JOINT FIRST ORDER SOC AND SOH ESTIMATION FRAMEWORK FOR LI-ION BATTERY BASED ON ECM-IGPR

Li Qian, Jiang Fan, Han Qiaoni, Zhang Ji’ang, Cheng Ze, Su Zhan, Ma Boyang

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 240-250.

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 240-250. DOI: 10.19912/j.0254-0096.tynxb.2023-0024

JOINT FIRST ORDER SOC AND SOH ESTIMATION FRAMEWORK FOR LI-ION BATTERY BASED ON ECM-IGPR

  • Li Qian1, Jiang Fan2, Han Qiaoni3, Zhang Ji’ang4, Cheng Ze3, Su Zhan1, Ma Boyang1
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Abstract

State of charge and state of health estimation of Li-ion batteries are important elements of battery management systems, but existing studies usually ignore the association between them and estimating them separately. Therefore, this paper proposes a joint estimation framework of SOC-SOH for Li-ion battery based on Equivalent Circuit Model-Improved Gaussian Process Regression, which updates the SOC estimation with the single-cycle prediction of SOH. The contents are as follow: At first, HFs in the Incremental Capacity curve were extracted and Principal Component Analysis was performed to realize the optimization of HFs. And then the IGPR model for battery aging was developed for SOH prediction. On this basis, the state space model of Li-ion battery was established based on the parameter identification results and capacity estimates, which was combined with the Particle Filter algorithm to update the SOC estimation of the latter cycle. Thus, the joint estimation of SOH and SOC is achieved. Lastly, the eight cells in the Oxford dataset were used to verify the accuracy and adaptability of the framework. Eight batteries from the Oxford dataset are used to validate the accuracy and adaptability of the framework, which achieves good results.

Key words

Li-ion battery / incremental capacity / joint state estimate / particle filter / Gaussian process regression

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Li Qian, Jiang Fan, Han Qiaoni, Zhang Ji’ang, Cheng Ze, Su Zhan, Ma Boyang. JOINT FIRST ORDER SOC AND SOH ESTIMATION FRAMEWORK FOR LI-ION BATTERY BASED ON ECM-IGPR[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 240-250 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0024

