Abstract

Monitoring the health condition as well as predicting the performance of lithium-ion batteries is crucial to the reliability and safety of electrical systems such as electric vehicles. However, estimating the discharge capacity and end-of-discharge (EOD) of a battery in real-time remains a challenge. Few works have been reported on the relationship between the capacity degradation of a battery and EOD. We introduce a new data-driven method that combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) models to predict the discharge capacity and the EOD using online condition monitoring data. The CNN model extracts long-term correlations among voltage, current, and temperature measurements and then estimates the discharge capacity. The BiLSTM model extracts short-term dependencies in condition monitoring data and predicts the EOD for each discharge cycle while utilizing the capacity predicted by the CNN as an additional input. By considering the discharge capacity, the BiLSTM model is able to use the long-term health condition of a battery to improve the prediction accuracy of its short-term performance. We demonstrated that the proposed method can achieve online discharge capacity estimation and EOD prediction efficiently and accurately.

References

1.
May
,
G. J.
,
Davidson
,
A.
, and
Monahov
,
B.
,
2018
, “
Lead Batteries for Utility Energy Storage: A Review
,”
J. Energy Storage
,
15
, pp.
145
157
.
2.
Wankmüller
,
F.
,
Thimmapuram
,
P. R.
,
Gallagher
,
K. G.
, and
Botterud
,
A.
,
2017
, “
Impact of Battery Degradation on Energy Arbitrage Revenue of Grid-Level Energy Storage
,”
J. Energy Storage
,
10
, pp.
56
66
.
3.
Richardson
,
R. R.
,
Osborne
,
M. A.
, and
Howey
,
D. A.
,
2019
, “
Battery Health Prediction Under Generalized Conditions Using a Gaussian Process Transition Model
,”
J. Energy Storage
,
23
, pp.
320
328
.
4.
Chen
,
Z.
,
Mi
,
C. C.
,
Fu
,
Y.
,
Xu
,
J.
, and
Gong
,
X.
,
2013
, “
Online Battery State of Health Estimation Based on Genetic Algorithm for Electric and Hybrid Vehicle Applications
,”
J. Power Sources
,
240
, pp.
184
192
.
5.
Mishra
,
M.
,
Martinsson
,
J.
,
Rantatalo
,
M.
, and
Goebel
,
K.
,
2018
, “
Bayesian Hierarchical Model-Based Prognostics for Lithium-Ion Batteries
,”
Reliab. Eng. Syst. Saf.
,
172
, pp.
25
35
.
6.
Dong
,
H.
,
Jin
,
X.
,
Lou
,
Y.
, and
Wang
,
C.
,
2014
, “
Lithium-Ion Battery State of Health Monitoring and Remaining Useful Life Prediction Based on Support Vector Regression-Particle Filter
,”
J. Power Sources
,
271
, pp.
114
123
.
7.
Saha
,
B.
,
Goebel
,
K.
,
Poll
,
S.
, and
Christophersen
,
J.
,
2008
, “
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework
,”
IEEE Trans. Instrum. Meas.
,
58
(
2
), pp.
291
296
.
8.
2019
, “
Battery Management System Market Size, Share & Trends Analysis Report by Battery Type (Lithium-Ion, Lead-acid, Nickel), by Topology (Centralized, Modular), by Application, and Segment Forecasts, 2019–2025
,” Market Analysis Report.
9.
Tao
,
L.
,
Ma
,
J.
,
Cheng
,
Y.
,
Noktehdan
,
A.
,
Chong
,
J.
, and
Lu
,
C.
,
2017
, “
A Review of Stochastic Battery Models and Health Management
,”
Renewable Sustainable Energy Rev.
,
80
, pp.
716
732
.
10.
Xiong
,
R.
,
Li
,
L.
, and
Tian
,
J.
,
2018
, “
Towards a Smarter Battery Management System: A Critical Review on Battery State of Health Monitoring Methods
,”
J. Power Sources
,
405
, pp.
18
29
.
11.
Pastor-Fernández
,
C.
,
Uddin
,
K.
,
Chouchelamane
,
G. H.
,
Widanage
,
W. D.
, and
Marco
,
J.
,
2017
, “
A Comparison Between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-Ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes Within Battery Management Systems
,”
J. Power Sources
,
360
, pp.
