Model Predictive Control-Based Fast Charging for Vehicular Batteries
Jingyu Yan,
Guoqing Xu,
Huihuan Qian,
Yangsheng Xu and
Zhibin Song
Additional contact information
Jingyu Yan: Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
Guoqing Xu: Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
Huihuan Qian: Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
Yangsheng Xu: Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
Zhibin Song: Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
Energies, 2011, vol. 4, issue 8, 1-19
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.
Keywords: battery fast charging; model predictive control; state of charge; genetic algorithm; electric vehicles (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2011
References: View complete reference list from CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
https://www.mdpi.com/1996-1073/4/8/1178/pdf (application/pdf)
https://www.mdpi.com/1996-1073/4/8/1178/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:4:y:2011:i:8:p:1178-1196:d:13603
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().