State of Health Estimation of Lithium Cobalt Oxide Batteries Based on ARX Identification Across Different Temperatures

Lithium-ion batteries (LiBs) undergo degradation influenced by storage and cycling conditions. Accurate state of health (SOH) assessment is crucial for predicting battery aging, which is generally marked by a decline in capacity (energy fade) or an increase in internal resistance (power fade). This study investigates the impulse response (IR) technique for assessing the SOH of lithium cobalt oxide batteries, addressing both capacity fade and rising internal resistance. The IR method relies on a predefined dataset that records the voltage response of the LiB to pulse current inputs across various states of charge (SOC), temperatures, and aging conditions to train a series of linear auto-regressive exogenous (ARX) models. This dataset is then used as a look-up table for subsequent SOH estimation under new operating conditions. The results demonstrate that the method can capture trends in capacity fade and resistance increase only when multiple battery temperatures are incorporated into the look-up table. In contrast, estimations based on ARX models trained at a single fixed temperature fail to provide reliable predictions of battery SOH.