An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System
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...
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MDPI AG
2020-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/24/8799 |
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| author | Md Ohirul Qays Yonis Buswig Hazrul Basri Md Liton Hossain Ahmed Abu-Siada Md Momtazur Rahman S. M. Muyeen |
| author_facet | Md Ohirul Qays Yonis Buswig Hazrul Basri Md Liton Hossain Ahmed Abu-Siada Md Momtazur Rahman S. M. Muyeen |
| author_sort | Md Ohirul Qays |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:14:06Z |
| format | Article |
| id | doaj.art-eae219d556be4a9da450f01eb9326586 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:14:06Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-eae219d556be4a9da450f01eb93265862023-11-20T23:59:37ZengMDPI AGApplied Sciences2076-34172020-12-011024879910.3390/app10248799An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid SystemMd Ohirul Qays0Yonis Buswig1Hazrul Basri2Md Liton Hossain3Ahmed Abu-Siada4Md Momtazur Rahman5S. M. Muyeen6Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan 94300, Sarawak, MalaysiaDepartment of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan 94300, Sarawak, MalaysiaDepartment of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan 94300, Sarawak, MalaysiaDepartment of Electrical and Computer Engineering, Curtin University, Kent Street, Bentley, Perth, WA 6102, AustraliaDepartment of Electrical and Computer Engineering, Curtin University, Kent Street, Bentley, Perth, WA 6102, AustraliaElectron Science Research Institute, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, AustraliaDepartment of Electrical and Computer Engineering, Curtin University, Kent Street, Bentley, Perth, WA 6102, AustraliaIn 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.https://www.mdpi.com/2076-3417/10/24/8799backpropagation neural network (BPNN)battery management system (BMS)dSPACE 1104energy storagePV-battery integrationstate of charge (SOC) |
| spellingShingle | Md Ohirul Qays Yonis Buswig Hazrul Basri Md Liton Hossain Ahmed Abu-Siada Md Momtazur Rahman S. M. Muyeen An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System Applied Sciences backpropagation neural network (BPNN) battery management system (BMS) dSPACE 1104 energy storage PV-battery integration state of charge (SOC) |
| title | An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System |
| title_full | An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System |
| title_fullStr | An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System |
| title_full_unstemmed | An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System |
| title_short | An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System |
| title_sort | intelligent controlling method for battery lifetime increment using state of charge estimation in pv battery hybrid system |
| topic | backpropagation neural network (BPNN) battery management system (BMS) dSPACE 1104 energy storage PV-battery integration state of charge (SOC) |
| url | https://www.mdpi.com/2076-3417/10/24/8799 |
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