Comparative Study on SOC Estimation Techniques for Optimal Battery Sizing for Hybrid Vehicles

Automotive Industry is growing at a very fast rate. Hence problems pertaining to the increasing CO2 levels in the atmosphere and the ever increasing fuel rates also increase. Electric and Hybrid electric technology has become the latest milestone for the automotive industry. In Hybrid Electric Vehicles (HEV), the reliable range of operation is characterized by batteries and battery state of charge (SOC), that describes its remaining capacity, is an important factor for providing the control strategy for the battery management system (BMS) in plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). Accuracy in estimation of the SOC is necessary not only to protect the battery, prevent it’s over discharge, and improve the battery life but also to allow the application to make rational control strategies to save energy. However, the chemical energy of a battery which is a chemical energy storage source cannot be directly accessed and this issue makes the estimation of the SOC of a battery difficult. Hence, estimation of the SOC accurately becomes very complex and is difficult to implement, as there are parametric uncertainties and the battery models are limited. In fact, in practice several examples of models of the estimation of the SOC are found which have poor accuracy and reliability. Hence a comparative study done in this paper on the various methods will help choose the right method based on the requirements and the application. This paper also reviews a case study on modeling and simulation of one of the methods of SOC estimation and efforts have been put in obtaining the performance of Li-ion batteries by calculating the SOC using Coulomb counting method in MATLAB Simulink. DOI: 10.17762/ijritcc2321-8169.16044

Computer vision based healthcare system for identification of diabetes & its types using AI

Diabetes mellitus, often known as diabetes, is an endocrine disorder that has a wide global impact today. Here is a requirement for an effective model that able prognoses diabetes and its types with more accurateness as early. Given the breadth and depth of existing studies, there is a pressing need for accurate and timely illness forecasting in the healthcare sector. Current circumstances need the creation and design of systems that are quicker to respond, more accurate, more durable, and more generalizable. For increasing the accurateness of prediction with best effectiveness innovative Artificial Intelligence and Machine Learning Model is proposed. This model predicts the diabetes class using the symptoms located into the data-set which is having the row as one rule of the system & this rule are need to understand and compile using feature