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

1             EXECUTIVE SUMMARY            37

• 1.1        Scope of This Report 37
• 1.2        AI Growth Drivers in the Battery Industry     37
  • 1.2.1    Net-zero targets and the electrification push           37
  • 1.2.2    Energy density, cost and critical-material constraints       37
  • 1.2.3    Regulatory pull: the EU Battery Passport    38
  • 1.2.4    Regional demand patterns: Europe, North America and East Asia            38
• 1.3        Challenges Facing the Rechargeable Battery Industry       38
• 1.4        How AI Applies Across the Battery Lifecycle              39
• 1.5        Five Benefits of AI: Development Speed, Cost, Performance, Sustainability and Market Responsiveness          40
• 1.6        Paradigm Shifts: From Experiment-Centred to Data-Centred Development        41
• 1.7        AI Disruption of the Battery Supply Chain  42
• 1.8        Use-Case Benchmarking and Maturity         43
• 1.9        AI in Batteries for Electric Vehicles  46
• 1.10     AI in Batteries for Stationary Storage and Data Centres     46
• 1.11     Interest by Region       47
• 1.12     Market Findings            48
  • 1.12.1 Battery diagnostics by capacity served and market value                49
  • 1.12.2 On-edge AI: diagnostics and performance enhancement                49
  • 1.12.3 Materials informatics, cell testing and second-life assessment  50
• 1.13     Three-Wave Adoption Framework    50
  • 1.13.1 Wave 1: Analytics and BMS intelligence (2027–2030)        50
  • 1.13.2 Wave 2: AI-native manufacturing and accelerated materials (2030–2034)          50
  • 1.13.3 Wave 3: Autonomous discovery and fully digital-twin-based development (2034–2037)           50
• 1.14     The Investment Surge               51
• 1.15     The Past Twelve Months         52
  • 1.15.1 Generative AI and foundation models enter battery R&D 52
  • 1.15.2 Leading cell makers launch company-wide AI transformation     52
  • 1.15.3 Battery passports approach enforcement amid calls for phasing              53
  • 1.15.4 Export controls reshape access to advanced battery technology               53
  • 1.15.5 Stationary storage and AI data centres become the growth engine           54
  • 1.15.6 Robotics, drones and aviation emerge as new battery markets   54
  • 1.15.7 EV demand slowdown sharpens focus on cost and productivity 54
• 1.16     Strategic Imperatives for Cell Makers, OEMs, Utilities and Software Vendors    55
• 1.17     Major Market Players 55

2             INTRODUCTION: AI AND THE BATTERY VALUE CHAIN        57

• 2.1        Defining AI-Driven Battery Technology          57
• 2.2        Machine Learning vs. Artificial Intelligence                58
• 2.3        The Battery Market Context  58
  • 2.3.1    Global Li-ion demand by application             58
  • 2.3.2    Li-ion vs. beyond-Li-ion demand      59
  • 2.3.3    Chemistry mix: NMC, LFP/LMFP, sodium-ion, solid-state, silicon anode               60
  • 2.3.4    Cost trajectory and pack-price outlook        62
• 2.4        Traditional Battery Development: Sequential, Experiment-Centred and Slow   63
• 2.5        What Has Changed: Why AI Is Deployable Now     64
  • 2.5.1    Connected-fleet data volumes          64
  • 2.5.2    Cloud and edge compute cost curves           64
  • 2.5.3    Open battery datasets and benchmarks     64
  • 2.5.4    Foundation models for chemistry and materials   64
• 2.6        The Economic Case for AI in Batteries           65
  • 2.6.1    R&D cost and time savings   65
  • 2.6.2    Manufacturing yield and defect reduction  65
  • 2.6.3    Lifetime extension and warranty cost reduction     66
• 2.7        Market Architecture: Nine AI Use-Case Arenas      67

3             MACHINE LEARNING FUNDAMENTALS FOR BATTERY APPLICATIONS    70

• 3.1        AI as a Moving Target: Definitions and Scope            70
• 3.2        The Importance of Data: Quality, Dimensionality and Standardised Structures                70
• 3.3        Learning Paradigms  71
  • 3.3.1    Supervised learning   71
  • 3.3.2    Unsupervised learning            71
  • 3.3.3    Problem classes: regression, classification, clustering and anomaly detection               72
  • 3.3.4    Reinforcement learning and the exploration–exploitation trade-off          72
  • 3.3.5    Semi-supervised and active learning             72
• 3.4        Classical Algorithms 73
  • 3.4.1    Support vector and relevance vector machines     73
  • 3.4.2    Decision trees, random forests and gradient boosting       73
  • 3.4.3    k-nearest neighbour and k-means clustering           74
  • 3.4.4    Principal component analysis            74
  • 3.4.5    Gaussian process regression              74
• 3.5        Neural Networks and Deep Learning             74
  • 3.5.1    The artificial neuron and the training process          74
  • 3.5.2    Feedforward, recurrent (LSTM, GRU) and convolutional networks             75
  • 3.5.3    Generative adversarial networks and transformers              75
  • 3.5.4    Graph neural networks for materials              75
  • 3.5.5    Universal machine-learned interatomic potentials              77
• 3.6        Natural Language Processing and Large Language Models            78
• 3.7        Physics-Informed and Hybrid Models           79
  • 3.7.1    Electrochemical (P2D, SPM) and equivalent circuit models           79
  • 3.7.2    Kalman and particle filtering                79
  • 3.7.3    Physics-informed neural networks  80
• 3.8        Generative AI, Foundation Models and Agentic AI 81
• 3.9        Transfer, Federated and Domain-Adaptive Learning            82
• 3.10     Explainability, Uncertainty Quantification, Under-Fitting and Over-Fitting            82
  • 3.10.1 Explainability 82
  • 3.10.2 Uncertainty quantification    82
  • 3.10.3 Fitting errors   82
• 3.11     The Inefficiency of Overuse: When Not to Apply Machine Learning           83
• 3.12     Edge vs. Cloud Deployment 83

