Theoretical bounds on the accuracy of state and parameter estimation for batteries
Paper in proceeding, 2017
Today it is standard to use equivalent circuit models to describe the dynamic behavior of Li-ion vehicle batteries. The parameters and states change with operating point and are therefore continuously estimated using bayesian observers, though without knowing to what degree the performance can be improved. Posterior Cramér-Rao Lower Bounds (CRLBs) can be used to theoretically quantify the optimal accuracy of bayesian estimators. In this paper we apply this to a second-order nonlinear equivalent-circuit model of a lithium-ion battery. It is shown, by numerical calculations, how the posterior Cramér-Rao Lower Bounds depend on the amplitude and frequency of the current, and on the slope of the Open Circuit Voltage (OCV) curve. Furthermore, it is investigated how much the accuracy is reduced in combined estimation of the states and the resistance compared to when the resistance is perfectly known. More importantly, it is also shown that the Mean Square Errors (MSE) of an Extended Kalman Filter (EKF) are close to the posterior CRLBs, which means that, under the investigated circumstances, it is not possible to significantly reduce the MSEs by replacing the EKF by any other observer.
Secondary batteries
Circuit simulation
Numerical calculation
Open circuit voltage
Kalman filters
Parameter estimation
Circuit theory
State and parameter estimations
Equivalent circuits
Theoretical bounds
Nonlinear equivalent circuit
Dynamic behaviors
Lithium-ion batteries
Equivalent circuit model
Extended Kalman filters
Mean square error
Electric batteries
Bayesian estimators
Optimal accuracy
Author
Anton Klintberg
Chalmers, Signals and Systems, Systems and control
Torsten Wik
Chalmers, Signals and Systems, Systems and control
B. Fridholm
Volvo Cars
American Control Conference
0743-1619 (ISSN)
4035-4041Subject Categories
Control Engineering
DOI
10.23919/ACC.2017.7963574