| 王建秋,何永泰,浦东玲,王小旦.融合物理先验与异方差高斯过程的锂离子电池剩余寿命预测[J].电子测量与仪器学报,2026,40(1):102-109 |
| 融合物理先验与异方差高斯过程的锂离子电池剩余寿命预测 |
| Lithium-ion battery remaining useful life prediction via physics-driven prior andheteroscedastic Gaussian process regression |
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| DOI： |
| 中文关键词: 三段物理先验模型 异方差高斯过程回归 不确定度量化 β-校准 |
| 英文关键词:three-segment physics prior model heteroscedastic Gaussian process regression uncertainty quantification β-calibration |
| 基金项目:国家自然科学基金（51566001）、云南省高校科技创新团队支持计划（2018038）项目资助 |
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| 中文摘要: |
| 针对现有纯数据驱动模型易过拟合且不确定度估计不足的问题，提出了一种混合物理-数据驱动框架（Phys+GPR）。该方法首先基于电池早期—加速—线性三阶段退化机理构建3段经验物理模型，提取物理先验容量；随后对物理残差引入异方差高斯过程回归（GPR）（两阶段GPR）分别估计残差均值与方差，并采用TreeBagger随机森林对均值预测进行二次修正；最后通过β-校准在训练集上确定置信区间尺度，实现全生命周期90%预测区间的可靠覆盖。在NASA提供的B0005、B0006、B0007、B0018四块电池上进行留一电池（LOBO）交叉验证，Phys+GPR在所有电池上均取得R2> 0.93的高精度预测，且90%预测区间覆盖率（PICP）在70%~92%，平均区间宽度（MPIW）在0.085~0.10 Ah，显著优于纯GPR、单指数物理+GPR及SVR基线方法。实验结果表明，该方法具备良好的跨电池泛化能力、可解释的物理先验机制以及稳健的不确定度量化性能，为电池健康管理与在线寿命预测提供了高置信度支持。 |
| 英文摘要: |
| To address the overfitting and unreliable uncertainty estimation of purely data-driven approaches, this paper proposes a hybrid physics-data framework (Phys + GPR) for battery prognostics. First, a three-segment empirical model, derived from the early, accelerated, and linear degradation stages of lithium-ion batteries, is employed to extract a physics-based capacity prior. The residuals between measured capacity and the prior are then modelled by a two-stage heteroscedastic Gaussian process regression (GPR), Stage 1 estimates the residual mean, Stage 2 estimates the input-dependent variance. A TreeBagger random-forest regressor further refines the mean prediction, and β-calibration is applied on the training set to scale the predictive intervals, ensuring a reliable 90% coverage throughout the battery lifetime. Leave-one-battery-out (LOBO) cross-validation on NASA cells B0005, B0006, B0007 and B0018 shows that Phys + GPR achieves R2>0.93 for all cells, with a 90% prediction-interval coverage probability (PICP) of 70%~92% and a mean prediction-interval width (MPIW) of 0.085~0.10 Ah—significantly outperforming pure GPR, single-exponential + GPR and SVR baselines. The results demonstrate superior cross-battery generalisation, interpretable physics priors and robust uncertainty quantification, providing high-confidence support for battery health management and online RUL prediction. |
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