301
318
.
12.
Dubarry
,
M.
,
Truchot
,
C.
, and
Liaw
,
B. Y.
,
2012
, “
Synthesize Battery Degradation Modes Via a Diagnostic and Prognostic Model
,”
J. Power Sources
,
219
, pp.
204
216
.
13.
Liu
,
D.
,
Yin
,
X.
,
Song
,
Y.
,
Liu
,
W.
, and
Peng
,
Y.
,
2018
, “
An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter
,”
IEEE Access
,
6
, pp.
40990
41001
.
14.
Dalal
,
M.
,
Ma
,
J.
, and
He
,
D.
,
2011
, “
Lithium-Ion Battery Life Prognostic Health Management System Using Particle Filtering Framework
,”
Proc. Inst. Mech. Eng., Part O J. Risk Reliab.
,
225
(
1
), pp.
81
90
.
15.
Saha
,
B.
,
Koshimoto
,
E.
,
Quach
,
C. C.
,
Hogge
,
E. F.
,
Strom
,
T. H.
,
Hill
,
B. L.
,
Vazquez
,
S. L.
, et al
,
2011
, “
Battery Health Management System for Electric UAVs
,”
Proceedings of 2011 Aerospace Conference
,
Big Sky, MT
,
Mar. 5–12
, IEEE, pp.
1
9
.
16.
Wassiliadis
,
N.
,
Adermann
,
J.
,
Frericks
,
A.
,
Pak
,
M.
,
Reiter
,
C.
,
Lohmann
,
B.
, and
Lienkamp
,
M.
,
2018
, “
Revisiting the Dual Extended Kalman Filter for Battery State-of-Charge and State-of-Health Estimation: A Use-Case Life Cycle Analysis
,”
J. Energy Storage
,
19
, pp.
73
87
.
17.
Plett
,
G. L.
,
2004
, “
Extended Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs: Part 3. State and Parameter Estimation
,”
J. Power Sources
,
134
(
2
), pp.
277
292
.
18.
He
,
H.
,
Xiong
,
R.
, and
Peng
,
J.
,
2016
, “
Real-Time Estimation of Battery State-of-Charge With Unscented Kalman Filter and RTOS μCOS-II Platform
,”
Appl. Energy
,
162
, pp.
1410
1418
.
19.
Schwunk
,
S.
,
Armbruster
,
N.
,
Straub
,
S.
,
Kehl
,
J.
, and
Vetter
,
M.
,
2013
, “
Particle Filter for State of Charge and State of Health Estimation for Lithium–Iron Phosphate Batteries
,”
J. Power Sources
,
239
, pp.
705
710
.
20.
Miao
,
Q.
,
Xie
,
L.
,
Cui
,
H.
,
Liang
,
W.
, and
Pecht
,
M.
,
2013
, “
Remaining Useful Life Prediction of Lithium-Ion Battery With Unscented Particle Filter Technique
,”
Microelectron. Reliab.
,
53
(
6
), pp.
805
810
.
21.
Song
,
X.
,
Yang
,
F.
,
Wang
,
D.
, and
Tsui
,
K.-L.
,
2019
, “
Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries
,”
IEEE Access
,
7
, pp.
88894
88902
.
22.
Li
,
X.
,
Wang
,
Z.
, and
Zhang
,
L.
,
2019
, “
Co-Estimation of Capacity and State-of-Charge for Lithium-Ion Batteries in Electric Vehicles
,”
Energy
,
174
, pp.
33
44
.
23.
Jiang
,
B.
,
Dai
,
H.
,
Wei
,
X.
, and
Xu
,
T.
,
2019
, “
Joint Estimation of Lithium-Ion Battery State of Charge and Capacity Within an Adaptive Variable Multi-timescale Framework Considering Current Measurement Offset
,”
Appl. Energy
,
253
, p.
113619
.
24.
Wei
,
Z.
,
Zhao
,
J.
,
Ji
,
D.
, and
Tseng
,
K. J.
,
2017
, “
A Multi-timescale Estimator for Battery State of Charge and Capacity Dual Estimation Based on an Online Identified Model
,”
Appl. Energy
,
204
, pp.
1264
1274
.
