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

王顺利; 张文霞; 郭子文; 邓潇; 刘广忱; 黄琦

doi:10.19799/j.cnki.2095-4239.2025.1047

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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

储能系统与工程 | 更新时间：2026-07-13

- 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
- Review of lithium-ion battery state of charge estimation based on physics-informed neural networks
- 储能科学与技术 2026年15卷第6期页码：2319-2337
- 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：
  
  王顺利（1985—），男，教授，博士生导师，从事新能源与储能系统研究，E-mail：497420789@qq.com。
- 基金信息：
  
  国家重点研发计划资助项目(2024YFB2408400);国家自然科学基金(62173281;52377217;U23A20651);四川省自然科学基金(2024NSFSC0013)
- DOI：10.19799/j.cnki.2095-4239.2025.1047
  中图分类号： TM 912
- 收稿：2025-11-19，
  
  修回：2025-12-18，
  
  纸质出版：2026-06-28
- 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

XU J J, CAI X Y, CAI S M, et al. High-energy lithium-ion batteries: Recent progress and a promising future in applications[J]. Energy & Environmental Materials, 2023, 6(5): e12450. DOI:10.1002/eem2.12450.

GAO Y L, PAN Z H, SUN J G, et al. High-energy batteries: Beyond lithium-ion and their long road to commercialisation[J]. Nano-Micro Letters, 2022, 14(1): 94. DOI:10.1007/s40820-022-00844-2.

雷亦鸣. 基于数据驱动的稳健SOC估计融合方法研究[D]. 西安: 西安理工大学, 2024.LEI Y M. Research on robust soc estimation fusion method based on data-driven approach[D]. Xi'an: Xi'an University of Technology, 2024.

SHI H T, WU Q Q, WANG S L, et al. Improved back-propagation neural network-multi-information gain optimization Kalman filter method for high-precision estimation of state-of-energy in lithium-ion batteries[J]. Energy, 2025, 335: 138214. DOI:10.1016/j.energy.2025.138214.

步传宇, 姜昆, 任军, 等. 基于改进型ASRCKF算法的锂离子电池荷电状态估计[J]. 广东电力, 2020, 33(10): 16-25. DOI:10.3969/j.issn.1007-290X.2020.010.003.

BU C Y, JIANG K, REN J, et al. Estimation of SOC of lithium-ion battery based on improved ASRCKF[J]. Guangdong Electric Power, 2020, 33(10): 16-25. DOI:10.3969/j.issn.1007-290X.2020.010.003.

LI J W, CHE Y H, ZHANG K, et al. Efficient battery fault monitoring in electric vehicles: Advancing from detection to quantification[J]. Energy, 2024, 313: 134150. DOI:10.1016/j.energy.2024.134150.

XIE J M, WEI X Y, BO X Q, et al. State of charge estimation of lithium-ion battery based on extended Kalman filter algorithm[J]. Frontiers in Energy Research, 2023, 11: 1180881. DOI:10.3389/fenrg.2023.1180881.

LIU Q H, YU Q Q. The lithium battery SOC estimation on square root unscented Kalman filter[J]. Energy Reports, 2022, 8: 286-294. DOI:10.1016/j.egyr.2022.05.079.

WU W B, ZENG J B, JIAN Q F, et al. A Li-ion battery state of charge estimation strategy based on the suboptimal multiple fading factor extended Kalman filter algorithm[J]. Processes, 2024, 12(5): DOI:10.3390/pr12050998.

SOLOMON O O, ZHENG W, CHEN J X, et al. State of charge estimation of lithium-ion battery using an improved fractional-order extended Kalman filter[J]. Journal of Energy Storage, 2022, 49: 104007. DOI:10.1016/j.est.2022.104007.

ZHANG M, YANG D F, DU J X, et al. A review of SOH prediction of Li-ion batteries based on data-driven algorithms[J]. Energies, 2023, 16(7): 1-28.

LIU P, WANG L Z, RANJAN R, et al. A survey on active deep learning: From model driven to data driven[J]. ACM Computing Surveys, 2022, 54(10s): 1-34. DOI:10.1145/3510414.

