RUL Estimation of Lithium-Ion Power Battery Based on DEKF Algorithm

A. Wang, Haitao Chen, Peng Jin, Jun Huang, Dong Feng, Minxin Zheng
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引用次数: 2

Abstract

Lithium-ion power batteries are the energy source for many complex electronic systems, and their Remaining Useful Life (RUL) is critical to the safety and reliability of power systems. The high-precision estimation of lithium-ion battery life has become one of the current research hotspots, and has attracted more and more scholars' attention. In order to realize the residual cycle life estimation of lithium ion power battery, an equivalent circuit model is established for lithium ion power battery. Based on the HPPC pulse experimental data, the second-order Thevenin equivalent circuit model is selected, combined with the equivalent circuit model and battery capacity model. The state equation and measurement equation of the double extended Kalman filter algorithm and the specific iterative recursive calculation process. The accuracy and adaptability of the algorithm are verified by the actual discharge conditions. Finally, based on the life data of this experimental object, the battery RUL estimation within a reasonable error range was made. It is of great significance to improve the safety, reliability and economic benefits of the system.
基于DEKF算法的锂离子动力电池RUL估计
锂离子动力电池是许多复杂电子系统的能量来源,其剩余使用寿命(RUL)对电力系统的安全性和可靠性至关重要。锂离子电池寿命的高精度估算已成为当前的研究热点之一,并引起了越来越多学者的关注。为了实现锂离子动力电池剩余循环寿命的估算,建立了锂离子动力电池的等效电路模型。基于HPPC脉冲实验数据,结合等效电路模型和电池容量模型,选择二阶Thevenin等效电路模型。给出了双扩展卡尔曼滤波算法的状态方程和测量方程以及具体的迭代递推计算过程。通过实际放电情况验证了算法的准确性和适应性。最后,根据实验对象的寿命数据,在合理的误差范围内对电池RUL进行估计。对提高系统的安全性、可靠性和经济效益具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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