Abstract
In battery management systems, the estimation of internal cell parameters has become an important research focus in the recent years. Exemplarily, this includes the tracking of parameters such as the internal cell impedances, the cell capacity, or the state-of-charge (SoC) of a battery. In general, the battery is considered to be a non-linear dynamic system. Hence, this paper compares the accuracy and the complexity of the extended Kalman filter (EKF) and the particle filter (PF), which are applied for the estimation of internal cell states such as the SoC and the battery’s transient response. The comparison shows that the PF yields better accuracy compared to the EKF under the given conditions. However, the EKF is computationally less complex compared to the PF.
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Pathuri-Bhuvana, V., Unterrieder, C., Fischer, J. (2013). Battery Internal State Estimation: Simulation Based Analysis on EKF and Auxiliary PF. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2013. EUROCAST 2013. Lecture Notes in Computer Science, vol 8111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53856-8_59
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DOI: https://doi.org/10.1007/978-3-642-53856-8_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53855-1
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