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- W2904090109 abstract "Lithium-ion batteries are currently the most prevalent energy storage devices. Accurate estimation of battery state-of-charge (SOC) is an important task in the battery management system for ensuring safety operations of battery packs. This paper presents a hybrid model which substitutes a nonlinear autoregressive exogenous (NARX) artificial neural network for traditional RC networks to represent the polarization effect of lithium-ion batteries. Based on the hybrid model, the extended Kalman filter method is adopted to estimate the battery SOC. Experiments on a lithium-ion battery cell were conducted to verify the feasibility and effectiveness of this method. The results show that the hybrid model has a good performance in modeling battery dynamic characteristics, and whereby the estimation of battery SOC achieves high accuracy and convergence speed." @default.
- W2904090109 created "2018-12-22" @default.
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- W2904090109 date "2018-10-01" @default.
- W2904090109 modified "2023-09-23" @default.
- W2904090109 title "State-of-Charge Estimation of Li-ion Batteries Based on A Hybrid Model Using Nonlinear Autoregressive Exogenous Neural Networks" @default.
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- W2904090109 doi "https://doi.org/10.1109/appeec.2018.8566627" @default.
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