4             BATTERY DATA INFRASTRUCTURE, SENSING AND COMPUTE      85

• 4.1        The Battery Data Problem      85
  • 4.1.1    Data scarcity, cost of cycling data and proprietary silos   85
    • 4.1.1.1 Laboratory data costs              85
    • 4.1.1.2 Proprietary silos           85
  • 4.1.2    Open datasets and benchmarks      85
• 4.2        The Data Pipeline: From BMS to AI   87
• 4.3        Advanced Sensing Hardware              88
  • 4.3.1    Voltage, current and temperature sensing 88
  • 4.3.2    On-board electrochemical impedance spectroscopy (EIS)            89
  • 4.3.3    Embedded fibre-optic, pressure and gas sensors 89
  • 4.3.4    Wireless BMS and chip-on-cell sensing       89
    • 4.3.4.1 Wireless BMS 89
    • 4.3.4.2 Chip-on-cell sensing 90
• 4.4        BMS Silicon and Edge-AI Processors              91
  • 4.4.1    Battery-monitoring ICs and analogue front ends   91
    • 4.4.1.1 Measurement accuracy          91
    • 4.4.1.2 Synchronisation           91
    • 4.4.1.3 Sampling rates and data resolution 91
    • 4.4.1.4 Cell balancing               91
    • 4.4.1.5 Communication and isolation            92
    • 4.4.1.6 Functional safety and diagnostics   92
    • 4.4.1.7 Integrated sensing beyond voltage and temperature           92
    • 4.4.1.8 New application segments   92
    • 4.4.1.9 Market structure          93
  • 4.4.2    Microcontrollers and neural processors for on-edge AI     93
  • 4.4.3    Battery-management ICs for AI-server backup units (BBUs)         93
• 4.5        Cloud Platforms and Connectivity   94
  • 4.5.1    Telematics, OTA updates and vehicle data access               94
  • 4.5.2    Battery-in-the-cloud architectures  94
• 4.6        Compute for Materials Discovery     95
  • 4.6.1    HPC and GPU clusters for DFT and molecular dynamics 95
  • 4.6.2    Machine-learned interatomic potentials     95
  • 4.6.3    Quantum computing for battery chemistry                95
• 4.7        Data Standards and Interoperability               95
  • 4.7.1    Battery passport data models            95
  • 4.7.2    OPC UA and IT/OT convergence in gigafactories    96
• 4.8        Cybersecurity and Data Governance             96

5             AI-DRIVEN BATTERY MATERIALS DISCOVERY (MATERIALS INFORMATICS)          98

• 5.1        Why AI for Battery Materials 98
  • 5.1.1    The attraction of AI: navigating a vast design space             98
  • 5.1.2    Traditional material discovery and the limits of DFT             98
  • 5.1.3    Materials informatics               99
  • 5.1.4    The workflow of AI-based research and development        99
• 5.2        Property Prediction and Material Grouping 101
  • 5.2.1    Datasets and descriptors      101
    • 5.2.1.1 Unsupervised grouping           102
  • 5.2.2    DFT plus AI interpolation: lithium diffusion, voltage window and stability            102
• 5.3        Inverting the Process: Inverse Design            102
  • 5.3.1    Informed selection vs. novel material formulation                102
  • 5.3.2    Virtual screening         103
  • 5.3.3    De novo and generative design          104
    • 5.3.3.1 Large-scale generative results            104
  • 5.3.4    Large language model interfaces and literature mining     104
• 5.4        Reducing Material Screening Time  105
  • 5.4.1    The case for AI in material screening              105
  • 5.4.2    Case studies of AI-based screening-time reduction            105
• 5.5        Cathode Materials      106
  • 5.5.1    High-nickel and cobalt-free layered oxides                106
  • 5.5.2    LFP and LMFP optimisation 106
  • 5.5.3    Disordered rock-salt and lithium-rich cathodes     106
• 5.6        Anode Materials           107
  • 5.6.1    Graphite and silicon-carbon composites   107
  • 5.6.2    Lithium-metal anodes and dendrite suppression 107
  • 5.6.3    Hard carbon for sodium-ion 107
• 5.7        Liquid Electrolytes      108
  • 5.7.1    Formulation and additive design      108
  • 5.7.2    Solvent stability prediction   108
• 5.8        Solid Electrolytes         109
  • 5.8.1    Sulfide, oxide, polymer and halide systems              109
  • 5.8.2    Case study: Microsoft and PNNL AI-screened solid electrolytes 110
• 5.9        AI-Based Optimisation of Cell Material Combinations      111
• 5.10     Autonomous (Self-Driving) Laboratories     111
  • 5.10.1 Robotic synthesis and characterisation       111
  • 5.10.2 Closed-loop active learning 112
  • 5.10.3 Agentic AI lab assistants       113
  • 5.10.4 European initiatives: BIG-MAP and Battery 2030+ 114
  • 5.10.5 Commercial self-driving laboratories            115
  • 5.10.6 Academic milestones and mobile robots   115
  • 5.10.7 Big-technology autonomous laboratories  115
• 5.11     Critical-Mineral Substitution and Supply-Risk Mitigation 116
• 5.12     Players in Materials Informatics for Batteries            116
• 5.13     Business Analysis      118
  • 5.13.1 Business models and partnerships 118
  • 5.13.2 Existing client–supplier relationships            118
  • 5.13.3 Differentiation               118
  • 5.13.4 Challenges: data quality, interpretability, compute and qualification      119
    • 5.13.4.1            Data quality and availability 119
    • 5.13.4.2            Gap between computation and experiment              119
    • 5.13.4.3            Interpretability and trust         119
    • 5.13.4.4            Computational resources      120
    • 5.13.4.5            Model generalisation 120
    • 5.13.4.6            Qualification and time to market      120
    • 5.13.4.7            Intellectual property and data ownership   120
    • 5.13.4.8            Regulatory and safety requirements               121
    • 5.13.4.9            Skills    121
  • 5.13.5 Pricing of materials informatics platforms 121
  • 5.13.6 Risks for SaaS business models        122
  • 5.13.7 Barriers to profitability             122
  • 5.13.8 Big-technology platforms and competition for dedicated players              123
  • 5.13.9 In-house development and consolidation  123
  • 5.13.10              Outlook             123
• 5.14     Market Forecast: Materials Informatics for Batteries 2027–2037 123

6             AI APPLICATIONS BY BATTERY CHEMISTRY               125

• 6.1        Advanced Li-Ion: High-Nickel, LFP/LMFP and the Path to 350 Wh/kg       125
• 6.2        Silicon and Silicon-Carbon Anodes 127
• 6.3        Lithium-Metal and Anode-Less Cells             127
• 6.4        Solid-State and Semi-Solid-State Batteries               128
  • 6.4.1    Commercial status    128
  • 6.4.2    Demand outlook         128
• 6.5        Lithium-Sulfur Batteries          130
• 6.6        Sodium-Ion Batteries                130
• 6.6.1    Commercial status    130
• 6.6.2    Demand outlook         131
• 6.7        LTO and Niobate Batteries     132
• 6.8        Zinc-Based and Aluminium-Ion Batteries    132
• 6.9        Redox Flow Batteries 132
• 6.10     Structural, Flexible, Printed and Other Emerging Formats               133
• 6.11     PFAS-Free Binders and Additives: Regulation-Driven Reformulation        133
• 6.12     AI Opportunity by Chemistry               134

