基于分段决策的混装电池组中磷酸铁锂电池荷电状态高精估算

陈峥; 张欢; 夏雪磊; 申江卫; 魏超; 魏福星

doi:10.19799/j.cnki.2095-4239.2025.0885

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基于分段决策的混装电池组中磷酸铁锂电池荷电状态高精估算

储能测试与评价 | 更新时间：2026-05-28

- 基于分段决策的混装电池组中磷酸铁锂电池荷电状态高精估算
- High-accuracy state of charge estimation for LFP batteries based in hybrid battery packs on a segmented decision strategy
- 储能科学与技术 2026年15卷第2期页码：604-615
- 作者机构：
  
  昆明理工大学交通工程学院，云南昆明 650500
- 作者简介：
  
  陈峥（1982—），男，教授，研究方向为动力电池管理与控制，E-mail：chen@kust.edu.cn；
  魏福星，讲师，研究方向为动力电池管理与控制，E-mail：wfx@kust.edu.cn。
- 基金信息：
  
  国家自然科学基金(52267022);云南省基础研究计划项目(202401AS070118);云南省“兴滇英才支持计划”云岭学者专项(KKRC202402005);云南省车路协同控制与运行安全创新团队(202505AS350024)
- DOI：10.19799/j.cnki.2095-4239.2025.0885
  中图分类号： TM 912
- 收稿：2025-10-09，
  
  修回：2025-10-29，
  
  纸质出版：2026-02-28
- 稿件说明：
移动端阅览
陈峥, 张欢, 夏雪磊, 等. 基于分段决策的混装电池组中磷酸铁锂电池荷电状态高精估算[J]. 储能科学与技术, 2026, 15(2): 604-615.

CHEN Zheng, ZHANG Huan, XIA Xuelei, et al. High-accuracy state of charge estimation for LFP batteries based in hybrid battery packs on a segmented decision strategy[J]. Energy Storage Science and Technology, 2026, 15(2): 604-615.
陈峥, 张欢, 夏雪磊, 等. 基于分段决策的混装电池组中磷酸铁锂电池荷电状态高精估算[J]. 储能科学与技术, 2026, 15(2): 604-615. DOI： 10.19799/j.cnki.2095-4239.2025.0885.

CHEN Zheng, ZHANG Huan, XIA Xuelei, et al. High-accuracy state of charge estimation for LFP batteries based in hybrid battery packs on a segmented decision strategy[J]. Energy Storage Science and Technology, 2026, 15(2): 604-615. DOI： 10.19799/j.cnki.2095-4239.2025.0885.

摘要

磷酸铁锂(LiFePO

LFP)与镍钴锰酸锂(LiNi

ₓ

NCM)电池串联构建的混合动力电池系统，是突破传统单一化学体系瓶颈的关键技术。然而，混装电池包中LFP电池具有平坦的电压平台特性，导致全工作区间的荷电状态(state of charge

SOC)估算精度受限，且在多算法切换时易出现SOC跳变现象。为此，本工作提出一种基于开路电压(open circuit voltage

OCV)曲线区间自适应划分的分段融合SOC估算方法。首先，考虑到LFP电池OCV斜率变化特征，设计了分段平滑策略，在高斜率区保持电压特征，在平台区增强平滑效果，并根据平滑OCV曲线的一阶差分斜率，设定自适应斜率阈值，将放电区间划分为前端高斜率区、中间平台区与后端高斜率区，为SOC算法选择提供明确依据；其次，构建分段估算框架：在高斜率区采用改进自适应扩展卡尔曼滤波进行SOC动态跟踪，在平台区则利用混合包中NCM电池的SOC进行映射估算。针对算法切换点SOC跳变问题，进一步提出梯度敏感的S型融合算法(gradient-sensitive adaptive blending

GSAB)，该算法通过量化切换点邻域的SOC梯度差异，动态调整融合函数参数以生成平滑过渡权重，抑制切换点的SOC跳变。结果表明，改进自适应扩展卡尔曼滤波算法在NCM电池上的均方根误差相较于传统扩展卡尔曼滤波算法降低63.70%；GSAB策略有效消除了算法切换时的SOC突变，使过渡区波动降低72.42%。最终，在城市道路循环工况下，LFP电池全区间SOC估算的平均绝对误差与均方根误差分别降至1.08%和1.31%，验证了所提方法能有效提升LFP电池SOC全区间

