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A battery capacity estimation method based on the equivalent circuit model and quantile regression using vehicle real-world operation data

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  • Jiang, Yan
  • Meng, Xin

Abstract

Battery capacity estimation is critical for evaluating the battery performance and safety. Currently, the sampling interval of battery operation data on the cloud is 10–30 s, and relatively poor data quality poses difficulties in battery capacity estimation. In this study, a battery capacity estimation method is proposed based on the battery equivalent circuit model and a quantile regression method using real-world vehicle operation data on the cloud. The battery parameters were identified based on the Thevenin battery model, and the state of charge (SOC) was estimated using a joint Kalman filter. Then, the discharge electric quantity–SOC data was fitted using quantile regression to obtain the preliminary capacity estimation results. To describe the battery capacity degradation along with driving mileage, this estimation result was further fitted using the Arrhenius empirical aging model and another quantile regression. The Nelder–Mead method was used to estimate the coefficients in quantile regression-related algorithms. Because quantile regression is not sensitive to the outliers of data points, it can reduce the impact of poor data quality on battery capacity estimation results. The proposed battery capacity estimation method was verified using real-world vehicle operation data, and the capacity estimation errors were within 3.2%.

Suggested Citation

  • Jiang, Yan & Meng, Xin, 2023. "A battery capacity estimation method based on the equivalent circuit model and quantile regression using vehicle real-world operation data," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223025203
    DOI: 10.1016/j.energy.2023.129126
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    2. García, Antonio & Micó, Carlos & Cobo, Mariany Chávez & Elkourchi, Imad & Vemula, Jagadish Babu, 2025. "A comprehensive methodology for characterization and electro-thermal modelling for a next-generation solid-state battery," Energy, Elsevier, vol. 322(C).
    3. Zeng, Jiawei & Wang, Shunli & Cao, Wen & Zhou, Yifei & Fernandez, Carlos & Guerrero, Josep M., 2024. "Battery asynchronous fractional-order thermoelectric coupling modeling and state of charge estimation based on frequency characteristic separation at low temperatures," Energy, Elsevier, vol. 307(C).
    4. Ni, Yulong & Song, Kai & Pei, Lei & Li, Xiaoyu & Wang, Tiansi & Zhang, He & Zhu, Chunbo & Xu, Jianing, 2025. "State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model," Applied Energy, Elsevier, vol. 385(C).
    5. Zhao, Xinwei & Liu, Yonggui & Xiao, Bin, 2025. "Enhanced prediction for battery aging capacity using an efficient temporal convolutional network," Energy, Elsevier, vol. 320(C).
    6. Liu, Wei & Teh, Jiashen & Alharbi, Bader, 2025. "An asynchronous electro-thermal coupling modeling method of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 324(C).

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