Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health

The objective of this paper is development of a remaining useful life (RUL) prediction algorithm based on estimation of parameters of an enhanced single particle model (eSPM) that could be implemented using vehicle charging data. First, we use data from an aging study conducted on LMO-NMC-cathode graphite-anode battery cells to develop an eSPM that can predict the evolution of parameters associated with the aging of the battery. In particular, the parameters we estimate in this work as indicators of state of health (SOH) are number of moles of cyclable lithium and Ohmic resistance. A method is demonstrated for estimating these parameters from experimental data, and it is shown that they are correlated with battery SOH measured from the experimental aging study. Finally, a composite SOH metric derived from the estimated eSPM parameters is used to design a RUL predictor based on a particle filter (PF) that can predict the RUL utilizing the evolution of the SOH metric. The RUL estimation algorithm is validated using experimental data collected on several LMO-NMC battery cells, showing that it is possible to infer battery SOH and RUL from charging data readily available in plug-in batteryelectric or hybrid vehicles.

Automated summary

This paper developed a remaining useful life (RUL) prediction algorithm based on estimation of parameters of an enhanced single particle model (eSPM) that could be implemented using vehicle charging data. The proposed method estimates parameters associated with battery aging, uses them to design a RUL predictor, and validates the algorithm using experimental data collected on LMO-NMC battery cells, demonstrating the feasibility of inferring battery state of health and RUL from readily available charging data in plug-in battery-electric or hybrid vehicles.