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An Innovative State-of-charge Estimation Method of Lithium-ion Battery Based on 5th-order Cubature Kalman Filter

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Abstract

The lithium-ion batteries have drawn much attention as the major energy storage system. However, the battery state estimation still suffers from inaccuracy under dynamic operational conditions, with the unstable environmental noise influencing the robustness of estimation. This paper presents a 5th-order cubature Kalman filter with improvements on adaptivity for real-time state-of-charge estimation. The second-order equivalent circuit model is developed for describing the characteristics of battery, and parameter identification is carried out according to particle swarm optimization. The developed method is validated in stable and dynamic conditions, and simulation results show a satisfactory consistency with the experimental results. The maximum estimation error under static conditions is less than 3% and the maximum error under dynamic conditions is 5%. Numerical analysis indicates that the method has better convergence and robustness than the traditional method under the disturbances of initial error, which demonstrates the potential for EV applications in harsh environments. The proposed method shows application potential for both online estimations and cloud-computing system, indicating its diverse application prospect in electric vehicles.

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Abbreviations

BMS:

Battery management system

CC:

Coulomb count

CLTC:

China light-duty vehicle test cycle

CKF:

Cubature Kalman filter

DOD:

Depth of discharge

ECM:

Equivalent circuit model

EKF:

Extended Kalman filter

EV:

Electric vehicle

HEV:

Hybrid electric vehicle

KF:

Kalman filter

MAE:

Maximum absolute error

NEDC:

New European drive cycle

OCV:

Open-circuit voltage

PEV:

Pure electric vehicle

PSO:

Particle swarm optimization

RC:

Resistance capacitor

RMSE:

Root mean squared error

SOC:

State of charge

UDDS:

Urban dynamometer driving schedule

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (2018YFB0105400).

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Authors and Affiliations

  1. School of Vehicle and Mobility, Tsinghua University, Beijing, China

    Huang Yi

  2. School of Transportation Science and Engineering, Beihang University, Beijing, China

    Shichun Yang, Sida Zhou, Xinan Zhou, Xiaoyu Yan & Xinhua Liu

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  1. Huang Yi
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  2. Shichun Yang
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  3. Sida Zhou
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  4. Xinan Zhou
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  5. Xiaoyu Yan
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  6. Xinhua Liu
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Correspondence to Xinhua Liu.

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On behalf of all the authors, the corresponding author states that there is no conflict of interest.

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Yi, H., Yang, S., Zhou, S. et al. An Innovative State-of-charge Estimation Method of Lithium-ion Battery Based on 5th-order Cubature Kalman Filter. Automot. Innov. 4, 448–458 (2021). https://doi.org/10.1007/s42154-021-00162-0

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  • DOI: https://doi.org/10.1007/s42154-021-00162-0

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