인공지능(AI) 배터리 기술 시장(2027-2037년)

Artificial intelligence is rapidly becoming a core technology across the battery value chain. It is changing how batteries are discovered, designed, tested, manufactured, managed, reused and recycled. The market for AI-driven battery technology spans AI software, AI-enabled services and AI-specific hardware, such as vision inspection systems and edge-AI battery management processors. It is set for strong growth over the next decade, as battery demand expands and AI becomes embedded in every stage of the lifecycle. The past twelve months have marked a turning point. AI has moved from pilot projects to company-wide transformation programmes at the world's leading cell makers, with targets for large productivity gains and AI governance overseen at board level. Recent developments include:

• Generative AI in R&D. Materials foundation models and generative design tools have entered commercial battery R&D. Battery developers now offer AI-driven materials discovery platforms that screen millions of candidate electrolytes and electrode materials.
• Sodium-ion. Sodium-ion batteries have entered mass production for passenger vehicles. Solid-state batteries are approaching commercial launch, and both chemistries rely heavily on AI-accelerated development.
• Regulation and trade. The EU battery passport, due from February 2027, is turning battery state-of-health data into a regulated asset. Export controls on advanced battery technology have made data, models and process know-how matters of national strategy.
• New demand. AI data centres, humanoid robots, drones and electric aircraft have emerged as new sources of battery demand. Each needs sophisticated battery intelligence.

Looking ahead, the fastest growth is expected in:

• Second-life assessment, as the first large wave of retired EV batteries arrives after 2030;
• Recycling, under circular-economy regulation;
• Digital twins, linking data across the battery lifecycle;
• Materials informatics, driven by next-generation chemistries.

Stationary storage and data centres are becoming the fastest-growing customer groups. On-edge AI will take a rapidly rising share of battery analytics as neural processors spread through battery management hardware. Adoption will follow three waves:

• 1. Analytics and BMS intelligence in the late 2020s.
• 2. AI-native manufacturing and accelerated materials discovery in the early 2030s.
• 3. Autonomous laboratories and fully digital-twin-based development by the mid-2030s.

Competitive advantage will go to companies that combine proprietary battery data, deep electrochemical expertise, validated physics-informed models and integration across the full battery lifecycle. Battery development is moving from an experiment-centred process to a data-centred one, and AI capability is becoming as strategically important as manufacturing scale.

The Global Market for Artificial Intelligence (AI) Battery Technology 2027–2037 is a comprehensive analysis of how AI is transforming every stage of the battery lifecycle, and of the commercial opportunities this creates. The report analyses nine AI use-case arenas:

• materials informatics;
• cell and pack design;
• cell testing and modelling;
• manufacturing and quality control;
• BMS and battery analytics;
• application operation;
• digital twins;
• second-life assessment;
• recycling.

Report contents include:

• Global market forecasts 2027–2037 by use case, AI technique, deployment, offering, chemistry, end use and region
• Bear, base and bull scenario analysis and value-pool distribution across the technology stack
• Analysis of machine learning, deep learning, physics-informed models, generative AI, foundation models and agentic AI for batteries
• Battery data infrastructure, public datasets, advanced sensing, BMS silicon and edge-AI processors
• AI-driven materials discovery, universal interatomic potentials and autonomous laboratories
• AI in cell and pack design, testing, lifetime prediction and gigafactory manufacturing
• Next-generation BMS, cloud analytics, on-edge AI and battery health certification
• AI for EV charging, V2G, BESS dispatch and data-centre battery management
• Battery digital twins across the lifecycle
• Second-life assessment, regrouping and AI in recycling
• AI opportunities by battery chemistry and end-use market
• Regional policy ecosystems, battery passports, AI regulation, safety standards and export controls
• AI adoption by cell makers, EV OEMs and platform companies
• Competitive landscape, partnerships, funding, M&A and patent analysis
• Profiles of 95 companies. Companies profiled include About:Energy, ACC (Automotive Cells Company), ACCURE Battery Intelligence, Addionics, Aegis Critical Energy Defence, Aionics, Altilium, Anabatic Semi, Analog Devices, Atinary Technologies, B2U Storage Solutions, BASF, BattGenie, Blue Solutions, BMW Group, Bosch, Breathe Battery Technologies, Brightfield AI/Voltus, Brill Power, CATL, Chemix, Circulor, Circunomics, Citrine Informatics, Cling Systems, Cognex, Connected Energy, CuspAI, Dassault Systemes, DellCon, DNV (Veracity), DP Technology, Dragonfly Energy, Dukosi, Dunia Innovations, Eatron Technologies, EcoPro, Electra Vehicles, Elisa IndustrIQ, Elysia Battery Intelligence (Fortescue Zero), EnPower Greentech, enspired, Envision AESC, Eonix Energy, EthonAI, EVE Energy, Fluence Energy, Gaussion, GBatteries, Glimpse Engineering, Google DeepMind, Huawei Digital Power, Intellegens and more....