25.
Shen
,
J.
,
Ma
,
W.
,
Xiong
,
J.
,
Shu
,
X.
,
Zhang
,
Y.
,
Chen
,
Z.
, and
Liu
,
Y.
,
2022
, “
Alternative Combined Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries in Wide Temperature Scope
,”
Energy
,
244
, p.
123236
.
26.
Ng
,
K. S.
,
Moo
,
C.-S.
,
Chen
,
Y.-P.
, and
Hsieh
,
Y.-C.
,
2009
, “
Enhanced Coulomb Counting Method for Estimating State-of-Charge and State-of-Health of Lithium-Ion Batteries
,”
Appl. Energy
,
86
(
9
), pp.
1506
1511
.
27.
Tong
,
S.
,
Klein
,
M. P.
, and
Park
,
J. W.
,
2015
, “
On-Line Optimization of Battery Open Circuit Voltage for Improved State-of-Charge and State-of-Health Estimation
,”
J. Power Sources
,
293
, pp.
416
428
.
28.
Weng
,
C.
,
Sun
,
J.
, and
Peng
,
H.
,
2014
, “
A Unified Open-Circuit-Voltage Model of Lithium-Ion Batteries for State-of-Charge Estimation and State-of-Health Monitoring
,”
J. Power Sources
,
258
, pp.
228
237
.
29.
Eddahech
,
A.
,
Briat
,
O.
,
Bertrand
,
N.
,
Deletage
,
J.-Y.
, and
Vinassa
,
J.-M.
,
2012
, “
Behavior and State-of-Health Monitoring of Li-Ion Batteries Using Impedance Spectroscopy and Recurrent Neural Networks
,”
Int. J. Electr. Power Energy Syst.
,
42
(
1
), pp.
487
494
.
30.
Galeotti
,
M.
,
Cinà
,
L.
,
Giammanco
,
C.
,
Cordiner
,
S.
, and
Di Carlo
,
A.
,
2015
, “
Performance Analysis and SOH (State of Health) Evaluation of Lithium Polymer Batteries Through Electrochemical Impedance Spectroscopy
,”
Energy
,
89
, pp.
678
686
.
31.
Tröltzsch
,
U.
,
Kanoun
,
O.
, and
Tränkler
,
H.-R.
,
2006
, “
Characterizing Aging Effects of Lithium Ion Batteries by Impedance Spectroscopy
,”
Electrochim. Acta
,
51
(
8–9
), pp.
1664
1672
.
32.
Salkind
,
A. J.
,
Fennie
,
C.
,
Singh
,
P.
,
Atwater
,
T.
, and
Reisner
,
D. E.
,
1999
, “
Determination of State-of-Charge and State-of-Health of Batteries by Fuzzy Logic Methodology
,”
J. Power Sources
,
80
(
1–2
), pp.
293
300
.
33.
Lin
,
H.-T.
,
Liang
,
T.-J.
, and
Chen
,
S.-M.
,
2012
, “
Estimation of Battery State of Health Using Probabilistic Neural Network
,”
IEEE Trans. Ind. Inform.
,
9
(
2
), pp.
679
685
.
34.
Wang
,
S.
,
Zhao
,
L.
,
Su
,
X.
, and
Ma
,
P.
,
2014
, “
Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression
,”
Energies
,
7
(
10
), pp.
6492
6508
.
35.
Yang
,
D.
,
Wang
,
Y.
,
Pan
,
R.
,
Chen
,
R.
, and
Chen
,
Z.
,
2018
, “
State-of-Health Estimation for the Lithium-Ion Battery Based on Support Vector Regression
,”
Appl. Energy
,
227
, pp.
273
283
.
36.
Severson
,
K. A.
,
Attia
,
P. M.
,
Jin
,
N.
,
Perkins
,
N.
,
Jiang
,
B.
,
Yang
,
Z.
,
Chen
,
M. H.
, et al
,
2019
, “
Data-Driven Prediction of Battery Cycle Life Before Capacity Degradation
,”
Nature Energy
,
4
(
5
), pp.
383
391
.
37.
Song
,
Y.
,
Li
,
L.
,
Peng
,
Y.
, and
Liu
,
D.
