Title :
Model-based electrochemical estimation of lithium-ion batteries
Author :
Smith, Kandler A. ; Rahn, Christopher D. ; Wang, Chao Yang
Author_Institution :
Pennsylvania State Univ., University Park, PA
Abstract :
A linear Kalman filter based on a reduced order electrochemical model is designed to estimate internal battery potentials, concentration gradients, and state of charge (SOC) from external current and voltage measurements. The estimates are compared with results from an experimentally validated one-dimensional nonlinear finite volume model of a 6 Ah hybrid electric vehicle battery. The linear filter gives, to within ~2%, performance in the 30%-70% SOC range, except in the case of severe current pulses that draw electrode surface concentrations to near saturation and depletion; however, the estimates recover as concentration gradients relax. With 4 to 7 states, the filter has low order comparable to empirical equivalent circuit models but provides estimates of the batterypsilas internal electrochemical state.
Keywords :
Kalman filters; battery powered vehicles; electrochemistry; finite volume methods; hybrid electric vehicles; secondary cells; concentration gradients; hybrid electric vehicle battery; linear Kalman filter; lithium-ion batteries; model-based electrochemical estimation; one-dimensional nonlinear finite volume model; reduced order electrochemical model; state of charge; Batteries; Electrodes; Equivalent circuits; Hybrid electric vehicles; Nonlinear filters; Predictive models; Solid modeling; State estimation; Vehicle dynamics; Voltage control;
Conference_Titel :
Control Applications, 2008. CCA 2008. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-2222-7
Electronic_ISBN :
978-1-4244-2223-4
DOI :
10.1109/CCA.2008.4629589