Abstract

The new generation of lithium-ion batteries (LIBs) possesses considerable energy density that arise the safety concern much more than before. One of the main issues associated with LIB safety is the heat generation and thermal runaway in LIBs. The importance of characterizing the heat generation in LIBs is reflected in numerous studies. The heat generation in LIBs can be related to energy efficiency as well. In this work, the heat generation in LIB is predicted using two different approaches (physics-based and machine learning-based approaches). A validated multiphysics-based and neural network-based models for commercial LIBs with lithium iron phosphate/graphite (LFP/G), lithium manganese oxide/graphite (LMO/G), and lithium cobalt oxide/graphite (LCO/G) electrodes are used to predict the heat generation toward shaping the LIB energy efficiency contours, illustrating the effect of the nominal capacity as a key parameter in the manufacturing process of the LIBs. The developed contours can provide the energy systems designers a comprehensive view over the accurate efficiency of LIBs when they need to incorporate LIBs into their devices. In addition, the effect of temperature on charge/discharge energy efficiency of LFP/graphite LIBs is obtained, and the performance of three typical LIBs in the market at a very low temperature is compared, which have a wide range of applications from consumer applications such as electric vehicles (EVs) to industrial applications such as uninterruptible power sources (UPSes).

References

1.
Lu
,
J.
,
Wu
,
T.
, and
Amine
,
K.
,
2017
, “
State-of-the-Art Characterization Techniques for Advanced Lithium-Ion Batteries
,”
Nat. Energy
,
2
(
3
), p.
17011
. 10.1038/nenergy.2017.11
2.
Zheng
,
J.
,
Sun
,
X.
,
Jia
,
L.
, and
Zhou
,
Y.
,
2020
, “
Electric Passenger Vehicles Sales and Carbon Dioxide Emission Reduction Potential in China’s Leading Markets
,”
J. Cleaner Prod.
,
243
, p.
118607
. 10.1016/j.jclepro.2019.118607
3.
Nazari
,
A.
, and
Nazari
,
A.
,
2018
, “
Experimental Investigation on Newtonian Drop Formation in Different Continuous Phase Fluids
,”
Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition IMECE2018
,
Pittsburgh, PA
,
Nov. 9–15
, p. V007T09A059https://doi.org/10.1115/IMECE2018-86602.
4.
Yang
,
F.
,
Mousavie
,
S. M. Ali
,
Oh
,
T. K.
,
Yang
,
T.
,
Lu
,
Y.
,
Farley
,
C.
,
Bodnar
,
R. J.
,
Niu
,
L.
,
Qiao
,
R.
, and
Li
,
Z.
,
2018
, “
Sodium-Sulfur Flow Battery for Low-Cost Electrical Storage
,”
Adv. Energy Mater.
,
8
(
11
), p.
1701991
. 10.1002/aenm.201701991
5.
Nazari
,
A.
,
Zadkazemi Derakhshi
,
A.
,
Nazari
,
A.
, and
Firoozabadi
,
B.
,
2018
, “
Drop Formation From a Capillary Tube: Comparison of Different Bulk Fluid on Newtonian Drops and Formation of Newtonian and Non-Newtonian Drops in Air Using Image Processing
,”
Int. J. Heat Mass Transfer
,
124
, pp.
912
919
. 10.1016/j.ijheatmasstransfer.2018.04.024
6.
Mousavi
,
S. M. Ali
,
Piavis
,
W.
, and
Turn
,
S.
,
2019
, “
Reforming of Biogas Using a Non-Thermal, Gliding-Arc, Plasma in Reverse Vortex Flow and Fate of Hydrogen Sulfide Contaminants
,”
Fuel Proces. Techn.
,
193
, pp.
378
391
. 10.1016/j.fuproc.2019.05.031
7.
Li
,
K.
, and
Tseng
,
K. J.
,
2015
, “
Energy Efficiency of Lithium-Ion Battery Used as Energy Storage Devices in Micro-Grid
,”
Industrial Electronics Society, IECON 2015-41st Annual Conference of the IEEE
,
Yokohama, Japan
,
Nov. 9–12
.
8.
Schimpe
,
M.
,
Naumann
,
M.
,
Truong
,
N.
,
Hesse
,
H. C.
,
Santhanagopalan
,
S.
,
Saxon
,
A.
, and
Jossen
,
A.
,
2018
, “
Energy Efficiency Evaluation of a Stationary Lithium-Ion Battery Container Storage System via Electro-Thermal Modeling and Detailed Component Analysis
,”
Appl. Energy
,
210
, pp.
