The Global Market for Artificial Intelligence (AI) Battery Technology 2027-2037

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

List of Tables

• Table 1. Quantified Benefits of AI by Development Stage
• Table 2. AI Use-Case Benchmarking: Value, Adoption Barriers and Time to Impact
• Table 3. TRL Assessment: AI Applications Across the Battery Value Chain
• Table 4. Global AI-Driven Battery Technology Market Summary 2027–2037 (US$M, Base Case)
• Table 5. Venture Funding in AI-for-Batteries Companies 2020–2026 (US$M)
• Table 6. Top Ten Strategic Conclusions Mapped to Stakeholder Type
• Table 7. Leading Companies by AI Use Case
• Table 8. Market Definition and Scope: Inclusions and Exclusions
• Table 9. Battery Chemistry Landscape and Relevance of AI
• Table 10. Reported Performance Gains from AI by Application
• Table 11. Nine AI Use-Case Arenas: Scope, Techniques, Customers and Market Size
• Table 12. Machine Learning Approaches and Battery Use Cases
• Table 13. Types of Neural Network and Their Battery Applications
• Table 14. Universal Machine-Learned Interatomic Potentials Used in Battery Research
• Table 15. Model-Based, Data-Driven and Hybrid Approaches Compared
• Table 16. Edge vs. Cloud Deployment Trade-offs for Battery AI
• Table 17. Major Public Battery Datasets and Their Uses
• Table 18. Advanced Battery Sensing Technologies and TRL
• Table 19. BMS IC and Edge-AI Silicon Suppliers
• Table 20. Case Studies of AI-Based Reduction in Material Screening Time
• Table 21. AI-Discovered Electrolyte and Solid-Electrolyte Candidates 2020–2026
• Table 22. Autonomous Battery Laboratories: Operators and Capabilities
• Table 23. Autonomous Battery Laboratories: Operators and Capabilities:
• Table 24. Companies in Materials Informatics for Batteries
• Table 25. AI Materials Discovery Business Models Compared
• Table 26. AI Opportunity Matrix by Battery Chemistry
• Table 27. Emerging Cell Architectures and AI Design Levers
• Table 28. Research Areas in AI-Based Battery Pack Design Optimisation
• Table 29. Battery Design and Simulation Software Vendors
• Table 30. Battery Validation Test Durations and Costs by Application
• Table 31. Data Forms for Cell Modelling
• Table 32. RUL Prediction Accuracy by Method (RMSE, MAPE)
• Table 33. Algorithmic Approaches for Different Testing Modes
• Table 34. Companies in AI for Cell Testing
• Table 35. AI Use Cases by Manufacturing Step
• Table 36. AI Inspection Technologies: Detection Capability and Throughput
• Table 37. Algorithmic Approaches in Manufacturing and Cell Assembly
• Table 38. Smart Battery Manufacturing Players
• Table 39. AI in Battery Manufacturing Market by Solution Type 2027–2037 (US$M)
• Table 40. Conventional vs. AI-Enabled BMS Capabilities
• Table 41. Health Indicators by Operating Environment and Data-Collection Condition
• Table 42. Deep Learning Models for Battery Time-Series Prediction and Anomaly Detection
• Table 43. Reported Safety and Performance Gains from AI-Enabled BMS
• Table 44. Companies in AI for Battery Diagnostics and Management
• Table 45. Business Models in Battery Diagnostics and Analytics
• Table 46. Battery Capacity Served by AI Diagnostics 2027–2037 (GWh)
• Table 47. Battery Diagnostics and BMS Analytics Market Value 2027–2037 (US$M)
• Table 48. Grid-Integration Models for Electric Vehicles: Case Studies
• Table 49. Case Studies of AI-Based EV Charging and Grid-Integration Technologies
• Table 50. Cloud Data Compression and Model Optimisation Case Studies
• Table 51. Digital-Twin-Based SoX Estimation and Cell-Balancing Functions
• Table 52. Comparison of Integrated Battery Digital Twin Platforms
• Table 53. Algorithmic Approaches and Data Inputs/Outputs for Second-Life Assessment
• Table 54. Second-Life Battery Applications: Requirements, Economics and the Role of AI
• Table 55. Companies in AI for Second-Life Battery Assessment
• Table 56. Recycling Techniques Compared, with AI Optimisation Levers
• Table 57. AI Applications by Recycling Process Step
• Table 58. EU Batteries Regulation Recovery and Recycled-Content Targets
• Table 59. Used-EV Battery Health Certification: Approaches and Users
• Table 60. AI Battery Applications in Electric Vehicles by Value-Chain Stage
• Table 61. AI Applications in Stationary Energy Storage by Segment
• Table 62. AI-Driven Battery Technology Market by End Use 2027–2037 (US$M)
• Table 63. Global AI-Driven Battery Technology Market 2027–2037 (US$M)
• Table 64. Market by Use Case 2027–2037 (US$M)
• Table 65. Market by AI Technique 2027–2037 (US$M)
• Table 66. Market by Deployment Mode 2027–2037 (US$M)
• Table 67. Market by Offering 2027–2037 (US$M)
• Table 68. Market by Battery Chemistry 2027–2037 (US$M)
• Table 69. Market by Region 2027–2037 (US$M)
• Table 70. Regional AI Adoption Focus by Use Case
• Table 71. Chinese Government Bodies and Their Role in Battery Policy
• Table 72. South Korean Battery Innovation Programmes and Funding
• Table 73. Comparative Battery Policy Matrix: Eight Economies
• Table 74. Key Battery Safety Standards and AI Relevance
• Table 75. AI Adoption by Battery Cell and Materials Companies
• Table 76. AI Adoption by EV OEMs
• Table 77. AI Platform Companies and Research Institutions in Battery Development
• Table 78.Business Models and Revenue Streams by AI Use Case
• Table 79. Key Partnerships Between Cell Makers, OEMs and AI Vendors 2022–2026
• Table 80. Funding Rounds in AI-for-Batteries Companies 2022–2026
• Table 81. M&A Transactions in AI-Battery Software and Services 2020–2026
• Table 82. Adoption Barriers and Mitigation Strategies
• Table 83. Key Forecast Assumptions by Use Case
• Table 84. Glossary of Terms and Abbreviations

