Artificial Intelligence for Renewable Energy Systems by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9781119761716
Published: 2022-01-19T00:00:00+00:00

5.1 Introduction

Sustainable energy sources lead to reduction in carbon footprint and thus increase the reliability of a system [1–4]. The State of Charge (SoC) is a parameter by which users can get details about the availability of battery capacity. The precise calculation and estimation of the available energy within a battery has always been a challenging task. Mathematically, SoC is a ratio of current capacity Q(t) to nominal capacity Qn given as

(5.1)

The precise calculation and modeling of the SoC will not only increases the system performance but also impacts cycle life in a longer run. Measurement and estimation of SoC remains one of the challenging tasks. SoC estimation is based on various sensor-based, filter-based, and data-driven techniques out of which Coulomb counting is the fundamental one. In [5], authors use Coulomb counting where current is integrated with respect to time. However, this method is inefficient due to the effect of temperature, discharge current, and cycle life of the battery. In order to overcome this, filter-based and data-driven estimation algorithms are utilized to estimate the SoC of the battery. In [6], authors use a Kalman Filter technique for SoC estimation. The Kalman filter is an estimator used to estimate the linear and nonlinear systems. Based on the present data of voltage, current, and temperature, it estimates the SoC with good accuracy and precision. While the estimation error of fully and partially charged battery is found to be around 0.5%. Apart from the Kalman filter, machine learning–based algorithms are also used to estimate the SoC of a battery. In [7], authors use a Support Vector Machine (SVM) approach, which is a statistical learning method used for estimating the SoC of a battery. The SoC of a Lithium Iron Manganese Phosphate (LiFeMnPO4) battery is estimated under the constant charging and variable load condition. The error in the proposed model is less than 4%, while the RMSE is 0.4% throughout the whole experiment. Apart from SVM, various other data-driven methods such as Neural Networks (NNs), Deep Neural Networks (DNNs), and Reinforcement Learning (RL) approach are used for the estimation of SoC.

Due to green gas emissions, the world is perpetually shifting toward renewable energy sources. Solar and wind energy being the pioneers in power portfolio. The on-field efficiency of solar panels is near about 18% to 21% [8]. However, due to the recent advancement in perovskite solar cells, reflectors, photonic crystals, and recycling photovoltaic (PV) cells, the efficiency of the solar cells is increasing drastically. Further, it is expected that, till 2030, the on-field efficiency of solar-based PV cells will be near about 30% [9].

Nowadays, the integration of solar PV arrays with batteries is gaining tremendous importance due to intermittency posed. Regarding this, many electric conveyances, grids, and space satellites are directly charging their batteries through solar PV arrays [10]. Mainly, two topologies exist for solar PV array with battery. The first one being stand-alone system, while the other being the grid-tied system. In the stand-alone system, solar PV is connected to the load via a battery.

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