多健康指标的锂离子电池健康状态无分布区间估计

郭子瑶; 张晓桐; 庞晓琼; 王竹晴

doi:10.19799/j.cnki.2095-4239.2025.1031

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多健康指标的锂离子电池健康状态无分布区间估计

储能测试与评价 | 更新时间：2026-04-29

- 多健康指标的锂离子电池健康状态无分布区间估计
- Automated distribution-free interval estimation for lithium-Ion battery state of health using multiple health indicators
- 储能科学与技术 2026年15卷第4期页码：1375-1386
- 作者机构：
  
  1.中北大学计算机科学与技术学院，山西太原 030051
  2.太原理工大学计算机科学与技术学院，山西太原 030024
- 作者简介：
  
  郭子瑶（1999—），女，硕士研究生，研究方向为复杂系统的故障预测与健康管理，E-mail：784465478@qq.com；
  庞晓琼，副教授，研究方向为复杂系统的故障预测与健康管理，E-mail：xqpang@nuc.edu.cn。
- 基金信息：
  
  山西省研究生教育创新计划项目(2024AL20)
- DOI：10.19799/j.cnki.2095-4239.2025.1031
  中图分类号： TM 912
- 收稿：2025-11-18，
  
  修回：2025-12-22，
  
  纸质出版：2026-04-28
- 稿件说明：
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郭子瑶, 张晓桐, 庞晓琼, 等. 多健康指标的锂离子电池健康状态无分布区间估计[J]. 储能科学与技术, 2026, 15(4): 1375-1386.

GUO Ziyao, ZHANG Xiaotong, PANG Xiaoqiong, et al. Automated distribution-free interval estimation for lithium-Ion battery state of health using multiple health indicators[J]. Energy Storage Science and Technology, 2026, 15(4): 1375-1386.
郭子瑶, 张晓桐, 庞晓琼, 等. 多健康指标的锂离子电池健康状态无分布区间估计[J]. 储能科学与技术, 2026, 15(4): 1375-1386. DOI： 10.19799/j.cnki.2095-4239.2025.1031.

GUO Ziyao, ZHANG Xiaotong, PANG Xiaoqiong, et al. Automated distribution-free interval estimation for lithium-Ion battery state of health using multiple health indicators[J]. Energy Storage Science and Technology, 2026, 15(4): 1375-1386. DOI： 10.19799/j.cnki.2095-4239.2025.1031.

摘要

针对现有锂离子电池健康状态(state of health，SOH)点估计方法难以量化估计不确定性的问题，本工作聚焦于更具实际应用价值的SOH区间估计方法。现有大多数区间估计方法是基于分布假设的，当现实数据偏离这些假设时，会引入估计偏差，进而影响估计的可靠性。为此，无分布的上下界估计(lower upper bound estimate，LUBE)方法逐渐受到关注，但其仍面临若干挑战：其一，损失函数不可微，导致模型优化困难；其二，部分研究采用Sigmoid函数将不可微的损失函数转变为可微损失函数，但Sigmoid函数的引入往往需人工调整斜率参数；其三，现有的研究多建立在容量这一理想健康指标上，然而容量的精确测量成本高昂，限制了该方法在实际场景中的适用性。为此，本工作提出一种面向多健康指标的锂离子电池SOH无分布区间估计方法。首先，采用核主成分分析(kernel principal component analysis， KPCA)方法对提取的多健康指标进行非线性降维。在此基础上，构建一个双输出神经网络模型，通过引入一种无需手动调整斜率参数的损失函数，使模型能够基于降维后的数据，稳定地输出高质量的预测区间。在公开可用的CALCE数据集上的实验结果表明，本工作所提方法不仅满足预定义置信水平的要求，还进一步提升了预测区间的质量。

Abstract

To address the limitations of conventional state of health (SOH) point estimation methods for lithium-ion batteries

this study develops a more practical SOH interval estimation method. Most previous interval estimation methods rely on distribution assumptions. However

when real-world battery data deviate from these assumptions

estimation biases may be introduced

consequently reducing estimation reliability. Consequently

the distribution-free lower upper bound estimation (LUBE) method has gradually attracted attention

although it still faces critical challenges. First

the loss function is non-differentiable

complicating model optimization. Second

several studies employed Sigmoid functions to transform non-differentiable loss functions into differentiable loss functions; however

this approach often requires manual slope-parameter tuning. Third

previous studies mostly rely on capacity as an ideal health indicator; however

accurately measuring capacity is costly

and this limits the real-world applicability of this method. To address these shortcomings

this study proposes a distribution-free SOH interval estimation method for quantifying LIB SOH using multiple health indicators. First

the kernel principal component analysis (KPCA) method is applied to reduce the dimensionality of the extracted health indicators. Based on this

a dual-output neural network model is constructed; this model introduces a loss function that eliminates the need for manual slope-parameter tuning

enabling it to stably output high-quality prediction intervals based on the reduced-dimensional data. Experimental results using the publicly available CALCE dataset demonstrate that the proposed method consistently meets nominal-confidence-level requirements while significantly improving prediction-interval quality.

关键词

Keywords

references

CHEN Y Q, KANG Y Q, ZHAO Y, et al. A review of lithium-ion battery safety concerns: The issues, strategies, and testing standards[J]. Journal of Energy Chemistry, 2021, 59: 83-99. DOI:10.1016/j.jechem.2020.10.017.

NI Y L, SONG K, PEI L, et al. State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model[J]. Applied Energy, 2025, 385: 125539. DOI:10.1016/j.apenergy.2025.125539.