References

[1] 刘红锐, 张彬, 刘威, 等. 一种大规模链式电池储能系统分层式并行均衡器[J]. 太阳能学报, 2023, 44(6): 91-98.
LIU H R, ZHANG B, LIU W, et al.Hierarchical parallel equalizer for large-scale chain battery energy storage system[J]. Acta energiae solaris sinica, 2023, 44(6): 91-98.
[2] 杨斌, 樊立萍, 高迎慧, 等. 超高功率密度锂离子电池放电性能及容量预估研究[J]. 太阳能学报, 2023, 44(11): 419-425.
YANG B, FAN L P, GAO Y H, et al.Reserach on discharge performance and capacity prediction of ultra-high power density lithium-ion batteries[J]. Acta energiae solaris sinica, 2023, 44(11): 419-425.
[3] 申永鹏, 孙嵩楠, 孙建彬, 等. 锂离子电池健康评估和寿命预测综述[J]. 太阳能学报, 2023, 44(6): 61-70.
SHEN Y P, SUN S N, SUN J B, et al.Research on influence of current characteristics on performance of lithium-ion[J]. Acta energiae solaris sinica, 2023, 44(6): 61-70.
[4] 刘玉洁, 赵巍, 孙孝峰, 等. 储能系统锂离子电池附加受控电压源等效电路模型研究[J]. 太阳能学报, 2023, 44(8): 1-9.
LIU Y J, ZHAO W, SUN X F, et al.Equivalent circuit model of lithium-ion batteries attached controlled voltage source in energy storage system[J]. Acta energiae solaris sinica, 2023, 44(8): 1-9.
[5] BASIA A, SIMEU-ABAZI Z, GASCARD E, et al.Review on state of health estimation methodologies for lithium-ion batteries in the context of circular economy[J]. CIRP journal of manufacturing science and technology, 2021, 32: 517-528.
[6] JYOTI J, SINGH B P, TRIPATHI S K.Recent advancements in development of different cathode materials for rechargeable lithium ion batteries[J]. Journal of energy storage, 2021, 43: 103112.
[7] 彭喜元, 彭宇, 刘大同. 数据驱动的故障预测[M]. 哈尔滨: 哈尔滨工业大学出版社, 2016.
PENG X Y, PENG Y, LIU D T.Data driven prognostics and health management[M]. Harbin: Harbin Institute of Technology Press, 2016.
[8] 王顺利, 于春梅, 毕效辉, 等. 新能源技术与电源管理[M]. 北京: 机械工业出版社, 2019.
WANG S L, YU C M, BI X H, et al.New energy technology and power management[M]. Beijing: China Machine Press, 2019.
[9] CHE Y B, LIU Y S, CHENG Z, et al.SOC and SOH identification method of Li-ion battery based on SWPSO-DRNN[J]. IEEE journal of emerging and selected topics in power electronics, 2021, 9(4): 4050-4061.
[10] WANG Z L, FENG G J, ZHEN D, et al.A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles[J]. Energy reports, 2021, 7: 5141-5161.
[11] HU X S, LI S B, PENG H E.A comparative study of equivalent circuit models for Li-ion batteries[J]. Journal of power sources, 2012, 198: 359-367.
[12] CHEN J, ZHANG Y, ZHU Q M, et al.Aitken based modified Kalman filtering stochastic gradient algorithm for dual-rate nonlinear models[J]. Journal of the Franklin Institute, 2019, 356(8): 4732-4746.
[13] MAWONOU K S R, EDDAHECH A, DUMUR D, et al. Improved state of charge estimation for Li-ion batteries using fractional order extended Kalman filter[J]. Journal of power sources, 2019, 435: 226710.
[14] DONG X L, ZHANG C P, JIANG J C.Evaluation of SOC estimation method based on EKF/AEKF under noise interference[J]. Energy procedia, 2018, 152: 520-525.
[15] LI Y, LIU K L, FOLEY A M, et al.Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review[J]. Renewable and sustainable energy reviews, 2019, 113: 109254.
[16] TIAN H X, QIN P L, LI K, et al.A review of the state of health for lithium-ion batteries: research status and suggestions[J]. Journal of cleaner production, 2020, 261: 120813.
[17] HOSSAIN LIPU M S, HANNAN M A, HUSSAIN A, et al. A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: challenges and recommendations[J]. Journal of cleaner production, 2018, 205: 115-133.
[18] 刘昊天, 王萍, 程泽. 一种编解码器模型的锂离子电池健康状态估算[J]. 中国电机工程学报, 2021, 41(5): 1851-1859, 28.
LIU H T, WANG P, CHENG Z.A novel method based on encoder-decoder framework for Li-ion battery state of health estimation[J]. Proceedings of the CSEE, 2021, 41(5): 1851-1859, 28.
[19] SUN T, XU B W, CUI Y F, et al.A sequential capacity estimation for the lithium-ion batteries combining incremental capacity curve and discrete Arrhenius fading model[J]. Journal of power sources, 2021, 484: 229248.
[20] LIU D T, ZHOU J B, LIAO H T, et al.A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics[J]. IEEE transactions on systems, man, and cybernetics: systems, 2015, 45(6): 915-928.
[21] GUO P Y, 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.
[22] 刘健, 陈自强, 黄德扬, 等. 基于等压差充电时间的锂离子电池寿命预测[J]. 上海交通大学学报, 2019, 53(9): 1058-1065.
LIU J, CHEN Z Q, HUANG D Y, et al.Remaining useful life prediction for lithium-ion batteries based on time interval of equal charging voltage difference[J]. Journal of Shanghai Jiao Tong University, 2019, 53(9): 1058-1065.
[23] 何冰琛, 杨薛明, 王劲松, 等. 基于PCA-GPR的锂离子电池剩余使用寿命预测[J]. 太阳能学报, 2022, 43(5): 484-491.
HE B C, YANG X M, WANG J S, et al.Prediction of remaining useful life of lithium-ion batteries based on PCA-GPR[J]. Acta energiae solaris sinica, 2022, 43(5): 484-491.
[24] WANG D, YANG F F, TSUI K L, et al.Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter[J]. IEEE transactions on instrumentation and measurement, 2016, 65(6): 1282-1291.
[25] TIAN J P, XIONG R, SHEN W X.State-of-health estimation based on differential temperature for lithium ion batteries[J]. IEEE transactions on power electronics, 2020, 35(10): 10363-10373.
[26] WIDODO A, SHIM M C, CAESARENDRA W, et al.Intelligent prognostics for battery health monitoring based on sample entropy[J]. Expert systems with applications, 2011, 38(9): 11763-11769.
[27] LI J F, LYU C, WANG L X, et al.Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter[J]. Journal of power sources, 2014, 268: 895-903.
[28] STROE D I, SCHALTZ E.Lithium-ion battery state-of-health estimation using the incremental capacity analysis technique[J]. IEEE transactions on industry applications, 2020, 56(1): 678-685.
[29] BIRKL C.Oxford battery degradation dataset 1[Z]. University of Oxford, Oxford, U.K., 2017.
[30] BOLE B, KULKARNI C, DAIGIE M.Randomized battery usage data set[Z]. NASA Ames Prognostics Data Repository, 2014.
[31] HAN X B, OUYANG M G, LU L G, et al.A comparative study of commercial lithium ion battery cycle life in electrical vehicle: aging mechanism identification[J]. Journal of power sources, 2014, 251: 38-54.
[32] 徐晶. 梯次利用锂离子电池容量和内阻变化特性研究[D]. 北京: 北京交通大学, 2014.
XU J.Research on the variation characteristics of capacity and internal resistance of lithium-ion batteries echelon use[D]. Beijing: Beijing Jiaotong University, 2014.
[33] FENG X N, LI J Q, OUYANG M G, et al.Using probability density function to evaluate the state of health of lithium-ion batteries[J]. Journal of power sources, 2013, 232: 209-218.
[34] RASMUSSEN C E, WILLIAMS C K I. Gaussian processes for machine learning[M]. Scotland: The MIT Press, 2005.
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