Dynamical Lithium-Ion Battery Model Identiﬁcationusing Electric Vehicle Operating Data for Resistance Estimation
(English)Manuscript (preprint) (Other academic)
State-of-health (SOH) estimates of batteries are essential on-board electric vehicles (EVs) in order to provide safe, reliable, and cost-eﬀective battery operation. Here, we present an approach for the estimation of the battery SOH indicator internal resistance. Battery models are constructed on the basis of ordinary EV operating data. The 10 s discharge resistance, which is an established battery ﬁgure-of-merit from laboratory testing, can be conveniently computed from the identiﬁed model parameters. Dynamical battery models based on a current input and a voltage output with model parameters dependent on temperature and state-of-charge (SOC) are derived using AutoRegressive with eXogenous input (ARX) models. The suggested method is validated with usage data from emulated EV operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately by the proposed model for a SOC and temperature range spanning typical EV operating conditions (average relative estimation error of 1.5%). The method even provides an uncertainty interval for the resistance estimations, which is found to be very narrow. The linear identiﬁcation of the model parameters and the resistance computation are very fast rendering the method suitable for on-board application.
Other Chemical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-173540OAI: oai:DiVA.org:kth-173540DiVA: diva2:853535
QS 20152015-09-142015-09-142015-09-14Bibliographically approved