Online Estimation of the Electrochemical Impedance Spectrum and Remaining Useful Life of Lithium-Ion Batteries

An electrochemical impedance spectrum (EIS) is considered to be one of the key indicators to monitor the health status of lithium-ion batteries. Experimental procedures to measure the MS of a battery are offline and require manual intervention. So, in order to monitor the state of health of a battery in real time, online methods for EIS estimation would be very useful. This paper presents an approach for estimation of the EIS of lithium-ion batteries based on a fractional-order equivalent circuit model (FOECM) which can be implemented online. First, the parameters of the fractional-order model are determined using recursive least-squares technique in conjunction with a fractional-order state variable filter based on current and voltage measurements. The parameters obtained are then used to generate the estimated EIS of the battery under different aging conditions. Thereafter, a regression model is obtained based on the estimated MS spectrum which can represent the degradation trend of the battery in terms of its internal resistance growth. Finally, the obtained regression model is used in the particle filtering framework to predict the remaining useful life (RUL) of the battery quite satisfactorily as compared to the RUL obtained based on the measured EIS data. Moreover, in order to justify the proposed RUL estimation method based on FOECM, comparative analyses with respect to other FOECM-based regression models and an integer order model have also been carried out.