Model Predictive Control-Based Fast Charging for Vehicular Batteries

Energies

Jingyu Yan¹  Guoqing Xu¹  Huihuan Qian¹  Yangsheng Xu¹ 

[1] Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China; E-Mails:

关键词: battery fast charging;    model predictive control;    state of charge;    genetic algorithm;    electric vehicles;   

DOI  :  10.3390/en4081178

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.