Abstract
It is essential to estimate the state of charge (SOC) of lead-acid batteries to improve the stability and reliability of photovoltaic systems. In this paper, we propose SOC estimation methods for a lead-acid battery using a feed-forward neural network (FFNN) and a recurrent neural network (RNN) with a gradient descent (GD), a levenberg–marquardt (LM), and a scaled conjugate gradient (SCG). Additionally, an adaptive neuro-fuzzy inference system (ANFIS) with a hybrid method was proposed. The voltage and current are used as input data of neural networks to estimate the battery SOC. Experimental results show that the RNN with LM has the best performance for the mean squared error, but the ANFIS has the highest convergence speed.
Highlights
Today, several environmental issues exist, including the depletion of fossil fuels and the dangers of nuclear power generation
Estimating the state of charge (SOC) from a battery is one of the most effective methods for estimating the SOC using open-circuit voltage (OCV), this method requires a condition in which the circuit is opened or no current flows, and it takes time to wait for the battery to stabilize internally
We propose methods for estimating the leadacid battery SOC using an feed-forward neural network (FFNN), an recurrent neural network (RNN), and an adaptive neuro-fuzzy inference system (ANFIS)
Summary
Several environmental issues exist, including the depletion of fossil fuels and the dangers of nuclear power generation. A battery is a device that generates electrical energy through a chemical reaction, and it has nonlinear characteristics in response to parameters such as the ambient temperature, internal resistance, and capacitance For these reasons, it is very difficult to estimate the SOC of a battery correctly. The open-circuit voltage (OCV) [4] method involves measuring the voltage in the no-load state This method is difficult to apply to real-time systems because it uses the measured OCV at the chemical equilibrium inside the battery. SOC estimation methods for a lead-acid battery using a feed-forward neural network (FFNN), a www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 9, No 9, 2018 recurrent neural network (RNN), and an adaptive neuro-fuzzy inference system (ANFIS) were proposed.
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