Table of Contents

1 EXECUTIVE SUMMARY

• 1.1 Scope of This Report
• 1.2 AI Growth Drivers in the Battery Industry
  • 1.2.1 Net-zero targets and the electrification push
  • 1.2.2 Energy density, cost and critical-material constraints
  • 1.2.3 Regulatory pull: the EU Battery Passport
  • 1.2.4 Regional demand patterns: Europe, North America and East Asia
• 1.3 Challenges Facing the Rechargeable Battery Industry
• 1.4 How AI Applies Across the Battery Lifecycle
• 1.5 Five Benefits of AI: Development Speed, Cost, Performance, Sustainability and Market Responsiveness
• 1.6 Paradigm Shifts: From Experiment-Centred to Data-Centred Development
• 1.7 AI Disruption of the Battery Supply Chain
• 1.8 Use-Case Benchmarking and Maturity
• 1.9 AI in Batteries for Electric Vehicles
• 1.10 AI in Batteries for Stationary Storage and Data Centres
• 1.11 Interest by Region
• 1.12 Market Findings
  • 1.12.1 Battery diagnostics by capacity served and market value
  • 1.12.2 On-edge AI: diagnostics and performance enhancement
  • 1.12.3 Materials informatics, cell testing and second-life assessment
• 1.13 Three-Wave Adoption Framework
  • 1.13.1 Wave 1: Analytics and BMS intelligence (2027–2030)
  • 1.13.2 Wave 2: AI-native manufacturing and accelerated materials (2030–2034)
  • 1.13.3 Wave 3: Autonomous discovery and fully digital-twin-based development (2034–2037)
• 1.14 The Investment Surge
• 1.15 The Past Twelve Months
  • 1.15.1 Generative AI and foundation models enter battery R&D
  • 1.15.2 Leading cell makers launch company-wide AI transformation
  • 1.15.3 Battery passports approach enforcement amid calls for phasing
  • 1.15.4 Export controls reshape access to advanced battery technology
  • 1.15.5 Stationary storage and AI data centres become the growth engine
  • 1.15.6 Robotics, drones and aviation emerge as new battery markets
  • 1.15.7 EV demand slowdown sharpens focus on cost and productivity
• 1.16 Strategic Imperatives for Cell Makers, OEMs, Utilities and Software Vendors
• 1.17 Major Market Players

2 INTRODUCTION: AI AND THE BATTERY VALUE CHAIN

• 2.1 Defining AI-Driven Battery Technology
• 2.2 Machine Learning vs. Artificial Intelligence
• 2.3 The Battery Market Context
  • 2.3.1 Global Li-ion demand by application
  • 2.3.2 Li-ion vs. beyond-Li-ion demand
  • 2.3.3 Chemistry mix: NMC, LFP/LMFP, sodium-ion, solid-state, silicon anode
  • 2.3.4 Cost trajectory and pack-price outlook
• 2.4 Traditional Battery Development: Sequential, Experiment-Centred and Slow
• 2.5 What Has Changed: Why AI Is Deployable Now
  • 2.5.1 Connected-fleet data volumes
  • 2.5.2 Cloud and edge compute cost curves
  • 2.5.3 Open battery datasets and benchmarks
  • 2.5.4 Foundation models for chemistry and materials
• 2.6 The Economic Case for AI in Batteries
  • 2.6.1 R&D cost and time savings
  • 2.6.2 Manufacturing yield and defect reduction
  • 2.6.3 Lifetime extension and warranty cost reduction
• 2.7 Market Architecture: Nine AI Use-Case Arenas

3 MACHINE LEARNING FUNDAMENTALS FOR BATTERY APPLICATIONS

• 3.1 AI as a Moving Target: Definitions and Scope
• 3.2 The Importance of Data: Quality, Dimensionality and Standardised Structures
• 3.3 Learning Paradigms
  • 3.3.1 Supervised learning
  • 3.3.2 Unsupervised learning
  • 3.3.3 Problem classes: regression, classification, clustering and anomaly detection
  • 3.3.4 Reinforcement learning and the exploration–exploitation trade-off
  • 3.3.5 Semi-supervised and active learning
• 3.4 Classical Algorithms
  • 3.4.1 Support vector and relevance vector machines
  • 3.4.2 Decision trees, random forests and gradient boosting
  • 3.4.3 k-nearest neighbour and k-means clustering
  • 3.4.4 Principal component analysis
  • 3.4.5 Gaussian process regression
• 3.5 Neural Networks and Deep Learning
  • 3.5.1 The artificial neuron and the training process
  • 3.5.2 Feedforward, recurrent (LSTM, GRU) and convolutional networks
  • 3.5.3 Generative adversarial networks and transformers
  • 3.5.4 Graph neural networks for materials
  • 3.5.5 Universal machine-learned interatomic potentials
• 3.6 Natural Language Processing and Large Language Models
• 3.7 Physics-Informed and Hybrid Models
  • 3.7.1 Electrochemical (P2D, SPM) and equivalent circuit models
  • 3.7.2 Kalman and particle filtering
  • 3.7.3 Physics-informed neural networks
• 3.8 Generative AI, Foundation Models and Agentic AI
• 3.9 Transfer, Federated and Domain-Adaptive Learning
• 3.10 Explainability, Uncertainty Quantification, Under-Fitting and Over-Fitting
  • 3.10.1 Explainability
  • 3.10.2 Uncertainty quantification
  • 3.10.3 Fitting errors
• 3.11 The Inefficiency of Overuse: When Not to Apply Machine Learning
• 3.12 Edge vs. Cloud Deployment