7             AI IN CELL AND PACK DESIGN           136

• 7.1        From Trial-and-Error to Simulation-Led Design       136
• 7.2        Multi-Variable Cell Design Optimisation      136
  • 7.2.1    Electrode thickness, particle size distribution, binder content and electrolyte ratio      136
  • 7.2.2    Genetic algorithms combined with reinforcement learning            137
  • 7.2.3    Coupling P2D physics models with AI to test design scenarios virtually 137
• 7.3        Electrode and Microstructure Design            138
  • 7.3.1    Image-based microstructure reconstruction            138
  • 7.3.2    Porosity, tortuosity and loading optimisation           138
  • 7.3.3    Thick-format electrodes          138
• 7.4        Cell Formats and Architectures         139
  • 7.4.1    Larger cell formats and tabless designs (Tesla 4680)          139
  • 7.4.2    Bipolar and dual-electrolyte architectures 139
    • 7.4.2.1 How bipolar cells work            139
    • 7.4.2.2 Benefits             139
    • 7.4.2.3 Challenges      140
    • 7.4.2.4 Commercial status    140
    • 7.4.2.5 How AI helps with bipolar designs   140
    • 7.4.2.6 Dual-electrolyte architectures           140
    • 7.4.2.7 Variants             141
    • 7.4.2.8 Dual-electrolyte challenges 141
    • 7.4.2.9 How AI helps with dual-electrolyte designs               141
  • 7.4.3    Commercial examples            141
    • 7.4.3.1 Large-format tabless cylindrical cells            141
    • 7.4.3.2 Long prismatic "blade" cells                142
    • 7.4.3.3 High-integration cell-to-pack systems          142
    • 7.4.3.4 Multi-layer electrodes              142
    • 7.4.3.5 Three-dimensional current collectors           142
    • 7.4.3.6 Thick electrodes and simplified cell designs            142
    • 7.4.3.7 Dry-electrode processing      142
    • 7.4.3.8 Bipolar designs            142
    • 7.4.3.9 The common thread  143
• 7.5        Cell Performance and Energy Density           143
• 7.6        AI-Based Battery Pack Design Structure Optimisation      144
  • 7.6.1    Why pack structure optimisation matters   144
  • 7.6.2    Key considerations in optimal pack design                145
  • 7.6.3    AI-based optimisation workflow       145
• 7.7        Research on AI-based pack design optimisation   146
• 7.8        Cell-to-Pack, Cell-to-Chassis and Hybrid Packs   148
  • 7.8.1    Cell-to-pack and cell-to-chassis      148
  • 7.8.2    Hybrid battery packs 148
• 7.9        Generative Design, Lightweighting and Thermal System Design  149
• 7.10     Design for Recycling and Sustainability       149
• 7.11     Battery Design and Simulation Software Landscape          150
• 7.12     Market Forecast: AI Cell and Pack Design 2027–2037       151

8             AI IN CELL TESTING, MODELLING AND LIFETIME PREDICTION   152

• 8.1        Traditional Cell Testing: Shortcomings and Challenges     152
• 8.2        AI for High-Throughput Automated Testing 153
• 8.3        Data Forms for Cell Modelling            153
• 8.4        AI for Design of Experiments and Anomalous Data Identification               154
• 8.5        Lifetime Modelling and Early-Life Prediction             154
• 8.6        Degradation Modelling            155
  • 8.6.1    SEI growth, lithium plating and active-material loss            155
  • 8.6.2    Incremental capacity, differential voltage and EIS health indicators         156
• 8.7        Remaining Useful Life (RUL) Prediction Methods   157
  • 8.7.1    Model-based approaches     157
  • 8.7.2    Stochastic process methods: Wiener, Gamma and Markov           157
  • 8.7.3    Classical machine learning approaches     158
  • 8.7.4    Deep learning approaches   158
  • 8.7.5    Hybrid fusion approaches     158
• 8.8        Temperature and Pressure Simulation          160
• 8.9        Data-Driven Cell Architecture Optimisation             161
• 8.10     AI Classification of Cell Cross-Section Defect Types          161
• 8.11     Algorithmic Approaches by Testing Mode   162
• 8.12     Test Laboratory Automation and Certification Implications            163
• 8.13     Players in AI for Cell Testing  164
• 8.14     Business Analysis      165
  • 8.14.1 Typical business models        165
  • 8.14.2 Differentiation               165
  • 8.14.3 Challenges      165
    • 8.14.3.1            Trust and acceptance               165
    • 8.14.3.2            Transferability across chemistries and protocols  166
    • 8.14.3.3            Capturing rare and late-emerging failure modes    166
    • 8.14.3.4            Data access and confidentiality        166
    • 8.14.3.5            Data quality and consistency              166
    • 8.14.3.6            Integration with laboratory infrastructure   166
    • 8.14.3.7            Regulatory and certification barriers              167
    • 8.14.3.8            Demonstrating commercial value    167
  • 8.14.4 Outlook             167
    • 8.14.4.1            Near term (2027–2030)           167
    • 8.14.4.2            Medium term (2030–2034)   167
    • 8.14.4.3            Longer term (2034–2037)      167
    • 8.14.4.4            Structural change       167
• 8.15     Market Forecast: AI Cell Testing 2027–2037             168

9             AI IN CELL ASSEMBLY AND MANUFACTURING       169

• 9.1        Overview of the Traditional Manufacturing Process             169
• 9.2        The Gigafactory Ramp-Up Problem 170
  • 9.2.1    Scrap rates during ramp-up 170
  • 9.2.2    The "valley of death" between start of production and target yield             170
• 9.3        Data Quality and Data Acquisition Challenges in Industrial Settings        171
• 9.4        AI Use Cases by Process Step             172
• 9.5        Vision AI for Defect Detection and Quality Control               173
  • 9.5.1    Coating non-uniformity, electrode surface defects and stacking misalignment               173
  • 9.5.2    X-ray and CT inspection          173
  • 9.5.3    Ultrasound and acoustic inspection              174
• 9.6        Streaming Process Analytics and Preventive Control          175
  • 9.6.1    Electrolyte injection volume, calendering pressure and drying conditions           175
  • 9.6.2    Predicting defects before they occur              175
• 9.7        Electrode Coating and Drying Simulation   176
• 9.8        Formation and Ageing: AI-Shortened Protocols      176
• 9.9        Algorithmic Approaches in Manufacturing and Cell Assembly     177
• 9.10     Factory Digital Twins, Virtual Commissioning and FAT/SAT            177
• 9.11     Predictive Maintenance, MES and IT/OT Integration             178
  • 9.11.1 Predictive maintenance         178
  • 9.11.2 Manufacturing execution systems   178
• 9.12     Robotics and Physical AI in Cell and Pack Assembly          178
• 9.13     Smart Battery Manufacturing Players            179
• 9.14     Business Analysis      180
  • 9.14.1 Types of smart manufacturing players          180
    • 9.14.1.1            Vertically integrated cell makers       180
    • 9.14.1.2            Industrial software and automation providers         180
    • 9.14.1.3            Equipment makers     181
    • 9.14.1.4            Inspection-technology providers      181
    • 9.14.1.5            Specialist AI analytics companies   181
    • 9.14.1.6            Battery data platforms             181
    • 9.14.1.7            System integrators and engineering firms   181
    • 9.14.1.8            Research institutions               181
  • 9.14.2 Challenges      182
    • 9.14.2.1            Upfront investment and uncertain payback              182
    • 9.14.2.2            Integration with heterogeneous equipment               182
    • 9.14.2.3            Data quality and genealogy  182
    • 9.14.2.4            Model maintenance and drift              182
    • 9.14.2.5            Skills shortages            183
    • 9.14.2.6            Organisational and cultural barriers               183
    • 9.14.2.7            Cybersecurity 183
    • 9.14.2.8            Data ownership and vendor lock-in 183
    • 9.14.2.9            Scaling beyond pilots               183
  • 9.14.3 Outlook             183
    • 9.14.3.1            Large cell makers        183
    • 9.14.3.2            New entrants and start-ups 184
    • 9.14.3.3            How solutions will adapt       184
    • 9.14.3.4            Policy support               184
    • 9.14.3.5            Next-generation chemistries               184
    • 9.14.3.6            Overall outlook             185
• 9.15     Market Forecast: AI in Battery Manufacturing 2027–2037 185

