A hybrid DNN-KF model for real-time SOC estimation of lithium-ion batteries under different ambient temperatures

Guanxu Chen, Shancheng Jiang, Min Xie, Fangfang Yang

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    7 Citations (Scopus)

    Abstract

    Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LFP) battery under different ambient temperatures is a long-standing problem in industry. In this paper, a hybrid model combining deep neural network and Kalman filter is proposed for SOC estimation of LFP battery under different ambient temperatures. After estimation via deep neural network, the estimated SOCs are further corrected using Kalman filter of high denoising capability. Data collected from dynamic stress test, US06 test and federal urban driving schedule under 25°C, 30°C, 40°C, and 50°C are used to verify the performance of proposed model, with the first two data as training set and the third data as testing set. Experimental results show that the proposed model can well meet the requirement of real-time estimation with mean absolute error within 2% and root mean square error within 2.4%. In addition, we also test the robustness of proposed model against different initial SOC values, and proves that the proposed model can well generalize the estimation to different initial values.
    Original languageEnglish
    Title of host publication2022 Global Reliability and Prognostics and Health Management Conference (PHM-Yantai)
    PublisherIEEE
    ISBN (Electronic)978-1-6654-9631-5
    DOIs
    Publication statusPublished - 2022
    Event2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022 - Yantai, China
    Duration: 13 Oct 202216 Oct 2022

    Publication series

    NameGlobal Reliability and Prognostics and Health Management Conference, PHM-Yantai

    Conference

    Conference2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022
    Country/TerritoryChina
    CityYantai
    Period13/10/2216/10/22

    Research Keywords

    • deep neural network
    • Kalman filter
    • lithium iron phosphate battery
    • SOC estimation

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