INSIGHT INTO SPATIAL ANALYSIS OF GPS BROADCAST ORBIT ERROR TOWARDS ORBITAL IMPROVEMENT

Broadcast orbits are compared against final orbit to get the error of broadcast orbit. The errors are analysed by presenting the error over space, especially longitude. The satellite trajectory is divided into three sector namely northern, southern, and transitional sectors. Spatial analysis show that the error is correlated with the latitude and longitude. Some consistency pattern can be observed from the distribution of the error in the spatial analysis. Standard deviation (SD) is used to quantify the consistency, providing more quantitative insights into the spatial analysis. Four patterns can be observed in the error distribution, namely consistency in northern and southern sector, consistency of transitional sector, changes after transitional sector, and correlation between ΔX component and ΔY component. The spatial analysis shows potential to be used in broadcast orbit error estimation and prediction. A model that uses this predicted broadcast orbit error as a correction will be designed in the future to improve the broadcast orbit accuracy.

Novel Technique for Estimation of Cell Parameters Using MATLAB/Simulink

The main source of power in Electric Vehicles (EVs) is derived from batteries. An efficient cell model is extremely important for the development of complex algorithms like core temperature estimation, State of Health (SOH) estimation and State of Charge (SOC) estimation. In this paper, a new methodology for improving the SOC estimation using Equivalent Cell Model (ECM) approach is proposed. The modeling and simulations were performed using MATLAB/Simulink software. In this regard, a Li polymer cell was modeled as a single Resistor-Capacitor (RC) pair (R0, R1 and C1) model using PowerTrain blockset in MATLAB/Simulink software. To validate the developed model, a NASA dataset was used as the reference dataset. The cell model was tuned against the NASA dataset for different currents in such a way that the error in the terminal voltages (difference in terminal voltage between the dataset and the ECM) is <±0.2 V. The mean error and the standard deviation of the error were 0.0529 and 0.0310 respectively. This process was performed by tuning the cell parameters. It was found that the cell parameters were independent of the nominal capacity of the cell. The cell parameters of Li polymer and the Li ion cells (NASA dataset) were found be almost identical. These parameters showed dependence on SOC and temperature. The major challenge in a battery management system is the parameter estimation and prediction of SOC, this is because the degradation of battery is highly nonlinear in nature. This paper presents the parameter estimation and prediction of state of charge of Li ion batteries by implementing different machine learning techniques. The selection of the best suited algorithm is finalized through the performance indices mainly by evaluating the values of R- Squared. The parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on Support Vector Machine (SVM) technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out. Later, these parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on SVM technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out.

Bivariate Compound Exponentiated Survival Function of the Lomax Distribution: Estimation and Prediction

In this paper, bivariate compound exponentiated survival function of the Lomax distribution is constructed based on the technique considered by AL-Hussaini (2011). Some properties of the distribution are derived. Maximum likelihood estimation and prediction of the future observations are considered. Also, Bayesian estimation and prediction are studied under squared error loss function. The performance of the proposed bivariate distribution is examined using a simulation study. Finally, a real data set is analyzed under the proposed distribution to illustrate its flexibility for real-life application.