10          NEXT-GENERATION BMS AND BATTERY ANALYTICS            187

• 10.1     Battery Management Systems: Purpose and Multi-Cell Pack Management        187
• 10.2     Limitations of Existing BMS and the Case for Next-Generation BMS         187
• 10.3     Next-Generation BMS Integrated with Big Data and AI        188
• 10.4     Data Pre-Processing for AI Model Development     190
  • 10.4.1 Pre-processing procedures  190
  • 10.4.2 Why health indicators must reflect operating environment and data-collection conditions     190
  • 10.4.3 Methods for extracting health indicators     190
  • 10.4.4 Limitations and worked examples   191
• 10.5     Data Structures and Forms for Diagnostics              192
• 10.6     State Estimation: SoC, SoH, SoP, SoE and Internal Temperature 193
• 10.7     Fault Detection and Anomaly Diagnosis     193
  • 10.7.1 Selecting deep learning models for battery time-series prediction            193
  • 10.7.2 Selecting deep learning models for anomaly detection and fault diagnosis       194
  • 10.7.3 Case study: data patterning and anomaly diagnosis for safe operation  194
• 10.8     Prescriptive AI: From Prediction to Recommended Action              195
• 10.9     AI-Optimised Charging: Adaptive and Degradation-Aware Protocols       196
• 10.10  Thermal Runaway Early Detection and Advanced Pack Sensors 196
  • 10.10.1              Advanced sensor technologies          197
  • 10.10.2              Sensor market outlook            197
  • 10.10.3              Integration and the path to predictive maintenance            198
• 10.11  Wireless BMS and Remote Monitoring          198
• 10.12  On-Edge AI       199
  • 10.12.1              Case study: real-time lifetime prediction on embedded Linux     199
  • 10.12.2              Samsung Galaxy S25 battery AI         199
  • 10.12.3              Eatron and Syntiant   199
  • 10.12.4              LG Energy Solution and Qualcomm                199
  • 10.12.5              Tesla BMS: optimisation over a journey        199
• 10.13  Cloud Battery Analytics: Fleet, Warranty, Residual Value and Insurance               200
• 10.14  The Battery Passport 200
• 10.15  Players in AI for Battery Diagnostics and Management      201
• 10.16  Business Analysis      202
  • 10.16.1              Business models         202
    • 10.16.1.1         Software as a service (SaaS)               202
    • 10.16.1.2         Embedded software licences              202
    • 10.16.1.3         Certification and reporting services                203
    • 10.16.1.4         Outcome-based models        203
    • 10.16.1.5         Bundled hardware and software       204
    • 10.16.1.6         Hybrid and platform models                204
  • 10.16.2              Differentiation               205
    • 10.16.2.1         Model accuracy and validation against real field outcomes           205
    • 10.16.2.2         Scale of data  205
    • 10.16.2.3         Physics-informed approaches           205
    • 10.16.2.4         Integration       206
  • 10.16.3              Challenges      207
    • 10.16.3.1         Data access    207
    • 10.16.3.2         Edge constraints         207
    • 10.16.3.3         Certification    207
    • 10.16.3.4         Price pressure               208
    • 10.16.3.5         Proving value  208
  • 10.16.4              Outlook             209
    • 10.16.4.1         Expansion across mobility    209
    • 10.16.4.2         Convergence of analytics and BMS 209
    • 10.16.4.3         Expansion across the lifecycle           210
    • 10.16.4.4         Global diffusion           210
    • 10.16.4.5         Market evolution          210
• 10.17  Market Forecast: Battery Diagnostics and BMS Analytics 2027–2037     210

11          AI FOR BATTERY OPERATION IN EVs, ENERGY STORAGE AND DATA CENTRES  213

• 11.1     AI for EV Operation Within the Power Grid  213
  • 11.1.1 Why grid-aware EV operation needs AI         213
  • 11.1.2 Case studies  213
    • 11.1.2.1            OEM joint platforms  214
    • 11.1.2.2            Energy-retailer tariffs with automated charging      214
    • 11.1.2.3            Utility managed-charging programmes       214
    • 11.1.2.4            Vehicle-to-grid services          214
    • 11.1.2.5            Large-scale pilots in China   214
• 11.2     AI-Based Charging for Optimal EV Battery Operation          216
  • 11.2.1 Why AI-based charging is needed     216
  • 11.2.2 Case studies  216
    • 11.2.2.1            Journey-level charging optimisation               216
    • 11.2.2.2            Ultra-high-power charging    216
    • 11.2.2.3            Battery swapping at scale     217
    • 11.2.2.4            Battery-as-a-service in emerging markets  217
    • 11.2.2.5            Adaptive charging software  217
    • 11.2.2.6            Depot charging for commercial fleets           217
    • 11.2.2.7            Research on AI-optimised fast charging      218
• 11.3     Smart Charging and Vehicle-to-Grid Platforms       219
  • 11.3.1 Smart charging and V1G         219
  • 11.3.2 Bidirectional charging              219
    • 11.3.2.1            The role of AI   219
  • 11.3.3 Degradation cost         219
  • 11.3.4 Scaling               219
• 11.4     AI-Based Environmental Control for Energy Storage Systems       220
  • 11.4.1 Why environmental control must reflect battery ageing characteristics 220
  • 11.4.2 Limitations of existing control strategies     220
  • 11.4.3 EIS image-based classification of operating environments             220
  • 11.4.4 Designing AI-based control strategies for optimal ESS operation                221
• 11.5     BESS Dispatch, Energy Trading and Augmentation Planning          222
  • 11.5.1 The economics of storage     222
  • 11.5.2 AI dispatch and trading platforms    222
  • 11.5.3 Augmentation planning          222
  • 11.5.4 Long-duration storage             222
• 11.6     Big Data Management in Cloud Servers       223
  • 11.6.1 Why cloud data management matters as data volumes grow       223
  • 11.6.2 Operational pattern images for large-scale EV data             223
  • 11.6.3 Data compression for storage efficiency     224
  • 11.6.4 Optimising lifetime prediction models in the cloud              224
• 11.7     Battery Storage for Data Centres, Commercial and Industrial Sites          225
  • 11.7.1 AI data centres as a driver of battery demand          225
    • 11.7.1.1            Demand outlook         225
  • 11.7.2 Battery backup units for AI servers  226
• 11.8     Battery Swapping, Battery-as-a-Service and New Business Models        227

