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Predicting lithium-ion battery degradation for efficient design and management

Abstract : 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.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02156553
Contributor : Christine Rouil <>
Submitted on : Friday, June 14, 2019 - 2:32:11 PM
Last modification on : Thursday, April 9, 2020 - 4:43:28 PM

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Sebastien Grolleau, Arnaud Delaille, Hamid Gualous. Predicting lithium-ion battery degradation for efficient design and management. 2013 World Electric Vehicle Symposium and Exhibition (EVS27), Nov 2013, Barcelona, Spain. pp.1-6, ⟨10.1109/evs.2013.6914799⟩. ⟨hal-02156553⟩

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