Abstract

Progress in the field of power electronics within electric vehicles has generally been driven by conventional engineering design principles and experiential learning. Power electronics is inherently a multidomain field where semiconductor physics and electrical, thermal, and mechanical design knowledge converge to achieve a practical realization of desired targets in the form of conversion efficiency, power density, and reliability. Due to the promising nature of artificial intelligence in delivering rapid results, engineers are starting to explore the ways in which it can contribute to making power electronics more compact and reliable. Here, we conduct a brief review of the foray of artificial intelligence in three distinct subtechnologies within a power electronics system in the context of electric vehicles: semiconductor devices, power electronics module design and prognostics, and thermal management design. The intent is not to report an exhaustive literature review, but to identify the state of the art and opportunities for artificial intelligence to play a meaningful role in power electronics design from a mechanical and thermal standpoint, as well as to discuss a few promising future research directions.

References

1.
Kumar
,
K. R.
, and
Kalavathi
,
M. S.
,
2018
, “
Artificial Intelligence Based Forecast Models for Predicting Solar Power Generation
,”
Mater. Today: Proc.
,
5
(
1
), pp.
796
802
.10.1016/j.matpr.2017.11.149
2.
Heinermann
,
J.
, and
Kramer
,
O.
,
2016
, “
Machine Learning Ensembles for Wind Power Prediction
,”
Renewable Energy
,
89
, pp.
671
679
.10.1016/j.renene.2015.11.073
3.
Dorokhova
,
M.
,
Ballif
,
C.
, and
Wyrsch
,
N.
,
2021
, “
Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach
,”
Front. Big Data
,
4
, p.
33
.10.3389/fdata.2021.586481
4.
Shi
,
J.
,
Gao
,
Y.
,
Wang
,
W.
,
Yu
,
N.
, and
Ioannou
,
P. A.
,
2020
, “
Operating Electric Vehicle Fleet for Ride-Hailing Services With Reinforcement Learning
,”
IEEE Trans. Intell. Transp. Syst.
,
21
(
11
), pp.
4822
4834
.10.1109/TITS.2019.2947408
5.
Shahriar
,
S.
,
Al-Ali
,
A. R.
,
Osman
,
A. H.
,
Dhou
,
S.
, and
Nijim
,
M.
,
2020
, “
Machine Learning Approaches for EV Charging Behavior: A Review
,”
IEEE Access
,
8
, pp.
168980
168993
.10.1109/ACCESS.2020.3023388
6.
Severson
,
K. A.
,
Attia
,
P. M.
,
Jin
,
N.
,
Perkins
,
N.
,
Jiang
,
B.
,
Yang
,
Z.
, and
Chen
,
M. H.
, et al.,
2019
, “
Data-Driven Prediction of Battery Cycle Life Before Capacity Degradation
,”
Nat. Energy
,
4
(
5
), pp.
383
391
.10.1038/s41560-019-0356-8
7.
Attia
,
P. M.
,
Grover
,
A.
,
Jin
,
N.
,
Severson
,
K. A.
,
Markov
,
T. M.
,
Liao
,
Y.-H.
, and
Chen
,
M. H.
, et al.,
2020
, “
Closed-Loop Optimization of Fast-Charging Protocols for Batteries With Machine Learning
,”
Nature
,
578
(
7795
), pp.
397
402
.10.1038/s41586-020-1994-5
8.
Zhao
,
S.
,
Blaabjerg
,
F.
, and
Wang
,
H.
,
2021
, “
An Overview of Artificial Intelligence Applications for Power Electronics
,”
IEEE Trans. Power Electron.
,
36
(
4
), pp.
4633
4658
.10.1109/TPEL.2020.3024914
9.
Bose
,
B. K.
,
2020
, “
Artificial Intelligence Techniques: How Can It Solve Problems in Power Electronics?: An Advancing Frontier
,”
IEEE Power Electron. Mag.
,
7
(
4
), pp.
19
27
.10.1109/MPEL.2020.3033607
10.
Kanechika
,
M.
,
Uesugi
,
T.
, and
Kachi
,
T.
,
2010
, “
Advanced SiC and GaN Power Electronics for Automotive Systems
,”
2010 International Electron Devices Meeting
,
San Francisco, CA
, Dec. 6–8, pp.
