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This paper describes a new method to increase the efficiency of the battery charging process, η, which is defined as the ratio of the energy accumulated in the battery over the actual energy supplied to it. Through several simulation results, it has been found that such efficiency is a function of the current profile applied to the battery during the charging process; hence, plots describing the energy loss in the battery, time taken to achieve a desired level of charge, and power needed as a function of the charging current, are shown. In order to find the optimal charging current profile, the mathematical model of the energy loss in the battery is developed and the problem of finding the optimal current profile is formulated as an Optimal Control problem. A model based on a Lithium-Ion Battery commercially available for PHEV is used as the plant to be controlled.

This paper presents the utilization of alternative correlation functions in the Kriging method for generating surrogate models (metamodels) for the performance of the bearings in an internal combustion engine. Originally, in the Kriging method an anisotropic exponential covariance function is developed by selecting optimal correlation parameters through optimization. In this paper an alternative nonparametric isotropic covariance approach is employed instead for generating the correlation functions. In this manner the covariance for spatial data is evaluated in a more straightforward manner. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points, which sample the design space.

The fuel spray behavior in the near nozzle region of a gasoline injector is challenging to predict due to existing pressure gradients and turbulences of the internal flow and in-nozzle cavitation. Therefore, statistical parameters for spray characterization through experiments must be considered. The characterization of spray velocity fields in the near-nozzle region is of particular importance as the velocity information is crucial in understanding the hydrodynamic processes which take place further downstream during fuel atomization and mixture formation. This knowledge is needed in order to optimize injector nozzles for future requirements. In this study, the results of three experimental approaches for determination of spray velocity in the near-nozzle region are presented. Two different injector nozzle types were measured through high-speed shadowgraph imaging, Laser Doppler Anemometry (LDA) and X-ray imaging.

In order to improve the electric vehicle endurance mileage and output characteristics of motor, the optimization of inverter loss and motor current ripple reduction are considered. Aiming at high inverter loss of traditional SVPWM and high current ripple of DPWM, an adjustable discontinuous pulse width modulation(ADPWM) is proposed, whose clamping angle α and clamping phase angle θ are variable. In order to accurately calculate the inverter loss, a switching instantaneous inverter loss model is proposed, and the ADPWM inverter loss and current total harmonic distortion(THD) was studied based on Simulink modeling and simulation. The simulation and experiment results show that the experiment can be accurately reflected by Simulink model; The inverter loss can be reduced by 15%-25% by introducing ADPWM while the current distortion rate is low. With the clamping angle α increasing, the inverter loss decreased significantly.

Accurate parameters of vehicle motion state are very important to the active safety of a vehicle. Currently the extended Kalman filter and unscented Kalman filter are widely used in estimation of the key state parameters, such as speed. In this situation, tire model must be used. The Magic Formula Tire Model is widely used in vehicle dynamics simulation because of its high versatility and accuracy. However, it requires a large number of parameters, which make the key state parameters of a real vehicle difficult to accurately obtain. Therefore, it is limited in real-time control of a vehicle. Firstly, the original Magic Formula Tire Model is simplified in this paper; then Jin Chi's Tire Model is introduced; thirdly, parameters of both the simplified Magic Formula and Jin Chi's Tire Model are identified using PSO (Particle Swarm Optimization) algorithm. Finally, Jin Chi's Tire Model is also used in parameters identification of experimental data.