State-of-health estimates of batteries are essential for on-board electric vehicles in order to provide safe, reliable, and cost-effective battery operation. This paper suggests a method to estimate the 10 s discharge resistance, which is an established battery figure-of-merit from laboratory testing, without performing the laboratory test. Instead, a state-of-health estimate of batteries is obtained using data directly from their operational use, e.g. on-board electric vehicles. It is shown that simple dynamical battery models, based on a current input and a voltage output, with model parameters dependent on temperature and state-of-charge, can be derived using AutoRegressive with eXogenous input models, whose order can be adjusted to describe the complex battery behavior. Then the 10 s discharge resistance can be conveniently computed from the identified model parameters. Moreover, the uncertainty of the estimated resistance values is provided by the method. The suggested method is validated with usage data from emulated electric vehicle operation of an automotive lithium- ion battery cell. The resistance values are estimated accurately for a state-of-charge and temperature range spanning typical electric vehicle operating conditions. The identification of the model parameters and the resistance computation are very fast, rendering the method suitable for on-board application.

In regard to the increasing market launch of plug-in hybrid electric vehicles (PHEVs), understanding battery pack performance under electric vehicle (EV) operating conditions is essential. As lifetime still remains an issue for battery packs, it is a necessity to monitor the battery pack's state-of-health (SOH) on-board. Standard performance tests for health evaluation do not apply since operation interruptions and additional testing equipment are beyond question during ordinary EV usage. We suggest a novel methodology of performance estimation from real-life battery data. On the basis of battery pack data collected during PHEV operation, a support vector machine model is constructed that serves as source for performance evaluation figures. The SOH indicator "10 s discharge resistance" as known from hybrid pulse power characterization (HPPC) tests is chosen to exemplify how performance degradation can be followed over a year.