Abstract
Predicting the remaining useful life (RUL) of lithium-ion batteries is critical to the safe and reliable use of batteries. Due to the highly non-linear degradation properties, the conventional approach requires sophisticated models and large amounts of operational data to accurately predict the RUL. A fused model data-driven approach ISE-PF-pSVR is presented in this paper. The semi-empirical degradation model is improved by considering the charging rate and temperature characteristics. And the use of particle filter (PF) algorithm updates degradation model parameters. Since the filtering algorithm obtains the observations by recursion, there is error accumulation. P-step support vector regression (SVR) is introduced to estimate the observation iteratively, so as to boost the precision of dynamic parameter update. In addition, the performance of ISE-PF-pSVR model is verified using three datasets with inconsistent degradation forms. It shows that the established RUL prediction model has remarkable universality, applicability and accuracy.
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Acknowledgements
This work was supported by the Natural Science Basic Research Program of Shaanxi Province (Grants Number 2021JZ-19).
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Feng, H., Xu, A. (2023). Model-Data Driven Fusion Method Considering Charging Rate and Temperature to Predict RUL of Lithium-Ion Battery. In: Nakamatsu, K., Kountchev, R., Patnaik, S., Abe, J.M. (eds) Advanced Intelligent Technologies for Information and Communication. ICAIT 2022. Smart Innovation, Systems and Technologies, vol 365. Springer, Singapore. https://doi.org/10.1007/978-981-99-5203-8_22
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DOI: https://doi.org/10.1007/978-981-99-5203-8_22
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