13.5.1
13.5.4
.
11.
Higashiwaki
,
M.
,
Kuramata
,
A.
,
Murakami
,
H.
, and
Kumagai
,
Y.
,
2017
, “
State-of-the-Art Technologies of Gallium Oxide Power Devices
,”
J. Phys. D: Appl. Phys.
,
50
(
33
), p.
333002
.10.1088/1361-6463/aa7aff
12.
Göke
,
S.
,
Staight
,
K.
, and
Vrijen
,
R.
,
2021
,
Scaling AI in the Sector That Enables It: Lessons for Semiconductor-Device Makers
,
McKinsey & Company
.https://www.mckinsey.com/industries/semiconductors/our-insights/scaling-ai-in-the-sector-that-enables-it-lessons-for-semiconductor-device-makers
13.
IRDS
,
2020
,
International Roadmap for Devices and Systems (IRDSTM)
,
IEEE
, Piscataway, NJ.https://irds.ieee.org/images/files/pdf/2020/2020IRDS_OSC.pdf
14.
Irani
,
K. B.
,
Cheng
,
J.
,
Fayyad
,
U. M.
, and
Qian
,
Z.
,
1993
, “
Applying Machine Learning to Semiconductor Manufacturing
,”
IEEE Expert
,
8
(
1
), pp.
41
47
.10.1109/64.193054
15.
Yuan-Fu
,
Y.
,
2019
, “
A Deep Learning Model for Identification of Defect Patterns in Semiconductor Wafer Map
,” 2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (
ASMC
),
Saratoga Springs, NY
, May 6–9, pp.
1
6
.10.1109/ASMC.2019.8791815
16.
Nakazawa
,
T.
, and
Kulkarni
,
D. V.
,
2018
, “
Wafer Map Defect Pattern Classification and Image Retrieval Using Convolutional Neural Network
,”
IEEE Trans. Semicond. Manuf.
,
31
(
2
), pp.
309
314
.10.1109/TSM.2018.2795466
17.
Jiang
,
D.
,
Lin
,
W.
, and
Raghavan
,
N.
,
2020
, “
A Novel Framework for Semiconductor Manufacturing Final Test Yield Classification Using Machine Learning Techniques
,”
IEEE Access
,
8
, pp.
197885
197895
.10.1109/ACCESS.2020.3034680
18.
Mirhoseini
,
A.
,
Goldie
,
A.
,
Yazgan
,
M.
,
Jiang
,
J. W.
,
Songhori
,
E.
,
Wang
,
S.
, and
Lee
,
Y.-J.
, et al.,
2021
, “
A Graph Placement Methodology for Fast Chip Design
,”
Nature
,
594
(
7862
), pp.
207
212
.10.1038/s41586-021-03544-w
19.
Khailany
,
B.
,
Ren
,
H.
,
Dai
,
S.
,
Godil
,
S.
,
Keller
,
B.
,
Kirby
,
R.
,
Klinefelter
,
A.
,
Venkatesan
,
R.
,
Zhang
,
Y.
,
Catanzaro
,
B.
, and
Dally
,
W. J.
,
2020
, “
Accelerating Chip Design With Machine Learning
,”
IEEE Micro
,
40
(
6
), pp.
23
32
.10.1109/MM.2020.3026231
20.
Singh
,
R.
,
Garg
,
R.
,
Patel
,
N. S.
, and
Braun
,
M. W.
,
2020
, “
Generative Adversarial Networks for Synthetic Defect Generation in Assembly and Test Manufacturing
,” 2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (
ASMC
),
Saratoga Springs, NY
, Aug. 24–26, pp.
1
5
.10.1109/ASMC49169.2020.9185242
21.
Hashimoto
,
M.
,
Ide
,
Y.
, and
Aritsugi
,
M.
,
2021
, “
Anomaly Detection for Sensor Data of Semiconductor Manufacturing Equipment Using a GAN
,”
Procedia Comput. Sci.
,
192
, pp.
873
882
.10.1016/j.procs.2021.08.090
22.
Dropka
,
N.
, and
Holena
,
M.
,
2020
, “
Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials
,”
Crystals
,
10
(
8
), p.
