基于物理信息引导的磷酸铁锂电池荷电状态估计方法

骆可; 强栋; 翁伟; 姚绒; 付俊成; 张轩铭; 孟锦豪

doi:10.19799/j.cnki.2095-4239.2026.0420

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基于物理信息引导的磷酸铁锂电池荷电状态估计方法

储能测试与评价 | 更新时间：2026-09-18

- 基于物理信息引导的磷酸铁锂电池荷电状态估计方法
- Physics-informed adaptive filtering method for state-of-charge estimation of lithium iron phosphate batteries
- 储能科学与技术 2026年15卷第9期页码：3824-3838
- 作者机构：
  
  1.特变电工新疆新能源股份有限公司，新疆维吾尔自治区乌鲁木齐 830011
  2.西安交通大学国家储能技术产教融合创新平台（中心）
  3.西安交通大学电气学院，陕西西安 710049
- 作者简介：
  
  骆可（1985—），男，工程师，研究方向为储能系统集成与运行优化，E-mail：luoke@tbea.com；
  孟锦豪，副教授，研究方向为储能系统设计与能量管理、电池建模与SOX估计、数据-机理混合的大数据电池诊断预警、电池系统测试方法与性能评价，E-mail：jinhao@xjtu.edu.cn。
- 基金信息：
  
  工信部制造业高质量发展专项(gx23225)
- DOI：10.19799/j.cnki.2095-4239.2026.0420
  中图分类号： TM 912
- 收稿：2026-05-15，
  
  修回：2026-06-12，
  
  纸质出版：2026-09-28
- 稿件说明：
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骆可, 强栋, 翁伟, 等. 基于物理信息引导的磷酸铁锂电池荷电状态估计方法[J]. 储能科学与技术, 2026, 15(9): 3824-3838.

LUO Ke, QIANG Dong, WENG Wei, et al. Physics-informed adaptive filtering method for state-of-charge estimation of lithium iron phosphate batteries[J]. Energy Storage Science and Technology, 2026, 15(9): 3824-3838.
骆可, 强栋, 翁伟, 等. 基于物理信息引导的磷酸铁锂电池荷电状态估计方法[J]. 储能科学与技术, 2026, 15(9): 3824-3838. DOI： 10.19799/j.cnki.2095-4239.2026.0420.

LUO Ke, QIANG Dong, WENG Wei, et al. Physics-informed adaptive filtering method for state-of-charge estimation of lithium iron phosphate batteries[J]. Energy Storage Science and Technology, 2026, 15(9): 3824-3838. DOI： 10.19799/j.cnki.2095-4239.2026.0420.

摘要

磷酸铁锂（lithium iron phosphate，LFP）电池在电动汽车与储能系统中应用广泛，但其电压平台区可观测性不足，表面力信号又易受温度漂移、机械滞后与老化效应耦合干扰，导致复杂工况下荷电状态（state of charge，SOC）估计精度下降。针对上述问题，本工作提出一种物理信息引导熵权自适应扩展卡尔曼滤波（physics-informed entropy-weighted adaptive extended Kalman filter，PI-EWAEKF）方法。首先，基于准静态标定结果构建开路电压（open-circuit voltage，OCV）-SOC与表面力-SOC物理映射关系；其次，采用增量力分析（incremental force analysis，IFA）提取抗漂移力学特征，并结合OCV梯度构建多源物理特征集；进一步，利用香农熵和物理梯度共同表征电压与表面力信号的实时置信度，并将其映射为AEKF测量噪声协方差矩阵的自适应调节因子，从而实现多源观测权重的动态分配。实验在25℃、35℃、45℃以及健康状态（state of health，SOH）约为 100% 和 91% 条件下开展，并采用联邦城市行驶工况（federal urban driving schedule，FUDS）、动态应力测试（dynamic stress test，DST）和新欧洲行驶工况（new European driving cycle，NEDC）进行验证。结果表明，相较于单电压观测模型，所提方法在25℃动态工况下将FUDS、DST和NEDC工况的均方根误差分别由5.11%、4.16%和3.62%降低至0.96%、0.97%和0.70%，最大降幅约为81.2%。在高温与老化耦合条件下，所提方法仍能保持较低误差水平，验证了其在复杂工况下的估计精度与鲁棒性。

Abstract

Lithium iron phosphate batteries are widely employed in electric vehicles and energy storage systems. However

their weak observability in the voltage plateau region

alongside the coupled effects of temperature drift

mechanical hysteresis

and aging on surface-force signals

degrades state of charge (SOC) estimation under complex operating conditions. To address these challenges

this study proposes a physics-informed entropy-weighted adaptive extended Kalman filter (PI-EWAEKF). First

physical mapping relationships for open-circuit voltage (OCV)-SOC and surface force-SOC are established using quasi-static calibration. Second

incremental force analysis is performed to extract drift-resistant mechanical features

which are combined with OCV gradients to construct a multisource physical feature set. Furthermore

Shannon entropy and physical gradients are integrated to characterize the real-time confidence of voltage and surface-force signals

which are then mapped into adaptive adjustment factors for the PI-EWAEKF measurement noise covariance matrix to dynamically allocate multisource observation weights. Experiments are conducted at 25℃

35℃

and 45℃ under approximately 100% and 91% state-of-health levels

with validation across the federal urban driving schedule (FUDS)

dynamic stress test (DST)

and new European driving cycle (NEDC) profiles. Compared with a voltage-only observation model at 25℃

the proposed method reduces the root-mean-square errors under FUDS

DST

and NEDC profiles from 5.11%

4.16%

and 3.62% to 0.96%

0.97%

and 0.70%

respectively

achieving a maximum reduction of approximately 81.2%. Even under coupled high-temperature and aging conditions

the proposed method still maintains low error

demonstrating strong estimation accuracy and robustness under complex operating conditions.

关键词

Keywords

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