Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

Md Ohirul Qays, Yonis Buswig, Md Liton Hossain and Ahmed Abu-Siada
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Md Ohirul Qays: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia
Yonis Buswig: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia
Md Liton Hossain: School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia
Ahmed Abu-Siada: School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia

Energies, 2020, vol. 13, issue 13, 1-16

Abstract: Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

Keywords: active battery balancing; backpropagation neural network; DC/DC Buck-boost converter; PV-battery integrated system; state of charge estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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