Jump to ContentJump to Main Navigation
Show Summary Details
More options …

it - Information Technology

Methods and Applications of Informatics and Information Technology

Editor-in-Chief: Conrad, Stefan / Molitor, Paul

6 Issues per year

See all formats and pricing
More options …
Volume 58, Issue 1


Modeling of battery life optimal charging strategies based on empirical mobility data

Jennifer Schoch
Published Online: 2016-02-09 | DOI: https://doi.org/10.1515/itit-2015-0043


Battery electric vehicles (EVs) have a more limited range and longer refueling, i. e. charging times accompanied by battery aging as conventional vehicles. Initial field studies about the behavior of EV users also have identified the presence of range anxiety, which is expressed by frequent recharging. Battery aging is a non-linear process with numerous interactions, but generally pronounced around higher state of charge (SoC) levels. Therefore, range anxiety potentially provokes elevated battery degradation due to the resulting operating conditions. Based on 2966 empirical driving profiles, I analyze different user groups as well as the effect of different charging strategies in a simulation-based analysis. In comparison to charging strategies, for instance as fast as possible (AFAP) charging or critical SoC charging (such that the next trip is feasible), I calculate the degradation optimal charging pattern based on a realistic battery cell aging model. The model is derived from accelerated aging tests of Li-NMC 18650 cells, but can be adjusted to individual degradation parameters, in order to account for different battery chemistries. Preliminary results show that degradation can be reduced by up to 46% by the optimized charging strategy as compared to AFAP or critical SoC charging.

Keywords: Battery aging; smart charging; range anxiety; electric vehicle

ACM CCS: Hardware→Power and energy

About the article

Jennifer Schoch

Jennifer Schoch received her M.Sc. in Electrical Engineering and Information Technology from the Karlsruhe Institute of Technology (KIT) in 2014. She is currently a PhD student at KIT. Her research interests include electric mobility, battery degradation and user behavior.

Karlsruhe Institute of Technology, Englerstr. 14, 76131 Karlsruhe

Accepted: 2015-11-06

Received: 2015-10-15

Published Online: 2016-02-09

Published in Print: 2016-02-01

Citation Information: it - Information Technology, Volume 58, Issue 1, Pages 22–28, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2015-0043.

Export Citation

©2016 Walter de Gruyter Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Ning Ai, Junjun Zheng, and Xiaochen Chen
Transportation Research Part D: Transport and Environment, 2018, Volume 59, Page 433

Comments (0)

Please log in or register to comment.
Log in