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- Title
A Method for State of Charge and State of Health Estimation of LithiumBatteries Based on an Adaptive Weighting Unscented Kalman Filter.
- Authors
Fang, Fengyuan; Ma, Caiqing; Ji, Yan
- Abstract
This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective function to optimize the traditional gradient descent algorithm is used. For SOC estimation, an adaptive weighting unscented Kalman filter algorithm is proposed to deal with the nonlinear update problem of the mean and covariance, which can substantially improve the estimation accuracy of the internal state of the lithium battery. Compared with fixed weights in the traditional unscented Kalman filtering algorithm, this algorithm adaptively adjusts the weights according to the state and measured values to improve the state estimation update accuracy. Finally, according to simulations, the errors of this algorithm are all lower than 1.63 %, which confirms the effectiveness of this algorithm.
- Subjects
KALMAN filtering; PARAMETER identification; DERIVATIVES (Mathematics); LITHIUM cells; NONLINEAR equations
- Publication
Energies (19961073), 2024, Vol 17, Issue 9, p2145
- ISSN
1996-1073
- Publication type
Article
- DOI
10.3390/en17092145