多元混合储能系统监测与预警技术综述

杨沛豪; 高永峰; 曹志华; 张艳军; 金森; 宋政湘; 王康; 熊连松; 贡晓旭; 王劼文; 王烽; 戴海鹏; 张译丹; 曾骞

doi:10.16516/j.ceec.2026-107

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多元混合储能系统监测与预警技术综述

新能源与储能系统 | 更新时间：2026-10-14

- 多元混合储能系统监测与预警技术综述
- A Review of Monitoring and Early Warning Technologies for Multi-Resource Hybrid Energy Storage Systems
- 南方能源建设 2026年13卷第4期页码：17-32
- 作者机构：
  
  1. 西安热工研究院有限公司,陕西,西安,710054
  2. 高效灵活煤电及碳捕集利用封存全国重点实验室,北京,102209
  3. 西安交通大学电气工程学院,陕西,西安,710048
  4. 北方联合电力有限责任公司,内蒙古,呼和浩特,010020
  5. 中国华能集团有限公司福建分公司,福建,福州,350007
  6. 国网辽宁省电力有限公司电力调度控制中心,辽宁,沈阳,110006
  7. 中国华能集团有限公司, 河北雄安新区,071000
  8. 国网陕西省电力有限公司电力调度控制中心,陕西,西安,710018
  9. 清华四川能源互联网研究院,四川,成都,610213
  10. 华能罗源发电有限责任公司,福建,福州,350600
- 作者简介：
  
  杨沛豪，1993-，男，高级工程师，博士，主要从事发电储能电气技术工作（e-mail）yangpeihao@tpri.com.cn
  曾骞，（e-mail）3329849532@qq.com
- 基金信息：
  
  国家重点研发计划“973”项目（2022YFB24052-00）;华能集团总部科技项目“火储联合调频场景下多元储能设备状态监测及智能运维平台开发”（HNKJ25-H159）
- DOI：10.16516/j.ceec.2026-107
  中图分类号： TM7;TM715
- 纸质出版：2026
- 稿件说明：
移动端阅览
杨沛豪, 高永峰, 曹志华, 等. 多元混合储能系统监测与预警技术综述[J]. 南方能源建设, 2026, 13(4): 17-32.

.
杨沛豪, 高永峰, 曹志华, 等. 多元混合储能系统监测与预警技术综述[J]. 南方能源建设, 2026, 13(4): 17-32. DOI： 10.16516/j.ceec.2026-107.

. DOI： 10.16516/j.ceec.2026-107.

摘要

目的

多元混合储能系统（Hybrid Energy Storage System，HESS）中异构储能单元在响应特性、退化机理与能量管理策略上的差异，使系统呈现强耦合、多时间尺度与跨层级动态交互特征，传统储能监测预警方法难以满足复杂运行场景下的安全感知需求。针对上述问题，文章围绕HESS安全监测与预警关键技术开展系统综述，旨在为多元储能系统安全运行与智能运维提供参考。

方法

从拓扑结构、单元特性与应用场景三个维度入手，首先对比分析了被动式、半主动式与全主动式拓扑的工程适配性；进而梳理了常规电气监测与专用传感技术（电化学阻抗谱、光纤布拉格光栅、声发射）的现状，剖析了多物理场耦合感知、状态解耦与可观测性、多时间尺度数据同步等关键技术挑战；最后对比了物理机理、数据驱动及混合驱动三类预警方法的性能差异与适用边界。

结果

研究表明，全主动式拓扑在状态感知精度与能量调控能力方面具有明显优势，但其系统成本与控制复杂度较高；相比之下，半主动式拓扑因兼顾经济性、灵活性与可扩展性，已成为当前工程应用中的主流方案。与此同时，多源异构数据融合与复杂耦合状态解耦仍是制约HESS安全预警精度与泛化能力的关键瓶颈。现有示范工程表明，融合机理模型与智能诊断算法的多级安全预警体系已具备分钟级超前预警能力，并显著提升了系统故障识别与运维决策水平。

结论

最后，结合当前技术发展趋势，文章认为未来HESS监测预警体系将向标准化感知、异构设备互联互通、人工智能辅助决策以及全生命周期数字孪生方向发展，从而推动多元混合储能系统安全监测由“被动告警”向“主动预测与智能管控”演进。

Abstract

Objective

In Hybrid Energy Storage Systems (HESS)

the differences among heterogeneous storage units in response characteristics

degradation mechanisms

and energy management strategies lead to strong coupling

multi-timescale interactions

and cross-hierar

chical dynamic behaviors. Traditional monitoring and early-warning methods for conventional energy storage systems are therefore insufficient to satisfy the safety perception requirements under complex operating conditions. To address these challenges

this paper presents a systematic review of key technologies for HESS safety monitoring and early warning

aiming to provide references for the safe operation and intelligent maintenance of multi-energy storage systems.