663
.10.3390/cryst10080663
23.
Shin
,
C. K.
, and
Park
,
S. C.
,
2000
, “
A Machine Learning Approach to Yield Management in Semiconductor Manufacturing
,”
Int. J. Prod. Res.
,
38
(
17
), pp.
4261
4271
.10.1080/00207540050205073
24.
Lee
,
H.
,
Smet
,
V.
, and
Tummala
,
R.
,
2020
, “
A Review of SiC Power Module Packaging Technologies: Challenges, Advances, and Emerging Issues
,”
IEEE J. Emerging Sel. Top. Power Electron.
,
8
(
1
), pp.
239
255
.10.1109/JESTPE.2019.2951801
25.
Broughton
,
J.
,
Smet
,
V.
,
Tummala
,
R. R.
, and
Joshi
,
Y. K.
,
2018
, “
Review of Thermal Packaging Technologies for Automotive Power Electronics for Traction Purposes
,”
ASME J. Electron. Packag.
,
140
(
4
), p.
040801
.10.1115/1.4040828
26.
Yang
,
Y.
,
Dorn-Gomba
,
L.
,
Rodriguez
,
R.
,
Mak
,
C.
, and
Emadi
,
A.
,
2020
, “
Automotive Power Module Packaging: Current Status and Future Trends
,”
IEEE Access
,
8
, pp.
160126
160144
.10.1109/ACCESS.2020.3019775
27.
Ji
,
B.
,
Song
,
X.
,
Sciberras
,
E.
,
Cao
,
W.
,
Hu
,
Y.
, and
Pickert
,
V.
,
2015
, “
Multiobjective Design Optimization of IGBT Power Modules Considering Power Cycling and Thermal Cycling
,”
IEEE Trans. Power Electron.
,
30
(
5
), pp.
2493
2504
.10.1109/TPEL.2014.2365531
28.
Dragičević
,
T.
,
Wheeler
,
P.
, and
Blaabjerg
,
F.
,
2019
, “
Artificial Intelligence Aided Automated Design for Reliability of Power Electronic Systems
,”
IEEE Trans. Power Electron.
,
34
(
8
), pp.
7161
7171
.10.1109/TPEL.2018.2883947
29.
Nwanoro
,
K. C.
,
Lu
,
H.
,
Yin
,
C.
, and
Bailey
,
C.
,
2018
, “
An Analysis of the Reliability and Design Optimization of Aluminium Ribbon Bonds in Power Electronics Modules Using Computer Simulation Method
,”
Microelectron. Reliab.
,
87
, pp.
1
14
.10.1016/j.microrel.2018.05.013
30.
Pang
,
Y. F.
,
Scott
,
E. P.
,
Chen
,
J. Z.
, and
Thole
,
K. A.
,
2005
, “
Thermal Design and Optimization Methodology for Integrated Power Electronics Modules
,”
ASME J. Electron. Packag.
,
127
(
1
), pp.
59
66
.10.1115/1.1849233
31.
Noor
,
A. K.
,
2017
, “
AI and the Future of the Machine Design
,”
ASME Mech. Eng.
,
139
(
10
), pp.
38
43
.10.1115/1.2017-Oct-2
32.
Evans
,
T. M.
,
Le
,
Q.
,
Mukherjee
,
S.
,
Al Razi
,
I.
,
Vrotsos
,
T.
,
Peng
,
Y.
, and
Mantooth
,
H. A.
,
2019
, “
PowerSynth: A Power Module Layout Generation Tool
,”
IEEE Trans. Power Electron.
,
34
(
6
), pp.
5063
5078
.10.1109/TPEL.2018.2870346
33.
Yang
,
S.
,
Xiang
,
D.
,
Bryant
,
A.
,
Mawby
,
P.
,
Ran
,
L.
, and
Tavner
,
P.
,
2010
, “
Condition Monitoring for Device Reliability in Power Electronic Converters: A Review
,”
IEEE Trans. Power Electron.
,
25
(
11
), pp.
2734
2752
.10.1109/TPEL.2010.2049377
34.
Kabir
,
A.
,
Bailey
,
C.
,
Lu
,
H.
, and
Stoyanov
,
S.
