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Grey system theory-based capacity estimation method for Li-ion batteries

机译:基于灰色系统理论的锂离子电池容量估计方法

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The SOC and SoH of Li-ion batteries are of prime importance in EVs and their condition monitoring techniques have been extensively studied. This paper proposes a grey system theory for predicting the battery capacity and healthy conditions in relation to their discharge cycles. Numerical results via grey system theory-based models are obtained based on the aging data from NASA prognostics data repository. Therefore, the accuracy for the SOC estimation can be examined and improved. In this paper, the accuracy of different grey models including GM (1,1), segmental GM (1,1), Verhulst model, sliding window Verhulst model are investigated and the sliding window Verhulst model is found to be effective for EV batteries.
机译:锂离子电池的SOC和SoH在电动汽车中至关重要,并且对其状态监控技术进行了广泛的研究。本文提出了一种灰色系统理论,用于预测电池容量和健康状况与其放电周期的关系。通过基于灰色系统理论的模型,基于来自NASA预后数据存储库的老化数据获得了数值结果。因此,可以检查和提高SOC估计的准确性。本文研究了GM(1,1),分段GM(1,1),Verhulst模型,滑动窗口Verhulst模型等不同灰色模型的精度,发现滑动窗口Verhulst模型对EV电池有效。

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