Method

Specifically

this study investigated HESS from the perspectives of topology

storage unit characteristics

and application scenarios. First

the engineering adaptability of passive

semi-active

and fully active topologies was comparatively analyzed. Subsequently

the development status of conventional electrical monitoring and specialized sensing technologies—including electrochemical impedance spectroscopy (EIS)

fiber Bragg grating (FBG)

and acoustic emission (AE)—was reviewed. Key technical challenges such as multi-physics coupling perception

state decoupling and observability

and multi-timescale data synchronization were further discussed. Finally

the performance differences and application boundaries of physics-based

data-driven

and hybrid-driven early-warning methods were comparatively analyzed.

Result

The results indicate that fully active topologies exhibit superior state perception accuracy and energy regulation capability

while suffering from higher system cost and control complexity. In contrast

semi-active topologies have become the mainstream engineering solution due to their balanced performance in terms of economy

flexibility

and scalability. Meanwhile

multi-source heterogeneous data fusion and complex coupled-state decoupling remain critical bottlenecks restricting the accuracy and generalization capability of HESS safety early-warning systems. Existing demonstration projects show that multi-level safety early-warning frameworks integrating physics-based models and intelligent diagnostic a

lgorithms have achieved minute-level early warning capabilities and significantly improved fault identification and operation-maintenance decision-making performance.

Conclusion

Considering current technological trends

future HESS monitoring and early-warning systems are expected to evolve toward standardized sensing

heterogeneous device interoperability

artificial intelligence-assisted decision-making

and full-lifecycle digital twins

thereby promoting the transition of energy storage safety management from "passive alarm" to "proactive prediction and intelligent control".

关键词

Keywords

references

WANG F F, YANG P W, ZHAO G J, et al. Development and challenge of flexible operation technology of thermal power units under new power system [J]. Power generation technology, 2024, 45(2): 189-198. DOI: 10.12096/j.2096-4528.pgt.23079.

GARG A, PANDEY A, KHANNA V, et al. Hybrid energy storage systems for renewable energy: roles, benefits, and implementation strategies [C]//IEEE. 2025 IEEE 53rd Photovoltaic Specialists Conference (PVSC), Montreal, QC, Canada, June 8-13, 2025. New York: IEEE, 2025: 1161-1166. DOI: 10.1109/PVSC59419.2025.11132656.

ABO-KHALIL A G, SOBHY A, ABDELKAREEM M A, et al. Advancements and challenges in hybrid energy storage systems: components, control strategies, and future directions [J]. International journal of thermofluids, 2023, 20: 100477. DOI: 10.1016/j.ijft.2023.100477.

LEÓN GÓMEZ J C, DE LEÓN ALDACO S E, AGUAYO ALQUICIRA J. A review of hybrid renewable energy systems: architectures, battery systems, and optimization techniques [J]. Eng, 2023, 4(2): 1446-1467. DOI: 10.3390/eng4020084.

CUI X, ZHAO Y, TAN R X, et al. Multi-modal hybrid energy storage configuration and power allocation in wind-pv-storage systems [J]. Smart Power, 2025, 53(4): 20-28. DOI: 10.20204/j.sp.2025.04003.

WANG Y F, JIANG W, ZHU C W, et al. Research on dynamic equivalent SOC estimation of hybrid energy storage system based on sliding mode observer [J]. Frontiers in energy research, 2021, 9: 711716. DOI: 10.3389/fenrg.2021.711716.

WANG G, LI X J, SUN Z G, et al. Modeling and state estimation of "lithium-ion battery-supercapacitor" hybrid energy storage system [J]. Electronic measurement technology, 2023, 46(1): 103-111. DOI: 10.19651/j.cnki.emt.2210266.

SHUAI Y, LI W B, YAN M. Model predictive control of battery-supercapacitor hybrid energy storage system [J]. High power laser and particle beams, 2025, 37(3): 035025. DOI: 10.11884/HPLPB202537.240417.

SONG Y M, LIU Y J, JIN G, et al. Review of energy management methods for lithium-ion battery/supercapacitor hybrid energy storage systems [J]. Energy storage science and technology, 2024, 13(2): 652-668. DOI: 10.19799/j.cnki.2095-4239.2023.0568.

