<2025> AI-Based Development Strategies for Battery Materials, Cells, Packs, and Recycling
The global
battery industry has been experiencing explosive growth driven by the
increasing demand for electric vehicles (EVs), energy storage systems (ESS),
and mobile devices. However, meeting the conflicting requirements of high
energy density, high safety, low cost, and long cycle life simultaneously
remains a significant challenge.
Traditional
battery development has relied on a sequential, experiment-centered process
that involves material exploration, cell design, prototype fabrication,
testing, and improvement. This process typically takes several years and
requires substantial R&D investment. To overcome these limitations, recent
efforts have actively integrated artificial intelligence (AI) and machine
learning (ML) technologies, significantly accelerating the speed and efficiency
of battery development.
AI
technologies enable the following transformative changes across the entire
battery development process.
·
Automated analysis and
optimization of vast materials data
· Predictive
modeling of electrode and electrolyte performance
·
Intelligent quality control
in manufacturing processes
·
Real-time prediction and
management of battery life and safety
First,
AI-based materials science (Materials Informatics) enables the discovery of new
materials within months, a process that previously took years. This technology
reduces experimental failure rates in material development, shortens
development time (from years to months), and cuts costs by up to 60%.
For
example, deep learning models can take chemical composition, crystal structure,
and electrochemical properties as inputs to rapidly recommend optimal
cathode–anode combinations. By combining DFT calculations with AI interpolation
methods, they can quickly predict values such as lithium diffusion rate,
voltage window, and stability. In 2024, Microsoft and PNNL developed an
AI-based electrolyte discovery platform that identified commercially promising
compositions out of more than 30,000 candidates in just 80 hours.
Second,
in cell design and performance simulation, AI also demonstrates powerful
capabilities. It can simultaneously consider dozens of variables—such as
electrode thickness, particle size distribution, binder content, and
electrolyte ratio—to derive the optimal cell structure. In practice, genetic
algorithms combined with reinforcement learning enable the automatic search for
designs that meet target energy density, cycle life, and safety requirements.
By integrating physics-based simulations (Pseudo-two-dimensional, P2D models)
with AI prediction models, it is possible to virtually test tens of thousands
of design scenarios.
For
example, in Tesla’s 4680 cell development, AI-based process optimization
techniques were reportedly applied to electrode coating speed and tab structure
design. These methods played a decisive role in reducing risks before prototype
fabrication and in identifying designs with strong potential for mass
production.
Third,
in manufacturing process monitoring and quality control, even microscopic
defects at the nano- or micro-scale can critically affect battery performance
and safety. AI enables real-time detection and control of such issues. For
example, Vision AI can automatically detect coating non-uniformities, electrode
surface defects, and stacking misalignments. Streaming analysis of process data
provides early warnings of deviations in electrolyte injection volume,
calendaring pressure, and drying conditions. In addition, AI-based process
control can predict the likelihood of defects in advance (preventive control),
reducing defect rates by 30–50%. Such smart factory implementation allows the
battery industry to achieve both consistent quality assurance and improved
productivity.
Fourth, AI
is applied to lifetime and safety prediction as well as operational
optimization. By learning from voltage, current, and temperature data generated
during battery use, AI can predict state of health (SOH) and safety risks. For
instance, Google Research developed an LSTM-based Remaining Useful Life (RUL)
model that was able to predict lifetime patterns using only the first 100
charge–discharge cycles. In large-scale ESS operations, AI has been used to
schedule charge–discharge patterns, achieving a 20% extension in lifetime and a
reduction in operating costs. In electric vehicles, AI-powered battery
management systems (BMS) help prevent overheating and lithium plating risks
during fast charging. This contributes to three key values: failure prevention,
reduced maintenance costs, and enhanced safety.
As outlined
above, AI applications in the battery industry can be summarized into the
following advantages:
(1) Innovation
in development speed – reducing the time
required for material development, design, and validation from years to months
(2) Cost
reduction – lowering R&D and
manufacturing costs by up to 50–60% through fewer experiments and reduced
defect rates
(3) Product
performance improvement – achieving
simultaneous gains in energy density, cycle life, and safety
(4) Enhanced
sustainability – enabling recycled material
design, optimization of used battery reuse, and reduction of carbon footprint
(5) Improved
market responsiveness – meeting diverse
industry demands quickly through customized battery designs
Finally,
looking at the industrial innovation and future prospects brought by the
application of AI to the battery industry, AI is driving the following paradigm
shifts:
- From closed,
experiment-centered approaches → to open, data-centered development
frameworks
- From company-centered
approaches → to the expansion of global collaborative AI data platforms
- Acceleration of
next-generation battery development (such as solid-state, sodium-ion, and
lithium–sulfur)
In
conclusion, applying AI to battery development is not merely a technological
upgrade but a strategic choice that will determine industrial competitiveness
and market leadership. Within the next 5–10 years, AI-based standardized
platforms for battery development are expected to be established, leading to
the widespread adoption of a fully digital twin–based development framework
that encompasses the entire lifecycle—from materials and design to
manufacturing and operation.
