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Online state of charge and state of power co-estimation of lithium-ion batteries based on fractional-order calculus and model predictive control theory

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  • Guo, Ruohan
  • Shen, Weixiang

Abstract

Accurate battery modelling is the cornerstone to state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries in electric vehicles. Due to strong battery nonlinearity over a broad frequency range, traditional integer-order models are incapable of capturing complex battery dynamics for SOC and SOP co-estimation. This paper proposes a fractional-order modified moving horizon estimation (FO-mMHE) algorithm and a fractional-order model predictive control (FO-MPC) algorithm. Firstly, a second-order FOM is constructed by performing a series of hybrid pulse tests at different SOC regions, and its model parameters are identified through a particle swarm optimization-genetic algorithm method. Secondly, online SOC estimation is converted into a constrained optimization problem in a past moving horizon and then solved by the FO-mMHE algorithm, which enables fast convergence speed and proactive smoothing of estimation outcomes. Thirdly, the FO-MPC algorithm is devised to manipulate the current sequence in a prediction horizon for maximizing discharge/charge power accumulation and determining battery SOP in real time. Moreover, different battery current–voltage behaviors are comprehensively researched in the prediction horizon over a whole battery operating range. The proposed co-estimation method is validated under different dynamic load profiles. The experimental results demonstrate a SOC estimation error reduction of up to 1.2 % compared with the commonly used fractional-order extended Kalman filter while the SOP estimation error could be limited below 0.35 W.

Suggested Citation

  • Guo, Ruohan & Shen, Weixiang, 2022. "Online state of charge and state of power co-estimation of lithium-ion batteries based on fractional-order calculus and model predictive control theory," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922012661
    DOI: 10.1016/j.apenergy.2022.120009
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    References listed on IDEAS

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    1. Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Lu, Jiahuan, 2021. "State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach," Applied Energy, Elsevier, vol. 291(C).
    2. Hu, Xiaosong & Jiang, Haifu & Feng, Fei & Liu, Bo, 2020. "An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management," Applied Energy, Elsevier, vol. 257(C).
    3. Varga, Bogdan Ovidiu, 2013. "Electric vehicles, primary energy sources and CO2 emissions: Romanian case study," Energy, Elsevier, vol. 49(C), pages 61-70.
    4. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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    Cited by:

    1. Lu, Xin & Chen, Ning & Li, Hui & Guo, Shiyu & Chen, Zengtao, 2023. "Simulation of the temperature distribution of lithium-ion battery module considering the time-delay effect of the porous electrodes," Energy, Elsevier, vol. 284(C).
    2. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    3. Tang, Aihua & Huang, Yukun & Liu, Shangmei & Yu, Quanqing & Shen, Weixiang & Xiong, Rui, 2023. "A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models," Applied Energy, Elsevier, vol. 348(C).

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