WANG J Y, YANG K, SONG Z X, et al. Primary frequency regulation strategy for battery-flywheel hybrid energy storage based on adaptive state of charge [J]. Electric Power Engineering Technology, 2024, 43(5): 122-130. DOI: 10.12158/j.2096-3203.2024.05.012.

ALOTTO P, GUARNIERI M, MORO F. Redox flow batteries for the storage of renewable energy: a review [J]. Renewable and sustainable energy reviews, 2014, 29: 325-335. DOI: 10.1016/j.rser.2013.08.001.

MALAVATU J, KEPPLINGER P, FAESSLER B. A comparative simulation study of single and hybrid battery energy storage systems for peak reduction and valley filling using norm-2 optimization [J]. Results in engineering, 2025, 25: 104389. DOI: 10.1016/j.rineng.2025.104389.

SU J L, CHEN H R, LIU R Q, et al. Power allocation strategy for hybrid energy storage systems in DC microgrids with differential power processing technique [J]. Journal of energy storage, 2025, 133: 117970. DOI: 10.1016/j.est.2025.117970.

HUANG D M, WU B, SHI S, et al. Capacity optimization of a hybrid energy storage system for wind power fluctuation suppression based on CEEMDAN-SAOA [J]. Power system protection and control, 2025, 53(15): 59-70. DOI: 10.19783/j.cnki.pspc.241419.

LI Y B, YANG K, CHEN J S, et al. Power distribution strategy for a hybrid energy storage system suitable for wind-storage microgrids [J]. Electrical measurement instrumentation, 2025, 62(2): 43-50. DOI: 10.19753/j.issn1001-1390.2025.02.006.

MAGHFIROH H, WAHYUNGGORO O, CAHYADI A I. Enhancing electric vehicle battery life with hybrid energy storage and energy management [C]//IEEE. 2024 International Conference on Technology and Policy in Energy and Electric Power (ICTPEP), Bali, Indonesia, September 3-5, 2024. New York: IEEE, 2024: 100-105. DOI: 10.1109/ICT-PEP63827.2024.10733380.

KHAN W, RENHAI F, AZIZ A, et al. Deep reinforcement learning-based energy management for design and control of off-grid renewable microgrids with dual-battery storage [J]. Energy exploration exploitation, 2026, 44(2): 821-869. DOI: 10.1177/01445987251387368.

ALI H H, FATHY A, KHAMIES M. Advanced control strategy based on hybrid energy storage system for frequency stability of interconnected power system with high renewables penetration [J]. Scientific reports, 2025, 15(1): 38483. DOI: 10.1038/s41598-025-23283-6.

LIN X, ZAMORA R. Controls of hybrid energy storage systems in microgrids: critical review, case study and future trends [J]. Journal of energy storage, 2022, 47: 103884. DOI: 10.1016/j.est.2021.103884.

State Administration for Market Regulation, Standardization Administration of China. Battery management system for electrical energy storage: GB/T 34131—2023 [S]. Beijing: Standards Press of China, 2023.

MEDDINGS N, HEINRICH M, OVERNEY F, et al. Application of electrochemical impedance spectroscopy to commercial Li-ion cells: a review [J]. Journal of power sources, 2020, 480: 228742. DOI: 10.1016/j.jpowsour.2020.228742.

YANG T, WEN X K, TAN Z K, et al. Research on SOC evaluation method of lithium battery for energy storage station in novel power system [J]. Electrical measurement instrumentation, 2025, 62(3): 30-37. DOI: 10.19753/j.issn1001-1390.2025.03.004.

ALHUSSEIN A N D, QAID M R T M, AGLIULLIN T, et al. Fiber Bragg grating sensors: design, applications, and comparison with other sensing technologies [J]. Sensors, 2025, 25(7): 2289. DOI: 10.3390/s25072289.

ZHANG K, YIN J X, HE Y Z. Acoustic emission detection and analysis method for health status of lithium ion batteries [J]. Sensors, 2021, 21(3): 712. DOI: 10.3390/s21030712.

ZHANG C, WANG D, WANG B, et al. Battery degradation minimization-oriented hybrid energy storage system for electric vehicles [J]. Energies, 2020, 13(1): 246. DOI: 10.3390/en13010246.

IEEE. IEEE standard for a precision clock synchronization protocol for networked measurement and control systems: IEEE Std 1588—2019 [S]. New York: IEEE, 2020. DOI: 10.1109/IEEESTD.2020.9120376.