XUE P C, QIU R, PENG C C, et al. Solutions for lithium battery materials data issues in machine learning: Overview and future outlook[J]. Advanced Science, 2024, 11(48): 2410065. DOI:10.1002/advs.202410065.

于程程. 基于数据驱动的锂电池状态及剩余充电时间估计方法研究[D]. 烟台: 烟台大学, 2024.YU C C. Research on state and remaining charge time estimation of lithium battery based on data-driven[D]. Yantai: Yantai University, 2024.

BASIT S, UYAR M. Data-driven prediction of copper leaching yield from brass waste using stacking ensemble learning[J]. Separation and Purification Technology, 2025, 378: 134691. DOI:10.1016/j.seppur.2025.134691.

ZHOU Q Q, TENG S, SITU Z X, et al. A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions[J]. Hydrology and Earth System Sciences, 2023, 27(9): 1791-1808. DOI:10.5194/hess-27-1791-2023.

WAHEED W, XU Q S. Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management[J]. Electric Power Systems Research, 2024, 232: 110376. DOI:10.1016/j.epsr.2024.110376.

SHI D P, ZHAO J Y, WANG Z H, et al. Spatial-temporal self-attention transformer networks for battery state of charge estimation[J]. Electronics, 2023, 12(12): DOI:10.3390/electronics12122598.

ZHANG Z P, FAN Y, TIAN J Q, et al. Online estimation of model parameters and state of charge for lithium-ion battery using multitimescale recurrent neural networks[J]. IEEE Transactions on Industrial Electronics, 2025, 72(8): 8119-8129. DOI:10.1109/TIE.2025.3528505.

WANG Y X, CHEN Z H, ZHANG W. Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning[J]. Energy, 2022, 244: 123178. DOI:10.1016/j.energy.2022.123178.

SHEN L, SUN Y, YU Z Y, et al. On efficient training of large-scale deep learning models[J]. ACM Computing Surveys, 2025, 57(3): 1-36. DOI:10.1145/3700439.

WEN H, KHAN F, AMIN M T, et al. Myths and misconceptions of data-driven methods: Applications to process safety analysis[J]. Computers & Chemical Engineering, 2022, 158: 107639. DOI:10.1016/j.compchemeng.2021.107639.

肖张华. 结合机理信息与数据驱动的锂离子电池荷电状态估计方法研究[D]. 北京: 北京化工大学, 2025.XIAO Z H. Hybrid mechanism-data-driven approach for state of charge estimation in lithium-ion batteries[D]. Beijing: Beijing University of Chemical Technology, 2025.

AHN J, LEE Y, HAN B, et al. A highly effective and robust structure-based LSTM with feature-vector tuning framework for high-accuracy SOC estimation in EV[J]. Energy, 2025, 325: 136134. DOI:10.1016/j.energy.2025.136134.

TAKYI-ANINAKWA P, WANG S L, LIU G C, et al. Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data[J]. Applied Energy, 2024, 363: 123056. DOI:10.1016/j.apenergy.2024.123056.

LUO K, ZHAO J S, WANG Y P, et al. Physics-informed neural networks for PDE problems: A comprehensive review[J]. Artificial Intelligence Review, 2025, 58(10): 323. DOI:10.1007/s10462-025-11322-7.

FALAS S, ASPROU M, KONSTANTINOU C, et al. Robust power system state estimation using physics-informed neural networks[J]. IEEE Transactions on Industrial Informatics, 2025, 21(10): 8057-8067. DOI:10.1109/TII.2025.3582293.

NASCIMENTO R G, VIANA F A C, CORBETTA M, et al. A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks[J]. Scientific Reports, 2023, 13: 13856. DOI:10.1038/s41598-023-33018-0.

WANG Y N, HAN X B, GUO D X, et al. Physics-informed recurrent neural network with fractional-order gradients for state-of-charge estimation of lithium-ion battery[J]. IEEE Journal of Radio Frequency Identification, 2022, 6: 968-971. DOI:10.1109/JRFID.2022.3211841.

LAWAL Z K, YASSIN H, LAI D T C, et al. Physics-informed neural network (PINN) evolution and beyond: A systematic literature review and bibliometric analysis[J]. Big Data and Cognitive Computing, 2022, 6(4): DOI:10.3390/bdcc6040140.