,
2012
, “
A Review of Data-Driven Prognostics in Power Electronics
,”
2012 35th International Spring Seminar on Electronics Technology
,
Bad Aussee, Austria
, May 9–13, pp.
189
192
.10.1109/ISSE.2012.6273136
35.
Wang
,
B.
,
Cai
,
J.
,
Du
,
X.
, and
Zhou
,
L.
,
2017
, “
Review of Power Semiconductor Device Reliability for Power Converters
,”
CPSS Trans. Power Electron. Appl.
,
2
(
2
), pp.
101
117
.10.24295/CPSSTPEA.2017.00011
36.
Hanif
,
A.
,
Yu
,
Y.
,
DeVoto
,
D.
, and
Khan
,
F.
,
2019
, “
A Comprehensive Review Toward the State-of-the-Art in Failure and Lifetime Predictions of Power Electronic Devices
,”
IEEE Trans. Power Electron.
,
34
(
5
), pp.
4729
4746
.10.1109/TPEL.2018.2860587
37.
Oh
,
H.
,
Han
,
B.
,
McCluskey
,
P.
,
Han
,
C.
, and
Youn
,
B. D.
,
2015
, “
Physics-of-Failure, Condition Monitoring, and Prognostics of Insulated Gate Bipolar Transistor Modules: A Review
,”
IEEE Trans. Power Electron.
,
30
(
5
), pp.
2413
2426
.10.1109/TPEL.2014.2346485
38.
Ali
,
S. H.
,
Heydarzadeh
,
M.
,
Dusmez
,
S.
,
Li
,
X.
,
Kamath
,
A. S.
, and
Akin
,
B.
,
2018
, “
Lifetime Estimation of Discrete IGBT Devices Based on Gaussian Process
,”
IEEE Trans. Ind. Appl.
,
54
(
1
), pp.
395
403
.10.1109/TIA.2017.2753722
39.
Alghassi
,
A.
,
Perinpanayagam
,
S.
, and
Samie
,
M.
,
2016
, “
Stochastic RUL Calculation Enhanced With TDNN-Based IGBT Failure Modeling
,”
IEEE Trans. Reliab.
,
65
(
2
), pp.
558
573
.10.1109/TR.2015.2499960
40.
Olivares
,
C.
,
Rahman
,
R.
,
Stankus
,
C.
,
Hampton
,
J.
,
Zedwick
,
A.
, and
Ahmed
,
M.
,
2021
, “
Predicting Power Electronics Device Reliability Under Extreme Conditions With Machine Learning Algorithms
,” e-print
arXiv:2107.10292
.10.48550/arXiv.2107.10292
41.
Peyghami
,
S.
,
Dragicevic
,
T.
, and
Blaabjerg
,
F.
,
2021
, “
Intelligent Long-Term Performance Analysis in Power Electronics Systems
,”
Sci. Rep.
,
11
(
1
), p.
7557
.10.1038/s41598-021-87165-3
42.
Xiong
,
Y.
,
Cheng
,
X.
,
Shen
,
Z. J.
,
Mi
,
C.
,
Wu
,
H.
, and
Garg
,
V. K.
,
2008
, “
Prognostic and Warning System for Power-Electronic Modules in Electric, Hybrid Electric, and Fuel-Cell Vehicles
,”
IEEE Trans. Ind. Electron.
,
55
(
6
), pp.
2268
2276
.10.1109/TIE.2008.918399
43.
Yin
,
C. Y.
,
Lu
,
H.
,
Musallam
,
M.
,
Bailey
,
C.
, and
Johnson
,
C. M.
,
2008
, “
A Prognostic Assessment Method for Power Electronics Modules
,”
2008 2nd Electronics System-Integration Technology Conference
,
Greenwich, UK
, Sept. 1–4, pp.
1353
1358
.10.1109/ESTC.2008.4684552
44.
van der Giessen
,
E.
,
Schultz
,
P. A.
,
Bertin
,
N.
,
Bulatov
,
V. V.
,
Cai
,
W.
,
Csányi
,
G.
, and
Foiles
,
S. M.
, et al.,
2020
, “
Roadmap on Multiscale Materials Modeling
,”
Modell. Simul. Mater. Sci. Eng.
,
28
(
4
), p.
