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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.

This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine without performing time consuming analyses. The metamodels are developed based on results from actual simulation solvers computed at a limited number of sample points, which sample the design space. A finite difference bearing solver is employed in this paper for generating information necessary to construct the metamodels. An optimal symmetric Latin hypercube algorithm is utilized for identifying the sampling points based on the number and the range of the variables that are considered to vary in the design space. The development of the metamodels is validated by comparing results from the metamodels with results from the actual bearing performance solver over a large number of evaluation points. Once the metamodels are established they are employed for performing probabilistic analyses.

A 2D finite element program, known as FAST_FORM2D, was developed at FTI to carry out section analysis in die design. Incremental method is employed and plane strain condition is assumed for 2D sections. Contact behavior and friction force are simulated by a developed algorithm. Therefore, the divergence problems related to the conventional contact techniques can be reduced or avoided. An adaptive mesh generation scheme is implemented to achieve computation efficiency. With the code, it is possible to evaluate tension, strain, thickness distributions and punch force at different stages for any 2D section cut from 3D panels. User can easily input or modify forming conditions to get the best solution.

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.