PAN R B, XIAO F, SHEN M Y. Ro-PINN: A reduced order physics-informed neural network for solving the macroscopic model of pedestrian flows[J]. Transportation Research Part C: Emerging Technologies, 2024, 163: 104658. DOI:10.1016/j.trc.2024.104658.

XU L, YANG C H, XU X D, et al. Physics-informed stochastic configuration network promoted model predictive control with multi-objective optimization[J]. Artificial Intelligence Review, 2025, 58(9): 281. DOI:10.1007/s10462-025-11278-8.

WANG J F, JIA Y K, YANG N, et al. Precise equivalent circuit model for Li-ion battery by experimental improvement and parameter optimization[J]. Journal of Energy Storage, 2022, 52: 104980. DOI:10.1016/j.est.2022.104980.

BARZACCHI L, LAGNONI M, DI RIENZO R, et al. Enabling early detection of lithium-ion battery degradation by linking electrochemical properties to equivalent circuit model parameters[J]. Journal of Energy Storage, 2022, 50: 104213. DOI:10.1016/j.est.2022.104213.

MAHESHWARI A, NAGESWARI S. Real-time state of charge estimation for electric vehicle power batteries using optimized filter[J]. Energy, 2022, 254: 124328. DOI:10.1016/j.energy. 2022.124328.

VENNAM G, SAHOO A. A dynamic SOH-coupled lithium-ion cell model for state and parameter estimation[J]. IEEE Transactions on Energy Conversion, 2023, 38(2): 1186-1196. DOI:10.1109/TEC.2022.3218344.

LIU F, SHAO C, SU W X, et al. Online joint estimator of key states for battery based on a new equivalent circuit model[J]. Journal of Energy Storage, 2022, 52: 104780. DOI:10.1016/j.est.2022.104780.

BEDWAL K, MOULIK B, VANDANA. Novel equivalent circuit battery model with adaptive parameters for hybrid state of charge estimation[J]. Journal of Energy Storage, 2025, 137: 118653. DOI:10.1016/j.est.2025.118653.

LIU Y S, WANG L C, LI D Z, et al. State-of-health estimation of lithium-ion batteries based on electrochemical impedance spectroscopy: A review[J]. Protection and Control of Modern Power Systems, 2023, 8(1): 41. DOI:10.1186/s41601-023-00314-w.

MIRANDA D, GONÇALVES R, WUTTKE S, et al. Overview on theoretical simulations of lithium-ion batteries and their application to battery separators[J]. Advanced Energy Materials, 2023, 13(13): 2203874. DOI:10.1002/aenm.202203874.

张艳岗, 董泽庆, 郑利锋, 等. 锂离子动力电池电化学建模进展及降阶方法研究[J]. 物理学报, 2025, 74(14): 375-390. DOI:10.7498/aps.74.20250591.

ZHANG Y G, DONG Z Q, ZHENG L F, et al. Research on electrochemical modeling and order reduction methods for lithium-ion power batteries[J]. Acta Physica Sinica, 2025, 74(14): 375-390. DOI:10.7498/aps.74.20250591.

郝天运. 基于数据融合的锂电池荷电状态估计[D]. 南京: 南京邮电大学, 2023.HAO T Y. SOE estimation for lithium-ion batteries based on data fusion[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023.

WANG J R, MENG J H, PENG Q, et al. Lithium-ion battery state-of-charge estimation using electrochemical model with sensitive parameters adjustment[J]. Batteries, 2023, 9(3): DOI:10.3390/batteries9030180.

ZHU G R, WU Z X, REN X T, et al. A self-correction single particle model of lithium-ion battery based on multi-population genetic algorithm[J]. Journal of Energy Storage, 2023, 71: 108005. DOI:10.1016/j.est.2023.108005.

TIAN A N, HE L Y, DONG K L, et al. An extended single-particle model based on physics-informed neural network for SOC state estimation of lithium-ion batteries[C]//Clean Energy Technology and Energy Storage Systems. Singapore: Springer, 2025: 300-316. DOI:10.1007/978-981-96-0232-2_24.

