Real-time SOC co-estimation algorithm for Li-ion batteries based on fast square-root unscented Kalman filters

ZHANG Jun-hui; LI Qing; CHEN Da-peng; ZHAO Ye

doi:10.13374/j.issn2095-9389.2020.07.30.002

Volume 43 Issue 7

Jul. 2021

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Article Navigation > Chinese Journal of Engineering > 2021 > 43(7): 976-984

ZHANG Jun-hui, LI Qing, CHEN Da-peng, ZHAO Ye. Real-time SOC co-estimation algorithm for Li-ion batteries based on fast square-root unscented Kalman filters[J]. Chinese Journal of Engineering, 2021, 43(7): 976-984. doi: 10.13374/j.issn2095-9389.2020.07.30.002

Citation:

ZHANG Jun-hui, LI Qing, CHEN Da-peng, ZHAO Ye. Real-time SOC co-estimation algorithm for Li-ion batteries based on fast square-root unscented Kalman filters[J]. Chinese Journal of Engineering, 2021, 43(7): 976-984. doi: 10.13374/j.issn2095-9389.2020.07.30.002

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Real-time SOC co-estimation algorithm for Li-ion batteries based on fast square-root unscented Kalman filters

doi: 10.13374/j.issn2095-9389.2020.07.30.002

ZHANG Jun-hui^{1, 2, 3, 4
,
,},
LI Qing^{1, 2, 4},
CHEN Da-peng^{1, 2, 3, 4},
ZHAO Ye¹

1.
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
2.
Jiangsu R&D Center for Internet of Things, Wuxi 214135, China
3.
Wuxi Internet of Things Innovation Center Co., Ltd., Wuxi 214135, China
4.
Institute of Microelectronic Technology of Kunshan, Kunshan 215347, China

More Information

Corresponding author: E-mail: zhangjunhui@ime.ac.cn
Received Date: 2020-07-30
Available Online: 2020-09-03
Publish Date: 2021-07-01

Abstract

Abstract

The Li-ion battery is an important energy source for electric vehicles (EVs), and the accurate estimation of the battery power state provides a reliable reference for balancing the battery packing and battery management system (BMS). It also has great practical significance for making full and reasonable utilization of batteries, and improving the battery life cycle and vehicle operation efficiency. Practical issues that must be addressed include the filtering divergence caused by the non-positive definite error covariance matrix in the standard unscented Kalman filter (UKF) and the state estimation errors that accumulate from the simplified mathematical modeling of the Li-ion battery, with its inherently strong non-linearity, time variation, and uncertainty. To resolve these issues, in this article, a real-time state co-estimation algorithm was proposed based on a fast square-root unscented Kalman filter (SR-UKF) framework. First, during the iteration process, the non-linear measurement function, which describes the propagation of each sigma point, is called by an unscented transform. A reduction in computational complexity can be achieved if the non-linear measurement function is quasi-linearized. Second, instead of a state error covariance matrix, the square root of the state error covariance matrix is used, which is obtained by QR decomposition and first-order updating of the Cholesky factor. This step deals with the problem that arises if the state error covariance matrix is negative definite due to the computational errors accumulated while performing recursive estimation with the standard UKF. This guarantees the numerical stability of the battery’s estimated state of charge (SOC) in real time. Third, the inner ohmic resistance and nominal capacity that indirectly characterize the state of health can be estimated online, and a highly precise SOC estimation can be realized due to the accuracy and efficiency of the battery model. Comparative experimental results confirm and validate the feasibility and robustness of the proposed fast SR-UKF algorithm and co-estimation strategy.
- state of charge,
- state of health,
- square-root unscented Kalman filter,
- co-estimation strategy,
- Li-ion battery

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References(25)

