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.
Highlights
In the last few decades, photovoltaics (PV) have been broadly used as a cost effective and reliable renewable energy source with the aim of reducing the reliance on fossil fuel used in conventional thermal generation [1]
Active battery balancing strategy accompanied by battery management system (BMS) for PV-Battery integrated system are conveyed together in the MATLAB-Arduino interfacing experience
Battery storage is an essential, but expensive, element for PV-battery off-grid systems that ensures the continuity of the power supply at night and during cloudy days
Summary
In the last few decades, photovoltaics (PV) have been broadly used as a cost effective and reliable renewable energy source with the aim of reducing the reliance on fossil fuel used in conventional thermal generation [1]. In [2], a detailed PV model is investigated using an artificial neural network (ANN). Adopting such model in practical PV systems increases the implementation time and complexity when compared to classical models. A maximum power point tracking (MPPT) system is essential for PV systems to yield the maximum accessible power irrespective the irregular solar irradiance and atmospheric condition. A combination of Adaptive neuro-fuzzy inference system (ANFIS) and hill climbing (HC) MPPT technique is presented in [3]. Reported results outperformed the tracking precision of the conventional MPPT technique. To increase the profit of the PV system during partial shading events, an adaptive perturb and observation (P&O) MPPT algorithm is proposed [4]
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