XIE J L, YU J H, LIU L Q, et al. Estimating the charge and temperature states for Li-ion batteries by coupling single particle kinetics and electrothermal effects[J]. IEEE/ASME Transactions on Mechatronics, 2025, 30(6): 4837-4848. DOI:10.1109/TMECH.2025.3561894.

SHUI Z Y, LI X H, FENG Y, et al. Combining reduced-order model with data-driven model for parameter estimation of lithium-ion battery[J]. IEEE Transactions on Industrial Electronics, 2023, 70(2): 1521-1531. DOI:10.1109/TIE.2022.3157980.

ZHOU B R, MENG C W, PANG T W, et al. A reduced-order method for battery modeling considering time-varying characteristics of the system[C ] //2025 American Control Conference (ACC). July 8-10, 2025 , Denver, CO, USA. IEEE, 2025: 2793-2798. DOI:10.23919/ACC63710.2025.11107672.

CUI Z H, WANG L C, LI Q, et al. A comprehensive review on the state of charge estimation for lithium-ion battery based on neural network[J]. International Journal of Energy Research, 2022, 46(5): 5423-5440. DOI:10.1002/er.7545.

SHRIVASTAVA P, NAIDU P A, SHARMA S, et al. Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications[J]. Journal of Energy Storage, 2023, 64: 107159. DOI:10.1016/j.est.2023.107159.

HE L Y, TIAN A N, DING T, et al. Lithium-ion battery state estimation based on adaptive physics-informed neural network of electrochemical model[J]. Measurement, 2026, 257: 118985. DOI:10.1016/j.measurement.2025.118985.

闫淏迪, 崔承刚, 陈辉, 等. 基于融合物理模型与数据驱动模型的电池SOH估计方法[J]. 电源技术, 2025, 49(6): 1183-1191. DOI:10.3969/j.issn.1002-087X.2025.06.013.

YAN H D, CUI C G, CHEN H, et al. Battery SOH estimation method based on fusion of physical model and data-driven model[J]. Chinese Journal of Power Sources, 2025, 49(6): 1183-1191. DOI:10.3969/j.issn.1002-087X.2025.06.013.

SAHU S, ACHARYA S, DUTT R, et al. NeuroECM: A physics-informed neural network fusion model for equivalent circuit model of Li-ion battery[C ] //2025 23rd IEEE Interregional NEWCAS Conference (NEWCAS). June 22-25, 2025 , Paris, France. IEEE, 2025: 626-630. DOI:10.1109/NewCAS64648.2025.11106964.

P A, R S V, CEPHAS I. Physics informed neural networks for reliable SOC estimation in lithium-ion battery management[C ] //2024 9th International Conference on Communication and Electronics Systems (ICCES). December 16-18, 2024 , Coimbatore, India. IEEE, 2025: 41-47. DOI:10.1109/ICCES63552. 2024.10860002.

KAJIURA Y, ESPIN J, ZHANG D. Physics-informed neural networks for discovering systems with unmeasurable states with application to lithium-ion batteries[C ] //2024 American Control Conference (ACC). July 10-12, 2024 , Toronto, ON, Canada. IEEE, 2024: 1959-1964. DOI:10.23919/ACC60939.2024.10644822.

WANG F J, ZHI Q Q, ZHAO Z B, et al. Inherently interpretable physics-informed neural network for battery modeling and prognosis[J]. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(1): 1145-1159. DOI:10.1109/TNNLS.2023.3329368.

CHEN L, CHANG C, LIU X Y, et al. Physics-informed neural networks for small sample state of health estimation of lithium-ion batteries[J]. Journal of Energy Storage, 2025, 122: 116559. DOI:10.1016/j.est.2025.116559.

DANG L J, YANG J J, LIU M Q, et al. Differential equation-informed neural networks for state-of-charge estimation[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 1000315. DOI:10.1109/TIM.2023.3334377.

GUO R H, HU C G, SHEN W X. An adaptive approach for battery state of charge and state of power co-estimation with a fractional-order multi-model system considering temperatures[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12): 15131-15145. DOI:10.1109/TITS.2023.3299270.