12          BATTERY DIGITAL TWINS        228

• 12.1     Digital Twin Concepts and Technologies     228
  • 12.1.1 Concept and expected benefits        228
  • 12.1.2 Components of a digital twin              228
  • 12.1.3 Implementation and use        229
  • 12.1.4 Key enabling technologies    230
  • 12.1.5 Optimising the digital twin    230
• 12.2     Digital Twins in Battery Development             230
  • 12.2.1 Applications in batteries         230
  • 12.2.2 Hierarchical structure of the battery digital twin    231
  • 12.2.3 Vision for the battery digital twin      231
  • 12.2.4 Battery modelling with digital twins 232
  • 12.2.5 Challenges in applying digital twins to batteries     232
    • 12.2.5.1            Limited observability 232
    • 12.2.5.2            Parameterisation        232
    • 12.2.5.3            Model fidelity versus computational cost   233
    • 12.2.5.4            Data continuity across the lifecycle                233
    • 12.2.5.5            Validation         233
    • 12.2.5.6            Model drift and maintenance              233
    • 12.2.5.7            Integration and interoperability          233
    • 12.2.5.8            Uncertainty quantification    234
    • 12.2.5.9            Cost and business case         234
    • 12.2.5.10         Data ownership and privacy 234
    • 12.2.5.11         Security              234
    • 12.2.5.12         Skills    234
• 12.3     Digital-Twin Functions             234
  • 12.3.1 SoX estimation and cell balancing  234
  • 12.3.2 Advanced fault diagnosis and RUL estimation        235
• 12.4     Extending Across the Lifecycle: Manufacturing, Thermal Management, Battery Passport and V2G                236
• 12.5     Battery Digital Twin Platforms             236
  • 12.5.1 Building a battery digital twin platform         236
  • 12.5.2 Comparison of integrated platforms              236
• 12.6     Digital Twins and Cloud BMS               237
  • 12.6.1 Trends in digital-twin-based state estimation          237
  • 12.6.2 The virtual battery model       238
  • 12.6.3 Digital twin BMS case studies             238
• 12.7     Market Forecast: Battery Digital Twins 2027–2037               239

13          SECOND-LIFE ASSESSMENT AND BATTERY REUSE             241

• 13.1     Second-Life Batteries Overview        241
  • 13.1.1 Determining the second-life stream               241
  • 13.1.2 End-of-life volumes   241
• 13.2     Safety Concerns, Regulation and the Battery Passport     242
  • 13.2.1 Safety risks      242
  • 13.2.2 Regulation and standards     243
  • 13.2.3 The battery passport 243
• 13.3     The Role of AI 243
• 13.4     AI-Based Rapid Diagnostics to Cut Reuse Costs   244
  • 13.4.1 Why rapid diagnostics are needed   244
  • 13.4.2 Key considerations when receiving used batteries                244
  • 13.4.3 Rapid SOH diagnosis using one-hot encoding         245
  • 13.4.4 Improving rapid SOH diagnosis with AdaBoost       245
  • 13.4.5 Research on AI-based rapid diagnostic technologies         245
• 13.5     AI-Based Regrouping for Battery Reuse        246
  • 13.5.1 Limitations of existing reuse processes       246
  • 13.5.2 Removing defective cells using RLS-deviation fault diagnosis     246
  • 13.5.3 RUL-based regrouping algorithms   246
• 13.6     Second-Life Applications and Economics 247
  • 13.6.1 Economics      249
  • 13.6.2 AI's contribution          249
• 13.7     Players in AI for Second-Life Assessment   249
• 13.8     Business Analysis      250
  • 13.8.1 Revenue streams        250
  • 13.8.2 Types of players            251
    • 13.8.2.1            Diagnostics specialists           251
    • 13.8.2.2            Marketplace operators            251
    • 13.8.2.3            Second-life system integrators          251
    • 13.8.2.4            OEMs and cell makers             251
    • 13.8.2.5            Recyclers          251
    • 13.8.2.6            Energy companies and utilities          252
    • 13.8.2.7            Insurers, financiers and certifiers     252
  • 13.8.3 Differentiation               252
    • 13.8.3.1            Speed and cost of assessment         252
    • 13.8.3.2            Accuracy and certified reliability       252
    • 13.8.3.3            Breadth of coverage  252
    • 13.8.3.4            Access to first-life data           252
    • 13.8.3.5            Integration across the value chain   252
    • 13.8.3.6            System design and operating capability      253
    • 13.8.3.7            Warranty strength       253
    • 13.8.3.8            Regulatory compliance and documentation             253
  • 13.8.4 Challenges      253
    • 13.8.4.1            Access to battery history       253
    • 13.8.4.2            Heterogeneity of batteries     253
    • 13.8.4.3            Economics under falling new-battery prices             253
    • 13.8.4.4            Competition with recycling   254
    • 13.8.4.5            Safety and liability      254
    • 13.8.4.6            Regulation and certification 254
    • 13.8.4.7            Logistics and scale    254
    • 13.8.4.8            Warranty and performance risk         254
    • 13.8.4.9            Access to finance       254
  • 13.8.5 Outlook             254
    • 13.8.5.1            Volume growth after 2030     255
    • 13.8.5.2            Convergence of first-life and second-life analytics               255
    • 13.8.5.3            Standardisation and certification     255
    • 13.8.5.4            Evolving business models     255
• 13.9     Market Forecast: AI Second-Life Assessment 2027–2037               256

14          AI IN BATTERY RECYCLING   257

• 14.1     Limitations of Existing Recycling Processes and the Case for AI  257
• 14.2     Recycling Technologies           258
  • 14.2.1 Hydrometallurgy          258
  • 14.2.2 Pyrometallurgy              258
  • 14.2.3 Direct recycling             258
  • 14.2.4 Other methods             258
• 14.3     AI Across the Recycling Process        259
  • 14.3.1 Sorting and chemistry identification              259
  • 14.3.2 Robotic and vision-guided disassembly      260
  • 14.3.3 Black-mass characterisation              260
  • 14.3.4 Hydrometallurgical and direct-recycling process control 260
• 14.4     Recycling Specific Components and Beyond-Li-Ion Chemistries               262
  • 14.4.1 Component recovery                262
  • 14.4.2 Beyond-lithium-ion chemistries        262
• 14.5     Traceability, Battery Passports and EU Targets        262
• 14.6     Recycling Players Using AI     263
  • 14.6.1 Chinese players            264
  • 14.6.2 Technology providers                264
• 14.7     Market Forecast: AI in Battery Recycling 2027–2037           264

