Deep Learning-Based State-of-Charge Estimation for Lithium-Ion Batteries Across the Entire Life Cycle

ZHANG Lin; WU Chunling; HUANG Xinrong; LI Yanbo

doi:10.7652/xjtuxb202410003

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Deep Learning-Based State-of-Charge Estimation for Lithium-Ion Batteries Across the Entire Life Cycle

更新时间：2025-07-28

- Deep Learning-Based State-of-Charge Estimation for Lithium-Ion Batteries Across the Entire Life Cycle
- Vol. 58, Issue 10, Pages: 36-43(2024)
- 作者机构：
  
  1. 长安大学能源与电气工程学院,西安,710064
  2. 长安大学陕西省交通新能源开发、应用与汽车节能技术重点实验室,西安,710064
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202410003
  CLC： TM912
- Online First：10 October 2024，
  
  Published：2024
- 稿件说明：
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ZHANG Lin, WU Chunling, HUANG Xinrong, et al. Deep Learning-Based State-of-Charge Estimation for Lithium-Ion Batteries Across the Entire Life Cycle[J]. 2024, 58(10): 36-43.
DOI：

ZHANG Lin, WU Chunling, HUANG Xinrong, et al. Deep Learning-Based State-of-Charge Estimation for Lithium-Ion Batteries Across the Entire Life Cycle[J]. 2024, 58(10): 36-43. DOI： 10.7652/xjtuxb202410003.

摘要

针对锂离子电池的健康状态(SOH)随着充放电循环次数的增加而持续退化

导致在整个寿命周期内准确估计电池的荷电状态(SOC)难度较高的问题

提出了一种采用深度学习的全寿命周期内锂离子电池SOC估计模型。该模型采用被估计时刻及之前多个历史时刻的电流、电压和温度组成的序列数据作为模型的输入

先采用一维卷积神经网络(1D CNN)提取序列的特征

再用门控循环单元(GRU)建立特征与SOC之间的非线性关系

然后采用贝叶斯优化方法(BO)对网络超参数进行寻优以提升预测的精度。采用两个公开数据集对所提出的模型进行验证

实验结果表明:所提模型在较宽的SOH范围内实现了精确的SOC预测

且预测精度显著优于采用单个深度学习模型的预测精度; 与CNN和BiLSTM模型相比

所提模型的均方根误差分别平均降低了15.16%和45.22%; 当输入序列的长度为10、数据采样间隔时间为1 min时

在两个数据集上预测的均方根误差均低于2%。

Abstract

Accurately estimating the state-of-charge(SOC)of lithium-ion batteries throughout their entire life cycle is challenging due to the continuous degradation of their state-of-health(SOH)with increasing charge-discharge cycles. To address this issue

a deep learning-based SOC estimation model for lithium-ion batteries is proposed. The model utilizes sequential data consisting of current

voltage and temperature measurements from the estimated time and preceding time steps as input. It leverages one-dimensional convolutional neural networks(1D CNN)to extract features from the sequence and uses gated recurrent units(GRU)to establish the nonlinear relationship between the features and SOC. Bayesian optimization(BO)is applied to optimize the network hyperparameters

enhancing prediction accuracy. The proposed model is validated using two publicly available datasets. Experimental results demonstrate that it achieves accurate SOC predictions within a wide range of SOH and outperforms single deep learning models in terms of prediction accuracy. Compared with CNN and BiLSTM models

the proposed model reduces the root mean square error by an average of 15.16% and 45.22%

respectively. When the input sequence length is set to 10 and the data sampling interval is 1 minute

the root mean square error of the predictions is below 2% for both datasets.

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