Estimation Approaches for States of Charge and Health of Lithium-ion Battery

Yihuan LI; Kang LI; James YU

doi:10.12096/j.2096-4528.pgt.21074

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Estimation Approaches for States of Charge and Health of Lithium-ion Battery

Carbon Neutrality | 更新时间：2025-07-04

- Estimation Approaches for States of Charge and Health of Lithium-ion Battery
- Power Generation Technology Vol. 42, Issue 5, Pages: 537-546(2021)
- 作者机构：
  
  1.利兹大学电子电气工程学院, 英国利兹LS2 9JT
  2.苏格兰电力公司, 英国格拉斯哥G2 5AD
- 作者简介：
- 基金信息：
  
  Engineering and Physical Sciences Research Council(EP/R030243/1)
- DOI：10.12096/j.2096-4528.pgt.21074
  CLC： TK02
- Received：07 June 2021，
  
  Published：31 October 2021
- 稿件说明：
移动端阅览
Yihuan LI, Kang LI, James YU. Estimation Approaches for States of Charge and Health of Lithium-ion Battery[J]. Power Generation Technology, 2021, 42(5): 537-546.
DOI：

Yihuan LI, Kang LI, James YU. Estimation Approaches for States of Charge and Health of Lithium-ion Battery[J]. Power Generation Technology, 2021, 42(5): 537-546. DOI： 10.12096/j.2096-4528.pgt.21074.

摘要

电池储能系统是实现碳中和最重要、最有效的手段之一，其大规模应用对电池运行过程中的安全性提出了更高的要求。实时准确的电池状态估计为保障电池的安全稳定运行提供重要信息，是电池管理系统（battery management system，BMS）的一项重要功能。然而，由于复杂的操作条件和电池内部的电化学反应，很难准确地评估电池的内部状态。针对电池荷电状态（state of charge，SOC）和健康状态（state of health，SOH）这2个电池系统中的重要参数，系统回顾了当前常用的SOC和SOH估计方法，总结了各种方法的特点及其在实际应用中所面临的主要挑战，并在此基础上对电池SOC和SOH估计技术未来的发展提出展望，为电池状态估计技术的进一步研究提供参考依据。

Abstract

Battery energy storage system is one of the most important and effective means to achieve carbon neutrality

its large-scale applications put forward higher requirements for the battery operating safety. Accurate and real-time battery state estimation is an important function of battery management systems

and it provides critical information for ensuring safe and reliable operation of batteries. However

it is not an easy task to accurately estimate the battery internal state due to the complex operating conditions and the complicated electrochemical reactions inside the battery. This paper provided a systematic review of the state-of-the-art techniques on the state of charge (SOC) and state of health (SOH) estimation of batteries. The advantages and main challenges existing in practical applications of different techniques were analyzed and compared. Then

the future development needs for battery SOC and SOH estimation techniques were prospected

which provides references for further research in battery state estimation

关键词

Keywords

references

阮绵晖, 郑建平, 刘尧, 等. 离网直流微网群混合储能容量优化配置方法[J]. 电力工程技术, 2021, 40(3): 99-105.

RUAN M H, ZHENG J P, LIU R, et al. Optimization configuration method for hybrid energy storage capacity of independent DC microgrid cluster[J]. Electric Power Engineering Technology, 2021, 40(3): 99-105.

严海波, 康林贤, 周冬. 考虑随机性的微电网日前调度与储能优化模型[J]. 电网与清洁能源, 2019, 35(11): 61-65.

YAN H B, KANG L X, ZHOU D. Optimal model of day-ahead dispatching and energy storage for micro-grid considering randomness[J]. Power System and Clean Energy, 2019, 35(11): 61-65.

TSIROPOULOS I, TARVYDAS D, LEBEDEVA N. Li-ion batteries for mobility and stationary storage applications scenarios for costs and market growth[R]. Luxembourg: Publications Office of the European Union, 2018.

蒋燕萍, 陈佩军, 陈海燕. 电动汽车集约型换电设施的设计研究[J]. 发电技术, 2019, 40(6): 535-539.

JIANG Y P, CHEN P J, CHEN H Y. Research and design on intensive power exchange facility of electric vehicles[J]. Power Generation Technology, 2019, 40(6): 535-539.

耿健, 杨冬梅, 高正平, 等. 含储能的冷热电联供分布式综合能源微网优化运行[J]. 电力工程技术, 2021, 40(1): 25-32.

