基于等效电路模型和扩展卡尔曼滤波的聚合物锂电池荷电状态估计

D. Anggraeni, B. Sudiarto, A. Subhan, N. Chasanah, C. E. Santosa, Desti Ika Suryanti, G. Prabowo, P. Priambodo
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引用次数: 3

摘要

电池管理系统的进步在小型无人机中具有决定性作用。它的作用之一是估计控罪状态。其中充电状态用于确定电池容量,以防止过放电和欠放电过程。此外,这对于维护小型无人机机载电气系统的电能分配过程的安全性非常重要。在本研究中,基于等效电路方法和EKF算法的5000 mAh锂聚合物电池建模被用于估计SoC,因为该方法具有低复杂度和低计算要求。估计结果表明,该方法的MAE和RSME均小于1%。此外,将噪声的确定加入到实际环境中进行了实现,结果表明,SoC估计对给定的干扰具有较好的适应性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SoC Estimation Lithium Polymer Battery Based on Equivalent Circuit Model and Extended Kalman Filter
The advancement of a battery management system is notably decisive in small unmanned aircraft. One of its roles is estimating the State of Charge. Where the State of Charge is used to determine the battery capacity to preventing over-discharging and under-discharging processes. Moreover, this is very important in maintaining the security of the electrical energy distribution process to the onboard electrical system on small-scale unmanned aircraft. In this study, the 5000 mAh Lithium Polymer battery modelling based on the Electrical Equivalent Circuit methods and the EKF algorithm was used to estimate the SoC, because this method provides low complexity and low computational requirements. The estimation results show that the MAE and RSME with this method showed less than 1%. Moreover, determination of noise is added to implemented in real conditions, where the results show that the SoC estimation is adaptive to a given disturbance.
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