Abstract

With the widespread development of new energy, the study of power lithium-ion batteries (LIBs) has broad prospects and great academic significance. The model and parameters are two essential prerequisites for LIB state estimation, which are used to provide a guarantee for the secure and convenient handling of LIBs. To obtain the reliable model and parameters, a simplified fractional order equivalent circuit model (FO-ECM) with high precision is presented in this article. The dynamic external electrical characteristic of LIBs is represented by the one-order FO-ECM, and then, the FO-ECM parameters are identified by the combination of Grunwald–Letnikov (G-L) definition-based factional order numerical calculation and noise compensation-based forgetting factor recursive least squares (FFRLS) method. The simplified FO-ECM can better characterize the nonlinear dynamic behaviors of LIBs, and the G-L definition-based FO-FFRLS algorithm can maintain good accuracy in the parameter estimation process. The results show that the simplified FO-ECM can improve the modeling precision and parameter identification performance compared with the common integer-order ECM in different test cycles.

References

1.
Deng
,
Z.
,
Hu
,
X.
,
Lin
,
X.
,
Kim
,
Y.
, and
Li
,
J.
,
2021
, “
Sensitivity Analysis and Joint Estimation of Parameters and States for All-Solid-State Batteries
,”
IEEE Trans. Trans. Elect.
, p.
1
.
2.
Saxena
,
S.
,
Xing
,
Y.
,
Kwon
,
D.
, and
Pecht
,
M.
,
2019
, “
Accelerated Degradation Model for C-Rate Loading of Lithium-Ion Batteries
,”
Int. J. Electr. Power Energy Syst.
,
107
(
5
), pp.
438
445
.
3.
Deng
,
Z.
,
Hu
,
X.
,
Lin
,
X.
,
Xu
,
L.
,
Li
,
J.
, and
Guo
,
W.
,
2021
, “
A Reduced-Order Electrochemical Model for All-Solid-State Batteries
,”
IEEE Trans. Trans. Elect.
,
7
(
2
), pp.
464
473
.
4.
Xiong
,
R.
,
Zhang
,
Y.
,
Wang
,
J.
,
He
,
H.
,
Peng
,
S.
, and
Pecht
,
M.
,
2019
, “
Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles
,”
IEEE Trans. Veh. Technol.
,
68
(
5
), pp.
4110
4121
.
5.
Fathabadi
,
H.
,
2018
, “
Plug-in Hybrid Electric Vehicles (PHEVs): Replacing Internal Combustion Engine with Clean and Renewable Energy Based Auxiliary Power Sources
,”
IEEE Trans. Power Electron.
,
33
(
11
), pp.
9611
9618
.
6.
Guo
,
D.
,
Yang
,
G.
,
Feng
,
X.
,
Han
,
X.
,
Lu
,
L.
, and
Ouyang
,
M.
,
2020
, “
Physics-based Fractional-Order Model with Simplified Solid Phase Diffusion of Lithium-Ion Battery
,”
J. Energy Storage
,
30
(
8
), p.
101404
.
7.
Lai
,
X.
,
He
,
L.
,
Wang
,
S.
,
Zhou
,
L.
,
Zhang
,
Y.
,
Sun
,
T.
, and
Zheng
,
Y.
,
2020
, “
Co-Estimation of State of Charge and State of Power for Lithium-Ion Batteries Based on Fractional Variable-Order Model
,”
J. Clean. Prod.
,
255
(
10
), p.
120203
.
8.
Ovejas
,
V. J.
, and
Cuadras
,
A.
,
2019
, “
State of Charge Dependency of the Overvoltage Generated in Commercial Li-Ion Cells
,”
J. Power Sources
,
418
(
4
), pp.
176
185
.
9.
Wang
,
Y.
,
Zhang
,
X.
,
Liu
,
C.
,
Pan
,
R.
, and
Chen
,
Z.
,
2018
, “
Multi-timescale Power and Energy Assessment of Lithium-Ion Battery and Supercapacitor Hybrid System Using Extended Kalman Filter
,”
J. Power Sources
,
389
(
6
), pp.
93
105
.
10.
Yang
,
Q.
,
Xu
,
J.
,
Li
,
X.
,
Xu
,
D.
, and
Cao
,
B.
,
2020
, “
State-of-health Estimation of Lithium-Ion Battery Based on Fractional Impedance Model and Interval Capacity
,”
Int. J. Electr. Power Energy Syst.
,
119
(
7
), p.
105883
.
11.
Wang
,
H.
,
Song
,
W.
,
Zio
,
E.
,
Kudreyko
,
A.
, and
Zhang
,
Y.
,
2020
, “
Remaining Useful Life Prediction for Lithium-Ion Batteries Using Fractional Brownian Motion and Fruit-Fly Optimization Algorithm
,”
Measurement
,
161
(
9
), p.
107904
.
12.
Hu
,
X.
,
Li
,
S.
, and
Peng
,
H.