4 BATTERY DATA INFRASTRUCTURE, SENSING AND COMPUTE

• 4.1 The Battery Data Problem
  • 4.1.1 Data scarcity, cost of cycling data and proprietary silos
    • 4.1.1.1 Laboratory data costs
    • 4.1.1.2 Proprietary silos
  • 4.1.2 Open datasets and benchmarks
• 4.2 The Data Pipeline: From BMS to AI
• 4.3 Advanced Sensing Hardware
  • 4.3.1 Voltage, current and temperature sensing
  • 4.3.2 On-board electrochemical impedance spectroscopy (EIS)
  • 4.3.3 Embedded fibre-optic, pressure and gas sensors
  • 4.3.4 Wireless BMS and chip-on-cell sensing
    • 4.3.4.1 Wireless BMS
    • 4.3.4.2 Chip-on-cell sensing
• 4.4 BMS Silicon and Edge-AI Processors
  • 4.4.1 Battery-monitoring ICs and analogue front ends
    • 4.4.1.1 Measurement accuracy
    • 4.4.1.2 Synchronisation
    • 4.4.1.3 Sampling rates and data resolution
    • 4.4.1.4 Cell balancing
    • 4.4.1.5 Communication and isolation
    • 4.4.1.6 Functional safety and diagnostics
    • 4.4.1.7 Integrated sensing beyond voltage and temperature
    • 4.4.1.8 New application segments
    • 4.4.1.9 Market structure
  • 4.4.2 Microcontrollers and neural processors for on-edge AI
  • 4.4.3 Battery-management ICs for AI-server backup units (BBUs)
• 4.5 Cloud Platforms and Connectivity
  • 4.5.1 Telematics, OTA updates and vehicle data access
  • 4.5.2 Battery-in-the-cloud architectures
• 4.6 Compute for Materials Discovery
  • 4.6.1 HPC and GPU clusters for DFT and molecular dynamics
  • 4.6.2 Machine-learned interatomic potentials
  • 4.6.3 Quantum computing for battery chemistry
• 4.7 Data Standards and Interoperability
  • 4.7.1 Battery passport data models
  • 4.7.2 OPC UA and IT/OT convergence in gigafactories
• 4.8 Cybersecurity and Data Governance

5 AI-DRIVEN BATTERY MATERIALS DISCOVERY (MATERIALS INFORMATICS)

• 5.1 Why AI for Battery Materials
  • 5.1.1 The attraction of AI: navigating a vast design space
  • 5.1.2 Traditional material discovery and the limits of DFT
  • 5.1.3 Materials informatics
  • 5.1.4 The workflow of AI-based research and development
• 5.2 Property Prediction and Material Grouping
  • 5.2.1 Datasets and descriptors
    • 5.2.1.1 Unsupervised grouping
  • 5.2.2 DFT plus AI interpolation: lithium diffusion, voltage window and stability
• 5.3 Inverting the Process: Inverse Design
  • 5.3.1 Informed selection vs. novel material formulation
  • 5.3.2 Virtual screening
  • 5.3.3 De novo and generative design
    • 5.3.3.1 Large-scale generative results
  • 5.3.4 Large language model interfaces and literature mining
• 5.4 Reducing Material Screening Time
  • 5.4.1 The case for AI in material screening
  • 5.4.2 Case studies of AI-based screening-time reduction
• 5.5 Cathode Materials
  • 5.5.1 High-nickel and cobalt-free layered oxides
  • 5.5.2 LFP and LMFP optimisation
  • 5.5.3 Disordered rock-salt and lithium-rich cathodes
• 5.6 Anode Materials
  • 5.6.1 Graphite and silicon-carbon composites
  • 5.6.2 Lithium-metal anodes and dendrite suppression
  • 5.6.3 Hard carbon for sodium-ion
• 5.7 Liquid Electrolytes
  • 5.7.1 Formulation and additive design
  • 5.7.2 Solvent stability prediction
• 5.8 Solid Electrolytes
  • 5.8.1 Sulfide, oxide, polymer and halide systems
  • 5.8.2 Case study: Microsoft and PNNL AI-screened solid electrolytes
• 5.9 AI-Based Optimisation of Cell Material Combinations
• 5.10 Autonomous (Self-Driving) Laboratories
  • 5.10.1 Robotic synthesis and characterisation
  • 5.10.2 Closed-loop active learning
  • 5.10.3 Agentic AI lab assistants
  • 5.10.4 European initiatives: BIG-MAP and Battery 2030+
  • 5.10.5 Commercial self-driving laboratories
  • 5.10.6 Academic milestones and mobile robots
  • 5.10.7 Big-technology autonomous laboratories
• 5.11 Critical-Mineral Substitution and Supply-Risk Mitigation
• 5.12 Players in Materials Informatics for Batteries
• 5.13 Business Analysis
  • 5.13.1 Business models and partnerships
  • 5.13.2 Existing client–supplier relationships
  • 5.13.3 Differentiation
  • 5.13.4 Challenges: data quality, interpretability, compute and qualification
    • 5.13.4.1 Data quality and availability
    • 5.13.4.2 Gap between computation and experiment
    • 5.13.4.3 Interpretability and trust
    • 5.13.4.4 Computational resources
    • 5.13.4.5 Model generalisation
    • 5.13.4.6 Qualification and time to market
    • 5.13.4.7 Intellectual property and data ownership
    • 5.13.4.8 Regulatory and safety requirements
    • 5.13.4.9 Skills
  • 5.13.5 Pricing of materials informatics platforms
  • 5.13.6 Risks for SaaS business models
  • 5.13.7 Barriers to profitability
  • 5.13.8 Big-technology platforms and competition for dedicated players
  • 5.13.9 In-house development and consolidation
  • 5.13.10 Outlook
• 5.14 Market Forecast: Materials Informatics for Batteries 2027–2037

6 AI APPLICATIONS BY BATTERY CHEMISTRY

• 6.1 Advanced Li-Ion: High-Nickel, LFP/LMFP and the Path to 350 Wh/kg
• 6.2 Silicon and Silicon-Carbon Anodes
• 6.3 Lithium-Metal and Anode-Less Cells
• 6.4 Solid-State and Semi-Solid-State Batteries
  • 6.4.1 Commercial status
  • 6.4.2 Demand outlook
• 6.5 Lithium-Sulfur Batteries
• 6.6 Sodium-Ion Batteries
  • 6.6.1 Commercial status
  • 6.6.2 Demand outlook
• 6.7 LTO and Niobate Batteries
• 6.8 Zinc-Based and Aluminium-Ion Batteries
• 6.9 Redox Flow Batteries
• 6.10 Structural, Flexible, Printed and Other Emerging Formats
• 6.11 PFAS-Free Binders and Additives: Regulation-Driven Reformulation
• 6.12 AI Opportunity by Chemistry

