AI-Driven Battery Technology 2025-2035: Technology, Innovation and Opportunities
Ten-year forecasts across five application areas for AI throughout the battery lifecycle, including technology benchmarking and data-driven market predictions. Over 20 company profiles.
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This report provides key insights into five different application areas for artificial intelligence in the battery industry, including discussion of technologies, supply-chain disruption and player innovations. Market forecasts cover the next decade with both quantitative and qualitative analysis. It is the most comprehensive overview for machine learning applications in the battery industry, and reveals the potential for significant disruption and acceleration of battery development, manufacturing and usage.
AI growth drivers
The need for net-zero has placed increasing pressure for electrification world-wide, with battery demand skyrocketing as a result. As the electric vehicle (EV) and battery energy storage system (BESS) industries grow, requirements for the batteries that power them become more demanding. Energy density is the most important factor, but cost and critical material proportions are also a major consideration. Faster battery development is needed to enable suitable batteries, as well as allow for more efficient management, manufacturing and recycling methods. Artificial intelligence (AI) will be a crucial part of the solution.
Visualization of AI usage throughout the battery lifecycle. Source: IDTechEx
In Europe, the desire for better sustainability and safety for large battery deployments has already led to regulatory support, including the planned Battery Passport initiative, whereby manufacturers and end-users will be required to track cell data from production to end-of-life. This has already resulted in growth of AI battery analytics, for both diagnostics and second-life assessment.
Meanwhile, for North America, the need for faster cell development and materials discovery will lead to uptake of materials informatics platforms and AI-assisted cell testing methods, while in East Asia, manufacturing- and development-related applications will fuel demand for AI-assisted battery technology. In the report, IDTechEx discusses the details of AI usage throughout the battery industry and across these three regions.
Emerging markets analyzed through the lens of experience
IDTechEx has provided the most comprehensive overview of AI technologies used throughout the battery life-cycle and supply chain, providing an overarching view of machine-learning methods generally as well as trends and growth drivers.
IDTechEx has gathered expertise in many sectors of the battery industry, through analysis of emerging and incumbent technologies, as well as in the two major application areas for AI in batteries: electric vehicles (EVs) and energy storage systems (ESS). As such, it is well positioned to provide critical analysis on disruptions to the battery supply chain, as well as discuss the maturity and value provided by different AI use-cases.
An overview of content
The report provides market analysis and technology assessment for artificial intelligence (AI) employed throughout the battery industry, looking at five distinct application areas. This includes:
A review of technologies and techniques used in different application areas:
- Overview of machine learning and artificial intelligence
- Evaluation of incumbent techniques and their disadvantages
- Discussion of how value can be generated through use of AI
- Benchmarking of AI use-cases
Market assessment for each application area:
- Mix of quantitative and qualitative analysis of markets for each application area (materials discovery, cell testing, manufacturing, in-life diagnostics and second-life assessment).
- Review of the problems facing the battery industry, including energy-density challenges and the need for net zero
- Examination of theoretical and practical value propositions for AI, compared with the incumbent
- Discussion of business models and revenue streams for different players in the battery industry
Market and player analysis throughout:
- Review of player technology and business models
- Analysis of growth drivers, especially in Europe, North America and East Asia
- Market forecasts over three sectors and qualitative predictions for the rest, with a discussion of methodology and scope for each.
| Report Metrics | Details |
|---|---|
| Forecast Period | 2025 - 2035 |
| Forecast Units | Global capacity (GWh), Market value (US$ millions) |
| Regions Covered | Worldwide |