References

[1]	Zhang H K, Wang Y F, Qi H, et al. Active battery equalization method based on redundant battery for electric vehicles. IEEE Trans Veh Technol, 2019, 68(8): 7531 doi: 10.1109/TVT.2019.2925742
[2]	安富強, 趙洪量, 程志, 等. 純電動車用鋰離子電池發展現狀與研究進展. 工程科學學報, 2019, 41(1):22 An F Q, Zhao H L, Chen Z, et al. Development status and research progress of power battery for pure electric vehicles. Chin J Eng, 2019, 41(1): 22
[3]	Afshar S, Morris K, Khajepour A. State-of-charge estimation using an EKF-based adaptive observer. IEEE Trans Control Syst Technol, 2019, 27(5): 1907 doi: 10.1109/TCST.2018.2842038
[4]	Petzl M, Danzer M A. Advancements in OCV measurement and analysis for lithium-ion batteries. IEEE Trans Energy Convers, 2013, 28(3): 675 doi: 10.1109/TEC.2013.2259490
[5]	Xiong R, Zhang Y Z, He H W, et al. A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries. IEEE Trans Ind Electron, 2018, 65(2): 1526 doi: 10.1109/TIE.2017.2733475
[6]	Partovibakhsh M, Liu G J. An adaptive unscented Kalman filtering approach for online estimation of model parameters and state-of-charge of lithium-ion batteries for autonomous mobile robots. IEEE Trans Control Syst Technol, 2015, 23(1): 357 doi: 10.1109/TCST.2014.2317781
[7]	Chaoui H, Ibe-Ekeocha C C. State of charge and state of health estimation for lithium batteries using recurrent neural networks. IEEE Trans Veh Technol, 2017, 66(10): 8773 doi: 10.1109/TVT.2017.2715333
[8]	Wu J, Zhang C B, Chen Z H. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Appl Energy, 2016, 173: 134 doi: 10.1016/j.apenergy.2016.04.057
[9]	Chun C Y, Cho B H, Kim J. Implementation of discharging/charging current sensorless state-of-charge estimator reflecting cell-to-cell variations in lithium-ion series battery packs. Int J Automot Technol, 2016, 17(5): 909 doi: 10.1007/s12239-016-0088-8
[10]	Weng C H, Sun J, Peng H. A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring. J Power Sources, 2014, 258: 228 doi: 10.1016/j.jpowsour.2014.02.026
[11]	Lavigne L, Sabatier J, Francisco J M, et al. Lithium-ion open circuit voltage (OCV) curve modelling and its ageing adjustment. J Power Sources, 2016, 324: 694 doi: 10.1016/j.jpowsour.2016.05.121
[12]	Paschero M, Storti G L, Rizzi A, et al. A novel mechanical analogy-based battery model for SoC estimation using a multi-cell EKF. IEEE Trans Sustainable Energy, 2016, 7(4): 1695 doi: 10.1109/TSTE.2016.2574755
[13]	章軍輝, 李慶, 陳大鵬, 等. 基于自適應UKF的鋰離子動力電池狀態聯合估計. 東北大學學報(自然科學版), 2020, 41(11):1557 Zhang J H, Li Q, Chen D P, et al. State Co-estimation algorithm for li-ion power batteries based on adaptive unscented kalman filters. J Northeast Univ Nat Sci, 2020, 41(11): 1557
[14]	Wang D, Yang F F, Tsui K L, et al. Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter. IEEE Trans Instrum Meas, 2016, 65(6): 1282 doi: 10.1109/TIM.2016.2534258
[15]	Takeno K, Ichimura M, Takano K, et al. Quick testing of batteries in lithium-ion battery packs with impedance-measuring technology. J Power Sources, 2004, 128(1): 67 doi: 10.1016/j.jpowsour.2003.09.045
[16]	Abraham D P, Knuth J L, Dees D W, et al. Performance degradation of high-power lithium-ion cells: electrochemistry of harvested electrodes. J Power Sources, 2007, 170(2): 465 doi: 10.1016/j.jpowsour.2007.03.071
[17]	Klass V, Behm M, Lindbergh G. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation. J Power Sources, 2014, 270: 262 doi: 10.1016/j.jpowsour.2014.07.116
[18]	El Mejdoubi A, Chaoui H, Gualous H, et al. Lithium-ion batteries health prognosis considering aging conditions. IEEE Trans Power Electron, 2019, 34(7): 6834 doi: 10.1109/TPEL.2018.2873247
[19]	Van der Merwe R, Wan E A. The square-root unscented Kalman filter for state and parameter-estimation // Proceedings of 2001 IEEE International Conference on Acoustics, Speech and Signal Processing. Salt Lake City, 2001: 3461
[20]	Aung H, Low K S, Goh S T. State-of-charge estimation of lithium-ion battery using square root spherical unscented Kalman filter (Sqrt-UKFST) in nanosatellite. IEEE Trans Power Electron, 2015, 30(9): 4774 doi: 10.1109/TPEL.2014.2361755
[21]	Gholizade-Narm H, Charkhgard M. Lithium-ion battery state of charge estimation based on square-root unscented Kalman filter. IET Power Electron, 2013, 6(9): 1833 doi: 10.1049/iet-pel.2012.0706
[22]	Lin H T, Liang T J, Chen S M. Estimation of battery state of health using probabilistic neural network. IEEE Trans Ind Inf, 2013, 9(2): 679 doi: 10.1109/TII.2012.2222650
[23]	Kim J, Cho B H. State-of-charge estimation and state-of-health prediction of a Li-ion degraded battery based on an EKF combined with a per-unit system. IEEE Trans Veh Technol, 2011, 60(9): 4249 doi: 10.1109/TVT.2011.2168987
[24]	Shen J N, Shen J J, He Y J, et al. Accurate state of charge estimation with model mismatch for Li-ion batteries: a joint moving horizon estimation approach. IEEE Trans Power Electron, 2019, 34(5): 4329 doi: 10.1109/TPEL.2018.2861730
[25]	Yu Q 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. IEEE Trans Veh Technol, 2017, 66(10): 8693 doi: 10.1109/TVT.2017.2709326