15          END-USE MARKETS   266

• 15.1     Electric Vehicles          266
  • 15.1.1 Sales in 2025 266
  • 15.1.2 Early 2026 and outlook            266
  • 15.1.3 Battery demand           266
  • 15.1.4 Passenger vehicles     267
    • 15.1.4.1            Development 267
    • 15.1.4.2            Manufacturing              267
    • 15.1.4.3            Operation         267
    • 15.1.4.4            Residual value and warranty                268
    • 15.1.4.5            Adoption           268
  • 15.1.5 Commercial vehicles and heavy trucks        268
    • 15.1.5.1            Market growth               268
    • 15.1.5.2            Distinctive battery requirements       268
    • 15.1.5.3            Infrastructure 269
  • 15.1.6 Two- and three-wheelers        269
    • 15.1.6.1            Swapping and BaaS   269
    • 15.1.6.2            AI requirements            269
    • 15.1.6.3            Chemistry and cost   270
  • 15.1.7 Regional dynamics    270
  • 15.1.8 Used-EV market and battery health certification    270
    • 15.1.8.1            The confidence gap   270
    • 15.1.8.2            Variation within models          270
    • 15.1.8.3            Certification in practice          270
    • 15.1.8.4            Toward mandatory certificates           271
    • 15.1.8.5            Assessment methods              271
• 15.2     Stationary Energy Storage     272
  • 15.2.1 Growth in 2025             272
  • 15.2.2 Outlook             272
  • 15.2.3 Drivers                272
  • 15.2.4 Utility-scale BESS       273
    • 15.2.4.1            Why AI matters              273
    • 15.2.4.2            Obstacles        274
  • 15.2.5 Commercial, industrial and residential storage     274
    • 15.2.5.1            New business models             274
  • 15.2.6 Data-centre backup and on-site storage     274
  • 15.2.7 Long-duration storage             275
  • 15.2.8 Regional dynamics    275
    • 15.2.8.1            China  275
    • 15.2.8.2            Other regions 275
• 15.3     Consumer Electronics and Wearables         276
• 15.4     Off-Highway Machines: Construction, Agriculture and Mining     277
• 15.5     Robotics, Drones and Physical AI Platforms             277
  • 15.5.1 Humanoid robots        277
    • 15.5.1.1            Energy demand            277
    • 15.5.1.2            Current battery packs              278
    • 15.5.1.3            The weight constraint               278
  • 15.5.2 Autonomous mobile robots and warehouse automation 279
  • 15.5.3 Drones               279
  • 15.5.4 AI applications              279
  • 15.5.5 Outlook             280
• 15.6     Aviation and eVTOL    281
  • 15.6.1 eVTOL aircraft                281
    • 15.6.1.1            Commercial progress              281
    • 15.6.1.2            Battery requirements                281
    • 15.6.1.3            AI applications in eVTOL         282
    • 15.6.1.4            Validation and explainability               282
  • 15.6.2 Hybrid-electric aircraft            282
  • 15.6.3 Battery technology roadmap for aviation     283
  • 15.6.4 Outlook             283
• 15.7     Marine and Rail            284
  • 15.7.1 Marine 284
  • 15.7.2 Rail       284
• 15.8     Defence             284
• 15.9     Medical Devices           285
• 15.10  Comparative Application Analysis  285

16          GLOBAL MARKET FORECASTS 2027–2037 287

• 16.1     Scope of Forecasts and Methodology           287
• 16.2     Total Market    287
• 16.3     By Use Case   288
  • 16.3.1 Leading segments      288
  • 16.3.2 Fastest growth              289
• 16.4     Battery Diagnostics by Capacity Served      289
• 16.5     By AI Technique            290
• 16.6     By Deployment Mode: Cloud, On-Edge and On-Premises               291
• 16.7     By Offering: Software, Services and AI-Enabled Hardware              292
• 16.8     By Battery Chemistry                292
  • 16.8.1 LFP and LMFP                292
  • 16.8.2 High-nickel NMC/NCA             292
  • 16.8.3 Next-generation chemistries               293
• 16.9     By Region         293
• 16.10  Scenario Analysis: Bear, Base and Bull Cases         294
• 16.11  Value Pool Distribution Across the Stack    296

17          REGIONAL MARKETS AND BATTERY POLICY ECOSYSTEMS           298

• 17.1     Regional Demand Patterns for Battery AI     298
  • 17.1.1 Europe: analytics and second-life assessment led by the Battery Passport        298
  • 17.1.2 North America: materials informatics and AI-assisted cell testing            298
  • 17.1.3 East Asia: manufacturing and development applications                298
• 17.2     China  299
  • 17.2.1 Institutional mapping and 15th Five-Year Plan priorities   299
  • 17.2.2 Solid-state and sodium-ion innovation policy          300
  • 17.2.3 Export controls on battery technology          300
  • 17.2.4 AI adoption by Chinese cell makers                301
    • 17.2.4.1            Scale of the ecosystem           301
    • 17.2.4.2            Demand drivers            301
    • 17.2.4.3            AI use  301
  • 17.2.5 Materials Genome Engineering          301
• 17.3     South Korea    302
  • 17.3.1 K-Battery strategy and next-generation R&D funding           302
    • 17.3.1.1            Institutions      302
    • 17.3.1.2            Strategy over time       302
    • 17.3.1.3            Demand measures    302
  • 17.3.2 Battery hubs: Pohang and Cheongju–Ochang         302
    • 17.3.2.1            Current conditions     303
  • 17.3.3 The "full-stack" AI, energy and battery strategy       303
  • 17.3.4 AI-based development strategies of Korean cell makers  303
  • 17.3.5 AI-driven science programmes          304
• 17.4     Japan  304
  • 17.4.1 Battery Industry Strategy and Green Transformation targets          304
    • 17.4.1.1            Progress and setbacks            305
    • 17.4.1.2            Industry consolidation            305
  • 17.4.2 NEDO programmes and the Advanced Battery Collaboration       305
  • 17.4.3 NIMS and AIST data-driven materials platforms     305
• 17.5     European Union           305
  • 17.5.1 Batteries Regulation, BATT4EU and Battery 2030+               305
    • 17.5.1.1            Research programmes            306
  • 17.5.2 National strategies: Germany, France, Hungary, Poland   306
    • 17.5.2.1            Europe's position at end-2025           306
  • 17.5.3 Industrial Accelerator Act and Battery Booster Facility      307
    • 17.5.3.1            Scale-up policy             307
    • 17.5.3.2            Implications for AI       307
• 17.6     United States 307
  • 17.6.1 DOE, ARPA-E and national laboratory programmes             307
  • 17.6.2 Policy shift toward stationary storage and defence               308
    • 17.6.2.1            Capacity build-out     308
    • 17.6.2.2            Policy shift       308
    • 17.6.2.3            Implications for AI       308
  • 17.6.3 The Materials Genome Initiative        308
• 17.7     United Kingdom           308
  • 17.7.1            AI strengths     308
  • 17.7.2         Market drivers                309
• 17.8     Canada and India       309
• 17.9     Rest of World: Morocco, Australia and Southeast Asia      309
• 17.10            Global cell manufacturing capacity                310

