State Estimation Of Battery Management System Using Particle Filter

Document Type : Primary Research paper

Authors

1 PG Scholar, Department of Instrumentation and Control Systems Engineering, PSG College of Technology, Coimbatore, India

2 Assistant Professor (Senior Grade), Department of Instrumentation and Control Systems Engineering, PSG College of Technology, Coimbatore, India

Abstract

The degradation of the environment and severe energy crisis made the
government to pay attention to techniques which results in minimum emission. In such
cases, batteries play a main role because they have high energy density and long service
life. Batteries are considered to be the best alternative for reducing the emission released.
Hence batteries are environmentally friendly in nature. A Battery Management System
(BMS) is required in order to maintain a good performance of the battery. Battery
Management System involves in continuously tracking the states of the battery and also
monitoring the states. Thus, with the help of BMS, the dynamic behavior of the batteries
can be analyzed continuously. An estimator serves to be a decision rule which estimates the
parameters of state based on the observations taken. Particle filter is one such estimator
which is based on Monte Carlo methodology and can be applied for both Gaussian and
Non-Gaussian distributions. A common problem faced is the phenomenon of degeneracy
where the weights of the particles tend to be negligible after few iterations. This can be
overcome by the proper choice of proposal density which plays a vital role and also by the
process of resampling. When the particle filter algorithm involves the resampling process,
it is referred as Sequential Importance Resampling Particle Filter (SIR-PF). This paper
deals with the implementation of SIR-PF for estimating the states of the battery and
analyzing the performance of the filter.

Keywords

Int. J. of Aquatic Science
Volume 12, Issue 2 - Serial Number 2
June 2021
Pages 3913-3924

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