GENG J, YANG D M, GAO Z P, et al. Optimal operation of distributed integrated energy microgrid with CCHP considering energy storage[J]. Electric Power Engineering Technology, 2021, 40(1): 25-32.

禹海峰, 潘力强, 吴亚茹, 等. 储能提升含高比例风电电力系统可靠性分析[J]. 电网与清洁能源, 2020, 36(6): 92-98.

YU H F, PAN L Q, WU Y R, et al. Analysis of energy storage improving the reliability of power system with high proportional wind power[J]. Power System and Clean Energy, 2020, 36(6): 92-98.

张剑波, 卢兰光, 李哲. 车用动力电池系统的关键技术与学科前沿[J]. 汽车安全与节能学报, 2012, 3(2): 87-104.

ZHANG J B, LU L G, LI Z. Key technologies and fundamental academic issues for traction battery systems[J]. Journal of Automotive Safety and Energy, 2012, 3(2): 87-104.

沈佳妮, 贺益君, 马紫峰. 基于模型的锂离子电池SOC及SOH估计方法研究进展[J]. 化工学报, 2018, 69(1): 309-316.

SHEN J N, HE Y J, MA Z F. Progress of model based SOC and SOH estimation methods for lithium-ion battery[J]. CIESC Journal, 2018, 69(1): 309-316.

KIM T, WANG Y, SAHINOGLU Z, et al. State of charge estimation based on a realtime battery model and iterative smooth variable structure filter[C]//2014 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA), Kuala Lumpur, Malaysia, 20-23 May, 2014: 132-137.

CHANG W Y. The state of charge estimating methods for battery: a review[J]. ISRN Applied Mathematics, 2015, 2013: 203-209.

TANG X, LIU B, GAO F. State of charge estimation of LiFePO 4 battery based on a gain-classifier observer[J ] . Energy Procedia, 2017, 105(5): 2071-2076.

XIONG R, CAO J, YU Q, et al. Critical review on the battery state of charge estimation methods for electric vehicles[J]. IEEE Access, 2017, 6: 1832-1843.

ATTANAYAKA A M, KARUNADASA J P, HEMAPALA K T. Estimation of state of charge for lithium-ion batteries: a review[J]. AIMS Energy, 2019, 7(2): 186-210.

LEE S, KIM J, LEE J, et al. State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge[J]. Journal of Power Sources, 2008, 185(2): 1367-1373.

ZHU L, SUN Z, DAI H, et al. A novel modeling methodology of open circuit voltage hysteresis for LiFePO 4 batteries based on an adaptive discrete Preisach model[J ] . Applied Energy, 2015, 155: 91-109.

KIM I S. The novel state of charge estimation method for lithium battery using sliding mode observer[J]. Journal of Power Sources, 2006, 163(1): 584-590.

CHEN X, SHEN W, CAO Z, et al. A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles[J]. Journal of Power Sources, 2014, 246: 667-678.

MASTALI M, VAZQUEZ-ARENAS J, FRASER R, et al. Battery state of the charge estimation using Kalman filtering[J]. Journal of Power Sources, 2013, 239: 294-307.

PLETT G L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3: State and parameter estimation[J]. Journal of Power Sources, 2004, 134(2): 277-292.

PLETT G L. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2: Simultaneous state and parameter estimation[J]. Journal of power sources, 2006, 161(2): 1369-1384.

YU Q, XIONG R, LIN C, et al. Lithium-ion battery parameters and state-of-charge joint estimation based on H-infinity and unscented Kalman filters[J]. IEEE Transactions on Vehicular Technology, 2017, 66(10): 8693-8701.

ZHANG Y, ZHANG C, ZHANG X. State-of-charge estimation of the lithium-ion battery system with time-varying parameter for hybrid electric vehicles[J]. IETControl Theory&Applications, 2014, 8(3): 160-167.

XIONG R, YU Q, LIN C. A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter[J]. Applied Energy, 2017, 207: 346-353.

CHARKHGARD M, FARROKHI M. State-of-charge estimation for lithium-ion batteries using neural networks and EKF[J]. IEEE transactions on industrial electronics, 2010, 57(12): 4178-4187.

HE H, XIONG R, ZHANG X, et al. State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model[J]. IEEE Transactions on vehicular technology, 2011, 60(4): 1461-1469.

