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State-of-charge (SOC) estimation of high power Ni-MH rechargeable battery with artificial neural network

机译：具有人工神经网络的高功率Ni-MH可充电电池的充电状态（SOC）估计

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摘要

This paper presents a three-layer feedforward backpropagation (HP) artificial neural network (ANN), whose output is battery state-of-charge (SOC), to estimate and predict SOC of high power Ni-MH rechargeable battery. Five ANN inputs are novelly selected to improve the accuracy of ANN prediction by the proposed method of correlation coefficient ranking based on correlation analysis of different variables and SOC. and they are: battery discharging current i, accumulated ampere hours Ah, battery terminal voltage V, time-average terminal voltage tav and twice time-average voltage ttav (i.e. timeaverage of tav). Meanwhile, six training sets are equally selected from thirteen data sets about constant current discharging (CCD) from 100% to 0% SOC and Levenberg-Marquardt training algorithm is selected. Comparisons between simulation and measurement verify the proposed ANN model. Especially, the ANN can satisfyingly estimate SOC of battery (pack) whose starting SOC (i.e. SOC{sub}0) is not originally known after about ten minutes (short time compared with the whole discharging process) constant load discharging (CLD), and most of absolute values of absolute errors are not more than 5%.
机译：本文介绍了三层前馈回来（HP）人工神经网络（ANN），其输出是电池充电状态（SOC），以估计和预测高功率Ni-MH可充电电池的SOC。五个ANN输入是新颖的选择，以提高基于不同变量和SOC的相关系数排序的所提出的相关系数排序方法的ANN预测的准确性。它们是：电池放电电流I，累积安培小时AH，电池端子电压V，时间平均端电压TAV和两次的时间平均电压TTAV（即TAV的Timeverage）。同时，六个训练集等于从100％到0％SOC和Levenberg-Marquardt训练算法的恒定电流放电（CCD）的十三个数据集中的六个训练集。仿真与测量之间的比较验证了建议的ANN模型。特别是，ANN可以满足估计电池（包装）的SOC，其起始SOC（即SOC {SUB} 0）在大约十分钟之后最初未知（与整个放电过程相比短的时间）恒定负载放电（CLD），以及绝对错误的大多数绝对值不超过5％。

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