An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System
| Autor: | S. M. Muyeen, Hazrul Mohamed Basri, Ahmed Abu-Siada, Liton Hossain, Ohirul Qays, Momtazur Rahman, Yonis.M.Yonis Buswig |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Battery (electricity)
Computer science 020209 energy state of charge (SOC) 02 engineering and technology lcsh:Technology Automotive engineering Maximum power point tracking lcsh:Chemistry Control theory Hardware_GENERAL 0202 electrical engineering electronic engineering information engineering Islanding General Materials Science backpropagation neural network (BPNN) Instrumentation lcsh:QH301-705.5 dSPACE 1104 Fluid Flow and Transfer Processes battery management system (BMS) lcsh:T energy storage Process Chemistry and Technology 020208 electrical & electronic engineering Photovoltaic system General Engineering PV-battery integration lcsh:QC1-999 Computer Science Applications State of charge lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Hybrid system lcsh:Engineering (General). Civil engineering (General) lcsh:Physics Power control |
| Zdroj: | Applied Sciences Volume 10 Issue 24 Applied Sciences, Vol 10, Iss 8799, p 8799 (2020) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10248799 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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