SUN F, HU X, ZOU Y, et al. Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles[J]. Energy, 2011, 36(5): 3531-3540.

GAO M Y, LIU Y Y, HE Z W. Battery state of charge online estimation based on particle filter[C]//4th International Congress on Image and Signal Processing, Shanghai, 2011: 2233-2236.

HE Y, LIU X, ZHANG C, et al. A new model for state-of-charge (SOC) estimation for high-power Li-ion batteries[J]. Applied Energy, 2013, 101: 808-814.

HOW D N, HANNAN M A, LIPU M H, 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.

HE W, WILLIARD N, CHEN C, et al. State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation[J]. International Journal of Electrical Power&Energy Systems, 2014,62: 783-791.

ISMAIL M, DLYMA R, ELRAKAYBI A, et al. Battery state of charge estimation using an Artificial Neural Network[C]//2017 IEEE Transportation Electrification Conference and Expo, Chicago, USA, 2017: 342-349.

CHEMALI E, KOLLMEYER P J, PREINDL M, et al. State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach[J]. Journal of Power Sources, 2018, 400: 242-255.

HOW D N, HANNAN M A, LIPU M S, et al. State-of-charge estimation of Li-ion battery in electric vehicles: a deep neural network approach[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5565-5574.

YANG F, LI W, LI C, et al. State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network[J]. Energy, 2019, 175: 66-75.

CHEMALI E, KOLLMEYER P J, PREINDL M, et al. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2017, 65(8): 6730-6739.

SAHINOGLU G O, PAJOVIC M, SAHINOGLU Z, et al. Battery state-of-charge estimation based on regular/recurrent Gaussian process regression[J]. IEEE Transactions on Industrial Electronics, 2017, 65(5): 4311-4321.

XIAO F, LI C, FAN Y, et al. State of charge estimation for lithium-ion battery based on Gaussian process regression with deep recurrent kernel[J]. International Journal of Electrical Power&Energy Systems, 2021, 124: 106369.

SHRIVASTAVA P, SOON T K, IDRIS M Y, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries[J]. Renewable and Sustainable Energy Reviews, 2019, 113: 109233.

BIRKL C R, ROBERTS M R, MCTURK E, et al. Degradation diagnostics for lithium ion cells[J]. Journal of Power Sources, 2017, 341: 373-386.

XIONG R, LI L, TIAN J. Towards a smarter battery management system: a critical review on battery state of health monitoring methods[J]. Journal of Power Sources, 2018, 405: 18-29.

OUYANG M, FENG X, HAN X, et al. A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery[J]. Applied Energy, 2016, 165: 48-59.

SADABADI K K, RAMESH P, TULPULE P, et al. Design and calibration of a semi-empirical model for capturing dominant aging mechanisms of a PbA battery[J]. Journal of Energy Storage, 2019, 24: 100789.

ZHANG D, DEY S, PEREZ H E, et al. Real-time capacity estimation of lithium-ion batteries utilizing thermal dynamics[J]. IEEE Transactions on Control Systems Technology, 2019, 28(3): 992-1000.

LIU K, ZOU C, LI K, et al. Charging pattern optimization for lithium-ion batteries with an electrothermal-aging model[J]. IEEE Transactions on Industrial Informatics, 2018, 14(12): 5463-5474.

YU Q, XIONG R, YANG R, et al. Online capacity estimation for lithium-ion batteries through joint estimation method[J]. Applied Energy, 2019, 255: 113817.

XIONG R, LI L, LI Z, et al. An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application[J]. Applied Energy, 2018, 219: 264-275.

ZHENG Y, QIN C, LAI X, et al. A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model[J]. Applied Energy, 2019, 251: 113327.

ZHANG C, LI K, MCLOONE S, et al. Battery modelling methods for electric vehicles-A review[C]//2014 European Control Conference, Strasbourg, France, 2014: 2673-2678.

LI S, PISCHINGER S, HE C, et al. A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test[J]. Applied Energy, 2018, 212: 1522-1536.

HU C, YOUN B D, CHUNG J. A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation[J]. Applied Energy, 2012, 92: 694-704.

CHEN C, XIONG R, SHEN W. A lithium-ion battery-in-the-loop approach to test and validate multiscale dual H infinity filters for state-of-charge and capacity estimation[J]. IEEE Transactions on Power Electronics, 2017, 33(1): 332-342.

