Dimensionless Tuning Procedure of the Kalman Filter for State-Of-Charge Estimators
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/67820
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Título
Dimensionless Tuning Procedure of the Kalman Filter for State-Of-Charge EstimatorsFecha de publicación
2023Editor
IEEE, Institute of Electrical and Electronics EngineersISBN
9798350331820ISSN
2162-4704Cita bibliográfica
I. Peñarrocha, E. Pérez, H. Beltran and C. Díaz-Sanahuja, "Dimensionless Tuning Procedure of the Kalman Filter for State-Of-Charge Estimators," IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, Singapore, Singapore, 2023, pp. 1-6, doi: 10.1109/IECON51785.2023.10312668.Tipo de documento
info:eu-repo/semantics/conferenceObjectVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/10312668Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
This paper presents a solution to the challenging problem of estimating the state of charge (SOC) of batteries using a Kalman filter algorithm. The algorithm requires knowledge of the dynamical model and its parameters, ... [+]
This paper presents a solution to the challenging problem of estimating the state of charge (SOC) of batteries using a Kalman filter algorithm. The algorithm requires knowledge of the dynamical model and its parameters, along with the covariance matrices associated with measurement noise, process noise, and initial estimation error. However, determining the values for the process noise and initial estimation error matrices is often difficult. To address this issue, we propose a novel method to tune these matrices based on a new tuning parameter, the measurement noise variance, and the expected slope of the open circuit voltage vs. state of charge curve. We demonstrate the effectiveness of the proposed approach through extensive simulations involving various batteries and operating conditions in an electrical driving scenario. We assess the performance of the Kalman filter estimation under noisy environments, wrong initial conditions, and modeling errors, obtaining dimensionless performance indices that quantify its behavior. By analyzing the simulation results, we establish a general design procedure for the process covariance matrix, where the user only needs to set the desired limits for the state of charge error in noisy environments and the convergence time under wrong initializations. This design procedure is applicable to batteries of any type, sampling period, or measurement noise level, providing a practical and efficient solution for accurate state of charge estimation [-]
Publicado en
IECON 2023-49th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 2023Entidad financiadora
Ministerio de Ciencia e Innovación | Universitat Jaume I
Identificador de la entidad financiadora
http://dx.doi.org/10.13039/501100011033
Código del proyecto o subvención
MCIN/PEICTI2021-2023/PID2021-125634OB-I00 | MCIN/PEICTI2021-2023/TED2021-130120B-C22 | UJI-B2021-35
Título del proyecto o subvención
Gestión de sistemas renovables con almacenamiento y control de sus convertidores para contribuir a la operación del futuro sistema eléctrico | Control descentralizado y protección para un sistema eléctrico futuro resiliente basado en convertidores