Parameter Optimization and Temperature Prediction of Friction Stir Welding for Aluminum Alloy; Experiment, Simulation

One of the most efficient methods for joining of aluminum alloys is friction stir welding  (FSW) process. In FSW, welding parameters and tool geometry affect the weld strength. Heat is generated by friction between the tool and the workpiece, is important to predict and identify the mechanical and micro-structural changes. In this study, first using the Taguchi approach a design of experiment technique to set the optimal process parameters is investigated. It is shown that with increasing the shoulder diameter, the tensile strength increases and with increasing the tool rotational speed the tensile strength decreases. The traverse speed has less effect. Moreover temperature distribution is investigated experimentally. Results are compared with the software based on finite element method, analytical method, and analytical-empirical method. The capabilities, weaknesses, and accuracy of each method are discussed and suggestion is given

A Two-Layer Model for Optimal Charging Scheduling of Electric Vehicle Parking Lots in Distribution Network

International audienc

ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

Large-scale utilization of electric vehicles (EVs) affects the total electricity demand considerably. Demand forecast is usually designed for the seasonally changing load patterns. However, with the high penetration of EVs, daily charging demand makes traditional forecasting methods less accurate. This paper presents an autoregressive integrated moving average (ARIMA) method for demand forecasting of conventional electrical load (CEL) and charging demand of EV (CDE) parking lots simultaneously. Our EV charging demand prediction model takes daily driving patterns and distances as an input to determine the expected charging load profiles. The parameters of the ARIMA model are tuned so that the mean square error (MSE) of the forecaster is minimized. We improve the accuracy of ARIMA forecaster by optimizing the integrated and auto-regressive order parameters. Furthermore, due to the different seasonal and daily pattern of CEL and CDE, the proposed decoupled demand forecasting method provides significant improvement in terms of error reduction. The impact of EV charging demand on the accuracy of the proposed load forecaster is also analyzed in two approaches: (1) integrated forecaster for CEL + CDE, and (2) decoupled forecaster that targets CEL and CDE independently. The forecaster outputs are used to formulate a chance-constrained day-ahead scheduling problem. The numerical results show the effectiveness of the proposed forecaster and its influence on the stochastic power system operation