The Global Market for Artificial Intelligence (AI) Battery Technology 2027-2037
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:
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:
- 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.
- Analytics and BMS intelligence in the late 2020s.
- AI-native manufacturing and accelerated materials discovery in the early 2030s.
- Autonomous laboratories and fully digital-twin-based development by the mid-2030s.
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 Syst?mes, 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....
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 2030
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 2025
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 2025
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
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 2030
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 2025
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 2025
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
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
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
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