Model Prediction and Rule Based Energy Management Strategy for Hybrid Energy Storage System

被引：2
|
作者：
Zhou, Shiyao [1 ]
Chen, Ziqiang [1 ]
机构：
[1] Shanghai Jiao Tong Univ, State Key Lab Ocean Engn, Collaborat Innovat Ctr Adv Ship & Deep Sea Explor, Shanghai, Peoples R China
来源：
PROCEEDINGS OF 2019 IEEE 3RD INTERNATIONAL ELECTRICAL AND ENERGY CONFERENCE (CIEEC) | 2019年
基金：
中国国家自然科学基金;
关键词：
energy management strategy; hybrid energy storage system; fuzzy logic control; hardware in loop; VEHICLE;
D O I：
10.1109/CIEEC47146.2019.CIEEC-2019186
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In this paper, a real-time energy management strategy is proposed for a plug-in hybrid electric vehicle with the hybrid energy storage system including a Ni-Co-Mn Li-ion battery pack and a Lithium Titanium-Oxide battery pack. Through modeling, a state-of-charge and state-of-power capability joint estimator is proposed to forecast the dynamic performance of battery packs. A model prediction and rule based energy management strategy using the joint estimator and fuzzy logic control is developed. For comparison, we analyzed a conventional rule based energy management strategy. Thereafter, a quantifiable battery degradation model and the hardware in loop tests are derived to evaluate two kinds of strategies. The results demonstrate that the overall performance of the hybrid energy storage system can be improved significantly by using the energy management strategy proposed in this paper.
引用
收藏
页码：427 / 432
页数：6
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