<2025> Battery Degradation Mitigation / Prediction / Diagnosis Technologies and Company Trends (Includes AI-Related Technologies)
Lithium-ion
batteries play a pivotal role across various applications, including electric
vehicles (EVs), energy storage systems (ESS), and portable electronic devices.
However, the gradual degradation of performance over time remains one of the
most critical factors determining battery lifespan, safety, and reliability.
Accurately diagnosing and predicting battery degradation in industrial settings
is not merely a technical challenge but also directly tied to economic
feasibility, policy value, and overall market competitiveness.
Battery
degradation is the fundamental cause of performance deterioration in
lithium-ion batteries. The higher the energy density and power output, the more
severe the degradation becomes, resulting in greater performance loss — hence,
a deep understanding of degradation mechanisms is essential.
Currently,
two main technology and market domains are being developed based on this
concept: battery end-of-life diagnostics and fast-charging applications.
Diagnostic
technology is essential in the process of reusing end-of-life batteries.
Several OEMs have been conducting businesses utilizing reused batteries for
years, and many other companies are currently preparing new ventures based on
battery reuse applications.
In
addition, for EVs to surpass the market share of conventional gasoline and
diesel vehicles, it is essential to ensure both long battery life and short
charging time, meaning fast charging capability. This represents a fundamental
requirement for the rapid expansion of the battery and EV markets, where
technologies that mitigate or suppress degradation under harsh conditions are
indispensable.
Battery
degradation prediction has evolved over several decades through physics-based
modeling and experimental approaches. However, these traditional methodologies
have revealed fundamental limitations in practical industrial applications,
leading to the emergence of artificial intelligence (AI) and machine learning
(ML) as new solutions. The constraints of conventional physical and empirical
models have created the need for a new paradigm in battery degradation
prediction, and AI and ML have risen as the answers to this demand.
Over the
past decade, the introduction of AI technologies into battery research has
advanced beyond simple data analysis, reaching a level of maturity that enables
real industrial applications. In particular, for large-scale battery systems
such as EVs and ESS, the rise of AI is now recognized not merely as a research
trend but as an inevitable turning point for the industry.
This report
is organized into twelve chapters. Chapters 1 to 3 discuss the need for
technologies based on the understanding of battery degradation and provide
fundamental knowledge related to degradation. Chapters 4 and 5 describe the
causes and effects of degradation, while Chapters 6 and 7 address degradation
mitigation strategies and diagnostic/predictive technologies. Chapter 8 focuses
on AI and machine learning–based approaches for battery degradation diagnosis
and prediction. Chapters 9 and 10 present the global status of related
companies as well as market trends and outlook for the relevant industries, and
Chapters 11 and 12 cover patents and the latest technological developments.
In this
report, we aim to enhance understanding of battery degradation and identify
opportunities amid current challenges. The report provides an in-depth
discussion of degradation mechanisms, introduces various AI and machine
learning–based technologies for degradation mitigation, diagnosis, and
prediction, and presents detailed insights into domestic and global companies,
market and industry trends, as well as notable patents and emerging
technologies.
1.
Overview--------------------------------------------------------------- 11
1.1.
Intensifying Technological Competition 12
1.2.
After-Sales Management Issues 16
1.3.
Environmental Pollution Issues 17
1.4.
Fast-Charging Issues 20
2.
Lithium Ion Battery-------------------------------------------------- 21
2.1.
Components 24
3.
Degradation ---------------------------------------------------------- 27
3.1.
What Is Degradation? 28
3.2.
Degradation Mechanism 31
4.
Materials--------------------------------------------------------------- 33
4.1.
Cathode 34
4.1.1.
Degradation by Cathode Material 35
4.1.1.1. Lithium nickel manganese cobalt
oxides (NCM) 36
4.1.1.2. Lithium Manganese Oxide (LMO) 41
4.1.1.3. Lithium ferro-phosphate (LFP) 41
4.1.2.
Factors Aggravating or Mitigating Degradation 42
4.1.2.1 TM dissolution 43
4.1.2.2. Intragranular and Intergranular
Cracks 44
4.1.2.3. Thermal Stability 45
4.1.3. Results of Degradation 46
4.2.
