This brief proposes an electrochemical model-based estimator of the Lithium-ion (Li-ion) concentration and temperature of a Li-ion cell. The use of the electrochemical approach allows for the estimation of the spatial distribution of lithium concentration and temperature. The estimation is based on a soft-constrained dual unscented Kalman filter (DUKF) designed on the pseudo-2-D model of a Li-ion cell. The dual structure, along with parallelization, reduces the computational complexity, whereas the soft-constraint improves convergence. A simulation analysis validates the approach showing bulk state of charge (SoC) estimation error lower than 1.5%, solid-phase lithium concentration estimation errors of less than 4%, and temperature estimation errors within 0.2 °C from the true value in any point of the cell.

Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering

Marelli S.;Corno M.
2021-01-01

Abstract

This brief proposes an electrochemical model-based estimator of the Lithium-ion (Li-ion) concentration and temperature of a Li-ion cell. The use of the electrochemical approach allows for the estimation of the spatial distribution of lithium concentration and temperature. The estimation is based on a soft-constrained dual unscented Kalman filter (DUKF) designed on the pseudo-2-D model of a Li-ion cell. The dual structure, along with parallelization, reduces the computational complexity, whereas the soft-constraint improves convergence. A simulation analysis validates the approach showing bulk state of charge (SoC) estimation error lower than 1.5%, solid-phase lithium concentration estimation errors of less than 4%, and temperature estimation errors within 0.2 °C from the true value in any point of the cell.
2021
Dual unscented Kalman filter
electrochemical-thermal model
Li-ion batteries
soft-constraint
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1165754
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