As the power demand for electric vehicles, the estimations of state of charge (SOC) and state of health (SOH) for the lithium-ion battery are important in a battery management system. In this paper, a method for SOC and SOH joint estimation based on a dual sliding mode observer (DSMO) has been presented considering the capacity fading factor. An equivalent circuit model with one resistor and capacitor network is built to represent the dynamic behaviors of lithium-ion batteries. Moreover, the uncertainties of model are considered to improve the accuracy of the battery model. One observer of DSMO is used to estimate the terminal voltage and SOC. The other observer is used to estimate the battery capacity and then to calculate SOH. The convergence of DSMO for SOC and SOH estimation is proved by the Lyapunov stability theory. Based on the proposed battery model with uncertainties, DSMO can avoid the chattering effects and improve the estimation accuracy. AMESim and Simulink co-simulation is adopted to evaluate the performance of DSMO. The results show that the proposed DSMO has good performance and robustness.

1.
L.
Lu
,
X.
Han
,
J.
Li
,
J.
Hua
, and
M.
Ouyang
, “
A review on the key issues for lithium-ion battery management in electric vehicles
,”
J. Power Sources
226
,
272
288
(
2013
).
2.
C.
Zou
,
C.
Manzie
,
D.
Nesic
, and
A. G.
Kallapur
, “
Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery
,”
J. Power Sources
335
,
121
130
(
2016
).
3.
T.
Kim
,
W.
Qiao
, and
L.
Qu
, “
Online SOC and SOH estimation for multicell lithium-ion batteries based on an adaptive hybrid battery model and sliding-mode observer
,” in
Proceedings of IEEE Conference on Energy Conversion Congress and Exposition
, Denver, Colorado (
2013
), pp.
292
298
.
4.
W.
Waag
,
C.
Fleischer
, and
D. U.
Sauer
, “
Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
,”
J. Power Sources
258
(
14
),
321
339
(
2014
).
5.
X.
Chen
,
W.
Shen
,
Z.
Cao
, and
A.
Kapoor
, “
Adaptive gain sliding mode observer for state of charge estimation based on combined battery equivalent circuit model in electric vehicles
,” in
Proceedings of
IEEE Conference on Industrial Electronics and Applications, Melbourne, Australia (
2013
), pp.
601
606
.
6.
G. L.
Plett
, “
Kalman-filter SOC estimation for LiPB HEV cells
,” in
Proceedings of IEEE 19th Electric Vehicle Symposium (EVS19)
, Busan, Korea (
2002
), pp.
527
538
.
7.
A.
Vasebi
,
S. M. T.
Bathaee
, and
M.
Partovibakhsh
, “
Predicting state of charge of lead-acid batteries for hybrid electric vehicles by extended Kalman filter
,”
Energy Convers. Manage.
49
(
1
),
75
82
(
2008
).
8.
G. L.
Plett
, “
Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2: Simultaneous state and parameter estimation
,”
J. Power Sources
161
(
2
),
1369
1384
(
2006
).
9.
T.
Weigert
,
Q.
Tian
, and
K.
Lian
, “
State-of-charge prediction of batteries and battery-supercapacitor hybrids using artificial neural networks
,”
J. Power Sources
196
(
8
),
4061
4066
(
2011
).
10.
A. J.
Salkind
,
C.
Fennie
,
P.
Singh
,
T.
Atwatere
, and
D. E.
Reisnerc
, “
Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology
,”
J. Power Sources
80
(
1–2
),
293
300
(
1999
).
11.
S.
Grolleau
,
A.
Delaille
,
H.
Gualous
,
P.
Gyan
,
R.
Revel
,
J.
Bernard
 et al, “
Calendar aging of commercial graphite/LiFePO4 cell-predicting capacity fade under time dependent storage conditions
,”
J. Power Sources
255
,
450
458
(
2014
).
12.
S.
Grolleau
,
A.
Delaille
, and
H.
Gualous
, “
Predicting lithium-ion battery degradation for efficient design and management
,” in
Proceedings of Electric Vehicle Symposium and Exhibition (EVS'27)
, Barcelona, Spain (
2013
), pp.
1
6
.
13.
J.
Schmalstieg
,
S.
Kabitz
,
M.
Ecker
, and
D. U.
Sauer
, “
From accelerated aging tests to a lifetime prediction model: Analyzing lithium-ion batteries
,” in
Proceedings of Electric Vehicle Symposium and Exhibition (EVS'27)
, Barcelona, Spain (
2013
), pp.
1
12
.
14.
Y.
Zou
,
X.
Hu
,
H.
Ma
, and
S. E.
Li
, “
Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles
,”
J. Power Sources
273
,
793
803
(
2015
).
15.
B. S.
Bhangu
,
P.
Stone
, and
D. A.
Bingham
, “
Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles
,”
IEEE Trans. Veh. Technol.
54
,
783
794
(
2005
).
16.
T. T. D.
Sousa
,
V. T.
Arioli
,
C. S.
Vieira
,
S. R. D.
Santos
, and
A. P.
Frana
, “
Comparison of different approaches for lead acid battery state of health estimation based on artificial neural networks algorithms
,” in
Proceedings of Evolving and Adaptive Intelligent Systems
, Natal, Brazil (
2016
), pp.
79
84
.
17.
G. L.
Plett
, “
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification
,”
J. Power Sources
134
(
2
),
262
276
(
2004
).
18.
G. L.
Plett
, “
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation
,”
J. Power Sources
134
(
2
),
277
292
(
2004
).
19.
C.
Hu
,
B. D.
Youn
, and
J.
Chung
, “
A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation
,”
Appl. Energy
92
,
694
704
(
2012
).
20.
Y.
Ma
,
B.
Li
,
Y.
Xie
, and
H.
Chen
, “
Estimating the state of charge of lithium-ion battery based on sliding mode observer
,”
IFAC-PapersOnLine
49
(
11
),
54
61
(
2016
).
21.
M.
Gholizadeh
and
F. R.
Salmasi
, “
Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model
,”
IEEE. Trans. Ind. Electron.
61
(
3
),
1335
(
2014
).
22.
I.
Kim
, “
A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer
,”
IEEE Trans. Power Electron.
25
,
1013
1022
(
2010
).
23.
S.
Dey
,
B.
Ayalew
, and
P.
Pisu
, “
Combined estimation of state-of-charge and state-of-health of Li-ion battery cells using SMO on electrochemical model
,” in
Proceedings of Evolving and Adaptive Intelligent Systems
, Nantes, France (
2016
), pp.
79
84
.
24.
M.
Yan
,
Z.
Xiuwen
, and
Z.
Jixing
, “
Lithium-ion battery state of charge estimation based on moving horizon
,” in
Proceedings of World Congress on Intelligent Control and Automation (WCICA)
, Shenyang, China (
2014
), pp.
5002
5007
.
25.
X.
Hu
,
F.
Sun
, and
Y.
Zou
, “
Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles
,”
Simul. Modell. Pract. Theory
34
,
1
11
(
2013
).
26.
F.
Zhang
,
G.
Liu
, and
L.
Fang
, “
A battery state of charge estimation method using sliding mode observer
,” in
Proceedings of World Congress on Intelligent Control and Automation (WCICA)
, Jinan, China (
2008
), pp.
989
994
.
You do not currently have access to this content.