Particularised Kalman filter for the state-of-charge estimation of second-life lithium-ion batteries and experimental validation
Fecha
2021Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
Impacto
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10.1109/EEEIC/ICPSEurope51590.2021.9584520
Resumen
A critical issue for a proper energy management of a lithium-ion (Li-ion) battery is the estimation of its state-of-charge (SOC). There are various methods available for the SOC estimation, being some of them robust and accurate, but requiring high computational power for its applicability, which is inconvenient for their use with the usual low-cost microcontrollers that build a typical BMS. This ...
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A critical issue for a proper energy management of a lithium-ion (Li-ion) battery is the estimation of its state-of-charge (SOC). There are various methods available for the SOC estimation, being some of them robust and accurate, but requiring high computational power for its applicability, which is inconvenient for their use with the usual low-cost microcontrollers that build a typical BMS. This contribution proposes an SOC estimation algorithm based on a simplified Kalman Filter, that combines a high accuracy with reduced computational requirements. The proposed simplifications result from a careful analysis of the Li-ion battery performance and linearization of processes that entail negligible loss of accuracy. The proposed algorithm is used to estimate the SOC of a second-life Li-ion battery operating in an experimental PV self-consumption facility. Its performance, in terms of accuracy, robustness and computational requirement, is compared with an Extended Kalman Filter (EKF), a Particle Filter (PF) and other low-performance estimation algorithms, proving its tradeoff between accuracy and computational cost. [--]
Materias
Lithium-ion battery,
State of charge,
Kalman Filter,
Estimation algorithm
Editor
IEEE
Publicado en
Dicorato, M. (Ed.).: 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe. IEEE, 2021, 1 - 6, 978-1-6654-3612-0
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicación /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Versión del editor
Entidades Financiadoras
This work has been supported by the Spanish State Research Agency
(AEI) under grant PID2019-111262RB-I00 /AEI/ 10.13039/501100011033,
the European Union under the H2020 project STARDUST (774094), the
Government of Navarra through research project 0011–1411–2018–000029
GERA and the Public University of Navarra under project ReBMS
PJUPNA1904.
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