Joint Estimation of State-of-charge and State-of-health for Lithium-ion Batteries under the Influence of Temperature and Aging

doi:10.3901/JME.2024.18.266

Abstract

Abstract: Lithium-ion batteries are widely used in electric vehicles, energy storage power stations and electronic products. Accurate state of charge(SOC) and state of health(SOH) are the basis for its safe and efficient application. However, the nonlinear dynamics of batteries due to temperature and aging state changes severely affect the accuracy of state estimation. Taking the nickel manganese cobalt lithium-ion battery as an example to carry out research: ① The open circuit voltage behavior characteristics of the battery at different aging stages, temperature and SOC ranges are analyzed, and an open circuit voltage model considering aging, temperature and SOC is proposed; ② The equivalent circuit model is established, and a joint estimation method of SOC and SOH of the battery based on multi-scale extended Kalman filter algorithm(MEKF) and fading memory approximate weighted total least squares algorithm(FMAWTLS) is proposed. Among them, MEKF is used to estimate model parameters at the macro scale, SOC is estimated at the micro scale, and FMAWTLS is used to estimated SOH; ③ The algorithm is verified by using battery data of different aging states and temperatures, and the results show that the maximum errors of SOC and SOH are both less than 3 %. The established open circuit voltage model and joint estimation method provide new ideas for SOC and SOH estimation under the influence of temperature and aging.

Key words: lithium-ion batteries, state of charge, state of health, extended Kalman filter, least squares method

CLC Number:

TM912

JIANG Shuai, CHEN Cheng, DUAN Yanzhou, XIONG Rui. Joint Estimation of State-of-charge and State-of-health for Lithium-ion Batteries under the Influence of Temperature and Aging[J]. Journal of Mechanical Engineering, 2024, 60(18): 266-275.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2024.18.266

