A Time-Varying Log-linear Model for Predicting the Resistance of Lithium-ion Batteries

The resistance offers insight into the efficiency and power capability of Lithium-ion (Li-ion) batteries. That is, it can describe the performance of the batteries. However, as with other performance parameters of Li-ion batteries, the resistance is dependent on the operating conditions and the age of the battery. Traditionally, to capture these dependencies, Li-ion cells are aged at different conditions by applying synthetic mission profiles, which are periodically stopped to measure the resistance at standard conditions. Even though accurate information about the
resistance behaviour are obtained, the measurements are timeconsuming. Therefore, we extract the resistance directly from a dynamic real-life profile. The extracted resistance is modelled as function of the state-of-charge (SOC). The parameters of the model are allowed to vary over time to account for increase in the resistance as the battery ages. In order to capture the variation in time of the parameters of the log-linear model are assumed to follow a vector auto-regressive (VAR) model. The estimated VAR is used to predict the long term behaviour of the expected internal resistance. The prediction of the long term behaviour will enable the calculation of the remaining useful life of the battery, allowing for the inclusion of future battery usage through the SOC.