Design and implementation of a high‐efficiency LiFePO4 battery charger for electric vehicle applications

This letter studies and implements a high‐efficiency LiFePO₄ battery charger. The modular design can satisfy the requirements of series/parallel charging for electric vehicle applications. A CC‐CC‐CV charging scheme is also realized to meet the characteristics of LiFePO₄ battery stacks. A 2 kW laboratory prototype is built and tested. The experimental results are shown to verify the feasibility of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

A Study of Structure Evolution in Pearlitic Steel Wire at Increasing Plastic Deformation

In this work was studied the influence of the plastic deformation's intensity under deep drawing on microstructures of a pearlitic steel containing 0.8% C. Varying the levels of deformation causes diverse dislocation movements as well as modified structural states in the individual phases of the pearlitic eutectoid steel. It is shown that in the course of plastic deformation there is a reduction of interlamellar distance in a pearlite and increase in dislocation density. In some parts partial spheroidisation cementite plates is observed. The bands formed in dislocation structure are found out. The analysis of failure mechanisms of steel with pearlite structure after plastic deformation is carried out. During the deformation of pearlite, the increase in stress at the phase boundary, owing to the elastic strain incompatibility between the ferrite and cementite phases, resembles the stress concentration at grain boundaries. Pores form at the interface between the surface of the ferritic matrix and the spheroidised carbide particles. Such micro pores occur by means of plastic deformation of the ferrite's interstices around the stronger cementite owing to the reduction of the ferritic interstices and their subsequent cracking.     

Convergence Analysis of Weighted Stochastic Gradient Identification Algorithms Based on Latest‐Estimation for ARX Models

In this paper, weighted stochastic gradient (WSG) algorithms for ARX models are proposed by modifying the standard stochastic gradient identification algorithms. In the proposed algorithms, the correction term is a weighting combination of the correction terms of the standard stochastic gradient (SG) algorithm in the current and last recursive steps. In addition, a latest estimation based WSG (LE‐WSG) algorithm is also established. The convergence performance of the proposed LE‐WSG algorithm is then analyzed. It is shown by a numerical example that both the WSG and LE‐WSG algorithms can possess faster convergence speed and higher convergence precision compared with the standard SG algorithms if the weighting factor is appropriately chosen.     

Hybrid recommendations for mobile commerce based on mobile phone features

Mobile data communications have evolved as the number of third generation (3G) subscribers has increased. The evolution has triggered an increase in the use of mobile devices, such as mobile phones, to conduct mobile commerce and mobile shopping on the mobile web. There are fewer products to browse on the mobile web; hence, one‐to‐one marketing with product recommendations is important. Typical collaborative filtering (CF) recommendation systems make recommendations to potential customers based on the purchase behaviour of customers with similar preferences. However, this method may suffer from the so‐called sparsity problem, which means there may not be sufficient similar users because the user‐item rating matrix is sparse. In mobile shopping environments, the features of users' mobile phones provide different functionalities for using mobile services; thus, the features may be used to identify users with similar purchase behaviour. In this paper, we propose a mobile phone feature (MPF)‐based hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF‐based method and a preference‐based method that uses association rule mining to extract recommendation rules from user groups and make recommendations. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.