Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System

Md Ohirul Qays, Joarder Akash (2020) Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System. Masters thesis, Universiti Malaysia Sarawak (UNIMAS).

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Abstract

Due to the reduction of fossil-fuel utilization PV-Battery integrated system is a preferable power supply in many areas of the world. Designing a supervisory controller that can harvest high energy density and prolong the battery lifetime is one of the major challenges in a battery energy storage system. A proper Battery Management System (BMS) monitors the battery charge status and takes decision to lengthen the battery lifetime. A regulatory State of Charge (SOC) estimation based on PV-Battery standalone system is presented in this research that significantly addresses the issues. The proposed control algorithm estimates SOC by Backpropagation Neural Network (BPNN) scheme and implements Maximum Power Point Tracking (MPPT) system of the solar panels to take decision for charging, discharging or islanding mode of the Lead-Acid battery bank. The proposed model is designed in MATLAB/SIMULINK software and the experimental prototype is assessed via dSPACE 1104 component. The proposed power control strategy is explored as robust as well as attained the effective objective of standalone PV-Battery Management System e.g. avoiding overcharging and deep-discharging manoeuvre under different solar radiations and temperatures. A case study is presented for several SOC estimation methodologies that demonstrate the effectiveness of the proposed strategy with 0.082% error.

Item Type: Thesis (Masters)
Additional Information: Thesis (MSc.) - Universiti Malaysia Sarawak , 2020.
Uncontrolled Keywords: Battery Management System (BMS), State of Charge (SOC) Estimation, Backpropagation Neural Network (BPNN), PV-Battery system.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: MD OHIRUL QAYS JOARDER
Date Deposited: 08 Dec 2020 05:26
Last Modified: 08 Dec 2020 05:26
URI: http://ir.unimas.my/id/eprint/33252

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