估算精度。

Abstract

Hybrid battery systems constructed by connecting lithium iron phosphate (LiFePO

LFP) and nickel cobalt manganese (LiNi

NCM) cells in series represent a key technology for overcoming the limitations of single-chemistry systems. However

the flat voltage plateau of LFP cells complicates state-of-charge (SOC) estimation

resulting in insufficient accuracy over the full operating range and abrupt SOC jumps during multi-algorithm switching. To address these issues

this study proposes a segmented fusion method for SOC estimation based on adaptive partitioning of the open-circuit voltage (OCV) curve. First

a piecewise smoothing strategy is designed for the LFP OCV curve according to variations in slope. This strategy preserves voltage characteristics in high-slope regions while enhancing smoothing in the plateau region. Based on the first-order derivative of the smoothed OCV curve

an adaptive slope threshold is established to divide the discharge process into a front high-slope region

a middle plateau region

and a rear high-slope region

providing a clear basis for algorithm selection. Subsequently

a segmented estimation framework was developed. An improved adaptive extended Kalman filter (AEKF) is applied for dynamic SOC tracking in the high-slope regions

while SOC in the plateau region is estimated by mapping from the SOC of the NCM battery in the hybrid pack. To mitigate SOC jumps at switching points

a gradient-sensitive adaptive blending (GSAB) algorithm was introduced. This algorithm quantifies SOC gradient differences near switching points and dynamically adjusts fusion function parameters to generate smooth transition weights

thereby suppressing SOC discontinuities. Experimental results show that the improved AEKF reduces the root mean square error (RMSE) of the NCM battery by 63.70% compared with the traditional extended Kalm

an filter (EKF). The GSAB strategy effectively eliminates SOC jumps during algorithm switching and reduces transition-zone fluctuations by 72.42%. Under an urban driving cycle

full-range SOC estimation for the LFP battery achieves a mean absolute error (MAE) of 1.08% and an RMSE of 1.31%

demonstrating the effectiveness of the proposed method in improving full-range SOC estimation accuracy.

关键词

Keywords

references

DENG J, BAE C, DENLINGER A, et al. Electric vehicles batteries: Requirements and challenges[J]. Joule, 2020, 4(3): 511-515. DOI:10.1016/j.joule.2020.01.013.

DARCOVICH K, LAURENT T, RIBBERINK H. Improved prospects for V2X with longer range 2nd generation electric vehicles[J]. eTransportation, 2020, 6: 100085. DOI:10.1016/j.etran.2020.100085.

朱成, 刘頔, 滕欣余, 等. 新能源汽车综合经济性对比分析及预测研究[J]. 汽车工程, 2023, 45(2): 333-340. DOI:10.19562/j.chinasae.qcgc.2023.02.018.

ZHU C, LIU D, TENG X Y, et al. Comparative analysis and forecast research on comprehensive economy of new energy vehicles[J]. Automotive Engineering, 2023, 45(2): 333-340. DOI:10.19562/j.chinasae.qcgc.2023.02.018.

郝峻丰, 朱璟, 岑官骏, 等. 锂电池百篇论文点评(2025.04.01—2025.05.31)[J]. 储能科学与技术, 2025, 14(7): 2884-2902. DOI:10.19799/j.cnki.2095-4239.2025.0586.

HAO J F, ZHU J, CEN G J, et al. Reviews of selected 100 recent papers for lithium batteries(Apr. 1, 2025 to May 31 2025)[J]. Energy Storage Science and Technology, 2025, 14(7): 2884-2902. DOI:10.19799/j.cnki.2095-4239.2025.0586.

NASERI T, MOUSAVI S M. Treatment of spent lithium iron phosphate (LFP) batteries[J]. Current Opinion in Green and Sustainable Chemistry, 2024, 47: 100906. DOI:10.1016/j.cogsc. 2024.100906.

KOVACHEV G, ELLERSDORFER C, GSTREIN G, et al. Safety assessment of electrically cycled cells at high temperatures under mechanical crush loads[J]. eTransportation, 2020, 6: 100087. DOI:10.1016/j.etran.2020.100087.

ZHAO T Y, LI W L, TRAVERSY M, et al. A review on the recycling of spent lithium iron phosphate batteries[J]. Journal of Environmental Management, 2024, 351: 119670. DOI:10.1016/j.jenvman.2023.119670.

YANG X G, LIU T, WANG C Y. Thermally modulated lithium iron phosphate batteries for mass-market electric vehicles[J]. Nature Energy, 2021, 6(2): 176-185. DOI:10.1038/s41560-020-00757-7.

HOW D N T, HANNAN M A, HOSSAIN LIPU M S, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review[J]. IEEE Access, 2019, 7: 136116-136136. DOI:10.1109/ACCESS.2019.2942213.

