engleski

Dual EKF-based State and Parameter Estimator for a LiFePO4 Battery Cell

This paper presents the design of a dual Extended Kalman Filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate (LiFePO4) cell. An experimentally- identified lumped- parameter equivalent battery electrical circuit model has been used as a basis for the design of both estimators. In the proposed estimation scheme, the parameter estimator has been used for the adaptation of the state (SoC) EKF-based estimator, which may be sensitive to nonlinear battery parameter map errors. In order to achieve smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator, a suitable weighting scheme has also been proposed. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator has been verified by means of computer simulations on the developed battery model subject to NEDC driving cycle-related operating regimes.

Battery modeling, Kalman filter, Estimation, Electric vehicles

nije evidentirano