LIU E H, WANG X, NIU G X, et al. Uncertainty management in lebesgue-sampling-based Li-ion battery SFP model for SOC estimation and RDT prediction[J]. IEEE/ASME Transactions on Mechatronics, 2023, 28(2): 611-620. DOI:10.1109/TMECH. 2022.3205244.

RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707. DOI:10.1016/j.jcp.2018.10.045.

YANG X Y, DU Y X, LI L H, et al. Physics-informed neural network for model prediction and dynamics parameter identification of collaborative robot joints[J]. IEEE Robotics and Automation Letters, 2023, 8(12): 8462-8469. DOI:10.1109/LRA. 2023.3329620.

CHEN S Q, ZHANG Q, WANG D F, et al. Physics-informed neural networks for degradation diagnosis of lithium-ion batteries via electrochemical impedance spectroscopy[J ] . Journal of Energy Storage, 2025, 14 0: 119127. DOI:10.1016/j.est.2025. 119127.

ZHOU K Q, QIN Y, YUEN C. Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve[J]. Journal of Energy Storage, 2024, 100: 113502. DOI:10.1016/j.est.2024.113502.

SCHREIBER C O, YOON H S, SHAH K. Understanding pitfalls and opportunities in estimating parameters of a physics-based battery model using a machine learning based method—a case study with long short-term memory neural network[J]. Journal of Electrochemical Energy Conversion and Storage, 2026, 23(2): 021103. DOI:10.1115/1.4069910.

TAO J J, WANG S L, CAO W, et al. A comprehensive review of multiple physical and data-driven model fusion methods for accurate lithium-ion battery inner state factor estimation[J]. Batteries, 2024, 10(12): 1-25.

KARNIADAKIS G E, KEVREKIDIS I G, LU L, et al. Physics-informed machine learning[J]. Nature Reviews Physics, 2021, 3(6): 422-440. DOI:10.1038/s42254-021-00314-5.

JI C, DAI J D, ZHAI C, et al. A review on lithium-ion battery modeling from mechanism-based and data-driven perspectives[J]. Processes, 2024, 12(9): DOI:10.3390/pr12091871.

ALI ALI H A, RAIJMAKERS L H J, TEMPEL H, et al. A hybrid electrochemical multi-particle model for Li-ion batteries[J]. Journal of the Electrochemical Society, 2024, 171(11): 110523. DOI:10.1149/1945-7111/ad92dd.

EL-DALAHMEH M. Physics modelling and adaptive signal processing filter for online condition monitoring and remaining useful life prediction of lithium-ion battery[D]. Middlesbrough: Teesside University, 2023.

HUANG Q, WANG S L, CHEN Z H, et al. Electrochemical modeling of energy storage lithium-ion battery[M]//Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs. Singapore: Springer Nature Singapore, 2023: 21-40. DOI:10.1007/978-981-99-5344-8_2.

JAYASINGHE A E, FERNANDO N, KUMARAWADU S, et al. Review on Li-ion battery parameter extraction methods[J]. IEEE Access, 2023, 11: 73180-73197. DOI:10.1109/ACCESS.2023. 3296440.

童剑城, 范斌, 林至诚, 等. 基于增广拉格朗日法的动态平衡物理信息神经网络[J]. 计算机工程与应用, 2025, 61(20): 170-181.

TONG J C, FAN B, LIN Z C, et al. Dynamic balanced physics-informed neural network based on augmented Lagrange method[J]. Computer Engineering and Applications, 2025, 61(20): 170-181.

ADAIKKAPPAN M, SATHIYAMOORTHY N. Modeling, state of charge estimation, and charging of lithium-ion battery in electric vehicle: A review[J]. International Journal of Energy Research, 2022, 46(3): 2141-2165. DOI:10.1002/er.7339.

D O P, BABU P S, V I, et al. Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms[J]. Scientific Reports, 2024, 14: 16036. DOI:10.1038/s41598-024-66997-9.

BEHNAMGOL V, ASADI M, MOHAMED M A A, et al. Comprehensive review of lithium-ion battery state of charge estimation by sliding mode observers[J]. Energies, 2024, 17(22): DOI:10.3390/en17225754.