http://www.cjmenet.com.cn/EN/Y2024/V60/I18/266

References

[1] XIONG Rui，HUANG Jintao，DUAN Yanzhou，et al. Enhanced Lithium-ion battery model considering critical surface charge behavior[J]. Applied Energy，2022，314：118915.
[2] 金建新，虞儒新，刘刚，等. 锂离子电池健康状态估算方法研究进展[J]. 电气工程学报，2024，19(1)：33-48. JIN Jianxin，YU Ruxin，LIU Gang，et al. Research progress on state-of-health estimating method for lithium-ion batteries[J]. Journal of Electrical Engineering，2024，19(1)：33-48.
[3] 李维聪，穆浩，沈恒龙，等. 固态锂电池在载运工具中的应用前景分析[J]. 电气工程学报，2022，17(4)：88-102. LI Weicong，MU Hao，SHEN Henglong，et al. Application prospect analysis of solid-state lithium battery in vehicle[J]. Journal of Electrical Engineering，2022，17(4)：88-102.
[4] LAI Xin，CHEN Quanwei，TANG Xiaopeng，et al. Critical review of life cycle assessment of lithium-ion batteries for electric vehicles：A lifespan perspective[J]. Etransportation，2022：100169.
[5] TRAN M K，DACOSTA A，MEVAWALLA A，et al. Comparative study of equivalent circuit models performance in four common lithium-ion batteries：LFP，NMC，LMO，NCA[J]. Batteries，2021，7(3)：51.
[6] LI Yihuan，LI Kang，LIU Xuan，et al. A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements[J]. Applied Energy，2022，325：119787.
[7] MOHAMMADI F. Lithium-ion battery state-of-charge estimation based on an improved Coulomb-Counting algorithm and uncertainty evaluation[J]. Journal of Energy Storage，2022，48：104061.
[8] OYEWOLE I，CHEHADE A，KIM Y. A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation[J]. Applied Energy，2022，312：118726.
[9] LUO Jiayi，PENG Jiankun，HE Hongwen. Lithium-ion battery SOC estimation study based on Cubature Kalman filter[J]. Energy Procedia，2019，158：3421-3426.
[10] WANG Yipeng，ZHAO Lin，CHENG Jianhua，et al. A state of charge estimation method of lithium-ion battery based on fused open circuit voltage curve[J]. Applied Sciences，2020，10(4)：1264.
[11] BABAEIYAZDI I，REZAEI-ZARE A，SHOKRZADEH S. State-of-charge prediction of degrading li-ion batteries using an adaptive machine learning approach[C]//2022 IEEE Power & Energy Society General Meeting (PESGM). New York：IEEE，2022：1-5.
[12] MOVASSAGH K，RAIHAN A，BALASINGAM B，et al. A critical look at coulomb counting approach for state of charge estimation in batteries[J]. Energies，2021，14(14)：4074.
[13] CHEN Cheng，XIONG Rui，YANG Ruixin，et al. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter[J]. Journal of Cleaner Production，2019，234：1153-1164.
[14] SUTANTO E，ASTAWA P E，FAHMI F，et al. Lithium-ion battery state-of-charge estimation from the voltage discharge profile using gradient vector and support vector machine[J]. Energies，2023，16(3)：1083.
[15] 杨淞元，田勇，田劲东. 基于iCEEMDAN和迁移学习的锂离子电池SOH估计[J]. 电气工程学报，2022，17(4)：2-10. YANG Songyuan，TIAN Yong，TIAN Jindong. State of health estimation of lithium-ion batteries based on iCEEMDAN and transfer learning[J]. Journal of Electrical Engineering，2022，17(4)：2-10.
[16] SHI Na，CHEN Zewang，NIU Mu，et al. State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification[J]. Journal of Energy Storage，2022，45：103518.
[17] LIU Xingtao，FAN Xiaojie，WANG Li，et al. State of charge estimation for power battery base on improved particle filter[J]. World Electric Vehicle Journal，2023，14(1)：8.
[18] CERAOLO M，LUTZEMBERGER G，POLI D，et al. Luenberger-based state-of-charge evaluation and experimental validation with lithium cells[J]. Journal of Energy Storage，2020，30：101534.
[19] MENG Shengya，LI Shihong，CHI Heng，et al. H∞ Observer based on descriptor systems applied to estimate the state of charge[J]. Entropy，2022，24(3)：420.
[20] YU Quanqing，XIONG Rui，YANG Ruixin，et al. Online capacity estimation for lithium-ion batteries through joint estimation method[J]. Applied Energy，2019，255：113817.
[21] 方德宇，楚潇，刘涛，等. 基于数据-模型驱动的锂离子电池健康状态估计[J]. 电气工程学报，2022，17(4)：20-31. FANG Deyu，CHU Xiao，LIU Tao，et al. Research on health assessment method of lithium-ion battery based on data-model hybrid drive[J]. Journal of Electrical Engineering，2022，17(4)：20-31.
[22] WANG Z，GLADWIN D T，SMITH M J，et al. Practical state estimation using Kalman filter methods for large-scale battery systems[J]. Applied Energy，2021，294：117022.
[23] LI Jiabo，YE Min，GAO Kangping，et al. Joint estimation of state of charge and state of health for lithium-ion battery based on dual adaptive extended Kalman filter[J]. International Journal of Energy Research，2021，45(9)：13307-13322.
[24] XU Yonghong，CHEN Xia，ZHANG Hongguang，et al. Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms[J]. International Journal of Energy Research，2022，46(14)：19615-19652.
[25] LIU Zheng，QIU Yuan，YANG Chunshan，et al. A state of charge estimation method for lithium-ion battery using PID compensator-based adaptive extended Kalman filter[J]. Complexity，2021，2021：1-14.
[26] PLETT G. Results of temperature-dependent LiPB cell modeling for HEV SOC estimation[C]//CD-ROM Proceedings of the 21st Electric Vehicle Symposium (EVS21)(Monaco：April 2005). 2005.
[27] VENNAM G，SAHOO A，AHMED S. A survey on lithium-ion battery internal and external degradation modeling and state of health estimation[J]. Journal of Energy Storage，2022，52：104720.
[28] PILLAI P，SUNDARESAN S，KUMAR P，et al. Open-circuit voltage models for battery management systems：A review[J]. Energies，2022，15(18)：6803.
[29] GUPTA J，MAURYA R，ARYA S R. Designing an on-board charger to efficiently charge multiple electric vehicles[J]. Chinese Journal of Electrical Engineering，2023，9(2)：38-56.
[30] MAHESHWARI A，NAGESWARI S. Real-time state of charge estimation for electric vehicle power batteries using optimized filter[J]. Energy，2022，254：124328.
[31] PLETT G L. Recursive approximate weighted total least squares estimation of battery cell total capacity[J]. Journal of Power Sources，2011，196(4)：2319-2331.
[32] LI Kuijie，LI Yalun，RUI Xinyu，et al. Experimental study on the effect of state of charge on failure propagation characteristics within battery modules[J]. Chinese Journal of Electrical Engineering，2023，9(3)：3-14.
[33] FARMANN A，SAUER D U. A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries[J]. Journal of Power Sources，2017，347：1-13.