AN F L, JIANG J C, ZHANG W G, et al. State of energy estimation for lithium-ion battery pack via prediction in electric vehicle applications[J ] . IEEE Transactions on Vehicular Technology, 2022, 71(1): 184-195. DOI:10.1109/TVT.2021.3125194.

潘海鸿, 吕治强, 李君子, 等. 基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2017, 32(21): 1-8. DOI:10.19595/j.cnki.1000-6753.tces.160837.

PAN H H, LÜ Z Q, LI J Z, et al. Estimation of lithium-ion battery state of charge based on grey prediction model-extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 1-8. DOI:10.19595/j.cnki.1000-6753.tces.160837.

黄鹏超, 鄂加强. 基于双自适应卡尔曼滤波的锂电池状态估算[J]. 储能科学与技术, 2022, 11(2): 660-666. DOI:10.19799/j.cnki.2095-4239.2021.0411.

HUANG P C, E J Q. State estimation of lithium-ion battery based on dual adaptive Kalman filter[J]. Energy Storage Science and Technology, 2022, 11(2): 660-666. DOI:10.19799/j.cnki.2095-4239.2021.0411.

乔家璐, 王顺利, 于春梅, 等. 基于加权多新息AEKF的锂电池SOC在线估算[J]. 储能科学与技术, 2021, 10(6): 2318-2325. DOI:10.19799/j.cnki.2095-4239.2021.0242.

QIAO J L, WANG S L, YU C M, et al. Novel multiple weighted-AEKF method for online state-of-charge estimation of lithium-ion batteries[J]. Energy Storage Science and Technology, 2021, 10(6): 2318-2325. DOI:10.19799/j.cnki.2095-4239.2021.0242.

CUI X D, SHEN W X, ZHENG J C. New on-line approach for lithium iron phosphate battery pack balancing based on state of charge[C]//2015 18th International Conference on Electrical Machines and Systems (ICEMS). October 25-28, 2015. Pattaya, Thailand. IEEE, 2015: 762-767. DOI:10.1109/icems.2015.7385136.

FARMANN A, SAUER D U. A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries[J]. Journal of Power Sources, 2017, 347: 1-13. DOI:10.1016/j.jpowsour.2017.01.098.

王林峰, 方杰, 曾士哲, 等. 电池包、方法和车辆: CN112599932A[P]. 2021-04-02.

蔡伟, 花睿, 曾士哲, 等. 一种电池系统、用于其SOC估算方法以及计算机装置和介质: CN112698223A[P]. 2021-04-23.

CHENG X Q, LIU X L, DENG H Y, et al. State of charge estimation of LiFePO 4 battery in AB hybrid battery packs[J ] . Journal of Energy Storage, 2025, 108: 115070. DOI:10.1016/j.est.2024.115070.

NASERI F, SCHALTZ E, STROE D I, et al. An enhanced equivalent circuit model with real-time parameter identification for battery state-of-charge estimation[J]. IEEE Transactions on Industrial Electronics, 2022, 69(4): 3743-3751. DOI:10.1109/TIE. 2021.3071679.

卿崇源, 陈少华, 李瑞鹏, 等. 基于FFRLS-DEKF的锂电池SOC-SOH联合估算研究[J]. 信息技术与信息化, 2024(3): 8-12. DOI:10.3969/j.issn.1672-9528.2024.03.002.

QING C Y, CHEN S H, LI R P, et al. Research on joint estimation of SOC-SOH of lithium battery based on FFRLS-DEKF[J]. Information Technology & Informatization, 2024(3): 8-12. DOI:10.3969/j.issn.1672-9528.2024.03.002.

潘豪, 王震, 康庄, 等. 光储孤岛直流微电网混合模式平滑切换策略[J/OL]. 太阳能学报, 1-10. [2025-10-21]. https://doi.org/10.19912/j.0254-0096.tynxb.2024-1671.

ZHANG Z Y, JIANG L, ZHANG L Z, et al. State-of-charge estimation of lithium-ion battery pack by using an adaptive extended Kalman filter for electric vehicles[J]. Journal of Energy Storage, 2021, 37: 102457. DOI:10.1016/j.est.2021.102457.

WANG G, JIN B, WANG M Z, et al. State of charge estimation for "LiFePO 4 -LiCo x Ni y Mn 1- x - y O 2 " hybrid battery pack[J ] . Journal of Energy Storage, 2023, 65: 107345. DOI:10.1016/j.est.2023.107345.

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