7 AI IN CELL AND PACK DESIGN

• 7.1 From Trial-and-Error to Simulation-Led Design
• 7.2 Multi-Variable Cell Design Optimisation
  • 7.2.1 Electrode thickness, particle size distribution, binder content and electrolyte ratio
  • 7.2.2 Genetic algorithms combined with reinforcement learning
  • 7.2.3 Coupling P2D physics models with AI to test design scenarios virtually
• 7.3 Electrode and Microstructure Design
  • 7.3.1 Image-based microstructure reconstruction
  • 7.3.2 Porosity, tortuosity and loading optimisation
  • 7.3.3 Thick-format electrodes
• 7.4 Cell Formats and Architectures
  • 7.4.1 Larger cell formats and tabless designs (Tesla 4680)
  • 7.4.2 Bipolar and dual-electrolyte architectures
    • 7.4.2.1 How bipolar cells work
    • 7.4.2.2 Benefits
    • 7.4.2.3 Challenges
    • 7.4.2.4 Commercial status
    • 7.4.2.5 How AI helps with bipolar designs
    • 7.4.2.6 Dual-electrolyte architectures
    • 7.4.2.7 Variants
    • 7.4.2.8 Dual-electrolyte challenges
    • 7.4.2.9 How AI helps with dual-electrolyte designs
  • 7.4.3 Commercial examples
    • 7.4.3.1 Large-format tabless cylindrical cells
    • 7.4.3.2 Long prismatic "blade" cells
    • 7.4.3.3 High-integration cell-to-pack systems
    • 7.4.3.4 Multi-layer electrodes
    • 7.4.3.5 Three-dimensional current collectors
    • 7.4.3.6 Thick electrodes and simplified cell designs
    • 7.4.3.7 Dry-electrode processing
    • 7.4.3.8 Bipolar designs
    • 7.4.3.9 The common thread
• 7.5 Cell Performance and Energy Density
• 7.6 AI-Based Battery Pack Design Structure Optimisation
  • 7.6.1 Why pack structure optimisation matters
  • 7.6.2 Key considerations in optimal pack design
  • 7.6.3 AI-based optimisation workflow
• 7.7 Research on AI-based pack design optimisation
• 7.8 Cell-to-Pack, Cell-to-Chassis and Hybrid Packs
  • 7.8.1 Cell-to-pack and cell-to-chassis
  • 7.8.2 Hybrid battery packs
• 7.9 Generative Design, Lightweighting and Thermal System Design
• 7.10 Design for Recycling and Sustainability
• 7.11 Battery Design and Simulation Software Landscape
• 7.12 Market Forecast: AI Cell and Pack Design 2027–2037

8 AI IN CELL TESTING, MODELLING AND LIFETIME PREDICTION

• 8.1 Traditional Cell Testing: Shortcomings and Challenges
• 8.2 AI for High-Throughput Automated Testing
• 8.3 Data Forms for Cell Modelling
• 8.4 AI for Design of Experiments and Anomalous Data Identification
• 8.5 Lifetime Modelling and Early-Life Prediction
• 8.6 Degradation Modelling
  • 8.6.1 SEI growth, lithium plating and active-material loss
  • 8.6.2 Incremental capacity, differential voltage and EIS health indicators
• 8.7 Remaining Useful Life (RUL) Prediction Methods
  • 8.7.1 Model-based approaches
  • 8.7.2 Stochastic process methods: Wiener, Gamma and Markov
  • 8.7.3 Classical machine learning approaches
  • 8.7.4 Deep learning approaches
  • 8.7.5 Hybrid fusion approaches
• 8.8 Temperature and Pressure Simulation
• 8.9 Data-Driven Cell Architecture Optimisation
• 8.10 AI Classification of Cell Cross-Section Defect Types
• 8.11 Algorithmic Approaches by Testing Mode
• 8.12 Test Laboratory Automation and Certification Implications
• 8.13 Players in AI for Cell Testing
• 8.14 Business Analysis
  • 8.14.1 Typical business models
  • 8.14.2 Differentiation
  • 8.14.3 Challenges
    • 8.14.3.1 Trust and acceptance
    • 8.14.3.2 Transferability across chemistries and protocols
    • 8.14.3.3 Capturing rare and late-emerging failure modes
    • 8.14.3.4 Data access and confidentiality
    • 8.14.3.5 Data quality and consistency
    • 8.14.3.6 Integration with laboratory infrastructure
    • 8.14.3.7 Regulatory and certification barriers
    • 8.14.3.8 Demonstrating commercial value
  • 8.14.4 Outlook
    • 8.14.4.1 Near term (2027–2030)
    • 8.14.4.2 Medium term (2030–2034)
    • 8.14.4.3 Longer term (2034–2037)
    • 8.14.4.4 Structural change
• 8.15 Market Forecast: AI Cell Testing 2027–2037

