## 1 Introduction  
Photovoltaics (PVs) have been widely used as a reliable and cost effective renewable energy source in order to reduce the dependency on fossil fuels conventional generation [1]. The performance of PV systems can be investigated through detailed modeling and simulation analysis [2]. In this regard, artificial neural network (ANN) model for PV system has been recently used instead of the classical one diode and five parameters (1D + 5P) model. While the ANN model revealed a better performance, the execution time is long with large data required for training and testing the ANN model. The PV cells are classified into two main categories, namely, double diode and single diode modules [3; 4]. While high accuracy PV panel is characterized by double diode module, the structural complexity depicts low analytical speed. Thus, the equitable trades between accuracy and easiness give advantage for single diode module to be adopted in various power electronics applications [5]. The implementation of PV systems presented in [6; 7; 8] suggested to conduct further research on PV-battery hybrid system. Irregularity of solar irradiance and atmospheric temperature degrades the efficiency of the solar panel. This calls for the adoption of a reliable maximum power point tracking (MPPT) scheme to harvest the maximum available power from the solar irradiance. The purpose of MPPT is to match the I-V operating point of the PV modules and yield the maximum available power from the electronic converter [9; 10]. Various online and offline MPPT techniques are presented in [11] in which, a combination of adaptive neuro-fuzzy inference system (ANFIS) and hill climbing (HC) MPPT technique is developed. Reported results outperform the tracking accuracy of the conventional MPPT technique. In [12], an adaptive perturb and observation (P&O) MPPT algorithm is developed to improve the yield of the PV system during partial shading events. The designed system is applicable for mono and polycrystalline-based PV panels. In [13], an improved P&O MPPT technique is developed and tested under a step and ramp changes of solar irradiation. The proposed method is reported to be of a faster tracking speed than the conventional method. Moreover, a modified P&O based MPPT technique implemented in MATLAB/SIMULINK software is presented in [14]. This algorithm was able to identify the maximum power point accurately compared to the conventional P&O scheme. Nevertheless, experimental validation of the proposed technique was recommended in future research accompanied by the impact of temperature parameter. Consequently, by following [15], P&O based MPPT system is used in this research to improve the working prototype in real-life application. Throughout the night-time plus cloudy days, only PV panel cannot afford to deliver sufficient power-supply that evocates in terms of energy storage system or another source of power-generation. PV-diesel system is widely used in remote area to provide uninterrupted power. However, the use of diesel generator may have severe consequences on the environment due to its dependency on fossil fuels [16]. Therefore, battery energy storage system (BESS) is another preferred option to maintain the power system efficiency in full-fledged [17; 18; 19]. The charging/discharging irregularity of PV-battery hybrid system decreases the battery operational life and affects the entire system efficiency. In general, lead acid battery comprises a flat terminal voltage in the range of 40% to 80% of the state of charge (SOC) [20]. As shown in Figure 1, the voltage variation in this range is less than 0.44 V. A minor difference of the open circuit voltage (VOC) in this scope causes a significant variation in the system performance as VOC is directly related to the SOC. Such slight voltage difference can hardly be used to persuade a proper charging and discharging process. Principally, SOC estimation accuracy ensures the integral efficiency of the battery storage system [21].