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- W4385657848 abstract "To ensure the safety and functionality, it is crucial to evaluate the state of battery system in electric vehicles. Describing battery dynamic characteristic with a combined state space model, this paper presents a novel adaptive nonlinear neural observer based approach for state of charge estimation, which is composed of linear discriminant function, nonlinear neural proportional-integral observer and extended Kalman filter. It incorporates the local linear approximation capability of extended Kalman filter with the nonlinear mapping, self-learning and self-adjusting capabilities of neural proportional-integral observer, which is used to compensate the deviation resulted from the underestimated initial state, process noise and measurement noise. Taking the samples collected from lithium-ion battery test system for example, simulation is carried out to verify the proposed method. Results show that it is capable of evaluating the state of charge of cell with a rapid convergence and an error <2 % while remaining unaffected by the unknown initial cell states and the underestimation of the process noise and the measurement noise." @default.
- W4385657848 created "2023-08-09" @default.
- W4385657848 creator A5076097045 @default.
- W4385657848 date "2023-11-01" @default.
- W4385657848 modified "2023-10-16" @default.
- W4385657848 title "A robust method for state of charge estimation of lithium-ion batteries using adaptive nonlinear neural observer" @default.
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- W4385657848 doi "https://doi.org/10.1016/j.est.2023.108480" @default.
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