Anode 49
4.2.1.
Degradation by Anode Material 49
4.2.1.1. Graphite-Based Anode 50
4.2.1.2. Li-Metal Anode 52
4.2.1.3. Silicon-Based Anode 53
4.2.1.4. Lithium Titanium Oxide (LTO)-Based
Anode 53
4.2.2.
Factors Aggravating or Mitigating Degradation 54
4.2.2.1. Temperature 55
4.2.2.2. Charging Current (C-rate) 56
4.2.2.3. State-of-Charge (SOC) 57
4.2.2.4. Additional Degradation
Induced by SEI 58
4.2.3.
Results of Degradation 60
4.2.3.1. Li Plating Reaction in Graphite
Anode 60
4.2.3.2. Additional Degradation Caused by SEI
Layer 61
4.3.
Electrolyte 63
4.4.
Degradation of Inactive Materials (Binder, Current Collector, Separator,
Components, etc.) 68
4.4.1.
Binder 68
4.4.2.
Current Collector 68
4.4.3.
Separator 69
4.4.4.
Cell Casing 69
4.5.
Other Degradation Factors (Aging Conditions, Ambient Temperature, Battery
Design, User Behavior, etc.) 70
4.6.
Interlinked Scenarios Among Degradation Mechanisms 71
4.6.1.
Positive-Feedback Scenario 71
4.6.2.
Negative-Feedback Scenario 72
5.
Results of Cell Degradation--------------------------------------- 73
5.1.
Performance Degradation 74
5.1.1.
Temporary Capacity Loss 74
5.1.2.
Permanent Capacity Loss 74
6.
Battery Degradation Mitigation Strategies ----------------- 78
6.1.
Improvement of Active Material Properties 78
6.1.1.
Cathode Active Material 78
6.1.1.1. Improvement Methods for Ni-Rich NCM
Cathodes 78
6.1.1.2. Inner Surface Modification 78
6.1.1.3. Outer Surface Modification 82
6.1.2.
Anode Active Material 87
6.1.2.1. Surface Coating 87
6.1.2.2. Adjustment of Electrolyte Amount 87
6.1.3.
Solid Electrolyte Interphase (SEI) 87
6.1.3.1. Electrolyte Additives 87
6.1.3.2. Lithium Salts 89
6.1.3.3. Solvents 90
6.2.
Charging Methods 91
6.2.1.
constant voltage (CV) 91
6.2.2.
constant current (CC) 92
6.2.3.
Constant Current/Constant Voltage (CC-CV) 92
6.2.4.
constant power (CP) 94
6.2.5.
Constant Power/Constant Voltage (CP-CV) 95
6.2.6.
Boost Charging 95
6.2.7.
varying current decay (VCD) 96
6.2.8.
multistage constant current (MCC) 97
6.2.9.
Pulse Charging 98
6.2.9.1. Precautions for Pulse Charging 98
6.2.10.
Trickle Charging 98
7.
Battery Degradation Diagnosis and Prediction
Technologies------------------------------------ 100
7.1.
Analysis Techniques by Degradation Mode 101
7.1.1.
Analysis of Structural Changes and Decomposition of Active Materials 101
7.1.2.
Particle Fracture Analysis 102
7.1.3.
SEI Layer Growth Analysis 103
7.1.4.
Li Plating Analysis 103
7.2.
Electrochemical Analysis Methods 105
7.2.1.
Cell Voltage and Capacity Analysis 105
7.2.2.
Resistance Analysis 110
7.3.
Non-Model-Based Analysis 112
7.3.1.
Diagnosis of Internal Battery Factors 113
7.3.2.
Diagnosis of External Battery Factors 114
7.4.
Model-Based Analysis 115
7.4.1.
Types of Models 116
7.4.1.1. Empirical Models 117
7.4.1.2. Physical Models 118
7.4.1.3. Single-particle models (SPM) 119
7.4.2.
SEI Layer Growth 120
7.4.3.
Li plating 121
7.4.4.
Structural Change and Decomposition of Cathode 121
7.4.5.
Particle Fracture 122
7.4.6.
