A framework fusing ensemble learning and quantile regression for lithium-ion battery life prediction

ZHENG Lihan; LIN Junnan; CHEN Ping; SHI Zhicong

doi:10.19799/j.cnki.2095-4239.2026.0054

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A framework fusing ensemble learning and quantile regression for lithium-ion battery life prediction

Energy Storage Test\: Methods and Evaluation | 更新时间：2026-07-13

- A framework fusing ensemble learning and quantile regression for lithium-ion battery life prediction
- Energy Storage Science and Technology Vol. 15, Issue 6, Pages: 2380-2391(2026)
- 作者机构：
  
  1.广东工业大学材料与能源学院，广东广州 510006
  2.电科电源股份有限公司，广东深圳 518000
  3.衡阳电科电源股份有限公司，湖南衡阳 421000
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2026.0054
  CLC： TM 912
- Received：19 January 2026，
  
  Revised：2026-01-29，
  
  Published：28 June 2026
- 稿件说明：
移动端阅览
郑立涵, 林俊楠, 陈平, 等. 基于集成学习和分位数回归的锂离子电池寿命预测方法[J]. 储能科学与技术, 2026, 15(6): 2380-2391.

ZHENG Lihan, LIN Junnan, CHEN Ping, et al. A framework fusing ensemble learning and quantile regression for lithium-ion battery life prediction[J]. Energy Storage Science and Technology, 2026, 15(6): 2380-2391.
郑立涵, 林俊楠, 陈平, 等. 基于集成学习和分位数回归的锂离子电池寿命预测方法[J]. 储能科学与技术, 2026, 15(6): 2380-2391. DOI： 10.19799/j.cnki.2095-4239.2026.0054.

ZHENG Lihan, LIN Junnan, CHEN Ping, et al. A framework fusing ensemble learning and quantile regression for lithium-ion battery life prediction[J]. Energy Storage Science and Technology, 2026, 15(6): 2380-2391. DOI： 10.19799/j.cnki.2095-4239.2026.0054.

摘要

锂离子电池寿命的精准预测是保障电池系统安全的关键。针对传统方法依赖复杂特征工程且难以量化预测不确定性的问题，本研究提出一种融合集成学习与分位数回归的锂离子电池寿命预测框架。该框架以搭载了注意力机制的门控循环单元为基学习器，结合引导聚集策略与分位数回归机制，仅需输入经滑动窗口处理的电池容量序列即可完成预测，避免了复杂的特征提取。通过五种不同容量规格电池退化数据验证表明，该框架在不同预测步长下均能保持较高的预测准确性，在300次循环的预测步长下，预测结果的均方根误差、平均绝对百分比误差和平均绝对误差分别低至0.4097%、0.3246%和0.1104%，显著优于传统点预测模型。与单一模型和基础门控循环单元模型相比，该模型在所有测试电池上均取得了最优且最稳定的预测性能，这得益于集成学习策略有效提升了模型的稳定性与泛化能力。在不确定性量化方面，所提框架生成的预测区间对测试电池的真实容量值均实现了有效覆盖，覆盖率显著高于对比模型，证明了其在预测不确定性评估方面的优势。本研究创新性地将集成学习与分位数回归相结合，在实现高精度点预测的同时输出具有95%置信度的预测区间，从而显著提升了锂离子电池寿命预测的准确性与不确定性量化能力。

Abstract

Accurate prediction of lithium-ion battery life is imperative for ensuring the safety of battery systems. Traditional methods rely on complex feature engineering and struggle to quantify prediction uncertainty. To address these limitations

this paper proposes a lithium-ion battery life prediction framework integrating ensemble learning and quantile regression. The proposed framework utilizes a Gated Recurrent Unit (GRU) augmented with an attention mechanism as the fundamental learner. Incorporating a Bootstrap Aggregating strategy and quantile regression

the approach necessitates only battery capacity sequences processed via a sliding window for prediction

thereby eliminating the requirement for complex feature extraction. Validation on degradation data from five lithium-ion batteries with varying capacity specifications demonstrates that the proposed framework maintains high prediction accuracy across different prediction horizons. At a prediction horizon of 300 cycles

the Root Mean Square Error

Mean Absolute Percentage Error

and Mean Absolute Error are as low as 0.4097%

0.3246%

and 0.1104%

respectively

significantly outperforming traditional point prediction models. A comparison of the proposed framework with single models and the standard GRU model reveals that the former attains superior and more stable prediction performance across all tested batteries. This enhancement can be attributed to the ensemble learning strategy

which augments model robustness and generalization. In the context of uncertainty quantification

the prediction intervals generated by the framework effectively encompass the actual capacity values of the test batteries. Furthermore

the coverage rates of the framework are notably higher than those of comparative models

underscoring its superiority in uncertainty assessment. This study innovatively combines ensemble learning with quantile regression

enabling high-precision point predictions while generating 95% confidence prediction intervals. Consequently

this combination significantly improves the accuracy and uncertainty quantification capability of lithium-ion battery life prediction.

关键词

Keywords

references

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