Review of lithium-ion battery state of charge estimation based on physics-informed neural networks

WANG Shunli; ZHANG Wenxia; GUO Ziwen; DEND Xiao; LIU Guangchen; HUANG Qi

doi:10.19799/j.cnki.2095-4239.2025.1047

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Review of lithium-ion battery state of charge estimation based on physics-informed neural networks

Energy Storage System and Engineering | 更新时间：2026-07-13

- Review of lithium-ion battery state of charge estimation based on physics-informed neural networks
- Energy Storage Science and Technology Vol. 15, Issue 6, Pages: 2319-2337(2026)
- 作者机构：
  
  1.内蒙古工业大学电力学院，内蒙古呼和浩特 010000
  2.西南科技大学信息工程学院，四川绵阳 621010
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.1047
  CLC： TM 912
- Received：19 November 2025，
  
  Revised：2025-12-18，
  
  Published：28 June 2026
- 稿件说明：
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王顺利, 张文霞, 郭子文, 等. 基于物理信息神经网络的锂离子电池荷电状态估计研究综述[J]. 储能科学与技术, 2026, 15(6): 2319-2337.

WANG Shunli, ZHANG Wenxia, GUO Ziwen, et al. Review of lithium-ion battery state of charge estimation based on physics-informed neural networks[J]. Energy Storage Science and Technology, 2026, 15(6): 2319-2337.
王顺利, 张文霞, 郭子文, 等. 基于物理信息神经网络的锂离子电池荷电状态估计研究综述[J]. 储能科学与技术, 2026, 15(6): 2319-2337. DOI： 10.19799/j.cnki.2095-4239.2025.1047.

WANG Shunli, ZHANG Wenxia, GUO Ziwen, et al. Review of lithium-ion battery state of charge estimation based on physics-informed neural networks[J]. Energy Storage Science and Technology, 2026, 15(6): 2319-2337. DOI： 10.19799/j.cnki.2095-4239.2025.1047.

摘要

锂离子电池荷电状态（state of charge，SOC）的精确估计是电池管理系统（battery management system，BMS）实现安全管控与高效调度的关键挑战。电池内在的强非线性、时变参数与复杂外部工况，使得传统方法面临固有局限：模型驱动方法在参数失配时精度骤降，而纯数据驱动方法则受制于数据依赖性强与物理一致性缺失的风险。物理信息神经网络（physics-informed neural networks，PINN）作为一种“机理嵌入”的新范式，通过将等效电路模型或电化学模型等电池物理模型的控制方程作为正则化约束嵌入损失函数，为应对上述挑战提供了创新解决方案。本综述详细介绍了PINN-SOC估计器的理论框架与设计路径，并通过与基于物理驱动方法、基于数据驱动方法、“数据-物理”融合方法系统性对比分析，结果表明PINN方法能够将SOC估计的均方根误差（root mean square error，RMSE）稳定在较低区间，且在未知工况下仍保持良好的精度和泛化性能。尽管优势显著，PINN方法的工程化应用仍面临训练稳定性、损失权重平衡与计算效率等挑战。未来的研究有望通过发展自适应训练算法与轻量化网络结构等关键技术，逐步克服现有瓶颈，推动PINN成为下一代智能BMS的最佳估计方案。

Abstract

Estimating the state of charge (SOC) for lithium-ion batteries is a critical challenge for enabling battery-management systems (BMSs) to achieve safe control and efficient scheduling. The strong inherent non-linearity and time-varying parameters within batteries

coupled with complex external operating conditions

impose inherent limitations on traditional approaches: model-driven methods experience a sharp decline in accuracy when parameters are mismatched

while purely data-driven methods are constrained by high data dependency and the risk of lacking physical consistency. Physics-informed neural networks (PINNs) represent a novel paradigm for embedding physical mechanisms. This approach offers an innovative solution to the aforementioned challenges by embedding the governing equations of battery physics models

such as equivalent-circuit models or electrochemical models

as regularization constraints within the loss function. In this review

we introduce the theoretical framework and design methodology of the PINN-SOC estimator in detail. A systematic comparative analysis with physics-driven

data-driven

and data-physics hybrid approaches demonstrates that the PINN method consistently maintains the root mean square error of SOC estimation within a low range

while retaining high accuracy and the capability for generalization under unknown operating conditions. Despite these notable strengths

the engineering application of the PINN method still faces challenges

such as the issues of training stability

weight-loss balancing

and computational efficiency. We expect future research to overcome existing bottlenecks gradually by developing key technologies

like adaptive-training algorithms and lightweight network architectures

thus positioning PINNs as the optimal solution for next-generation intelligent BMS.

关键词

Keywords

references

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