Remaining Useful Life Prediction Method of Lithium‑Ion Battery Based on Bi‑LSTM Network Under Multi‑State Influence

ZHANG Hao; HU Chang-hua; DU Dang-bo; PEI Hong; ZHANG Jian-xun

doi:10.12263/DZXB.20210207

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Remaining Useful Life Prediction Method of Lithium‑Ion Battery Based on Bi‑LSTM Network Under Multi‑State Influence

PAPERS | 更新时间：2025-12-11

- Remaining Useful Life Prediction Method of Lithium‑Ion Battery Based on Bi‑LSTM Network Under Multi‑State Influence
- ACTA ELECTRONICA SINICA Vol. 50, Issue 3, Pages: 619-624(2022)
- 作者机构：
  
  火箭军工程大学导弹工程学院, 陕西西安 710025
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210207
  CLC： TP206+.3
- Received：02 February 2021，
  
  Revised：2021-06-01，
  
  Published：25 March 2022
- 稿件说明：
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张浩,胡昌华,杜党波等.多状态影响下基于Bi‑LSTM网络的锂电池剩余寿命预测方法[J].电子学报,2022,50(03):619-624.

ZHANG Hao,HU Chang-hua,DU Dang-bo,et al.Remaining Useful Life Prediction Method of Lithium‑Ion Battery Based on Bi‑LSTM Network Under Multi‑State Influence[J].ACTA ELECTRONICA SINICA,2022,50(03):619-624.
张浩,胡昌华,杜党波等.多状态影响下基于Bi‑LSTM网络的锂电池剩余寿命预测方法[J].电子学报,2022,50(03):619-624. DOI： 10.12263/DZXB.20210207.

ZHANG Hao,HU Chang-hua,DU Dang-bo,et al.Remaining Useful Life Prediction Method of Lithium‑Ion Battery Based on Bi‑LSTM Network Under Multi‑State Influence[J].ACTA ELECTRONICA SINICA,2022,50(03):619-624. DOI： 10.12263/DZXB.20210207.

摘要

现有基于深度学习的锂电池剩余寿命（Remaining Useful Life

RUL）预测方法中，锂电池多个内部状态所蕴含的寿命信息未得到充分考虑.鉴于此，提出了一种融合电池容量、阻抗与温度三个内部状态的RUL预测模型.首先，引入双向长短时记忆（Bi‑directional Long Short‑Term Memory

Bi‑LSTM）网络学习三种状态数据的时间相关性.其次，利用dropout技术与Bayesian变分推断技术间的等价性实现了RUL预测结果的不确定性量化，得到了预测结果的95%置信区间与概率密度分布（Probability Density Function

PDF），并分析了不同dropout率对预测不确定性的影响.最后，通过四种不同的深度学习模型框架与两种内部状态输入方案的对比实验，验证了本文方法的有效性.

Abstract

The life information contained in multiple internal states of lithium-ion battery is not fully considered in the existing RUL(Remaining Useful Life) prediction methods of lithium-ion battery based on deep learning. In view of this

a RUL prediction model that integrates the three internal states include battery capacity

impedance and temperature is proposed. The Bi-LSTM(Bi-directional Long Short-Term Memory) network is introduced to learn the correlation about time of the data of the three states firstly. Secondly

the equivalence between dropout technology and Bayesian variational inference technology is used to quantify the uncertainty of the RUL prediction results. The 95% confidence interval and PDF(Probability Distribution Function) of the RUL prediction results are obtained

and the effect on the prediction uncertainty of different dropout rates is analyzed. Finally

the effectiveness of this method is verified through the comparative experiments of four different deep learning model and two input schemes of internal state.

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references

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