MAO Y S, CAI Z H, JIAO X N, et al. Multi-timescale optimization scheduling of integrated energy systems oriented towards generalized energy storage services [J]. Scientific reports, 2025, 15(1): 8549. DOI: 10.1038/s41598-025-92601-9.

BAI X Y, PENG D H, CHEN Y X, et al. Three-dimensional electrochemical-magnetic-thermal coupling model for lithium-ion batteries and its application in battery health monitoring and fault diagnosis [J]. Scientific reports, 2024, 14(1): 10802. DOI: 10.1038/s41598-024-61526-0.

SUN H T, TIAN W Y, YUE J K, et al. Lithium-ion battery heterogeneous electrochemical-thermal-mechanical multiphysics coupling model and characterization of microscopic properties [J]. Journal of power sources, 2025, 629: 235970. DOI: 10.1016/j.jpowsour.2024.235970.

CHENG Z X, MIN Y Y, QIN P, et al. A distributed thermal-pressure coupling model of large-format lithium iron phosphate battery thermal runaway [J]. Applied energy, 2025, 378: 124875. DOI: 10.1016/j.apenergy.2024.124875.

HAN C L, HU L J, OUYANG D X, et al. Research on thermal runaway warning threshold and machine learning model for large capacity energy storage batteries based on electric-thermal-mechanical-gas coupling [J]. Process safety and environmental protection, 2026, 208: 108471. DOI: 10.1016/j.psep.2026.108471.

YE J D, HUANG J X, KUANG J J, et al. Data-driven methods for early warning of battery thermal runaway: a review of multi-signal fusion and machine learning approaches [J]. Journal of energy storage, 2025, 133: 118043. DOI: 10.1016/j.est.2025.118043.

YANG Z G, GU Z J, TAO Q Y, et al. Early warning technology for common characteristic resistances of lithium-ion batteries with thermal runaway [J]. Chemical communications, 2024, 60(1): 87-90. DOI: 10.1039/D3CC05086F.

GUO J C, WU H, MA T, et al. Scenario-adaptive hierarchical optimisation framework for design in hybrid energy storage systems [J]. Nature communications, 2026, 17(1): 657. DOI: 10.1038/s41467-025-67377-1.

LI M C, WANG L, WANG Y J, et al. A framework for states co-estimation of hybrid energy storage systems based on fractional-order theory [J]. IEEE journal of emerging and selected topics in power electronics, 2023, 11(1): 224-233. DOI: 10.1109/JESTPE.2021.3135019.

IEC. Electrical energy storage (EES) systems — Part 5-2: safety requirements for grid-integrated EES systems — Electrochemical-based systems: IEC 62933—5—2: 2025 [S]. IEC, 2025.

SAIF A A, MOHAMED M, MOHAMMED S, et al. Comparative control strategies and evaluation of hybrid energy storage in DC microgrids under dynamic load disturbances [C]//IEEE. 2025 IEEE International Conference on Energy Technologies for Future Grids (ETFG), Wollongong, Australia, December 7-11, 2025. New York: IEEE, 2025: 1-6. DOI: 10.1109/ETFG61999.2025.11401195.

DONG G Z, LIN M Q. Model-based thermal anomaly detection for lithium-ion batteries using multiple-model residual generation [J]. Journal of energy storage, 2021, 40: 102740. DOI: 10.1016/j.est.2021.102740.

MA N, YANG D F, RIAZ S, et al. Aging mechanism and models of supercapacitors: a review [J]. Technologies, 2023, 11(2): 38. DOI: 10.3390/technologies11020038.

MADANI S S, ZIEBERT C, VAHDATKHAH P, et al. Recent progress of deep learning methods for health monitoring of lithium-ion batteries [J]. Batteries, 2024, 10(6): 204. DOI: 10.3390/batteries10060204.

ZHAO Y W, DENG J J, LIU P, et al. Enhancing battery durable operation: multi-fault diagnosis and safety evaluation in series-connected lithium-ion battery systems [J]. Applied energy, 2025, 377: 124632. DOI: 10.1016/j.apenergy.2024.124632.

DING H L, YAN S K. Lithium battery state-of-health prediction method combined Diffusion with Transformer [J]. Electrical measurement instrumentation, 2026, 63(5): 140-151. DOI: 10.19753/j.issn1001-1390.2026.05.015.

VALIZADEH A, AMIRHOSSEINI M H. Machine learning in lithium-ion battery: applications, challenges, and future trends [J]. SN computer science, 2024, 5(6): 717. DOI: 10.1007/s42979-024-03046-2.

