Abstract
Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.
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The different versions of the original document can be found in:
- http://dx.doi.org/10.3390/en4081178 under the license https://creativecommons.org/licenses/by
- https://www.mdpi.com/1996-1073/4/8/1178/pdf under the license http://creativecommons.org/licenses/by/3.0/
- https://doaj.org/toc/1996-1073 under the license cc-by
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Published on 01/01/2011
Volume 2011, 2011
DOI: 10.3390/en4081178
Licence: Other
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