YE M, GUO H, XIONG R, et al. An online model-based battery parameter and state estimation method using multi-scale dual adaptive particle filters[J]. Energy Procedia, 2017, 105: 4549-4554.

WENG C, CUI Y, SUN J, et al. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression[J]. Journal of Power Sources, 2013, 235: 36-44.

郭琦沛, 张彩萍, 高洋, 等. 基于容量增量曲线的三元锂离子电池健康状态估计方法[J]. 全球能源互联网, 2018, 1(2): 82-89.

GUO Q P, ZHANG C P, GAO Y, et al. Incremental capacity curve based state of health estimation for LNMCO lithium-ion batteries[J]. Journal of Global Energy Interconnection, 2018, 1(2): 82-89.

LI Y, ABDEL-MONEM M, GOPALAKRISHNAN R, et al. A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter[J]. Journal of Power Sources, 2018, 373: 40-53.

ZHENG L, ZHU J, LU DD, et al. Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries[J]. Energy, 2018, 150: 759-769.

TANG X, ZOU C, YAO K, et al. A fast estimation algorithm for lithium-ion battery state of health[J]. Journal of Power Sources, 2018, 396: 453-458.

WU Y, KEIL P, SCHUSTER S F, et al. Impact of temperature and discharge rate on the aging of a LiCoO 2 /LiNi 0.8 Co 0.15 Al 0.05 O 2 lithium-ion pouch cell[J ] . Journal of The Electrochemical Society, 2017, 164(7): 1438-1445..

RICHARDSON R R, BIRKL C R, OSBORNE M A, et al. Gaussian process regression for in situ capacity estimation of lithium-ion batteries[J]. IEEE Transactions on Industrial Informatics, 2018, 15(1): 127-138.

LI Y, SHENG H, CHENG Y, et al. State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis[J]. Applied Energy, 2020, 277: 115504.

FENG X, WENG C, HE X, et al. Online state-of-health estimation for Li-ion battery using partial charging segment based on support vector machine[J]. IEEE Transactions on Vehicular Technology, 2019, 68(9): 8583-8592.

LI Y, ZOU C, BERECIBAR M, et al. Random forest regression for online capacity estimation of lithium-ion batteries[J]. Applied Energy, 2018, 232: 197-210.

LIU K, LI Y, HU X, et al. Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries[J]. IEEE Transactions on Industrial Informatics, 2019, 16(6): 3767-3777.

LI X, YUAN C, LI X, et al. State of health estimation for Li-ion battery using incremental capacity analysis and Gaussian process regression[J]. Energy, 2020, 190: 116467.

GUO P, CHENG Z, YANG L. A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction[J]. Journal of Power Sources, 2019, 412: 442-450.

LI X, WANG Z, ZHANG L. Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles[J]. Energy, 2019, 174: 33-44.

YOU G W, PARK S, OH D. Diagnosis of electric vehicle batteries using recurrent neural networks[J]. IEEE Transactions on Industrial Electronics, 2017, 64(6): 4885-4893.

SHEN S, SADOUGHI M, CHEN X, et al. A deep learning method for online capacity estimation of lithium-ion batteries[J]. Journal of Energy Storage, 2019, 25: 100817.

KAUR K, GARG A, CUI X, et al. Deep learning networks for capacity estimation for monitoring SOH of Li-ion batteries for electric vehicles[J]. International Journal of Energy Research, 2021, 45(2): 3113-3128.

TAN Y, ZHAO G. Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2019, 67(10): 8723-8731.

LI Y, LI K, LIU X, et al. Lithium-ion battery capacity estimation: a pruned convolutional neural network approach assisted with transfer learning[J]. Applied Energy, 2021, 285: 116410.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research Status and Prospect of Lithium-Ion Battery Modelling

Health Status Assessment of Lithium-ion Battery Based on Fuzzy Comprehensive Evaluation

Related Author

ZHANG Xiaowei

YI Zhenxiao

WANG Kai

LI Jianlin

PENG Yuchen

WANG Qian

JIANG Xiaoxia

WANG Lei

Related Institution

CNOOC Petrochemical Engineering Company Limited

College of Electrical Engineering, Qingdao University

Weihai Innovation Research Institute, Qingdao University

Shandong Suoxiang Intelligent Technology Company Limited

National User-Side Energy Storage Innovation Research and Development Center (North China University of Technology), Shijingshan District

AI问答

⁰