PENG S M, ZHANG A, LIU D D, et al. State-of-charge estimation of lithium-ion batteries based on dual-coefficient tracking improved square-root unscented Kalman filter [J]. Batteries, 2023, 9(8): 392. DOI: 10.3390/batteries9080392.

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.

DONON B, GEORGE S, SEYLER T, et al. Graph neural networks for power systems [J]. IEEE power energy magazine, 2020, 18(2): 40-49. DOI: 10.1109/MPE.2019.2958873.

E L X, WANG J, YANG R X, et al. A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors [J]. Green energy and intelligent transportation, 2025, 4(3): 100291. DOI: 10.1016/j.geits.2025.100291.

WASEEM M, AHMAD M, PARVEEN A, et al. Battery technologies and functionality of battery management system for EVs: current status, key challenges, and future prospectives [J]. Journal of power sources, 2023, 580: 233349. DOI: 10.1016/j.jpowsour.2023.233349.

NAZARALIZADEH S, BANERJEE P, SRIVASTAVA A K, et al. Battery energy storage systems: a review of energy management systems and health metrics [J]. Energies, 2024, 17(5): 1250. DOI: 10.3390/en17051250.

LIU K L, PENG Q, CHE Y H, et al. Transfer learning for battery smarter state estimation and ageing prognostics: recent progress, challenges, and prospects [J]. Advances in applied energy, 2023, 9: 100117. DOI: 10.1016/j.adapen.2022.100117.

HANNAN M A, WALI S B, KER P J, et al. Battery energy-storage system: a review of technologies, optimization objectives, constraints, approaches, and outstanding issues [J]. Journal of energy storage, 2021, 42: 103023. DOI: 10.1016/j.est.2021.103023.

FANG Z Y, SHEK J K H, SUN W. A review of grid-connected hybrid energy storage systems: sizing configurations, control strategies, and future directions [J]. Journal of energy storage, 2025, 118: 116226. DOI: 10.1016/j.est.2025.116226.

ZHAI X Y, PENG Y F, GUO X P. Edge-cloud collaboration for low-latency, low-carbon, and cost-efficient operations [J]. Computers and electrical engineering, 2024, 120: 109758. DOI: 10.1016/j.compeleceng.2024.109758.

ZUGSCHWERT C, GÖSCHL S, MARTIN IBANEZ F, et al. Development of a multi-timescale method for classifying hybrid energy storage systems in grid applications [C]//IEEE. 2021 6th IEEE Workshop on the Electronic Grid (eGRID), New Orleans, LA, USA, November 8-10, 2021. New York: IEEE, 2021: 1-7. DOI: 10.1109/eGRID52793.2021.9662136.

YANG Z P, ZHENG K, ZHENG H L, et al. Multi-task learning framework for fault detection in energy storage system lithium-ion batteries: from degradation to slight overcharge [J]. Journal of energy storage, 2025, 127: 117164. DOI: 10.1016/j.est.2025.117164.

QU J B, SHEN J L, LI W H, et al. Diagnosing inconsistencies in battery energy storage systems: a framework integrating electrical, thermal, and aging perspectives [J]. Applied energy, 2026, 405: 127203. DOI: 10.1016/j.apenergy.2025.127203.

DU Y, WANG Z L, WEI D. Emerging sensing systems based on triboelectric nanogenerator [J]. Nano energy, 2025, 143: 111292. DOI: 10.1016/j.nanoen.2025.111292.

LIU L D, SHANG Y R, BERBILLE A, et al. Self-powered sensing platform based on triboelectric nanogenerators towards intelligent mining industry [J]. Nature communications, 2025, 16(1): 5141. DOI: 10.1038/s41467-025-60418-9.

JI L, LI H R, LI B T, et al. Residual correction-based fault location method for distribution networks using digital twin technology [J]. Smart power, 2025, 53(5): 90-98. DOI: 10.20204/j.sp.2025.05012.

LI J L, SHANG Y L, GU X, et al. Large language model-based fault diagnosis for lithium-ion batteries in cloud-edge systems [J]. Cell reports physical science, 2026, 7(4): 103210. DOI: 10.1016/j.xcrp.2026.103210.

FARAKHOR A, WU D, CHEN P G, et al. Economic optimal power management of second-life battery energy storage systems [J]. Journal of energy storage, 2025, 133: 117680. DOI: 10.1016/j.est.2025.117680.

SALAORNI D, BIANCHI F, COLNAGO S, et al. A novel digital twin for battery energy storage systems in micro-grids [J]. Journal of energy storage, 2025, 132: 117745. DOI: 10.1016/j.est.2025.117745.

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