TIAN J Q, LIU X H, LI S Q, et al. Lithium-ion battery health estimation with real-world data for electric vehicles[J]. Energy, 2023, 270: 126855. DOI:10.1016/j.energy.2023.126855.

ZHANG D F, LI W C, HAN X D, et al. Evolving Elman neural networks based state-of-health estimation for satellite lithium-ion batteries[J]. Journal of Energy Storage, 2023, 59: 106571. DOI:10.1016/j.est.2022.106571.

CAI L, LIN J D, LIAO X Y. A data-driven method for state of health prediction of lithium-ion batteries in a unified framework[J]. Journal of Energy Storage, 2022, 51: 104371. DOI:10.1016/j.est.2022.104371.

朱冰, 夏天. 多元宇宙优化估算锂离子电池的SOC与SOH[J]. 电池, 2024, 54(5): 688-692. DOI:10.19535/j.1001-1579.2024.05.017.ZHU B, XIA T. Estimation of SOC and SOH for Li-ion battery by multi-verse optimization[J]. Battery Bimonthly, 2024, 54(5): 688-692. DOI:10.19535/j.1001-1579.2024.05.017

LI H J, WANG S K, YANG L, et al. SOH estimation method for lithium-ion battery packs under real-world operating conditions based on a new attenuated model without additional experiments[J]. Energy, 2025, 330: 136802. DOI:10.1016/j.energy.2025.136802.

OKOSHI T, YAMADA K, HIRASAWA T, et al. Battery condition monitoring (BCM) technologies about lead-acid batteries[J]. Journal of Power Sources, 2006, 158(2): 874-878. DOI:10.1016/j.jpowsour.2005.11.008.

CHEN L P, XIE S Q, LOPES A M, et al. A new SOH estimation method for lithium-ion batteries based on model-data-fusion[J]. Energy, 2024, 286: 129597. DOI:10.1016/j.energy.2023.129597.

LIN M Q, YOU Y Q, MENG J H, et al. Lithium-ion batteries SOH estimation with multimodal multilinear feature fusion[J]. IEEE Transactions on Energy Conversion, 2023, 38(4): 2959-2968. DOI:10.1109/TEC.2023.3282017.

GONG D L, GAO Y, KOU Y L, et al. State of health estimation for lithium-ion battery based on energy features[J]. Energy, 2022, 257: 124812. DOI:10.1016/j.energy.2022.124812.

GOU B, XU Y, FENG X. State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method[J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 10854-10867. DOI:10.1109/TVT.2020.3014932.

CHEN C, TAO G Y, SHI J T, et al. A lithium-ion battery degradation prediction model with uncertainty quantification for its predictive maintenance[J]. IEEE Transactions on Industrial Electronics, 2024, 71(4): 3650-3659. DOI:10.1109/TIE.2023.3274874.

HE Y, BAI W Y, WANG L L, et al. SOH estimation for lithium-ion batteries: An improved GPR optimization method based on the developed feature extraction[J]. Journal of Energy Storage, 2024, 83: 110678. DOI:10.1016/j.est.2024.110678.

WANG Z P, YUAN C G, LI X Y. Lithium battery state-of-health estimation via differential thermal voltammetry with Gaussian process regression[J ] . IEEE Transactions on Transportation Electrification, 2021, 7(1): 16-25. DOI:10.1109/TTE.2020.3028784.

KHOSRAVI A, NAHAVANDI S, CREIGHTON D, et al. Lower upper bound estimation method for construction of neural network-based prediction intervals[J]. IEEE Transactions on Neural Networks, 2011, 22(3): 337-346. DOI:10.1109/TNN.2010. 2096824.

PEARCE T, BRINTRUP A, ZAKI M, et al. High-quality prediction intervals for deep learning: A distribution-free, ensembled approach[C]//Stockholm: Proceedings of the 35th International Conference on Machine Learning, 2018, 80: 4075-4084.

PANG X Q, GUO Z Y, JIA J F, et al. A distribution-free interval estimation method for lithium-ion battery state of health[J]. Ionics, 2025, 31(8): 7939-7952. DOI:10.1007/s11581-025-06470-3.

WILLIARD N, HE W, OSTERMAN M, et al. Comparative analysis of features for determining state of health in lithium-ion batteries[J]. International Journal of Prognostics and Health Management, 2013, 4(1): 14-20. DOI:10.36001/ijphm.2013.v4i1.1437.

HE W, WILLIARD N, OSTERMAN M, et al. Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method[J]. Journal of Power Sources, 2011, 196(23): 10314-10321. DOI:10.1016/j.jpowsour.2011.08.040.

XING Y J, MA E W M, TSUI K L, et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries[J]. Microelectronics Reliability, 2013, 53(6): 811-820. DOI:10.1016/j.microrel.2012.12.003.

WENG C H, CUI Y J, SUN J, et al. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression[J]. Journal of Power Sources, 2013, 235: 36-44. DOI:10.1016/j.jpowsour.2013.02.012.

PANG X Q, LIU X Y, JIA J F, et al. A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression[J]. Microelectronics Reliability, 2021, 127: 114405. DOI:10.1016/j.microrel.2021.114405.

LV Z H, SONG Y, HE C L, et al. Remaining useful life prediction for lithium-ion batteries incorporating spatio-temporal information[J]. Journal of Energy Storage, 2024, 88: 111626. DOI:10.1016/j.est.2024.111626.

SHRIVASTAVA N A, KHOSRAVI A, PANIGRAHI B K. Prediction interval estimation of electricity prices using PSO-tuned support vector machines[J]. IEEE Transactions on Industrial Informatics, 2015, 11(2): 322-331. DOI:10.1109/TII.2015.2389625.

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