High-accuracy state of charge estimation for LFP batteries based in hybrid battery packs on a segmented decision strategy

CHEN Zheng; ZHANG Huan; XIA Xuelei; SHEN Jiangwei; WEI Chao; WEI Fuxing

doi:10.19799/j.cnki.2095-4239.2025.0885

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High-accuracy state of charge estimation for LFP batteries based in hybrid battery packs on a segmented decision strategy

Energy Storage Test\: Methods and Evaluation | 更新时间：2026-05-28

- High-accuracy state of charge estimation for LFP batteries based in hybrid battery packs on a segmented decision strategy
- Energy Storage Science and Technology Vol. 15, Issue 2, Pages: 604-615(2026)
- 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：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.0885
  CLC： TM 912
- Received：09 October 2025，
  
  Revised：2025-10-29，
  
  Published：28 February 2026
- 稿件说明：
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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

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