| Segments Covered | Materials informatics for batteries, AI-assisted cell testing, smart battery manufacturing, cloud-based diagnostics, on-edge diagnostics, second-life assessment |
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Further information
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| 1. | EXECUTIVE SUMMARY |
| 1.1. | The scope of this report |
| 1.2. | Who should read this report? |
| 1.3. | Research methodology |
| 1.4. | Clarifying terms: machine learning vs artificial intelligence |
| 1.5. | Inefficiencies of overuse |
| 1.6. | Under- and over-fitting |
| 1.7. | Challenges facing the rechargeable battery industry |
| 1.8. | How AI can be applied throughout the battery lifecycle |
| 1.9. | AI disruptions to the battery supply chain |
| 1.10. | Use-case benchmarking |
| 1.11. | Use-case maturity comparison |
| 1.12. | AI in batteries for EVs |
| 1.13. | AI in batteries for BESS |
| 1.14. | Interest by region |
| 1.15. | Scope of forecasts |
| 1.16. | Methodologies |
| 1.17. | Diagnostics by capacity served |
| 1.18. | Diagnostics by market value |
| 1.19. | On-edge AI: diagnostics |
| 1.20. | On-edge AI: performance enhancement |
| 1.21. | Cell testing by market value |
| 1.22. | Second-life assessment by market value |
| 1.23. | AI will see significant usage throughout the battery industry |
| 2. | MACHINE LEARNING APPROACHES: AN OVERVIEW |
| 2.1. | An introduction to AI - shifting goalposts |
| 2.2. | Machine learning as a subset of artificial intelligence |
| 2.3. | Machine learning approaches |
| 2.4. | The importance of data - quality and dimensionality |
| 2.5. | Standardizing data structures |
| 2.6. | Supervised learning |
| 2.7. | Unsupervised learning |
| 2.8. | Problem classes in supervised and unsupervised learning |
| 2.9. | Reinforcement learning |
| 2.10. | Semi-supervised and active learning |
| 2.11. | The ɛ parameter: exploitation vs. exploration |
| 2.12. | Neural networks - an introduction |
| 2.13. | An artificial neuron in the training process |
| 2.14. | Types of neural network |
| 2.15. | Support vector machines |
| 2.16. | Decision tree methods |
| 2.17. | k-nearest neighbor (kNN) |
| 2.18. | k-means clustering |
| 2.19. | Principal component analysis |
| 3. | MATERIAL DISCOVERY |
| 3.1. | Overview |
| 3.1.1. | Material discovery in batteries - the attraction of AI |
| 3.1.2. | Traditional material discovery and DFT |
| 3.1.3. | An introduction to Materials Informatics |
| 3.1.4. | Property prediction and material grouping |
| 3.1.5. | Datasets and descriptors |
| 3.1.6. | The golden grail - inverting the process |
| 3.1.7. | Informed selection vs. novel material formulation |
| 3.1.8. | Virtual screening |
| 3.1.9. | De novo design |
| 3.1.10. | Integration of LLM interface |
| 3.1.11. | Electrodes |
| 3.1.12. | Electrolytes |
| 3.1.13. | Problem and algorithm classes |
| 3.2. | Players in materials informatics for batteries |
| 3.2.1. | BIG-MAP |
| 3.2.2. | Microsoft Quantum - Azure Open AI |
| 3.2.3. | Umicore |
| 3.2.4. | Wildcat Discovery Technologies |
| 3.2.5. | Schrödinger - an overview |
| 3.2.6. | Schrödinger technical details |
| 3.2.7. | Eonix Energy |
| 3.2.8. | Citrine Informatics |
| 3.2.9. | Morrow Batteries |
| 3.2.10. | Chemix |
| 3.2.11. | Aionics |
| 3.2.12. | SES AI |
| 3.2.13. | SES AI batteries |
| 3.3. | Business analysis for AI in battery material discovery |
| 3.3.1. | Business models/partnerships |
| 3.3.2. | Existing client-supplier relationships |
| 3.3.3. | Differentiation |
| 3.3.4. | Challenges |
| 3.3.5. | Materials informatics will see increasing use in the battery industry over the next decade |
| 4. | CELL TESTING AND MODELLING |
| 4.1. | Overview |
| 4.1.1. | Traditional cell testing - shortcomings and challenges |
| 4.1.2. | AI for high-throughput automated testing |
| 4.1.3. | Data forms for cell modelling |
| 4.1.4. | AI for design of experiment (DoE) and anomalous data identification |
| 4.1.5. | AI for lifetime modelling |
| 4.1.6. | AI for degradation modelling |
| 4.1.7. | AI for temperature and pressure simulation |
| 4.1.8. | Data driven cell architecture optimization |
| 4.1.9. | Algorithmic approaches for different testing modes |
| 4.2. | Players in AI for cell testing |
| 4.2.1. | Stanford, MIT and Toyota Research Institute |
| 4.2.2. | StoreDot - a data-first approach |
| 4.2.3. | StoreDot's batteries |
| 4.2.4. | Safion |
| 4.2.5. | TWAICE |
| 4.2.6. | Oorja Energy |
| 4.2.7. | Addionics |
| 4.2.8. | Monolith AI |
| 4.2.9. | Speedgoat |
| 4.2.10. | DNV Energy Systems via Veracity |
| 4.2.11. | NOVONIX and SandboxAQ |
| 4.2.12. | Cell testing players summary |
