CONDITIONAL EXTENDED KALMAN FILTER FOR BATTERY MODEL PARAMETER IDENTIFICATION

被引：0
|
作者：
Li, Yonghua [1 ]
Wang, Xu [2 ]
机构：
[1] Ford Motor Co, Res & Adv Engn, Vehicle Controls & Syst Engn, Dearborn, MI 48104 USA
[2] Ford Motor Co, Elect Powertrain Engn, Battery & EE Syst Architecture, Dearborn, MI 48121 USA
来源：
7TH ANNUAL DYNAMIC SYSTEMS AND CONTROL CONFERENCE, 2014, VOL 2 | 2014年
关键词：
D O I：
暂无
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 this paper a conditional Extended Kalman Filter is applied to battery model parameter and state estimations. A decision logic, based on battery input and output data, is designed such that parameter update is stopped when persistent excitation conditions are not met. Persistent excitation conditions are represented by a simpler, easier to implement set of calibrations. Examples, both from desktop simulation, and real world vehicle testing, have been provided to support the validity of this algorithm. The proposed strategy has been successfully deployed in production FHEV/PHEV vehicles.
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页数：7
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