9 AI IN CELL ASSEMBLY AND MANUFACTURING

• 9.1 Overview of the Traditional Manufacturing Process
• 9.2 The Gigafactory Ramp-Up Problem
  • 9.2.1 Scrap rates during ramp-up
  • 9.2.2 The "valley of death" between start of production and target yield
• 9.3 Data Quality and Data Acquisition Challenges in Industrial Settings
• 9.4 AI Use Cases by Process Step
• 9.5 Vision AI for Defect Detection and Quality Control
  • 9.5.1 Coating non-uniformity, electrode surface defects and stacking misalignment
  • 9.5.2 X-ray and CT inspection
  • 9.5.3 Ultrasound and acoustic inspection
• 9.6 Streaming Process Analytics and Preventive Control
  • 9.6.1 Electrolyte injection volume, calendering pressure and drying conditions
  • 9.6.2 Predicting defects before they occur
• 9.7 Electrode Coating and Drying Simulation
• 9.8 Formation and Ageing: AI-Shortened Protocols
• 9.9 Algorithmic Approaches in Manufacturing and Cell Assembly
• 9.10 Factory Digital Twins, Virtual Commissioning and FAT/SAT
• 9.11 Predictive Maintenance, MES and IT/OT Integration
  • 9.11.1 Predictive maintenance
  • 9.11.2 Manufacturing execution systems
• 9.12 Robotics and Physical AI in Cell and Pack Assembly
• 9.13 Smart Battery Manufacturing Players
• 9.14 Business Analysis
  • 9.14.1 Types of smart manufacturing players
    • 9.14.1.1 Vertically integrated cell makers
    • 9.14.1.2 Industrial software and automation providers
    • 9.14.1.3 Equipment makers
    • 9.14.1.4 Inspection-technology providers
    • 9.14.1.5 Specialist AI analytics companies
    • 9.14.1.6 Battery data platforms
    • 9.14.1.7 System integrators and engineering firms
    • 9.14.1.8 Research institutions
  • 9.14.2 Challenges
    • 9.14.2.1 Upfront investment and uncertain payback
    • 9.14.2.2 Integration with heterogeneous equipment
    • 9.14.2.3 Data quality and genealogy
    • 9.14.2.4 Model maintenance and drift
    • 9.14.2.5 Skills shortages
    • 9.14.2.6 Organisational and cultural barriers
    • 9.14.2.7 Cybersecurity
    • 9.14.2.8 Data ownership and vendor lock-in
    • 9.14.2.9 Scaling beyond pilots
  • 9.14.3 Outlook
    • 9.14.3.1 Large cell makers
    • 9.14.3.2 New entrants and start-ups
    • 9.14.3.3 How solutions will adapt
    • 9.14.3.4 Policy support
    • 9.14.3.5 Next-generation chemistries
    • 9.14.3.6 Overall outlook
• 9.15 Market Forecast: AI in Battery Manufacturing 2027–2037

10 NEXT-GENERATION BMS AND BATTERY ANALYTICS

• 10.1 Battery Management Systems: Purpose and Multi-Cell Pack Management
• 10.2 Limitations of Existing BMS and the Case for Next-Generation BMS
• 10.3 Next-Generation BMS Integrated with Big Data and AI
• 10.4 Data Pre-Processing for AI Model Development
  • 10.4.1 Pre-processing procedures
  • 10.4.2 Why health indicators must reflect operating environment and data-collection conditions
  • 10.4.3 Methods for extracting health indicators
  • 10.4.4 Limitations and worked examples
• 10.5 Data Structures and Forms for Diagnostics
• 10.6 State Estimation: SoC, SoH, SoP, SoE and Internal Temperature
• 10.7 Fault Detection and Anomaly Diagnosis
  • 10.7.1 Selecting deep learning models for battery time-series prediction
  • 10.7.2 Selecting deep learning models for anomaly detection and fault diagnosis
  • 10.7.3 Case study: data patterning and anomaly diagnosis for safe operation
• 10.8 Prescriptive AI: From Prediction to Recommended Action
• 10.9 AI-Optimised Charging: Adaptive and Degradation-Aware Protocols
• 10.10 Thermal Runaway Early Detection and Advanced Pack Sensors
  • 10.10.1 Advanced sensor technologies
  • 10.10.2 Sensor market outlook
  • 10.10.3 Integration and the path to predictive maintenance
• 10.11 Wireless BMS and Remote Monitoring
• 10.12 On-Edge AI
  • 10.12.1 Case study: real-time lifetime prediction on embedded Linux
  • 10.12.2 Samsung Galaxy S25 battery AI
  • 10.12.3 Eatron and Syntiant
  • 10.12.4 LG Energy Solution and Qualcomm
  • 10.12.5 Tesla BMS: optimisation over a journey
• 10.13 Cloud Battery Analytics: Fleet, Warranty, Residual Value and Insurance
• 10.14 The Battery Passport
• 10.15 Players in AI for Battery Diagnostics and Management
• 10.16 Business Analysis
  • 10.16.1 Business models
    • 10.16.1.1 Software as a service (SaaS)
    • 10.16.1.2 Embedded software licences
    • 10.16.1.3 Certification and reporting services
    • 10.16.1.4 Outcome-based models
    • 10.16.1.5 Bundled hardware and software
    • 10.16.1.6 Hybrid and platform models
  • 10.16.2 Differentiation
    • 10.16.2.1 Model accuracy and validation against real field outcomes
    • 10.16.2.2 Scale of data
    • 10.16.2.3 Physics-informed approaches
    • 10.16.2.4 Integration
  • 10.16.3 Challenges
    • 10.16.3.1 Data access
    • 10.16.3.2 Edge constraints
    • 10.16.3.3 Certification
    • 10.16.3.4 Price pressure
    • 10.16.3.5 Proving value
  • 10.16.4 Outlook
    • 10.16.4.1 Expansion across mobility
    • 10.16.4.2 Convergence of analytics and BMS
    • 10.16.4.3 Expansion across the lifecycle
    • 10.16.4.4 Global diffusion
    • 10.16.4.5 Market evolution
• 10.17 Market Forecast: Battery Diagnostics and BMS Analytics 2027–2037