,
2012
, “
A Comparative Study of Equivalent Circuit Models for Li-Ion Batteries
,”
J. Power Sources
,
198
(
1
), pp.
359
367
.
13.
Bian
,
X.
,
Liu
,
L.
, and
Yan
,
J.
,
2019
, “
A Model for State-of-Health Estimation of Lithium ion Batteries Based on Charging Profiles
,”
Energy
,
177
(
6
), pp.
57
65
.
14.
Li
,
S.
,
Hu
,
M.
,
Li
,
Y.
, and
Gong
,
C.
,
2019
, “
Fractional-order Modeling and SOC Estimation of Lithium-Ion Battery Considering Capacity Loss
,”
Int. J. Energy Res.
,
43
(
1
), pp.
417
429
.
15.
Zhou
,
D.
,
Zhang
,
K.
,
Ravey
,
A.
,
Gao
,
F.
, and
Miraoui
,
A.
,
2016
, “
Parameter Sensitivity Analysis for Fractional-Order Modeling of Lithium-Ion Batteries
,”
Energies
,
9
(
3
), p.
123
.
16.
Roger
,
M. C.
,
2018
, “
A new Parameter Identification Algorithm for a Class of Second Order Nonlinear Systems: An on-Line Closed-Loop Approach
,”
Int. J. Control Autom. Syst.
,
16
(
3
), pp.
1142
1155
.
17.
Mastalia
,
M.
,
Samadania
,
E.
,
Farhadb
,
S.
,
Frasera
,
R.
, and
Fowlerc
,
M.
,
2016
, “
Three-dimensional Multi-Particle Electrochemical Model of LiFePO4 Cells Based on a Resistor Network Methodology
,”
Electrochim. Acta
,
190
(
2
), pp.
574
587
.
18.
Chen
,
N.
,
Zhang
,
P.
,
Dai
,
J.
, and
Gui
,
W.
,
2020
, “
Estimating the State-of-Charge of Lithium-Ion Battery Using an H-Infinity Observer Based on Electrochemical Impedance Model
,”
IEEE Access
,
8
(
2
), pp.
26872
26884
.
19.
Linghu
,
J.
,
Kang
,
L.
,
Liu
,
M.
,
Luo
,
X.
,
Feng
,
Y.
, and
Lu
,
C.
,
2019
, “
Estimation for State-of-Charge of Lithium-Ion Battery Based on an Adaptive High-Degree Cubature Kalman Filter
,”
Energy
,
189
(
12
), p.
116204
.
20.
Huang
,
D.
,
Chen
,
Z.
,
Zheng
,
C.
, and
Li
,
H.
,
2019
, “
A Model-Based State-of-Charge Estimation Method for Series-Connected Lithium-Ion Battery Pack Considering Fast-Varying Cell Temperature
,”
Energy
,
185
(
10
), pp.
847
861
.
21.
Bi
,
Y.
, and
Choe
,
S.
,
2020
, “
An Adaptive Sigma-Point Kalman Filter with State Equality Constraints for Online State-of-Charge Estimation of a Li(NiMnCo)O2/Carbon Battery Using a Reduced-Order Electrochemical Model
,”
Appl. Energy
,
258
(
1
), p.
113925
.
22.
Zhang
,
W.
,
Wang
,
L.
,
Wang
,
L.
, and
Liao
,
C.
,
2018
, “
An Improved Adaptive Estimator for State-of-Charge Estimation of Lithium-Ion Batteries
,”
J. Power Sources
,
402
(
10
), pp.
422
433
.
23.
Nejad
,
S.
,
Gladwin
,
D.
, and
Stone
,
D.
,
2016
, “
A Systematic Review of Lumped-Parameter Equivalent Circuit Models for Real-Time Estimation of Lithium-Ion Battery States
,”
J. Power Sources
,
316
(
6
), pp.
183
196
.
24.
Mastali
,
M.
,
Vazquez-Arenas
,
J.
,
Fraser
,
R.
,
Fowler
,
M.
,
Afshar
,
S.
, and
Stevens
,
M.
,
2013
, “
Battery State of the Charge Estimation Using Kalman Filtering
,”
J. Power Sources
,
239
(
10
), pp.
294
307
.
25.
Allafi
,
W.
,
Uddin
,
K.
, and
Zhang
,
C.
,
2017
, “
On-line Scheme for Parameter Estimation of Nonlinear Lithium ion Battery Equivalent Circuit Models Using the Simplified Refined Instrumental Variable Method for a Modified Wiener Continuous-Time Model
,”
Appl. Energy
,
204
(
10
), pp.
497
508
.
26.
Li
,
Y.
,
Chen
,
J.
, and
Lan
,
F.
,
2020
, “
Enhanced Online Model Identification and State of Charge Estimation for Lithium-Ion Battery Under Noise Corrupted Measurements by Bias Compensation Recursive Least Squares
,”
J. Power Sources
,
456
(
4
), p.
227984
.