18          REGULATION, STANDARDS AND TECHNOLOGY SOVEREIGNTY 312

• 18.1     Battery Safety Standards and AI Validation               312
• 18.2     Battery Passport and Data-Sharing Rules   313
  • 18.2.1 Scope of the passport             313
  • 18.2.2 What it means for AI  314
  • 18.2.3 Wider data-access rules        314
• 18.3     AI Regulation: EU AI Act, US and China Frameworks           314
  • 18.3.1 The EU AI Act  314
  • 18.3.2 Implications for battery AI     315
  • 18.3.3 United States 315
  • 18.3.4 China  315
• 18.4     Functional Safety of AI-Based BMS (ISO 26262, ISO 21448, ISO/PAS 8800)        315
• 18.5     Standards Development: IEC, SAE, IEEE and National Programmes        316
• 18.6     PFAS Restrictions and Materials Regulation             317
• 18.7     Export Controls and Technology Sovereignty            317

19          AI ADOPTION BY BATTERY MAKERS, EV OEMs AND PLATFORM COMPANIES     320

• 19.1     Battery Cell and Materials Companies         320
• 19.2     EV OEMs           323
• 19.4     Platform Companies and Research Institutions     325

20          COMPETITIVE LANDSCAPE, INVESTMENT AND OUTLOOK            329

• 20.1     Competitive Dynamics by Use Case              329
• 20.2     Types of Players: Start-ups, Software Incumbents, Cell Makers and OEMs         330
• 20.3     Business Models and Revenue Streams      331
• 20.4     Strategic Partnerships and Joint Ventures   332
• 20.5     Investment and Funding         333
  • 20.5.1 Strategic investors      333
• 20.6     M&A Landscape          334
• 20.7     Patent Landscape      335
  • 20.7.1 Growth               335
  • 20.7.2 By jurisdiction               335
• 20.8     Barriers to Adoption  336
  • 20.8.1 Technical: data scarcity, generalisation and interpretability           336
  • 20.8.2 Economic: ROI, payback periods and fragmented buyers               336
  • 20.8.3 Organisational: skills shortages in data science and electrochemistry   337
• 20.9     Emerging Frontiers 2030–2037          338
  • 20.9.1 Smart cells with embedded sensing and self-healing functions 338
  • 20.9.2 Quantum–AI hybrid materials discovery      338
  • 20.9.3 Agentic AI across the battery lifecycle           338

21          COMPANY PROFILES                339 (95 company profiles)

22          APPENDIX        434

• 22.1     Research Methodology           434
• 22.2     Forecast Assumptions            437
• 22.3     Glossary of Terms and Abbreviations             439

23          REFERENCES 441

List of Tables

• Table 1. Quantified Benefits of AI by Development Stage 42
• Table 2. AI Use-Case Benchmarking: Value, Adoption Barriers and Time to Impact       45
• Table 3. TRL Assessment: AI Applications Across the Battery Value Chain           46
• Table 4. Global AI-Driven Battery Technology Market Summary 2027–2037 (US$M, Base Case)           49
• Table 5. Venture Funding in AI-for-Batteries Companies 2020–2026 (US$M)      52
• Table 6. Top Ten Strategic Conclusions Mapped to Stakeholder Type       56
• Table 7. Leading Companies by AI Use Case            57
• Table 8. Market Definition and Scope: Inclusions and Exclusions              58
• Table 9. Battery Chemistry Landscape and Relevance of AI           62
• Table 10. Reported Performance Gains from AI by Application     67
• Table 11. Nine AI Use-Case Arenas: Scope, Techniques, Customers and Market Size  68
• Table 12. Machine Learning Approaches and Battery Use Cases                74
• Table 13. Types of Neural Network and Their Battery Applications             77
• Table 14. Universal Machine-Learned Interatomic Potentials Used in Battery Research             79
• Table 15. Model-Based, Data-Driven and Hybrid Approaches Compared             81
• Table 16. Edge vs. Cloud Deployment Trade-offs for Battery AI     85
• Table 17. Major Public Battery Datasets and Their Uses    87
• Table 18. Advanced Battery Sensing Technologies and TRL            91
• Table 19. BMS IC and Edge-AI Silicon Suppliers      94
• Table 20. Case Studies of AI-Based Reduction in Material Screening Time          106
• Table 21. AI-Discovered Electrolyte and Solid-Electrolyte Candidates 2020–2026        111
• Table 22. Autonomous Battery Laboratories: Operators and Capabilities             115
• Table 23. Autonomous Battery Laboratories: Operators and Capabilities:           116
• Table 24. Companies in Materials Informatics for Batteries            117
• Table 25. AI Materials Discovery Business Models Compared      122
• Table 26. AI Opportunity Matrix by Battery Chemistry         135
• Table 27. Emerging Cell Architectures and AI Design Levers           144
• Table 28. Research Areas in AI-Based Battery Pack Design Optimisation             148
• Table 29. Battery Design and Simulation Software Vendors           151
• Table 30. Battery Validation Test Durations and Costs by Application      153
• Table 31. Data Forms for Cell Modelling      154
• Table 32. RUL Prediction Accuracy by Method (RMSE, MAPE)       160
• Table 33. Algorithmic Approaches for Different Testing Modes     163
• Table 34. Companies in AI for Cell Testing  165
• Table 35. AI Use Cases by Manufacturing Step        173
• Table 36. AI Inspection Technologies: Detection Capability and Throughput      175
• Table 37. Algorithmic Approaches in Manufacturing and Cell Assembly                178
• Table 38. Smart Battery Manufacturing Players       180
• Table 39. AI in Battery Manufacturing Market by Solution Type 2027–2037 (US$M)        186
• Table 40. Conventional vs. AI-Enabled BMS Capabilities 189
• Table 41. Health Indicators by Operating Environment and Data-Collection Condition               192
• Table 42. Deep Learning Models for Battery Time-Series Prediction and Anomaly Detection   196
• Table 43. Reported Safety and Performance Gains from AI-Enabled BMS             199
• Table 44. Companies in AI for Battery Diagnostics and Management      202
• Table 45. Business Models in Battery Diagnostics and Analytics 205
• Table 46. Battery Capacity Served by AI Diagnostics 2027–2037 (GWh) 212
• Table 47. Battery Diagnostics and BMS Analytics Market Value 2027–2037 (US$M)      212
• Table 48. Grid-Integration Models for Electric Vehicles: Case Studies     216
• Table 49. Case Studies of AI-Based EV Charging and Grid-Integration Technologies      219
• Table 50. Cloud Data Compression and Model Optimisation Case Studies         225
• Table 51. Digital-Twin-Based SoX Estimation and Cell-Balancing Functions       236
• Table 52. Comparison of Integrated Battery Digital Twin Platforms            238
• Table 53. Algorithmic Approaches and Data Inputs/Outputs for Second-Life Assessment       244
• Table 54. Second-Life Battery Applications: Requirements, Economics and the Role of AI       249
• Table 55. Companies in AI for Second-Life Battery Assessment  251
• Table 56. Recycling Techniques Compared, with AI Optimisation Levers               260
• Table 57. AI Applications by Recycling Process Step            262
• Table 58. EU Batteries Regulation Recovery and Recycled-Content Targets        263
• Table 59. Used-EV Battery Health Certification: Approaches and Users 272
• Table 60. AI Battery Applications in Electric Vehicles by Value-Chain Stage        273
• Table 61. AI Applications in Stationary Energy Storage by Segment           277
• Table 62. AI-Driven Battery Technology Market by End Use 2027–2037 (US$M) 287
• Table 63. Global AI-Driven Battery Technology Market 2027–2037 (US$M)          289
• Table 64. Market by Use Case 2027–2037 (US$M) 290
• Table 65. Market by AI Technique 2027–2037 (US$M)         292
• Table 66. Market by Deployment Mode 2027–2037 (US$M)            292
• Table 67. Market by Offering 2027–2037 (US$M)   293
• Table 68. Market by Battery Chemistry 2027–2037 (US$M)            294
• Table 69. Market by Region 2027–2037 (US$M)      295
• Table 70. Regional AI Adoption Focus by Use Case              300
• Table 71. Chinese Government Bodies and Their Role in Battery Policy  300
• Table 72. South Korean Battery Innovation Programmes and Funding     304
• Table 73. Comparative Battery Policy Matrix: Eight Economies    311
• Table 74. Key Battery Safety Standards and AI Relevance                314
• Table 75. AI Adoption by Battery Cell and Materials Companies 323
• Table 76. AI Adoption by EV OEMs   326
• Table 77. AI Platform Companies and Research Institutions in Battery Development   328
• Table 78.Business Models and Revenue Streams by AI Use Case              332
• Table 79. Key Partnerships Between Cell Makers, OEMs and AI Vendors 2022–2026    333
• Table 80. Funding Rounds in AI-for-Batteries Companies 2022–2026     334
• Table 81. M&A Transactions in AI-Battery Software and Services 2020–2026    335
• Table 82. Adoption Barriers and Mitigation Strategies         338
• Table 83. Key Forecast Assumptions by Use Case 439
• Table 84. Glossary of Terms and Abbreviations       440