11 AI FOR BATTERY OPERATION IN EVs, ENERGY STORAGE AND DATA CENTRES

• 11.1 AI for EV Operation Within the Power Grid
  • 11.1.1 Why grid-aware EV operation needs AI
  • 11.1.2 Case studies
    • 11.1.2.1 OEM joint platforms
    • 11.1.2.2 Energy-retailer tariffs with automated charging
    • 11.1.2.3 Utility managed-charging programmes
    • 11.1.2.4 Vehicle-to-grid services
    • 11.1.2.5 Large-scale pilots in China
• 11.2 AI-Based Charging for Optimal EV Battery Operation
  • 11.2.1 Why AI-based charging is needed
  • 11.2.2 Case studies
    • 11.2.2.1 Journey-level charging optimisation
    • 11.2.2.2 Ultra-high-power charging
    • 11.2.2.3 Battery swapping at scale
    • 11.2.2.4 Battery-as-a-service in emerging markets
    • 11.2.2.5 Adaptive charging software
    • 11.2.2.6 Depot charging for commercial fleets
    • 11.2.2.7 Research on AI-optimised fast charging
• 11.3 Smart Charging and Vehicle-to-Grid Platforms
  • 11.3.1 Smart charging and V1G
  • 11.3.2 Bidirectional charging
    • 11.3.2.1 The role of AI
  • 11.3.3 Degradation cost
  • 11.3.4 Scaling
• 11.4 AI-Based Environmental Control for Energy Storage Systems
  • 11.4.1 Why environmental control must reflect battery ageing characteristics
  • 11.4.2 Limitations of existing control strategies
  • 11.4.3 EIS image-based classification of operating environments
  • 11.4.4 Designing AI-based control strategies for optimal ESS operation
• 11.5 BESS Dispatch, Energy Trading and Augmentation Planning
  • 11.5.1 The economics of storage
  • 11.5.2 AI dispatch and trading platforms
  • 11.5.3 Augmentation planning
  • 11.5.4 Long-duration storage
• 11.6 Big Data Management in Cloud Servers
  • 11.6.1 Why cloud data management matters as data volumes grow
  • 11.6.2 Operational pattern images for large-scale EV data
  • 11.6.3 Data compression for storage efficiency
  • 11.6.4 Optimising lifetime prediction models in the cloud
• 11.7 Battery Storage for Data Centres, Commercial and Industrial Sites
  • 11.7.1 AI data centres as a driver of battery demand
    • 11.7.1.1 Demand outlook
  • 11.7.2 Battery backup units for AI servers
• 11.8 Battery Swapping, Battery-as-a-Service and New Business Models

12 BATTERY DIGITAL TWINS

• 12.1 Digital Twin Concepts and Technologies
  • 12.1.1 Concept and expected benefits
  • 12.1.2 Components of a digital twin
  • 12.1.3 Implementation and use
  • 12.1.4 Key enabling technologies
  • 12.1.5 Optimising the digital twin
• 12.2 Digital Twins in Battery Development
  • 12.2.1 Applications in batteries
  • 12.2.2 Hierarchical structure of the battery digital twin
  • 12.2.3 Vision for the battery digital twin
  • 12.2.4 Battery modelling with digital twins
  • 12.2.5 Challenges in applying digital twins to batteries
    • 12.2.5.1 Limited observability
    • 12.2.5.2 Parameterisation
    • 12.2.5.3 Model fidelity versus computational cost
    • 12.2.5.4 Data continuity across the lifecycle
    • 12.2.5.5 Validation
    • 12.2.5.6 Model drift and maintenance
    • 12.2.5.7 Integration and interoperability
    • 12.2.5.8 Uncertainty quantification
    • 12.2.5.9 Cost and business case
    • 12.2.5.10 Data ownership and privacy
    • 12.2.5.11 Security
    • 12.2.5.12 Skills
• 12.3 Digital-Twin Functions
  • 12.3.1 SoX estimation and cell balancing
  • 12.3.2 Advanced fault diagnosis and RUL estimation
• 12.4 Extending Across the Lifecycle: Manufacturing, Thermal Management, Battery Passport and V2G
• 12.5 Battery Digital Twin Platforms
  • 12.5.1 Building a battery digital twin platform
  • 12.5.2 Comparison of integrated platforms
• 12.6 Digital Twins and Cloud BMS
  • 12.6.1 Trends in digital-twin-based state estimation
  • 12.6.2 The virtual battery model
  • 12.6.3 Digital twin BMS case studies
• 12.7 Market Forecast: Battery Digital Twins 2027–2037

