A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries

被引:229
作者
Xiong, Rui [1 ]
Zhang, Yongzhi [1 ,2 ]
He, Hongwen [1 ]
Zhou, Xuan [3 ]
Pecht, Michael G. [2 ]
机构
[1] Beijing Inst Technol, Dept Vehicle Engn, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
[2] Univ Maryland, CALCE, College Pk, MD 20742 USA
[3] Kettering Univ, Elect & Comp Engn Dept, Flint, MI 48504 USA
基金
中国国家自然科学基金;
关键词
Double scale; lithium-ion battery; particle filtering (PF); remaining available energy; state of charge (SOC); OF-CHARGE ESTIMATION; ELECTRIC VEHICLES; MANAGEMENT-SYSTEMS; PARAMETER-ESTIMATION; DYNAMIC CURRENTS; PROGNOSTICS; FRAMEWORK; HYBRID; MODEL; PACKS;
D O I
10.1109/TIE.2017.2733475
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order for the battery management system (BMS) in an electric vehicle to function properly, accurate and robust indication of the energy state of the lithium-ion batteries is necessary. This robustness requires that the energy state can be estimated accurately even when the working conditions of batteries change dramatically. This paper implements battery remaining available energy prediction and state-of-charge (SOC) estimation against testing temperature uncertainties, as well as inaccurate initial SOC values. A double-scale particle filtering method has been developed to estimate or predict the system state and parameters on two different time scales. The developed method considers the slow time-varying characteristics of the battery parameter set and the quick time-varying characteristics of the battery state set. In order to select the preferred battery model, the Akaike information criterion (AIC) is used to make a tradeoff between the model prediction accuracy and complexity. To validate the developed double-scale particle filtering method, two different kinds of lithium-ion batteries were tested at three temperatures. The experimental results show that, with 20% initial SOC deviation, the maximum remaining available energy prediction and SOC estimation errors are both within 2%, even when the wrong temperature is indicated. In this case, the developed double-scale particle filtering method is expected to be robust in practice.
引用
收藏
页码:1526 / 1538
页数:13
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