,
2018
, “
Lithium-Ion Battery Remaining Useful Life Prediction Based on GRU-RNN
,”
Proceedings of 2018 12th International Conference on Reliability, Maintainability, and Safety (ICRMS)
,
Shanghai, China
,
Oct. 17–19
, IEEE, pp.
317
322
.
38.
Wei
,
J.
,
Dong
,
G.
, and
Chen
,
Z.
,
2017
, “
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression
,”
IEEE Trans. Ind. Electron.
,
65
(
7
), pp.
5634
5643
.
39.
Li
,
F.
, and
Xu
,
J.
,
2015
, “
A New Prognostics Method for State of Health Estimation of Lithium-Ion Batteries Based on a Mixture of Gaussian Process Models and Particle Filter
,”
Microelectron. Reliab.
,
55
(
7
), pp.
1035
1045
.
40.
Klass
,
V.
,
Behm
,
M.
, and
Lindbergh
,
G.
,
2014
, “
A Support Vector Machine-Based State-of-Health Estimation Method for Lithium-Ion Batteries Under Electric Vehicle Operation
,”
J. Power Sources
,
270
, pp.
262
272
.
41.
Kulkarni
,
C.
,
Hogge
,
E.
, and
Quach
,
C. C.
,
2018
, “
Remaining Flying Time Prediction Implementing Battery Prognostics Framework for Electric UAV’s
,”
AIAA Propulsion and Energy Forum
,
Cincinnati, OH
,
July 9–11
.
42.
Bole
,
B.
,
Daigle
,
M.
, and
Gorospe
,
G.
,
2014
, “
Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft
,”
European Conference of the PHM Society 2014
,
Nantes, France
,
July 8–10
.
43.
Eleftheroglou
,
N.
,
Mansouri
,
S. S.
,
Loutas
,
T.
,
Karvelis
,
P.
,
Georgoulas
,
G.
,
Nikolakopoulos
,
G.
, and
Zarouchas
,
D.
,
2019
, “
Intelligent Data-Driven Prognostic Methodologies for the Real-Time Remaining Useful Life Until the End-of-Discharge Estimation of the Lithium-Polymer Batteries of Unmanned Aerial Vehicles With Uncertainty Quantification
,”
Appl. Energy
,
254
, p.
113677
.
44.
Gu
,
J.
,
Wang
,
Z.
,
Kuen
,
J.
,
Ma
,
L.
,
Shahroudy
,
A.
,
Shuai
,
B.
,
Liu
,
T.
, et al
,
2018
, “
Recent Advances in Convolutional Neural Networks
,”
Pattern Recogn.
,
77
, pp.
354
377
.
45.
Hochreiter
,
S.
, and
Schmidhuber
,
J.
,
1997
, “
Long Short-Term Memory
,”
Neur. Comput.
,
9
(
8
), pp.
1735
1780
.
46.
Cleveland
,
W. S.
, and
Loader
,
C.
,
1996
, “Smoothing by Local Regression: Principles and Methods,”
Statistical Theory and Computational Aspects of Smoothing
,
W.
Härdle
and
M. G.
Schimek
, eds.,
Springer
,
New York
, pp.
10
49
.
47.
Street
,
J. O.
,
Carroll
,
R. J.
, and
Ruppert
,
D.
,
1988
, “
A Note on Computing Robust Regression Estimates Via Iteratively Reweighted Least Squares
,”
Am. Stat.
,
42
(
2
), pp.
152
154
.
48.
Saha
,
B.
, and
Goebel
,
K.
,
2007
, “
Battery Data Set
,”
NASA AMES Prognostics Data Repository
.
49.
Sbarufatti
,
C.
,
Corbetta
,
M.
,
Giglio
,
M.
, and
Cadini
,
F.
,
2017
, “
Adaptive Prognosis of Lithium-Ion Batteries Based on the Combination of Particle Filters and Radial Basis Function Neural Networks
,”
J. Power Sources
,
344
, pp.
128
140
.
50.
Bai
,
G.
,
Wang
,
P.
,
Hu
,
C.
, and
Pecht
,
M.
,
2014
, “
A Generic Model-Free Approach for Lithium-Ion Battery Health Management
,”
Appl. Energy
,
135
, pp.
247
260
.
You do not currently have access to this content.