Prediction of available capacity of power battery based on autoregressive integrated moving average model

Liwen Zhu; Liyu Zhang; Runze Gao; Zhen Wang

doi:10.1117/12.3025557

28 February 2024 Prediction of available capacity of power battery based on autoregressive integrated moving average model

Liwen Zhu, Liyu Zhang, Runze Gao, Zhen Wang

Author Affiliations +

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 1307133 (2024) https://doi.org/10.1117/12.3025557
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

As a common battery for new energy vehicles at home and abroad, the capacity loss of lithium-ion battery has always been the focus of attention. Accurately identifying the maximum available capacity of the battery in the actual use process is a key point and difficulty in the current development of power battery technology. In this paper, the influence of the voltage, current and temperature parameters of power battery on the available capacity of lithium-ion battery is explained, and predicted by ARIMA model. After obtaining lithium battery measurement data set in NASA, ADF single root test and difference method were used to stabilize the raw data of lithium battery capacity, and to evaluate the effectiveness of various estimated parameters. After strict evaluation, ARIMA (1,1,1) was identified as the best fit model, and relatively accurate prediction results were obtained. The results show that the ARIMA model prediction method can more accurately predict the available capacity of lithium battery in electric vehicles.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liwen Zhu, Liyu Zhang, Runze Gao, and Zhen Wang "Prediction of available capacity of power battery based on autoregressive integrated moving average model", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 1307133 (28 February 2024); https://doi.org/10.1117/12.3025557

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KEYWORDS

Batteries

Data modeling

Autoregressive models

Lithium

Statistical modeling

Model based design

Performance modeling

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