State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator

Daoming Sun, Xiaoli Yu, Chongming Wang, Cheng Zhang, Rui Huang, Quan Zhou, Taz Amietszajew, Rohit Bhagat

    Research output: Contribution to journalArticlepeer-review

    108 Citations (Scopus)
    329 Downloads (Pure)


    Adaptive extended Kalman filter (AEKF) is widely used for lithium-ion battery (LIBs) state of charge (SOC) estimation. Innovation covariance matrix (ICM) of AEKF is estimated by fixed-length error innovation sequence (EIS) (the difference between measured and estimated voltages), which doesn't consider the distribution change of EIS. However, the distribution of EIS will change due to load current dynamics or error of battery model. Failing to consider the distribution change of EIS will lead to SOC estimation inaccuracy. To address this problem, this paper proposed an intelligent adaptive extended Kalman filter (IAEKF) method that can detect the moment of distribution change of EIS by the maximum likelihood function. Then, the ICM is updated based on the EIS after that moment to improve the SOC estimation accuracy. Results show that the proposed IAEKF method improves SOC estimation accuracy. Compared to that of the AEKF, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) of SOC based on IAEKF decrease significantly by 43.34% and 55.80%, respectively, while the computation time only increases by 4.59%. In the end, the effect of initial parameters on the SOC estimation accuracy was analysed. It is found that the proposed IAEKF method is robust against parameter uncertainties.

    Original languageEnglish
    Article number119025
    Early online date8 Oct 2020
    Publication statusPublished - 1 Jan 2021

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy, 214, (2021) DOI: 10.1016/

    © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


    Natural Science Foundation of China under grant number No. 51806189 and 51476143


    • Distribution change
    • Innovation covariance matrix
    • Intelligent adaptive extended kalman filter
    • Lithium-ion battery
    • State of charge

    ASJC Scopus subject areas

    • Civil and Structural Engineering
    • Building and Construction
    • Modelling and Simulation
    • Renewable Energy, Sustainability and the Environment
    • Fuel Technology
    • Energy Engineering and Power Technology
    • Pollution
    • Energy(all)
    • Mechanical Engineering
    • Industrial and Manufacturing Engineering
    • Management, Monitoring, Policy and Law
    • Electrical and Electronic Engineering


    Dive into the research topics of 'State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator'. Together they form a unique fingerprint.

    Cite this