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

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 sy...

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Main Author:	Md Ohirul Qays, Joarder Akash
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://ir.unimas.my/id/eprint/33252/4/Artificial%20Neural%20Network%20based%20Battery%20Management%20System%20on%20State%20of%20Charge.pdf
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id	my-unimas-ir.33252
record_format	uketd_dc
spelling	my-unimas-ir.332522023-03-20T08:12:23Z Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System 2020-07-01 Md Ohirul Qays, Joarder Akash TK Electrical engineering. Electronics Nuclear engineering 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. Universiti Malaysia Sarawak (UNIMAS) 2020-07 Thesis http://ir.unimas.my/id/eprint/33252/ http://ir.unimas.my/id/eprint/33252/4/Artificial%20Neural%20Network%20based%20Battery%20Management%20System%20on%20State%20of%20Charge.pdf text en validuser masters Universiti Malaysia Sarawak (UNIMAS) Faculty of Engineering Fundamental Research Grant Scheme FRGS/1/2017/TK10/UNIMAS/03/1, Ministry of Higher Education, Malaysia.
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Md Ohirul Qays, Joarder Akash Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System
description	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.
format	Thesis
qualification_level	Master's degree
author	Md Ohirul Qays, Joarder Akash
author_facet	Md Ohirul Qays, Joarder Akash
author_sort	Md Ohirul Qays, Joarder Akash
title	Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System
title_short	Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System
title_full	Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System
title_fullStr	Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System
title_full_unstemmed	Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System
title_sort	artificial neural network based battery management system on state of charge estimation for optimal operation of photovoltaic-battery integrated system
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Engineering
publishDate	2020
url	http://ir.unimas.my/id/eprint/33252/4/Artificial%20Neural%20Network%20based%20Battery%20Management%20System%20on%20State%20of%20Charge.pdf
_version_	1783728423915487232

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

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