Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data Madeleine Ecker * , Jochen B. Gerschler, Jan Vogel, Stefan Käbitz, Friedrich Hust, Philipp Dechent, Dirk Uwe Sauer Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jaegerstrasse 17-19, D-52066 Aachen, Germany highlights < Extended accelerated aging tests on lithium-ion batteries. < Semi-empirical aging model based on extended calendar aging data. < Impedance-based electro-thermal model coupled to aging model. < Lifetime prediction under real application condition possible concerning capacity fade. article info Article history: Received 28 February 2012 Received in revised form 27 April 2012 Accepted 6 May 2012 Available online 12 May 2012 Keywords: Lithium-ion Aging Lifetime prognosis Battery model HEV abstract Battery lifetime prognosis is a key requirement for successful market introduction of electric and hybrid vehicles. This work aims at the development of a lifetime prediction approach based on an aging model for lithium-ion batteries. A multivariable analysis of a detailed series of accelerated lifetime experiments representing typical operating conditions in hybrid electric vehicle is presented. The impact of temperature and state of charge on impedance rise and capacity loss is quantified. The investigations are based on a high-power NMC/graphite lithium-ion battery with good cycle lifetime. The resulting mathematical functions are physically motivated by the occurring aging effects and are used for the parameterization of a semi-empirical aging model. An impedance-based electric-thermal model is coupled to the aging model to simulate the dynamic interaction between aging of the battery and the thermal as well as electric behavior. Based on these models different drive cycles and management strategies can be analyzed with regard to their impact on lifetime. It is an important tool for vehicle designers and for the implementation of business models. A key contribution of the paper is the parameterization of the aging model by experimental data, while aging simulation in the literature usually lacks a robust empirical foundation. Ó 2012 Elsevier B.V. All rights reserved. 1. Introduction Lifetime prediction for lithium-ion batteries under real opera- tion conditions is a key issue for a reliable integration of the battery not only into the vehicle but also for stationary applications and for warranty issues. As aging tests using real operation conditions are very time and cost intensive, accelerated aging tests are discussed to be a powerful method. To extrapolate data obtained from accelerated aging test to real life conditions, aging models are required. So far simple model approaches for lifetime predictions have been reported in literature, like e.g. approaches based on neuronal networks [1]. These approaches usually lack the ability to make extrapolations to conditions that were not used in the learning test set. On the other hand, physic-chemical models have been developed, focusing on the description of single aging mechanisms like formation of solid electrolyte interphase (SEI) [2,3], mechanical stresses [4], etc. These models have been used for parameter studies, helpful to understand the ongoing process. Nevertheless they are not appropriate for fast lifetime predictions, as they are difficult to parameterize and only describe single mechanisms. This work aims at a compromise between physico- chemical and simple neuronal network model approaches. A physical approach based on an impedance model, able to extrap- olate the data from accelerated aging tests to get real life condition lifetime predictions is presented here. It is an important goal of this semi-empirical approach to derive a set of equations for describing * Corresponding author. Tel.: þ49 241 8096977; fax: þ49 241 8092203. E-mail address: er@isea.rwth-aachen.de (M. Ecker). Contents lists available at SciVerse ScienceDirect Journal of Power Sources journal homepage: www.elsevier.com/locate/jpowsour 0378-7753/$ e see front matter Ó 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jpowsour.2012.05.012 Journal of Power Sources 215 (2012) 248e257