Silicon additives 122
7.5.
Diagnosis and Prediction Using Machine Learning / Artificial Intelligence 123
7.5.1.
Background of ML/AI-Based Diagnostic Technologies 124
7.5.2.
Performance and Safety Prediction 125
7.5.3.
Degradation and Lifetime Prediction 126
7.5.4.
On-Line Estimation Techniques 135
7.6.
Post-Mortem Analysis 144
7.6.1.
Precautions During Cell Disassembly 146
7.6.2.
Cell Opening Procedures and Component Separation Methods 147
7.6.3.
Physical Analysis Techniques 149
7.6.3.1. X-ray Diffraction (XRD) 149
7.6.3.2. Microscopy and Electron Diffraction 151
7.6.4.
Chemical Analysis Techniques 153
7.6.4.1. Elemental Analysis 153
7.6.4.2. Gas Measurement 155
7.6.4.3. Impurity and Organic Compound
Analysis 155
7.6.5.
Thermal Stability Analysis 156
8.
AI and Machine Learning-Based Battery Degradation Diagnosis and Prediction
(Advanced)--------------- 159
8.1.
Overview of Battery Degradation Prediction 159
8.1.1.
Industrial Importance of Battery Degradation Prediction 159
8.1.2.
Limitations of Traditional Physics-Based Models 160
8.1.3.
Emergence of Artificial Intelligence 163
8.1.4.
Data-Driven Degradation Diagnosis Techniques 165
8.1.5.
Probabilistic Models and Uncertainty Quantification 166
8.1.6.
Integration of Physical and AI Models (Physics-Informed Machine Learning, PIML) 167
8.1.7.
Industrial Implications and Policy/Market Linkages 169
8.2.
Quantification of Predictive Uncertainty and Reliability Validation 170
8.2.1.
Background and Problem Definition 170
8.2.2.
Types of Uncertainty and Modeling Principles 171
8.2.3.
Evaluation Metrics and Calibration Protocols 172
8.2.4.
Data Splitting and Distribution Shift Validation 174
8.2.5.
Implementation of Uncertainty by Model Type 176
8.2.6.
Improving Data Efficiency Using Chemistry-Aware or Synthetic Data 178
8.3.
Physics-Informed / Hybrid Modeling 182
8.3.1.
Problem Definition and Background 182
8.3.2.
Mathematical Framework 183
8.3.3.
Performance Comparison and Limitations 185
8.3.4.
Industrial Implications 188
8.3.5.
Conclusions and Future Directions 189
8.4.
Explainability and Interpretable AI 190
8.4.1.
Background and Need for Explainability 190
8.4.2.
Classification of Interpretation Methods 191
8.4.3.
Case-Based Explanations 193
8.4.4.
Industrial Implications 194
8.4.5.
Technical Limitations and Future Challenges 195
8.5.
Multi-Factor and Complex Degradation Mechanism Modeling 196
8.5.1.
Background and Problem Definition 196
8.5.2.
Multi-Factor Datasets and Analysis 197
8.5.3.
Combined Chemical and Physical Factor Model 198
8.5.4.
Hybrid Models and Interpretability 201
8.5.5.
Industrial Implications 202
8.6.
Multiscale Electrode Modeling and AI Integration 203
8.6.1.
Necessity of Electrode-level Modeling 203
8.6.1.1.
Background and Problem Definition 203
8.6.1.2.
Technical Supplementation with AI 203
8.6.2.
Particle-level Electrode Modeling and the Role of AI 204
8.6.2.1.
Background and Problem Definition 204
8.6.2.2.
Integration of Model and AI 204
8.6.2.3.
Industrial Implications 209
8.6.3.
Analysis of Electrode Porous Structure and AI Integration 210
8.6.3.1.
Background and Problem Definition 210
8.6.3.2.
Mathematical Modeling 210
8.6.3.3.
Experimental and Visualization Techniques 215
8.7.
Summary and Future Tasks 222
8.7.1.
Comprehensive Discussion 222
8.7.2.
Remaining Limitations 223
8.7.3.
Future Tasks 224
8.7.4.
Conclusion 224
9.