List of Figures

• Figure 1. AI Applications Across the Battery Lifecycle, from Materials to Recycling
• Figure 2. AI Disruption Points in the Battery Supply Chain
• Figure 3. Use-Case Maturity Comparison
• Figure 4. Regional Adoption Focus by AI Use Case
• Figure 5. Global Market Forecast by Use Case 2027–2037 (US$M)
• Figure 6. Three-Wave Adoption Timeline 2027–2037
• Figure 7. Global Li-Ion Battery Demand by Application 2020–2037 (GWh)
• Figure 8. Li-Ion vs. Beyond-Li-Ion Battery Demand 2026–2036 (GWh)
• Figure 9. Li-Ion Pack Price 2013–2037 (US$/kWh)
• Figure 10. Traditional vs. AI-Enabled Battery Development Workflow
• Figure 11. Development-Cost Reduction from AI/ML Across Battery R&D Stages
• Figure 12. AI/ML Technique Taxonomy for Battery Applications
• Figure 13. Data Flow from Cell Sensor to Cloud Analytics Platform
• Figure 14. Traditional vs. AI-Accelerated Materials Development Timeline
• Figure 15. AI-Based Materials R&D Workflow
• Figure 16. High-Throughput Screening and Prediction of Electrode Active Materials
• Figure 17. Data-Driven Prediction of Li-Ion Electrolyte Solvent Stability
• Figure 18. Microsoft/PNNL AI Screening Funnel for Solid-Electrolyte Candidates
• Figure 19. Closed-Loop Autonomous Laboratory Workflow
• Figure 20. Materials Informatics for Batteries Market 2027–2037 (US$M)
• Figure 21. Li-Ion Energy-Density Roadmap to 350+ Wh/kg
• Figure 22. Solid-State and Semi-Solid-State Battery Demand 2026–2036 (GWh)
• Figure 23. Sodium-Ion Battery Demand 2026–2036 (GWh)
• Figure 24. BEV Cell Energy Density 2015–2037 (Wh/kg and Wh/L)
• Figure 25. Workflow of AI-Based Battery Pack Design Structure Optimisation
• Figure 26. AI Cell and Pack Design Software Market 2027–2037 (US$M)
• Figure 27. Lithium-Ion Capacity Fade Mechanisms
• Figure 28. RUL Prediction Methods Taxonomy
• Figure 29. AI Cell Testing Market 2027–2037 (US$M)
• Figure 30. Li-Ion Cell Manufacturing Process Flow and AI Intervention Points
• Figure 31. Gigafactory Yield Curve With and Without AI Process Control
• Figure 32. AI in Battery Manufacturing Market by Region 2027–2037 (US$M)
• Figure 33. Next-Generation BMS Architecture: Embedded, Edge and Cloud Layers
• Figure 34. Advanced Battery Pack Sensor Market 2026–2036 (US$M)
• Figure 35. On-Edge vs. Cloud Battery AI Revenue 2027–2037 (US$M)
• Figure 36. EIS Image-Based Classification of ESS Operating Environments
• Figure 37. Operational Pattern Image Generation for Large-Scale EV Data
• Figure 38. Data-Centre Battery Demand 2026–2036 (GWh)
• Figure 39. Components of a Digital Twin
• Figure 40. Hierarchical Structure of the Battery Digital Twin: Material to System
• Figure 41. Digital Twin and Cloud BMS: Virtual Battery Model Architecture
• Figure 42. Battery Digital Twin Market 2027–2037 (US$M)
• Figure 43. End-of-Life EV Battery Volumes 2027–2037 (GWh)
• Figure 44. AI-Based Rapid SOH Diagnosis Workflow for Used Batteries
• Figure 45. RUL-Based Regrouping of Used Batteries
• Figure 46. AI Second-Life Assessment Market 2027–2037 (US$M)
• Figure 47. AI in Battery Recycling Market 2027–2037 (US$M)
• Figure 48. Li-Ion Battery Pack Demand for xEVs 2026–2036 (GWh)
• Figure 49. Battery Energy-Density Requirements for Humanoid Robots, AMRs and Drones
• Figure 50. End-Use Market Share 2027 vs. 2037
• Figure 51. Global Market 2027–2037, Base Case (US$M)
• Figure 52. Battery Capacity Served by AI Diagnostics 2027–2037 (GWh)
• Figure 53. Regional Market Share 2027 vs. 2037
• Figure 54. Bear, Base and Bull Scenarios 2027–2037 (US$M)
• Figure 55. Value Pool by Stack Layer, 2037
• Figure 56. Global Battery Cell Capacity by Region 2025–2037 (GWh)
• Figure 57. Battery and AI Regulatory Timeline 2023–2037
• Figure 58. Market Map: AI-Driven Battery Technology Companies by Use Case
• Figure 59. Funding by Use Case and Region 2020–2026 (US$M)
• Figure 60. AI-Battery Patent Filings by Country 2015–2025