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State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods

机译：两种方法结合估计的离网系统荷电状态

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The state of charge (SOC) estimation plays important role in the battery energy storage system (BESS). Nowadays many semiconductor companies are paying more and more attention and investment to support many researchers to implement the state of charge for the batteries storage. the key to optimize the batteries storage is determine SOC value based on accuracy methods. a number of brief methods for SOC determination have been studied and compared with traditional methods the adaptive methods shown precise result because didn't consider the dynamic effect of the batteries. In this paper, we use combination methods to estimate the SOC for lead-acid battery storage under two charge techniques namely Maximum Power Point Tracking - Plus Width Module (MPPT-PWM) when considering the effect of voltage drops on the estimation of SOC. The model uses the coulomb counting as an algorithm to determine the SOC and set it as a target in the backpropagation function in artificial neural network in MATLAB program (R2016a 64-bit (win64)). The simulation results show that the model is very precise to estimate the SOC in realistic operation.
机译：充电状态（SOC）估计在电池储能系统（BESS）中起着重要作用。如今，许多半导体公司越来越关注和投资以支持许多研究人员实现电池存储的充电状态。优化电池存储的关键是基于精度方法确定SOC值。研究了许多用于确定SOC的简要方法，并且与传统方法相比，自适应方法显示了精确的结果，因为它没有考虑电池的动态影响。在考虑电压降对SOC估算的影响时，本文采用组​​合方法估算铅酸蓄电池在两种充电技术下的SOC，即最大功率点跟踪-加宽模块（MPPT-PWM）。该模型使用库仑计数作为算法来确定SOC，并将其设置为MATLAB程序（R2016a 64位（win64））中的人工神经网络的反向传播函数中的目标。仿真结果表明，该模型可以非常精确地估算实际运行中的SOC。

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