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World Electric Vehicle Journal is published by MDPI from Volume 9 issue 1 (2018). Articles in this Issue were published by The World Electric Vehicle Association (WEVA) and its member the European Association for e-Mobility (AVERE), the Electric Drive Transportation Association (EDTA), and the Electric Vehicle Association of Asia Pacific (EVAAP). They are hosted by MDPI on as a courtesy and upon agreement with AVERE.
Open AccessArticle

Predicting lithium-ion battery degradation for efficient design and management

Laboratory for Electrical Storage, CEA//LITEN//DTS/LSE, 73377 Le-Bourget-du-Lac, France
Université de Caen Basse Normandie, LUSAC Normandie rue Louis Aragon, 50130 Cherbourg-Octeville, France
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2013, 6(3), 549-554;
Published: 27 September 2013
Being able to predict the Li-ion battery degradation is necessary for applications such as electric vehicles (EVs) and hybrid ones (HEVs). Most of the time, battery life prediction is based on accelerated cycling datasets obtained under different conditions. However, cell aging occurs not only during cycling but also at rest (calendar mode), the latter representing about 90 % of its lifetime. In this work, an empirical model of a 12 Ah commercial graphite/nickel-manganese-cobalt (C/NMC) cell accounting for calendar aging is presented. An innovative accelerated aging protocol representative of a battery usage likely to be encountered in real-world is also proposed. Experimental results tend to prove that a state-of-charge (SoC) range management can extend the battery lifetime significantly, mainly due to the calendar aging effect. Furthermore, results show that even a low battery usage, limited to 10 % of the total time, has a detrimental effect on the cell lifetime that a pure calendar aging model is unable to predict.
Keywords: lithium battery; battery calendar life; battery management; modelling lithium battery; battery calendar life; battery management; modelling
MDPI and ACS Style

Grolleau, S.; Delaille, A.; Gualous, H. Predicting lithium-ion battery degradation for efficient design and management. World Electr. Veh. J. 2013, 6, 549-554.

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