27.
Xu
,
Y.
,
Hu
,
M.
,
Zhou
,
A.
,
Li
,
Y.
,
Li
,
S.
,
Fu
,
C.
, and
Gong
,
C.
,
2020
, “
State of Charge Estimation for Lithium-Ion Batteries Based on Adaptive Dual Kalman Filter
,”
Appl. Math. Model.
,
77
(
1
), pp.
1255
1272
.
28.
Lai
,
X.
,
Zheng
,
Y.
, and
Sun
,
T.
,
2018
, “
A Comparative Study of Different Equivalent Circuit Models for Estimating State-of-Charge of Lithium-Ion Batteries
,”
Electrochim. Acta
,
259
(
1
), pp.
566
577
.
29.
Zhou
,
C.
,
Zhang
,
L.
,
Hu
,
X.
,
Zhang
,
Z.
,
Wik
,
T.
, and
Pecht
,
M.
,
2018
, “
A Review of Fractional-Order Techniques Applied to Lithium-Ion Batteries, Lead-Acid Batteries, and Supercapacitors
,”
J. Power Sources
,
390
(
6
), pp.
286
296
.
30.
Zou
,
C.
,
Hu
,
X.
,
Dey
,
S.
,
Zhang
,
L.
, and
Tang
,
X.
,
2018
, “
Nonlinear Fractional-Order Estimator With Guaranteed Robustness and Stability for Lithium-Ion Batteries
,”
IEEE Trans. Ind. Electron.
,
65
(
7
), pp.
5951
5961
.
31.
Ma
,
Y.
,
Zhou
,
X.
,
Li
,
B.
, and
Chen
,
H.
,
2016
, “
Fractional Modeling and SOC Estimation of Lithium-Ion Battery
,”
IEEE/CAA J. Autom. Sin.
,
3
(
3
), pp.
281
287
.
32.
Zou
,
Y.
,
Li
,
S.
,
Shao
,
B.
, and
Wang
,
B.
,
2016
, “
State-Space Model with non-Integer Order Derivatives for Lithium-Ion Battery
,”
Appl. Energy
,
161
(
1
), pp.
330
336
.
33.
Lu
,
X.
,
Li
,
H.
, and
Chen
,
N.
,
2019
, “
An Indicator for the Electrode Aging of Lithium-Ion Batteries Using a Fractional Variable Order Model
,”
Electrochim. Acta
,
299
(
3
), pp.
378
387
.
34.
Zhu
,
Q.
,
Xu
,
M.
,
Liu
,
W.
, and
Zheng
,
M.
,
2019
, “
A State of Charge Estimation Method for Lithium-Ion Batteries Based on Fractional Order Adaptive Extended Kalman Filter
,”
Energy
,
187
(
11
), p.
115880
.
35.
Sierociuk
,
D.
,
2016
, “
Dual Estimation of Fractional Variable Order Based on the Unscented Fractional Order Kalman Filter for Direct and Networked Measurements
,”
Circuits, Syst. Signal Process.
,
35
(
6
), pp.
2055
2082
.
36.
Rahman
,
M.
,
Anwar
,
S.
, and
Izadian
,
A.
,
2016
, “
Electrochemical Model Parameter Identification of a Lithium-Ion Battery Using Particle Swarm Optimization Method
,”
J. Power Sources
,
307
(
4
), pp.
86
97
.
37.
Yu
,
P.
,
Wang
,
S.
, and
Yu
,
C.
,
2019
, “
SOC Estimation of Lithium Batteries Based on Improved Fractional-Order Extended Kalman
,”
Energy Storage Sci. Technol.
,
8
(
05
), pp.
868
873
.
38.
Wang
,
B.
,
Li
,
S.
,
Peng
,
H.
, and
Liu
,
Z.
,
2015
, “
Fractional-order Modeling and Parameter Identification for Lithium-Ion Batteries
,”
J. Power Sources
,
293
(
10
), pp.
151
161
.
39.
Chen
,
Y.
,
Huang
,
D.
,
Zhu
,
Q.
,
Liu
,
W.
,
Liu
,
C.
, and
Xiong
,
N.
,
2017
, “
A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter
,”
Energies
,
10
(
9
), p.
1313
.
40.
Wang
,
J.
,
Zhang
,
L.
,
Xu
,
D.
,
Zhang
,
P.
, and
Zhang
,
G.
,
2019
, “
A Simplified Fractional Order Equivalent Circuit Model and Adaptive Online Parameter Identification Method for Lithium-Ion Batteries
,”
Math. Probl. Eng.
,
10
(
e0187152
), pp.
1
8
.
41.
Tian
,
J.
,
Xiong
,
R.
, and
Yu
,
Q.
,
2018
, “
Fractional Order Model Based Incremental Capacity Analysis for Degradation State Recognition of Lithium-Ion Batteries
,”
IEEE Trans. Ind. Electron.
,
66
(
2
), pp.
1576
1584
.
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