211
229
. 10.1016/j.apenergy.2017.10.129
9.
Meister
,
P.
,
Jia
,
H.
,
Li
,
J.
,
Kloepsch
,
R.
,
Winter
,
M.
, and
Placke
,
T.
,
2016
, “
Best Practice: Performance and Cost Evaluation of Lithium Ion Battery Active Materials With Special Emphasis on Energy Efficiency
,”
Chem. Mater.
,
28
(
20
), pp.
7203
7217
. 10.1021/acs.chemmater.6b02895
10.
He
,
W.
,
Williard
,
N.
,
Chen
,
C.
, and
Pecht
,
M.
,
2014
, “
State of Charge Estimation for Li-Ion Batteries Using Neural Network Modeling and Unscented Kalman Filter-Based Error Cancellation
,”
Int. J. Electr. Power Energy Syst.
,
62
, pp.
783
791
. 10.1016/j.ijepes.2014.04.059
11.
Chemali
,
E.
,
Kollmeyer
,
P. J.
,
Preindl
,
M.
, and
Emadi
,
A.
,
2018
, “
State-of-Charge Estimation of Li-Ion Batteries Using Deep Neural Networks: A Machine Learning Approach
,”
J. Power Sources
,
400
, pp.
242
255
. 10.1016/j.jpowsour.2018.06.104
12.
Sahinoglu
,
G. O.
,
Pajovic
,
M.
,
Sahinoglu
,
Z.
,
Wang
,
Y.
,
Orlik
,
P. V.
, and
Wada
,
T.
,
2017
, “
Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression
,”
IEEE Trans. Ind. Electron.
,
65
(
5
), pp.
4311
4321
. 10.1109/TIE.2017.2764869
13.
Choi
,
Y.
,
Ryu
,
S.
,
Park
,
K.
, and
Kim
,
H.
,
2019
, “
Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles
,”
IEEE Access
,
7
, pp.
75143
75152
. 10.1109/ACCESS.2019.2920932
14.
Patil
,
M. A.
,
Tagade
,
P.
,
Hariharan
,
K. S.
,
Kolake
,
S. M.
,
Song
,
T.
,
Yeo
,
T.
, and
Doo
,
S.
,
2015
, “
A Novel Multistage Support Vector Machine Based Approach for Li Ion Battery Remaining Useful Life Estimation
,”
Appl. Energy
,
159
, pp.
285
297
. 10.1016/j.apenergy.2015.08.119
15.
Liu
,
D.
,
Zhou
,
J.
,
Pan
,
D.
,
Peng
,
Y.
, and
Peng
,
X.
,
2015
, “
Lithium-Ion Battery Remaining Useful Life Estimation With an Optimized Relevance Vector Machine Algorithm With Incremental Learning
,”
Measurement
,
63
, pp.
143
151
. 10.1016/j.measurement.2014.11.031
16.
Severson
,
K. A.
,
Attia
,
P. M.
,
Jin
,
N.
,
Perkins
,
N.
,
Jiang
,
B.
,
Yang
,
Z.
,
Chen
,
M. H.
,
Aykol
,
M.
,
Herring
,
P. K.
,
Fraggedakis
,
D.
,
Bazant
,
M. Z.
,
Harris
,
S. J.
,
Chueh
,
W. C.
, and
Braatz
,
R. D.
,
2019
, “
Data-Driven Prediction of Battery Cycle Life Before Capacity Degradation
,”
Nat. Energy
,
4
(
5
), pp.
383
391
. 10.1038/s41560-019-0356-8
17.
Sidhu
,
M. S.
,
Ronanki
,
D.
, and
Williamson
,
S.
, “
State of Charge Estimation of Lithium-Ion Batteries Using Hybrid Machine Learning Technique
,”
IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society
,
Lisbon, Portugal, Portugal
,
Oct. 14–17
.
18.
Yarahmadi
,
M.
,
Mahan
,
R.
,
McFall
,
K.
, and
Barkhi Ashraf
,
A.
,
2020
, “
Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network
,”
Remote Sensing
,
12
(
1
), p.
176
. 10.3390/rs12010176
19.
Ma
,
J.
,
Xu
,
S.
,
Shang
,
P.
,
Ding
,
Y.
,
Qin
,
W.
,
Cheng
,
Y.
,
Lu
,
C.
,
Su
,
Y.
,
Chong
,
J.
,
Jin
,
H.
, and
Lin
,
Y.
