle (MPV) is built to study vehicle ride comfort by multi-body system dynamic theory. Virtual test rigs are developed to perform natural body frequency tests and random road input tests on the complete vehicle multi-body dynamic model. By comparing simulation results with field test results, the accuracy of the model is validated and the feasibility of virtual test rigs is established.