List of Figures

• Figure 1. AI Applications Across the Battery Lifecycle, from Materials to Recycling        40
• Figure 2. AI Disruption Points in the Battery Supply Chain               43
• Figure 3. Use-Case Maturity Comparison   45
• Figure 4. Regional Adoption Focus by AI Use Case               47
• Figure 5. Global Market Forecast by Use Case 2027–2037 (US$M)           49
• Figure 6. Three-Wave Adoption Timeline 2027–2037          51
• Figure 7. Global Li-Ion Battery Demand by Application 2020–2037 (GWh)           59
• Figure 8. Li-Ion vs. Beyond-Li-Ion Battery Demand 2026–2036 (GWh)     60
• Figure 9. Li-Ion Pack Price 2013–2037 (US$/kWh) 62
• Figure 10. Traditional vs. AI-Enabled Battery Development Workflow      63
• Figure 11. Development-Cost Reduction from AI/ML Across Battery R&D Stages           65
• Figure 12. AI/ML Technique Taxonomy for Battery Applications    77
• Figure 13. Data Flow from Cell Sensor to Cloud Analytics Platform           88
• Figure 14. Traditional vs. AI-Accelerated Materials Development Timeline           100
• Figure 15. AI-Based Materials R&D Workflow           101
• Figure 16. High-Throughput Screening and Prediction of Electrode Active Materials     103
• Figure 17. Data-Driven Prediction of Li-Ion Electrolyte Solvent Stability 109
• Figure 18. Microsoft/PNNL AI Screening Funnel for Solid-Electrolyte Candidates           110
• Figure 19. Closed-Loop Autonomous Laboratory Workflow           113
• Figure 20. Materials Informatics for Batteries Market 2027–2037 (US$M)             124
• Figure 21. Li-Ion Energy-Density Roadmap to 350+ Wh/kg              126
• Figure 22. Solid-State and Semi-Solid-State Battery Demand 2026–2036 (GWh)            129
• Figure 23. Sodium-Ion Battery Demand 2026–2036 (GWh)            131
• Figure 24. BEV Cell Energy Density 2015–2037 (Wh/kg and Wh/L)              144
• Figure 25. Workflow of AI-Based Battery Pack Design Structure Optimisation   146
• Figure 26. AI Cell and Pack Design Software Market 2027–2037 (US$M)               151
• Figure 27. Lithium-Ion Capacity Fade Mechanisms             156
• Figure 28. RUL Prediction Methods Taxonomy         159
• Figure 29. AI Cell Testing Market 2027–2037 (US$M)           168
• Figure 30. Li-Ion Cell Manufacturing Process Flow and AI Intervention Points   170
• Figure 31. Gigafactory Yield Curve With and Without AI Process Control               171
• Figure 32. AI in Battery Manufacturing Market by Region 2027–2037 (US$M)     186
• Figure 33. Next-Generation BMS Architecture: Embedded, Edge and Cloud Layers       189
• Figure 34. Advanced Battery Pack Sensor Market 2026–2036 (US$M)     197
• Figure 35. On-Edge vs. Cloud Battery AI Revenue 2027–2037 (US$M)     212
• Figure 36. EIS Image-Based Classification of ESS Operating Environments         221
• Figure 37. Operational Pattern Image Generation for Large-Scale EV Data           223
• Figure 38. Data-Centre Battery Demand 2026–2036 (GWh)           226
• Figure 39. Components of a Digital Twin     229
• Figure 40. Hierarchical Structure of the Battery Digital Twin: Material to System              231
• Figure 41. Digital Twin and Cloud BMS: Virtual Battery Model Architecture          238
• Figure 42. Battery Digital Twin Market 2027–2037 (US$M)               240
• Figure 43. End-of-Life EV Battery Volumes 2027–2037 (GWh)      242
• Figure 44. AI-Based Rapid SOH Diagnosis Workflow for Used Batteries 246
• Figure 45. RUL-Based Regrouping of Used Batteries            247
• Figure 46. AI Second-Life Assessment Market 2027–2037 (US$M)            256
• Figure 47. AI in Battery Recycling Market 2027–2037 (US$M)        265
• Figure 48. Li-Ion Battery Pack Demand for xEVs 2026–2036 (GWh)           267
• Figure 49. Battery Energy-Density Requirements for Humanoid Robots, AMRs and Drones     279
• Figure 50. End-Use Market Share 2027 vs. 2037    286
• Figure 51. Global Market 2027–2037, Base Case (US$M) 288
• Figure 52. Battery Capacity Served by AI Diagnostics 2027–2037 (GWh)              290
• Figure 53. Regional Market Share 2027 vs. 2037    294
• Figure 54. Bear, Base and Bull Scenarios 2027–2037 (US$M)       295
• Figure 55. Value Pool by Stack Layer, 2037 297
• Figure 56. Global Battery Cell Capacity by Region 2025–2037 (GWh)      311
• Figure 57. Battery and AI Regulatory Timeline 2023–2037               319
• Figure 58. Market Map: AI-Driven Battery Technology Companies by Use Case 330
• Figure 59. Funding by Use Case and Region 2020–2026 (US$M) 334
• Figure 60. AI-Battery Patent Filings by Country 2015–2025     336

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

PDF + Excel download.

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

PDF + Excel download + Print Edition.

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