043001
.10.1088/1361-651X/ab7150
45.
Moreno
,
G.
,
Narumanchi
,
S.
,
Feng
,
X.
,
Anschel
,
P.
,
Myers
,
S.
, and
Keller
,
P.
,
2021
, “
Electric-Drive Vehicle Power Electronics Thermal Management: Current Status, Challenges, and Future Directions
,”
ASME J. Electron. Packag.
,
144
(
1
), p.
011004
.10.1115/1.4049815
46.
Jones-Jackson
,
S.
,
Rodriguez
,
R.
, and
Emadi
,
A.
,
2021
, “
Jet Impingement Cooling in Power Electronics for Electrified Automotive Transportation: Current Status and Future Trends
,”
IEEE Trans. Power Electron.
,
36
(
9
), pp.
10420
10435
.10.1109/TPEL.2021.3059558
47.
Wu
,
T.
,
Ozpineci
,
B.
,
Chinthavali
,
M.
,
Wang
,
Z.
,
Debnath
,
S.
, and
Campbell
,
S.
,
2017
, “
Design and Optimization of 3D Printed Air-Cooled Heat Sinks Based on Genetic Algorithms
,” 2017 IEEE Transportation Electrification Conference and Expo (
ITEC
),
Chicago, IL
, June 22–24, pp.
650
655
.10.1109/ITEC.2017.7993346
48.
Michalak
,
A.
,
Zaman
,
M. S.
,
Tayyara
,
O.
,
Nasr
,
M.
,
Da Silva
,
C.
,
Mills
,
J. K.
,
Trescases
,
O.
, and
Amon
,
C. H.
,
2020
, “
A Thermal Management Design Methodology for Advanced Power Electronics Utilizing Genetic Optimization and Additive Manufacturing Techniques
,” 2020 19th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (
ITherm
),
Orlando, FL
, July 21–23, pp.
547
557
.10.1109/ITherm45881.2020.9190444
49.
Gurpinar
,
E.
,
Sahu
,
R.
, and
Ozpineci
,
B.
,
2021
, “
Heat Sink Design for WBG Power Modules Based on Fourier Series and Evolutionary Multi-Objective Multi-Physics Optimization
,”
IEEE Open J. Power Electron.
,
2
, pp.
559
569
.10.1109/OJPEL.2021.3119518
50.
Dede
,
E. M.
,
Joshi
,
S. N.
, and
Zhou
,
F.
,
2015
, “
Topology Optimization, Additive Layer Manufacturing, and Experimental Testing of an Air-Cooled Heat Sink
,”
ASME J. Mech. Des.
,
137
(
11
), p.
111403
.10.1115/1.4030989
51.
Andresen
,
M.
, and
Liserre
,
M.
,
2014
, “
Impact of Active Thermal Management on Power Electronics Design
,”
Microelectron. Reliab.
,
54
(
9–10
), pp.
1935
1939
.10.1016/j.microrel.2014.07.069
52.
Murdock
,
D. A.
,
Ramos
,
J. E.
,
Connors
,
J. J.
, and
Lorenz
,
R. D.
,
2003
, “
Active Thermal Control of Power Electronics Modules
,”
38th IAS Annual Meeting on Conference Record of the Industry Applications Conference
,
Salt Lake City, UT
, Oct. 12–16, Vol.
3
, pp.
1511
1515
.10.1109/IAS.2003.1257756
53.
Kim
,
K.
,
Kim
,
J. S.
,
Jeong
,
S.
,
Park
,
J.-H.
, and
Kim
,
H. K.
,
2021
, “
Cybersecurity for Autonomous Vehicles: Review of Attacks and Defense
,”
Comput. Secur.
,
103
, p.
102150
.10.1016/j.cose.2020.102150
54.
Celaya
,
J. R.
,
Saxena
,
A.
,
Kulkarni
,
C. S.
,
Saha
,
S.
, and
Goebel
,
K.
,
2012
, “
Prognostics Approach for Power MOSFET Under Thermal-Stress Aging
,”
2012 Proceedings Annual Reliability and Maintainability Symposium
,
Reno, NV
, Jan. 23–26, pp.
1
6
.10.1109/RAMS.2012.6175487
You do not currently have access to this content.