13 SECOND-LIFE ASSESSMENT AND BATTERY REUSE

• 13.1 Second-Life Batteries Overview
  • 13.1.1 Determining the second-life stream
  • 13.1.2 End-of-life volumes
• 13.2 Safety Concerns, Regulation and the Battery Passport
  • 13.2.1 Safety risks
  • 13.2.2 Regulation and standards
  • 13.2.3 The battery passport
• 13.3 The Role of AI
• 13.4 AI-Based Rapid Diagnostics to Cut Reuse Costs
  • 13.4.1 Why rapid diagnostics are needed
  • 13.4.2 Key considerations when receiving used batteries
  • 13.4.3 Rapid SOH diagnosis using one-hot encoding
  • 13.4.4 Improving rapid SOH diagnosis with AdaBoost
  • 13.4.5 Research on AI-based rapid diagnostic technologies
• 13.5 AI-Based Regrouping for Battery Reuse
  • 13.5.1 Limitations of existing reuse processes
  • 13.5.2 Removing defective cells using RLS-deviation fault diagnosis
  • 13.5.3 RUL-based regrouping algorithms
• 13.6 Second-Life Applications and Economics
  • 13.6.1 Economics
  • 13.6.2 AI's contribution
• 13.7 Players in AI for Second-Life Assessment
• 13.8 Business Analysis
  • 13.8.1 Revenue streams
  • 13.8.2 Types of players
    • 13.8.2.1 Diagnostics specialists
    • 13.8.2.2 Marketplace operators
    • 13.8.2.3 Second-life system integrators
    • 13.8.2.4 OEMs and cell makers
    • 13.8.2.5 Recyclers
    • 13.8.2.6 Energy companies and utilities
    • 13.8.2.7 Insurers, financiers and certifiers
  • 13.8.3 Differentiation
    • 13.8.3.1 Speed and cost of assessment
    • 13.8.3.2 Accuracy and certified reliability
    • 13.8.3.3 Breadth of coverage
    • 13.8.3.4 Access to first-life data
    • 13.8.3.5 Integration across the value chain
    • 13.8.3.6 System design and operating capability
    • 13.8.3.7 Warranty strength
    • 13.8.3.8 Regulatory compliance and documentation
  • 13.8.4 Challenges
    • 13.8.4.1 Access to battery history
    • 13.8.4.2 Heterogeneity of batteries
    • 13.8.4.3 Economics under falling new-battery prices
    • 13.8.4.4 Competition with recycling
    • 13.8.4.5 Safety and liability
    • 13.8.4.6 Regulation and certification
    • 13.8.4.7 Logistics and scale
    • 13.8.4.8 Warranty and performance risk
    • 13.8.4.9 Access to finance
  • 13.8.5 Outlook
    • 13.8.5.1 Volume growth after
    • 13.8.5.2 Convergence of first-life and second-life analytics
    • 13.8.5.3 Standardisation and certification
    • 13.8.5.4 Evolving business models
• 13.9 Market Forecast: AI Second-Life Assessment 2027–2037

14 AI IN BATTERY RECYCLING

• 14.1 Limitations of Existing Recycling Processes and the Case for AI
• 14.2 Recycling Technologies
  • 14.2.1 Hydrometallurgy
  • 14.2.2 Pyrometallurgy
  • 14.2.3 Direct recycling
  • 14.2.4 Other methods
• 14.3 AI Across the Recycling Process
  • 14.3.1 Sorting and chemistry identification
  • 14.3.2 Robotic and vision-guided disassembly
  • 14.3.3 Black-mass characterisation
  • 14.3.4 Hydrometallurgical and direct-recycling process control
• 14.4 Recycling Specific Components and Beyond-Li-Ion Chemistries
  • 14.4.1 Component recovery
  • 14.4.2 Beyond-lithium-ion chemistries
• 14.5 Traceability, Battery Passports and EU Targets
• 14.6 Recycling Players Using AI
  • 14.6.1 Chinese players
  • 14.6.2 Technology providers
• 14.7 Market Forecast: AI in Battery Recycling 2027–2037

15 END-USE MARKETS

• 15.1 Electric Vehicles
  • 15.1.1 Sales in
  • 15.1.2 Early 2026 and outlook
  • 15.1.3 Battery demand
  • 15.1.4 Passenger vehicles
    • 15.1.4.1 Development
    • 15.1.4.2 Manufacturing
    • 15.1.4.3 Operation
    • 15.1.4.4 Residual value and warranty
    • 15.1.4.5 Adoption
  • 15.1.5 Commercial vehicles and heavy trucks
    • 15.1.5.1 Market growth
    • 15.1.5.2 Distinctive battery requirements
    • 15.1.5.3 Infrastructure
  • 15.1.6 Two- and three-wheelers
    • 15.1.6.1 Swapping and BaaS
    • 15.1.6.2 AI requirements
    • 15.1.6.3 Chemistry and cost
  • 15.1.7 Regional dynamics
  • 15.1.8 Used-EV market and battery health certification
    • 15.1.8.1 The confidence gap
    • 15.1.8.2 Variation within models
    • 15.1.8.3 Certification in practice
    • 15.1.8.4 Toward mandatory certificates
    • 15.1.8.5 Assessment methods
• 15.2 Stationary Energy Storage
  • 15.2.1 Growth in
  • 15.2.2 Outlook
  • 15.2.3 Drivers
  • 15.2.4 Utility-scale BESS
    • 15.2.4.1 Why AI matters
    • 15.2.4.2 Obstacles
  • 15.2.5 Commercial, industrial and residential storage
    • 15.2.5.1 New business models
  • 15.2.6 Data-centre backup and on-site storage
  • 15.2.7 Long-duration storage
  • 15.2.8 Regional dynamics
    • 15.2.8.1 China
    • 15.2.8.2 Other regions
• 15.3 Consumer Electronics and Wearables
• 15.4 Off-Highway Machines: Construction, Agriculture and Mining
• 15.5 Robotics, Drones and Physical AI Platforms
  • 15.5.1 Humanoid robots
    • 15.5.1.1 Energy demand
    • 15.5.1.2 Current battery packs
    • 15.5.1.3 The weight constraint
  • 15.5.2 Autonomous mobile robots and warehouse automation
  • 15.5.3 Drones
  • 15.5.4 AI applications
  • 15.5.5 Outlook
• 15.6 Aviation and eVTOL
  • 15.6.1 eVTOL aircraft
    • 15.6.1.1 Commercial progress
    • 15.6.1.2 Battery requirements
    • 15.6.1.3 AI applications in eVTOL
    • 15.6.1.4 Validation and explainability
  • 15.6.2 Hybrid-electric aircraft
  • 15.6.3 Battery technology roadmap for aviation
  • 15.6.4 Outlook
• 15.7 Marine and Rail
  • 15.7.1 Marine
  • 15.7.2 Rail
• 15.8 Defence
• 15.9 Medical Devices
• 15.10 Comparative Application Analysis

