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Current estimation using Thevenin battery model

机译:使用戴维南电池模型的电流估算

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摘要

Current sensor is an important part of Battery Management System (BMS). Current information of a battery is very important to estimate the value of State of Charge (SOC) as a component of fault detection. Fault detection is very important to implement in BMS because of its function to protect battery from damage caused by over discharge and overcharge. The issue here is expensive current sensors. To overcome this issue, this research aims to design a current estimation algorithm which is based on a sensorless current method where the battery is modeled in a Thevenin equivalent circuit model. The Thevenin model is then formed into autoregressive exogenous (ARX) model and the parameters are extracted by using MATLAB identification toolbox. This research uses lithium polymer battery with a capacity of 2200 mAh and the tests conducted in this research are constant pulse load test and load variation test to see the performance of the algorithm. The results show that the current estimation using Thevenin model results better than the one using RC model as shown in the estimation test with constant pulse load and load variation.
机译:电流传感器是电池管理系统(BMS)的重要组成部分。电池的电流信息对于估计充电状态(SOC)的值非常重要,它是故障检测的一部分。故障检测对于在BMS中实施非常重要,因为它具有保护电池免受过放电和过充电引起的损坏的功能。这里的问题是昂贵的电流传感器。为了克服这个问题,本研究旨在设计一种基于无传感器电流方法的电流估算算法,该电流以戴维南等效电路模型建模。然后将戴维宁模型形成自回归外生(ARX)模型,并使用MATLAB识别工具箱提取参数。本研究使用容量为2200 mAh的锂聚合物电池,并且该研究进行的测试是恒定脉冲负载测试和负载变化测试,以了解算法的性能。结果表明,在恒定脉冲负载和负载变化的情况下,使用戴维宁模型进行的电流估计结果要优于使用RC模型进行的估计。

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