基于组合方法的两电荷控制器下离网系统电荷状态估计

W. Karrar, Zhen Zhang
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引用次数: 1

摘要

电池荷电状态(SOC)估计在电池储能系统(BESS)中起着重要的作用。目前,许多半导体公司越来越重视并投入资金支持许多研究人员实现电池的充电状态存储。电池储能优化的关键是基于精度方法确定荷电状态值。研究了几种简单的电池荷电状态测定方法,并与传统方法进行了比较,自适应方法由于没有考虑电池的动态影响,结果较准确。本文在考虑电压降对荷电状态估计影响的情况下,采用最大功率点跟踪-加宽模块(MPPT- PWM)两种充电技术对铅酸蓄电池荷电状态进行了组合估算。该模型在MATLAB程序(R2016a 64位(win64))中使用库仑计数作为确定SOC的算法,并将其设置为人工神经网络反向传播函数中的目标。仿真结果表明,该模型能较准确地估计实际运行中的SOC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods
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.
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