Enhanced EKF Method for State-of-Charge Estimation of Electric Vehicles' Li-ion Batteries under Highly Dynamic Power Profiles

被引：0
|
作者：
Wadi, Ali [1 ]
Abdel-Hafez, Mamoun F. [1 ]
Hussein, Ala A. [2 ]
机构：
[1] Amer Univ Sharjah, Dept Mech Engn, Sharjah, U Arab Emirates
[2] Prince Mohammad Bin Fand Univ, Dept Elect Engn, Khobar, Saudi Arabia
来源：
2021 4TH INTERNATIONAL SYMPOSIUM ON ADVANCED ELECTRICAL AND COMMUNICATION TECHNOLOGIES (ISAECT) | 2021年
关键词：
Battery; electric vehicle; dual auto-covariance least square; extended Kalman filter; state-of-charge; MODEL;
D O I：
10.1109/ISAECT53699.2021.9668406
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper proposes a highly-accurate state-of-charge (SOC) estimation technique to meet highly dynamic power requirements in electric vehicles (EVs). The proposed technique is a dual auto-covariance least square (DALS) integrated with an extended Kalman filter (EKF) in which the SOC and the model parameters are simultaneously estimated. The proposed technique is used to estimate the SOC of a 12.8-V lithium-ion (Li-ion) battery pack through a Dynamic Stress Test (DST) cycles. Results show a remarkable improvement in the estimation accuracy using the proposed technique.
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页数：6
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