Overview of Companies Related to Battery Degradation
9.1.
Korea 226
9.1.1.
Wonik PNE 226
9.1.2.
PMGrow 228
9.1.3.
MinTech 231
9.1.4.
NSys 235
9.1.5.
Cammsys 240
9.1.6.
SL Corporation 241
9.1.7. Bumyung 242
9.1.8.
Haedong Engineering 245
9.1.9.
nanu (AIMS) 247
9.1.10.
SIcell 249
9.1.11.
poen 252
9.1.12.
Max Science 255
9.1.13.
ODA Technology 259
9.1.14. MONA 264
9.1.15.
ELT Co., Ltd. 266
9.1.16.
Quantum Solution Co., Ltd. 269
9.1.17.
Waton 274
9.1.18.
MAK 279
9.1.19.
HYUNDAI KEFICO 281
9.1.20.
Nexcon Technology Co., Ltd. 282
9.2
North America 287
9.2.1
AMP 287
9.2.2.
Nuvation energy 290
9.2.3.
Intel Corporation 293
9.2.4.
Analog Devices, Inc. 294
9.2.5.
Ewert Energy Systems 298
9.2.6.
STAFL Systems, LLC. 300
9.2.7.
Sensata Technologies 302
9.2.8.
Exponential Power, Inc. 305
9.2.9.
REDARC Electronics 306
9.2.10.
KPM Power Inc 309
9.2.11.
Eberspaecher Vecture 312
9.2.12.
QNOVO 318
9.2.13.
Electrovaya Inc 321
9.2.14.
Liminal Insights, Inc 324
9.2.15.
Visteon 326
9.3
Europe 330
9.3.1.
STMicroelectronics 330
9.3.2.
Leclanche 333
9.3.3.
Eatron technologies 337
9.3.4.
BAMOMAS 341
9.3.5.
BMS Powersafe 344
9.4
Japan 347
9.4.1.
FDK Corporation 347
9.4.2.
4R Energy 349
9.4.3.
TOYO SYSTEM 352
9.4.4.
MARELLI 354
9.4.5.
Hitachi Astemo 357
9.5
China 363
9.5.1.
BYD 363
9.5.2.
CATL New Energy Technology Co., Ltd. 366
9.5.3.
Shenzhen PACE Electronic Technology Co., Ltd. 368
9.5.4.
Pylontech-Pylon Technologies Co., Ltd. 370
9.5.5.
Findreams battery 374
9.5.6.
HUASU 376
9.5.7.
SINOEV (Octillion) 382
9.5.8.
LIGAO Technology 383
9.5.9.
UAES 390
9.5.10.
Intron 393
9.6.
Others 398
9.6.1.
ION Energy Inc 398
10.
Market Overview and Outlook------------------------------- 402
10.1.
BMS 402
10.1.1.
Global BMS Market Outlook (2021–2030) 402
10.1.2.
BMS Suppliers by EV Model (2012–2024) 403
10.2
Fast Chargers 418
10.2.1.
Global Market Status 418
10.2.2.
U.S. Fast Charger Market Outlook (2021–2030) 418
10.2.3.
Status by Major U.S. Cities 419
10.2.4.
Regional Status of Fast Chargers in Korea 420
10.3.
Global Battery Diagnosis Market 422
10.3.1.
Global Battery Diagnosis and Repair Market Trend 422
10.3.2.
Global Battery Equipment Market Trend 423
11.
Patents Related to Battery Degradation Suppression and Diagnosis
(2017–2021)---------- 427
11.1
Domestic Patents 427
12.
Latest Technologies Related to Degradation
Diagnosis------------------------------------------ 474
12.1.
Analysis of Charge Transfer Resistance Behavior 474
12.2.
Local Li Plating Analysis Due to Temperature Nonuniformity 479
12.3.
IR Drop Analysis 483
12.4.
Incremental Capacity Analysis 487
12.5.
Differential Voltage Analysis 490
12.6.
Graphite Anode Interface Analysis Under Fast-charging Conditions 494
12.7.
Development of Anode Coating Materials and Impedance Analysis 497
13.
References------------------------------------------------------- 500