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Article

An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System

1
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan 94300, Sarawak, Malaysia
2
Department of Electrical and Computer Engineering, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia
3
Electron Science Research Institute, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(24), 8799; https://doi.org/10.3390/app10248799
Received: 16 October 2020 / Revised: 1 December 2020 / Accepted: 5 December 2020 / Published: 9 December 2020
(This article belongs to the Section Energy)
In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly. View Full-Text
Keywords: backpropagation neural network (BPNN); battery management system (BMS); dSPACE 1104; energy storage; PV-battery integration; state of charge (SOC) backpropagation neural network (BPNN); battery management system (BMS); dSPACE 1104; energy storage; PV-battery integration; state of charge (SOC)
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MDPI and ACS Style

Qays, M.O.; Buswig, Y.; Basri, H.; Hossain, M.L.; Abu-Siada, A.; Rahman, M.M.; Muyeen, S.M. An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System. Appl. Sci. 2020, 10, 8799. https://doi.org/10.3390/app10248799

AMA Style

Qays MO, Buswig Y, Basri H, Hossain ML, Abu-Siada A, Rahman MM, Muyeen SM. An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System. Applied Sciences. 2020; 10(24):8799. https://doi.org/10.3390/app10248799

Chicago/Turabian Style

Qays, Md O., Yonis Buswig, Hazrul Basri, Md L. Hossain, Ahmed Abu-Siada, Md M. Rahman, and S. M. Muyeen 2020. "An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System" Applied Sciences 10, no. 24: 8799. https://doi.org/10.3390/app10248799

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