| 4.3. | Business analysis for AI in cell testing |
| 4.3.1. | Typical business models |
| 4.3.2. | Differentiation |
| 4.3.3. | Challenges |
| 4.3.4. | AI is well-placed to revolutionize the cell testing process for battery development, but it will take time |
| 5. | CELL ASSEMBLY AND MANUFACTURING |
| 5.1. | Overview |
| 5.1.1. | Overview of traditional manufacturing process |
| 5.1.2. | Data quality challenges |
| 5.1.3. | Data acquisition challenges in industrial settings |
| 5.1.4. | AI for defect detection and quality control |
| 5.1.5. | AI for manufacturing process efficiency |
| 5.1.6. | Algorithmic approaches in manufacturing and cell assembly |
| 5.1.7. | Digital twins |
| 5.1.8. | FAT/SAT |
| 5.2. | Smart battery manufacturing players |
| 5.2.1. | CATL - smart factories |
| 5.2.2. | CATL - manufacturing process optimization |
| 5.2.3. | Siemens Xcelerator |
| 5.2.4. | Samsung Robotic Laboratory: ASTRAL |
| 5.2.5. | Voltaiq |
| 5.2.6. | BMW Group and University of Zagreb |
| 5.2.7. | EthonAI |
| 5.2.8. | Elisa IndustrIQ |
| 5.2.9. | Smart battery manufacturing players summary |
| 5.3. | Business analysis for smart battery manufacturing |
| 5.3.1. | Types of smart battery manufacturing players |
| 5.3.2. | Challenges |
| 5.3.3. | Smart factories could become standard for larger players, but startups will struggle to adopt |
| 6. | BATTERY MANAGEMENT SYSTEM ANALYTICS |
| 6.1. | Overview |
| 6.1.1. | Battery management in mobility and ESS - the need for accurate diagnostics |
| 6.1.2. | Management of multi-cell battery packs - a basic example |
| 6.1.3. | The purpose of a BMS |
| 6.1.4. | The data pipeline - from BMS to AI |
| 6.1.5. | Data structures and forms for diagnostics |
| 6.1.6. | Fault detection and analysis |
| 6.1.7. | SoH and SoC determination for lifetime optimization |
| 6.1.8. | The genesis of 'prescriptive' AI |
| 6.1.9. | Algorithmic approaches to battery system management |
| 6.1.10. | The Battery Passport |
| 6.2. | Players in AI for battery diagnostics and management |
| 6.2.1. | ACCURE Battery Intelligence |
| 6.2.2. | TWAICE |
| 6.2.3. | BattGenie |
| 6.2.4. | volytica diagnostics |
| 6.2.5. | On-edge AI |
| 6.2.6. | Samsung: Battery AI in S25 |
| 6.2.7. | Eatron and Syntient |
| 6.2.8. | LG Energy Solution and Qualcomm |
| 6.2.9. | Tesla BMS: optimization over a journey |
| 6.2.10. | Cell diagnostics players summary |
| 6.3. | Business analysis for AI-assisted battery diagnostics and management |
| 6.3.1. | Business models |
| 6.3.2. | Differentiation |
| 6.3.3. | Challenges |
| 6.3.4. | Data-focused battery analytics will take off in Europe and see growth in the wider mobility industry |
| 7. | SECOND LIFE ASSESSMENT |
| 7.1. | Overview |
| 7.1.1. | Second-life batteries: an overview |
| 7.1.2. | Determining the second-life stream |
| 7.1.3. | Safety concerns and regulations |
| 7.1.4. | The battery passport |
| 7.1.5. | The use of AI |
| 7.1.6. | Algorithmic approaches and data inputs/outputs |
| 7.2. | Players in AI for second-life battery assessment |
| 7.2.1. | ReJoule |
| 7.2.2. | volytica diagnostics and Cling Systems |
| 7.2.3. | NOVUM |
| 7.2.4. | DellCon |
| 7.2.5. | Second-life assessment player summary |
| 7.3. | Business analysis for AI-assisted second-life assessment |
| 7.3.1. | Revenue streams - somewhat ambiguous |
| 7.3.2. | Types of players |
| 7.3.3. | Differentiation |
| 7.3.4. | Challenges |
| 7.3.5. | AI for second-life assessment in batteries will become the norm in Europe |
| 8. | FORECASTS |
| 8.1. | Diagnostics by capacity served |
| 8.2. | Diagnostics by market value |
| 8.3. | Cell testing by market value |
| 8.4. | Second-life assessment by market value |
| 9. | COMPANY PROFILES |
| 9.1. | ACCURE |
| 9.2. | Addionics |
| 9.3. | Aionics Inc. |
| 9.4. | BattGenie Inc. |
| 9.5. | Chemix |
| 9.6. | Eatron Technologies |
| 9.7. | Elisa IndustrIQ |
| 9.8. | Eonix Energy |
| 9.9. | EthonAI |
| 9.10. | Monolith AI |
| 9.11. | Oorja Energy |
| 9.12. | ReJoule |
| 9.13. | Safion GmbH |
| 9.14. | Schrödinger Update |
| 9.15. | SES AI |
| 9.16. | Silver Power Systems |
| 9.17. | StoreDot |
| 9.18. | TWAICE |
| 9.19. | Voltaiq |
| 9.20. | volytica diagnostics |
| 9.21. | Wildcat Discovery Technologies |
| 10. | APPENDIX A: DATA CENTRES DRIVING BATTERY DEMAND |
| 10.1. | A note on battery demand |
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Cloud-based AI battery diagnostics market expected to grow at a CAGR of 23.4% over 10 years
Report Statistics
| slides | 190 |
|---|---|
| Companies | 21 |
| Forecasts to | 2035 |
| Published | Nov 2024 |
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