KHAN A, NAQVI I H, HASSAN N U. A comprehensive analysis of battery EV (BEV) performance under conflicting metrics through strategic state of charge management[J]. IEEE Transactions on Transportation Electrification, 2025, 11(4): 9613-9629. DOI:10.1109/TTE.2025.3562088.

VIEIRA R, KOLLMEYER P, PITAULT L, et al. Comprehensive comparison of machine learning and Kalman filter battery state of charge estimators[J]. IEEE Access, 2025, 13: 36321-36338. DOI:10.1109/ACCESS.2025.3545380.

LIU Y F, HE Y J, BIAN H D, et al. A review of lithium-ion battery state of charge estimation based on deep learning: Directions for improvement and future trends[J]. Journal of Energy Storage, 2022, 52: 104664. DOI:10.1016/j.est.2022.104664.

WANG L X, DUAN J D, ZHAO K, et al. Online state of charge estimation of LiFePO 4 battery based on EKF-AUKF algorithm with reference compensation for estimation results[J ] . Journal of Energy Storage, 2024, 100: 113504. DOI:10.1016/j.est.2024.113504.

WANG Y Q, CHENG Y, XIONG Y, et al. Estimation of battery open-circuit voltage and state of charge based on dynamic matrix control-extended Kalman filter algorithm[J]. Journal of Energy Storage, 2022, 52: 104860. DOI:10.1016/j.est.2022.104860.

WANG F J, ZHAI Z, ZHAO Z B, et al. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis[J]. Nature Communications, 2024, 15: 4332. DOI:10.1038/s41467-024-48779-z.

XU K K, HE T L, YANG P, et al. A new online SOC estimation method using broad learning system and adaptive unscented Kalman filter algorithm[J]. Energy, 2024, 309: 132920. DOI:10.1016/j.energy.2024.132920.

SUN G Q, LIU Y F, LIU X W. A method for estimating lithium-ion battery state of health based on physics-informed machine learning[J]. Journal of Power Sources, 2025, 627: 235767. DOI:10.1016/j.jpowsour.2024.235767.

ZHOU Z H, ZHAN M J, WU B G, et al. A novel adaptive unscented Kalman filter algorithm for SOC estimation to reduce the sensitivity of attenuation coefficient[J]. Energy, 2024, 307: 132598. DOI:10.1016/j.energy.2024.132598.

WEN P F, YE Z S, LI Y, et al. Physics-informed neural networks for prognostics and health management of lithium-ion batteries[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(1): 2276-2289. DOI:10.1109/TIV.2023.3315548.

YANG L, HE M J, REN Y T, et al. Physics-informed neural network for co-estimation of state of health, remaining useful life, and short-term degradation path in lithium-ion batteries[J]. Applied Energy, 2025, 398: 126427. DOI:10.1016/j.apenergy. 2025.126427.

SHI Q, JIANG Z X, WANG Z, et al. State of charge estimation by joint approach with model-based and data-driven algorithm for lithium-ion battery[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3000610. DOI:10.1109/TIM.2022.3199253.

HOU J Y, XU J, LIN C P, et al. State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method[J]. Energy, 2024, 290: 130056. DOI:10.1016/j.energy.2023.130056.

YU B J, WANG G, ZHU E N, et al. Predicting lithium-ion battery state of charge with long short-term memory network enhanced extended Kalman filter[J]. Journal of Energy Storage, 2025, 132: 117849. DOI:10.1016/j.est.2025.117849.

YANG K, TANG Y G, ZHANG S J, et al. A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism[J]. Energy, 2022, 244: 123233. DOI:10.1016/j.energy.2022.123233.

CUI Z H, KANG L, LI L W, et al. A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures[J]. Renewable Energy, 2022, 198: 1328-1340. DOI:10.1016/j.renene.2022.08.123.

LIU B Y, WANG H Y, TSENG M L, et al. State of charge estimation for lithium-ion batteries based on improved barnacle mating optimizer and support vector machine[J]. Journal of Energy Storage, 2022, 55: 105830. DOI:10.1016/j.est.2022.105830.