This report
comprehensively covers not only various papers and reports presenting practical
results achieved through AI in recent battery development but also the latest
corporate development trends. It is expected to greatly contribute to
understanding both the remarkable roles AI is currently playing in wide-ranging
areas such as R&D, design, and manufacturing, as well as the future
prospects of AI applications in the battery industry.
The
strong points of this report are as follows:
① Key insights from the
latest papers on battery material development using AI and machine learning
(ML)
② Information and core
content on AI technologies applied to battery cell/pack manufacturing processes
③ Comprehensive
coverage of AI-based operational technologies for the optimal management of
EVs, ESS, and data centers
④ Complete overview of
AI applications in reuse and recycling of end-of-life batteries
⑤ Analysis of
next-generation BMS technologies based on big data and AI
⑥ Comprehensive
information on the application of Digital Twin technology in battery
development
⑦ Latest development
trends in AI adoption by battery (materials) companies and EV OEMs
[Battery cell solid electrolyte
material development – Microsoft/PNNL, discovery of 18 new halogen-based
candidate materials using AI]
[Battery cell liquid electrolyte material development – ① Data-driven analysis for predicting LIB electrolyte solvent stability]
[High-throughput screening and prediction of electrode active materials]
Contents
1. AI Technology
in Battery Cell/Pack Manufacturing Processes
1.1 AI Technology for Battery Cell Material
Development
1.1.1 Principles and Algorithms of Machine
Learning (ML)
1.1.2 Natural Language Processing and Large
Language Models
1.1.3 Workflow of AI-Based Research and
Development
1.1.4 Overview of AI/ML-Based Battery
Material Development
1.1.5 Necessity of AI Technology for Battery
Cell Material Development
1.1.6 AI-Based Development of Battery Cell
Cathode Materials
1.1.7 AI-Based Development of Battery Cell
Anode Materials
1.1.8 AI-Based Development of Battery Cell
Liquid Electrolyte Materials
1.1.9 AI-Based Development of Battery Cell
Solid Electrolyte Materials
1.1.10 AI-Based Optimization Technology for
Battery Cell Materials
1.2 AI Technology for Reducing Battery Cell
Material Screening Time
1.2.1 Necessity of Applying AI Technology to
Reduce Material Screening Time
1.2.2 Case Studies of AI-Based Reduction in
Cell Material Screening Time
1.2.3 Application of AI Technology for
Classifying Cell Cross-Section Defect Types
1.3 AI Technology for Optimizing Battery
Pack Design Structure
1.3.1 Necessity of Battery Pack Design
Structure Optimization
1.3.2 Analysis of Key Considerations in
Optimal Battery Pack Design
1.3.3 Workflow of AI-Based Battery Pack
Design Structure Optimization
1.3.4 AI-Based Battery Pack Design Structure
Optimization
1.3.5 Research on AI-Based Battery Pack
Design Structure Optimization
2. AI Technologies
for Battery Application Operation
2.1 AI-Based Charging Technologies for
Optimal EV Operation
2.1.1 Necessity of AI Technologies for
Optimal EV Operation within the Power Grid
2.1.2 Case Studies of AI Technologies for
Optimal EV Operation within the Power Grid
2.1.3 Necessity of AI-Based Charging
Technologies for Optimal EV Battery Operation
2.1.4 Case Studies of AI-Based Charging
Technologies for Optimal EV Battery Operation
2.2 AI-Based
External Environment Control Technologies for Optimal ESS Operation
2.2.1 Necessity of
External Environment Control Considering Battery Aging Characteristics
2.2.2 Limitations
of Existing External Environment Control Technologies/Strategies in ESS
Operation
2.2.3 Research on
External Environment Classification for ESS Operation Using EIS Image-Based
Analysis
2.2.4 Design of
AI-Based External Environment Control Strategies for Optimal ESS Operation
2.3 Data
Management and Operation Technologies to Improve Cloud Server Efficiency in Big
Data Environments
2.3.1 Necessity of
Big Data Management and Operation in Cloud Servers Due to Increasing Data
Importance
2.3.2 Case Studies
on Generating Operational Pattern Images for Large-Scale EV Data Management