16 GLOBAL MARKET FORECASTS 2027–2037

• 16.1 Scope of Forecasts and Methodology
• 16.2 Total Market
• 16.3 By Use Case
  • 16.3.1 Leading segments
  • 16.3.2 Fastest growth
• 16.4 Battery Diagnostics by Capacity Served
• 16.5 By AI Technique
• 16.6 By Deployment Mode: Cloud, On-Edge and On-Premises
• 16.7 By Offering: Software, Services and AI-Enabled Hardware
• 16.8 By Battery Chemistry
  • 16.8.1 LFP and LMFP
  • 16.8.2 High-nickel NMC/NCA
  • 16.8.3 Next-generation chemistries
• 16.9 By Region
• 16.10 Scenario Analysis: Bear, Base and Bull Cases
• 16.11 Value Pool Distribution Across the Stack

17 REGIONAL MARKETS AND BATTERY POLICY ECOSYSTEMS

• 17.1 Regional Demand Patterns for Battery AI
  • 17.1.1 Europe: analytics and second-life assessment led by the Battery Passport
  • 17.1.2 North America: materials informatics and AI-assisted cell testing
  • 17.1.3 East Asia: manufacturing and development applications
• 17.2 China
  • 17.2.1 Institutional mapping and 15th Five-Year Plan priorities
  • 17.2.2 Solid-state and sodium-ion innovation policy
  • 17.2.3 Export controls on battery technology
  • 17.2.4 AI adoption by Chinese cell makers
    • 17.2.4.1 Scale of the ecosystem
    • 17.2.4.2 Demand drivers
    • 17.2.4.3 AI use
  • 17.2.5 Materials Genome Engineering
• 17.3 South Korea
  • 17.3.1 K-Battery strategy and next-generation R&D funding
    • 17.3.1.1 Institutions
    • 17.3.1.2 Strategy over time
    • 17.3.1.3 Demand measures
  • 17.3.2 Battery hubs: Pohang and Cheongju–Ochang
    • 17.3.2.1 Current conditions
  • 17.3.3 The "full-stack" AI, energy and battery strategy
  • 17.3.4 AI-based development strategies of Korean cell makers
  • 17.3.5 AI-driven science programmes
• 17.4 Japan
  • 17.4.1 Battery Industry Strategy and Green Transformation targets
    • 17.4.1.1 Progress and setbacks
    • 17.4.1.2 Industry consolidation
  • 17.4.2 NEDO programmes and the Advanced Battery Collaboration
  • 17.4.3 NIMS and AIST data-driven materials platforms
• 17.5 European Union
  • 17.5.1 Batteries Regulation, BATT4EU and Battery 2030+
    • 17.5.1.1 Research programmes
  • 17.5.2 National strategies: Germany, France, Hungary, Poland
    • 17.5.2.1 Europe's position at end-2025
  • 17.5.3 Industrial Accelerator Act and Battery Booster Facility
    • 17.5.3.1 Scale-up policy
    • 17.5.3.2 Implications for AI
• 17.6 United States
  • 17.6.1 DOE, ARPA-E and national laboratory programmes
  • 17.6.2 Policy shift toward stationary storage and defence
    • 17.6.2.1 Capacity build-out
    • 17.6.2.2 Policy shift
    • 17.6.2.3 Implications for AI
  • 17.6.3 The Materials Genome Initiative
• 17.7 United Kingdom
  • 17.7.1 AI strengths
  • 17.7.2 Market drivers
• 17.8 Canada and India
• 17.9 Rest of World: Morocco, Australia and Southeast Asia
• 17.10 Global cell manufacturing capacity

18 REGULATION, STANDARDS AND TECHNOLOGY SOVEREIGNTY

• 18.1 Battery Safety Standards and AI Validation
• 18.2 Battery Passport and Data-Sharing Rules
  • 18.2.1 Scope of the passport
  • 18.2.2 What it means for AI
  • 18.2.3 Wider data-access rules
• 18.3 AI Regulation: EU AI Act, US and China Frameworks
  • 18.3.1 The EU AI Act
  • 18.3.2 Implications for battery AI
  • 18.3.3 United States
  • 18.3.4 China
• 18.4 Functional Safety of AI-Based BMS (ISO 26262, ISO 21448, ISO/PAS 8800)
• 18.5 Standards Development: IEC, SAE, IEEE and National Programmes
• 18.6 PFAS Restrictions and Materials Regulation
• 18.7 Export Controls and Technology Sovereignty

19 AI ADOPTION BY BATTERY MAKERS, EV OEMs AND PLATFORM COMPANIES

• 19.1 Battery Cell and Materials Companies
• 19.2 EV OEMs
• 19.4 Platform Companies and Research Institutions

20 COMPETITIVE LANDSCAPE, INVESTMENT AND OUTLOOK

• 20.1 Competitive Dynamics by Use Case
• 20.2 Types of Players: Start-ups, Software Incumbents, Cell Makers and OEMs
• 20.3 Business Models and Revenue Streams
• 20.4 Strategic Partnerships and Joint Ventures
• 20.5 Investment and Funding
  • 20.5.1 Strategic investors
• 20.6 M&A Landscape
• 20.7 Patent Landscape
  • 20.7.1 Growth
  • 20.7.2 By jurisdiction
• 20.8 Barriers to Adoption
  • 20.8.1 Technical: data scarcity, generalisation and interpretability
  • 20.8.2 Economic: ROI, payback periods and fragmented buyers
  • 20.8.3 Organisational: skills shortages in data science and electrochemistry
• 20.9 Emerging Frontiers 2030–2037
  • 20.9.1 Smart cells with embedded sensing and self-healing functions
  • 20.9.2 Quantum–AI hybrid materials discovery
  • 20.9.3 Agentic AI across the battery lifecycle

21 COMPANY PROFILES (95 company profiles)

22 APPENDIX

• 22.1 Research Methodology
• 22.2 Forecast Assumptions
• 22.3 Glossary of Terms and Abbreviations

23 REFERENCES