,
2020
, “
Cycle Life Test Optimization for Different LiIion Power Battery Formulations Using a Hybrid Remaining-Useful-Life Prediction Method
,”
Appl. Energy
,
262
, p.
114490
. 10.1016/j.apenergy.2020.114490
20.
Feng
,
F.
,
Teng
,
S.
,
Liu
,
K.
,
Xie
,
J.
,
Xie
,
Y.
,
Liu
,
B.
, and
Li
,
K.
,
2020
, “
Co-Estimation of Lithium-Ion Battery State of Charge and State of Temperature Based on a Hybrid Electrochemical-Thermal-Neural-Network Model
,”
J. Power Sources
,
455
, p.
227935
. 10.1016/j.jpowsour.2020.227935
21.
Lan
,
C.
,
Xu
,
J.
,
Qiao
,
Y.
, and
Ma
,
Y.
,
2016
, “
Thermal Management for High Power Lithium-Ion Battery by Minichannel Aluminum Tubes
,”
Appl. Therm. Eng.
,
101
, pp.
284
292
. 10.1016/j.applthermaleng.2016.02.070
22.
Smith
,
J.
,
Hinterberger
,
M.
,
Schneider
,
C.
, and
Koehler
,
J.
,
2016
, “
Energy Savings and Increased Electric Vehicle Range Through Improved Battery Thermal Management
,”
Appl. Therm. Eng.
,
101
, pp.
647
656
. 10.1016/j.applthermaleng.2015.12.034
23.
Zhao
,
J.
,
Rao
,
Z.
,
Huo
,
Y.
,
Liu
,
X.
, and
Li
,
Y.
,
2015
, “
Thermal Management of Cylindrical Power Battery Module for Extending the Life of New Energy Electric Vehicles
,”
Appl. Therm. Eng.
,
85
, pp.
33
43
. 10.1016/j.applthermaleng.2015.04.012
24.
Lu
,
L.
,
Han
,
X.
,
Li
,
J.
,
Hua
,
J.
, and
Ouyang
,
M.
,
2013
, “
A Review on the Key Issues for Lithium-Ion Battery Management in Electric Vehicles
,”
J. Power Sources
,
226
, pp.
272
288
. 10.1016/j.jpowsour.2012.10.060
25.
Farhad
,
S.
, and
Nazari
,
A.
,
2019
, “
Introducing the Energy Efficiency Map of Lithium-Ion Batteries
,”
Int. J. Energy Res.
,
43
(
2
), pp.
931
944
. 10.1002/er.4332
26.
Nazari
,
A.
, and
Farhad
,
S.
,
2017
, “
Heat Generation in Lithium-Ion Batteries With Different Nominal Capacities and Chemistries
,”
Appl. Therm. Eng.
,
125
, pp.
1501
1517
. 10.1016/j.applthermaleng.2017.07.126
27.
Newman
,
J.
, and
Tiedemann
,
W.
,
1975
, “
Porous-Electrode Theory With Battery Applications
,”
AIChE J.
,
21
(
1
), pp.
25
41
. 10.1002/aic.690210103
28.
Cai
,
L.
, and
White
,
R. E.
,
2011
, “
Mathematical Modeling of a Lithium Ion Battery With Thermal Effects in COMSOL Inc. Multiphysics (MP) Software
,”
J. Power Sources
,
196
(
14
), pp.
5985
5989
. 10.1016/j.jpowsour.2011.03.017
29.
Nazari
,
A.
,
2016
,
Heat Generation in Lithium-ion Batteries
,
University of Akron
,
Akron, OH
. http://rave.ohiolink.edu/etdc/view?acc_num=akron1469445487
30.
Nazari
,
A.
,
Esmaeeli
,
R.
,
Hashemi
,
S. R.
,
Aliniagerdroudbari
,
H.
, and
Farhad
,
S.
,
2018
, “
The Effect of Temperature on Lithium-ion Battery Energy Efficiency With Graphite/LiFePO4 Electrodes at Different Nominal Capacities
,”
ASME Power and Energy
,
Lake Buena Vista, FL
,
June 24–28
.
31.
Nazari
,
A.
,
Esmaeeli
,
R.
,
Hashemi
,
S. R.
,
Aliniagerdroudbari
,
H.
, and
Farhad
,
S.
,
2018
, “
Low-Temperature Energy Efficiency of Lithium-Ion Batteries
,”
ASME 2018 International Mechanical Engineering Congress and Exposition
,
Pittsburgh, PA
,
Nov. 9–15
.
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