Comparative analysis of Direct and Indirect Model Reference Adaptive Control by Extended Kalman Filter
Volume-3 | Issue-3
Smart Wires and Modular FACTS Controllers for Smart Grid Applications: A Review
Volume-3 | Issue-4
Integrated Renewable Energy Management System for Reduced Hydrogen Consumption using Fuel Cell
Volume-3 | Issue-1
Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
Volume-3 | Issue-3
Artificial Intelligence based Business Process Automation for Enhanced Knowledge Management
Volume-3 | Issue-2
Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
Volume-3 | Issue-2
Design of Effective Smart Communication System for Impaired People
Volume-2 | Issue-4
Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
Volume-3 | Issue-2
A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Power Transfer Capability Recognition in Deregulated System under Line Outage Condition Using Power World Simulator
Volume-3 | Issue-4
Transformer Oil Diagnostic Tests Analysis using Statistical Correlation Technique
Volume-4 | Issue-3
Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model
Volume-3 | Issue-4
Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Volume-3 | Issue-3
Performance Analysis of Multiple Pico Hydro Power Generation
Volume-2 | Issue-2
Energy Efficient Data Mining Approach for Estimating the Diabetes
Volume-3 | Issue-2
Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
Volume-3 | Issue-2
A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
Volume-3 | Issue-2
Volume - 2 | Issue - 1 | march 2020
Published
28 April, 2020
This article deals with Remaining Useful Life (RUL) estimation of Lead Acid Battery using a probabilistic approach which is Bayesian inference of Linear Regression. RUL estimation of lead acid battery plays a very crucial role as it can prevent the catastrophic failure for the system in which it is used to serve as a power supply mainly in automobiles. Although there are various methods for age estimation of lead acid battery, machine learning algorithms always played a major role in the same. In this paper we have implemented one such algorithm for the RUL estimation. Bayesian approach is a probabilistic method which can be used for predicting the RUL of the battery. Firstly, we present a framework for feature extraction and then the RUL estimation model is trained on Bayesian inference of Linear Regression. The proposed approach is then applied to the collected dataset from five differently aged batteries which have undergone some charging/discharging and load cycle test. The experiment result shows that the proposed approach can improve the accuracy of RUL estimation than the regular methods.
KeywordsProbabilistic Model RUL Lead-acid Battery SOC MCMC Prediction
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