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

Abstract

This work 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 (LiFePO 4 ) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

Modeling, Control System Design and Preliminary Experimental Verification of a Hybrid Power Unit Suitable for Multirotor UAVs

“…The modeling approach used herein is based on the model precision requirements and other factors as simulation speed and others. The approach based on the quasi-static Thevenin model [33,34] (see Figure 2a) used herein to characterize the key aspects of battery operation, i.e., quasi-steady-state terminal voltage and overall inner resistance corresponding to heat power losses.…”

State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

“…The extended KF (EKF) method mathematically transforms the standard (linear matrix) KF to fit a nonlinear system. Besides, several EKF-based methods, such as lazy EKF and robust EKF, are proposed to estimate the SoC of the battery and in different scenarios for vehicle onboard battery and microgrid energy storage units [15,16]. The Dual EKF (DEKF) adopted in [17,18] to estimate the SoC of the battery and the parameter of the model shows faster convergence in shorter calculation time.…”

“…where D w and D v in Equations (14) and (15) are the covariance of the process noise w k and the measurement noise v k , respectively.…”

Damping Optimum-Based Design of Control Strategy Suitable for Battery/Ultracapacitor Electric Vehicles

“…The simulation model comprising the hybrid battery/ultracapacitor energy storage equipped with the previously designed DC bus control system and the traction motor drive within the hypothetical EVs is parameterized based on realistic power-train component data obtained from [43,[56][57][58][59].…”