HOSSAIN LIPU M S, HANNAN M A, HUSSAIN A, et al. Real-time state of charge estimation of lithium-ion batteries using optimized random forest regression algorithm[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(1): 639-648. DOI:10.1109/TIV.2022.3161301.

PSAROS A F, KAWAGUCHI K, KARNIADAKIS G E. Meta-learning PINN loss functions[J]. Journal of Computational Physics, 2022, 458: 111121. DOI:10.1016/j.jcp.2022.111121.

JI W Q, QIU W L, SHI Z Y, et al. Stiff-PINN: Physics-informed neural network for stiff chemical kinetics[J]. The Journal of Physical Chemistry A, 2021, 125(36): 8098-8106. DOI:10.1021/acs.jpca.1c05102.

ZHANG S H, ZHANG C, WANG B S. CRK-PINN: A physics-informed neural network for solving combustion reaction kinetics ordinary differential equations[J]. Combustion and Flame, 2024, 269: 113647. DOI:10.1016/j.combustflame.2024.113647.

LI K Q, YIN Z Y, ZHANG N, et al. A PINN-based modelling approach for hydromechanical behaviour of unsaturated expansive soils[J]. Computers and Geotechnics, 2024, 169: 106174. DOI:10.1016/j.compgeo.2024.106174.

FU H J, LIU Z G, CUI K X, et al. Physics-informed neural network for spacecraft lithium-ion battery modeling and health diagnosis[J]. IEEE/ASME Transactions on Mechatronics, 2024, 29(5): 3546-3555. DOI:10.1109/TMECH.2023.3348519.

SU C, LIANG J W, HE Z S. E-PINN: A fast physics-informed neural network based on explicit time-domain method for dynamic response prediction of nonlinear structures[J]. Engineering Structures, 2024, 321: 118900. DOI:10.1016/j.engstruct.2024.118900.

WANG C, XIAO Q Q, ZHOU Z K, et al. A data-assisted physics-informed neural network (DA-PINN) for fretting fatigue lifetime prediction[J]. International Journal of Mechanical System Dynamics, 2024, 4(3): 361-373. DOI:10.1002/msd2.12127.

MENG X H, LI Z, ZHANG D K, et al. PPINN: Parareal physics-informed neural network for time-dependent PDEs[J]. Computer Methods in Applied Mechanics and Engineering, 2020, 370: 113250. DOI:10.1016/j.cma.2020.113250.

WANG Y F, ZHONG L L. NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs[J]. Journal of Computational Physics, 2024, 496: 112603. DOI:10.1016/j.jcp.2023.112603.

LIU Y, LIU W, YAN X S, et al. Adaptive transfer learning for PINN[J]. Journal of Computational Physics, 2023, 490: 112291. DOI:10.1016/j.jcp.2023.112291.

PRANTIKOS K, CHATZIDAKIS S, TSOUKALAS L H, et al. Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients[J]. Scientific Reports, 2023, 13: 16840. DOI:10.1038/s41598-023-43325-1.

YANG X Y, ZHOU Z X, LI L H, et al. Collaborative robot dynamics with physical human-robot interaction and parameter identification with PINN[J]. Mechanism and Machine Theory, 2023, 189: 105439. DOI:10.1016/j.mechmachtheory.2023.105439.

NILPUENG K, KASEETHONG P, MESGARPOUR M, et al. A novel temperature prediction method without using energy equation based on physics-informed neural network (PINN): A case study on plate- circular/square pin-fin heat sinks[J]. Engineering Analysis with Boundary Elements, 2022, 145: 404-417. DOI:10.1016/j.enganabound.2022.09.032.

MENG Z, QIAN Q C, XU M Q, et al. PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation[J]. Computer Methods in Applied Mechanics and Engineering, 2023, 414: 116172. DOI:10.1016/j.cma.2023. 116172.

CHIU P H, WONG J C, OOI C, et al. CAN-PINN: A fast physics-informed neural network based on coupled-automatic-numerical differentiation method[J]. Computer Methods in Applied Mechanics and Engineering, 2022, 395: 114909. DOI:10.1016/j.cma.2022.114909.

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