Variable recursive least square algorithm for online battery equivalent circuit model parameters identification for electric vehicles

被引:1
|
作者
El Marghichi, Mouncef [1 ]
Loulijat, Azeddine [1 ]
El Hantati, Issam [2 ]
机构
[1] Hassan First Univ, Fac Sci & Technol, FST Settat, Km 3,Rd Casablanca,BP 577, Settat, Morocco
[2] Hassan II Univ Casablanca, High Sch Technol ESTC, Lab Mech Prod & Ind Engn LMPGI, Route El Jadida,Km 7, Casablanca 8012, Morocco
关键词
Recursive least squares (RLS); Variable recursive least squares (VRLSs); Adaptive forgetting factor recursive least squares (AFFRLS); Lithium battery; LITHIUM-ION BATTERY; STATE-OF-CHARGE; KALMAN FILTER; VOLTAGE TESTS;
D O I
10.1007/s00202-023-02064-2
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
In state-of-charge (SOC) estimation approaches which rely on electric circuit models, the accuracy of the model's parameters is influenced by factors such as battery aging and temperature, leading to SOC estimation errors. To tackle this issue effectively, a constant update of battery parameters is proposed. Our novel approach introduces the variable recursive least squares (VRLSs) algorithm, specifically designed to upgrade the parameters of a 2-capacitor-resistor network and accurately evaluate the terminal voltage of the battery. To assess the effectiveness of the VRLS algorithm, we conducted a comparison with two other methods: recursive least squares (RLS) and adaptive forgetting factor RLS (AFFRLS) algorithms. We employed experimental data from the CALCE Battery Research Group, with the Samsung and the A123 lithium-ion cells for testing. The results of the tests revealed that VRLS outperformed both AFFRLS and RLS methods. Notably, VRLS demonstrated significantly lower distribution in high error range and exhibited superior predictive performance indicators, including root-mean-square error, mean absolute error, and mean absolute percentage error across all tests.
引用
收藏
页码:2425 / 2445
页数:21
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