2.3.3 Case Studies
on Data Compression for Improved Storage Efficiency in Cloud Servers
2.3.4 Case Studies
on Improving Lifetime Prediction Algorithm Performance Through Model
Optimization in Cloud Servers
3. AI Technologies
for Battery Reuse/Recycling After Use
3.1 AI-Based Material Recovery and Process
Optimization Technologies for Reducing Recycling Costs of Used Batteries
3.1.1 Necessity of AI Adoption Due to
Limitations of Existing Battery Recycling Processes
3.1.2 Application of AI Technologies in
Battery Recycling Processes
3.2 AI-Based Rapid
Diagnostic Technologies for Reducing Reuse Costs of Used Batteries
3.2.1 Necessity of
Rapid Diagnostic Technologies for Used Batteries
3.2.2 Key
Considerations When Receiving Used Batteries
3.2.3 Research on
SOH Rapid Diagnosis Algorithms Using One-Hot Encoding
3.2.4 Research on
Enhancing SOH Rapid Diagnosis Algorithms Using the Adaboost Algorithm
3.2.5 Research on
AI-Based Rapid Diagnostic Technologies for Reducing Reuse Costs of Used
Batteries
3.3 AI-Based
Regrouping Technologies for Battery Reuse After Use
3.3.1 Limitations
of Existing Battery Reuse Processes
3.3.2 Research on
Fault Diagnosis Using RLS Deviation for Removal Prior to Regrouping of
Defective Batteries
3.3.3 Research on
RUL-Based Regrouping Algorithms for Used Batteries
4. Next-Generation
BMS Technologies Based on Big Data & AI
4.1 Limitations of Existing BMS and
Necessity of Introducing Next-Generation BMS Based on Big Data & AI
4.1.1 Overview of Battery Management Systems
4.1.2 Limitations of Existing BMS and
Necessity of Next-Generation BMS
4.1.3 Next-Generation BMS Technologies
Integrated with Big Data & AI
4.2 Data
Pre-Processing for AI Model Development
4.2.1 Data
Pre-Processing Procedures for AI Model Development
4.2.2 Necessity of
Extracting Health Indicators Reflecting Application Operating Environments and
Data Collection Conditions
4.2.3 Methods for
Extracting Health Indicators Reflecting Application Operating Environments and
Data Collection Conditions
4.2.4 Limitations
of Applying Health Indicators Reflecting Application Operating Environments and
Data Collection Conditions
4.2.5 Examples of
Extracting Health Indicators Reflecting Application Operating Environments and
Data Collection Conditions
4.3 Case Studies
of AI Applications in BMS for Various Purposes
4.3.1 Theories of
Artificial Intelligence
4.3.2 Selection of
Deep Learning Models for Battery Time-Series Data Prediction
4.3.3 Selection of
Deep Learning Models for Battery Anomaly Detection and Fault Diagnosis
4.3.4 Case Study
on Real-Time Lifetime Prediction Algorithms Using Embedded Linux Systems for
Optimal Battery Operation
4.3.5 Case Study
on Data Patterning and Anomaly Diagnosis for Safe Battery Operation
5. Digital Twin in
Battery Development
5.1 Digital Twin
Concepts & Technologies
5.1.1 Concept and
Expected Effects of Digital Twin
5.1.2 Components
of Digital Twin
5.1.3
Implementation and Utilization of Digital Twin
5.1.4 Key
Technologies for Implementing Digital Twin
5.1.5 Optimization
of Digital Twin
5.2 Digital Twin
in Battery Development
5.2.1 Application
of Digital Twin in Batteries
5.2.2 Hierarchical
Structure of Battery Digital Twin
5.2.3 Vision of
Battery Digital Twin
5.2.4 Battery
Modeling Using Digital Twin
5.2.5 Challenges
of Applying Digital Twin to Batteries
5.2.6 Summary of
SoX Estimation and Cell Balancing Functions Based on Digital Twin
5.2.7 Application
of Digital Twin in Advanced Fault Diagnosis and RUL (Remaining Useful Life)
Estimation
5.2.8 Expansion to
Manufacturing Optimization, TMS, Passport, and V2G Across the Full Lifecycle
5.2.9
Establishment of Battery Digital Twin Platform
5.2.10 Comparison
of Integrated Digital Twin Platforms
5.2.11 Trends in
Battery State Estimation Based on Digital Twin Models and Cloud BMS
5.2.12 Utilization
of Digital Twin and Cloud BMS: Virtual Battery Model
5.2.13 Digital
Twin BMS [1] ~ [5]
6. Current Status
of AI Applications in Battery (Materials) and EV Companies
6.1 Current Status of AI Applications in
Battery Companies
6.2 Current Status of AI Applications in EV
Companies
6.3 Current Status of AI Applications in Platform Specialized Companies (Institutions)