State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm

ZHANG Xiaowei; YI Zhenxiao; WANG Kai

doi:10.12096/j.2096-4528.pgt.24068

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State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm

Energy Storage | 更新时间：2026-06-29

- State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm
- Power Generation Technology Vol. 46, Issue 6, Pages: 1154-1163(2025)
- 作者机构：
  
  1.中海油石化工程有限公司，山东省济南市 250101
  2.青岛大学电气工程学院，山东省青岛市 266071
  3.青岛大学威海创新研究院，山东省威海市 264200
  4.山东索想智能科技有限公司，山东省潍坊市 261101
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52037005)
- DOI：10.12096/j.2096-4528.pgt.24068
  CLC： TK 02;TM 912
- Received：13 May 2024，
  
  Revised：2024-07-20，
  
  Published：31 December 2025
- 稿件说明：
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张效伟,衣振晓,王凯.基于改进自适应蜜獾算法优化时间卷积网络的车载锂离子电池健康状态估计[J].发电技术,2025,46(06):1154-1163.

ZHANG Xiaowei,YI Zhenxiao,WANG Kai.State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm[J].Power Generation Technology,2025,46(06):1154-1163.
张效伟,衣振晓,王凯.基于改进自适应蜜獾算法优化时间卷积网络的车载锂离子电池健康状态估计[J].发电技术,2025,46(06):1154-1163. DOI： 10.12096/j.2096-4528.pgt.24068.

ZHANG Xiaowei,YI Zhenxiao,WANG Kai.State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm[J].Power Generation Technology,2025,46(06):1154-1163. DOI： 10.12096/j.2096-4528.pgt.24068.

摘要

目的

锂离子电池作为新能源汽车的重要动力来源，准确的健康状态(state of health，SOH)估计对于设计安全可靠的汽车电池管理系统至关重要。传统方法普遍存在忽略容量恢复及特征有效性不足等问题，严重影响估算精度。为此，提出了一种虑及电池容量恢复的锂离子电池SOH估算方法。

方法

将中值绝对偏差与Savitzky-Golay滤波相结合，在数据预处理阶段有效去除异常值和噪声以提高特征的有效性，然后进行特征分解，去除冗余信息，减轻模型计算负担。将高度相关的特征作为时间卷积网络模型的输入，降低数据维度并减轻神经网络计算复杂度。提出了一种改进的自适应蜜獾算法以优化网络的超参数，加快模型收敛并提高网络性能。

结果

所提方法具有较高的准确性，均方根误差和平均绝对误差均低于0.007。

结论

所提方法具有较高的鲁棒性，能够对车载锂离子电池SOH进行有效估计，并能满足实际应用需求。

Abstract

Objectives

Lithium-ion batteries

as an important power source for new energy vehicles

require accurate state of health (SOH) estimation to design safe and reliable battery management systems. Traditional methods often overlook issues such as capacity recovery and insufficient feature effectiveness

which significantly affects estimation accuracy. To address these issues

a novel SOH estimation method for lithium-ion batteries that considers battery capacity recovery is proposed.

Methods

By combining the median absolute deviation with the Savitzky-Golay filter in the data preprocessing stage

the model effectively removes outliers and noise to improve the effectiveness of the features. Subsequently

feature decomposition is performed to remove redundant information and alleviate the computational load of the model. Highly correlated features are then selected as inputs for the temporal convolutional network model

reducing data dimensionality and simplifying the computational complexity of the neural network. Furthermore

an improved self-adaptive honey badger algorithm is proposed to optimize the hyperparameters of the network

accelerating model convergence and enhancing network performance.

Results

The proposed method has a high level of accuracy

with both the root mean squared error and the mean absolute error being lower than 0.007.

Conclusions

The proposed method exhibits high robustness

enabling effective SOH estimation of on-board lithium-ion batteries and meeting requirements of practical application.

关键词

Keywords

references

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