2025年-2035年人工智能驱动电池技术：技术、创新和机遇
在电池生命周期内，未来十年里人工智能将在五大应用领域发挥重要作用，包括技术基准设定以及数据驱动的市场预测。超过20家公司简介。
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本报告剖析了人工智能在电池行业的五大应用领域，涵盖技术革新、供应链中断及参与者创新等多个方面。同时，报告提供了未来十年的市场预测定量与定性分析。这是迄今为止对电池行业中机器学习应用的最全面概述，揭示了其在电池开发、制造及使用过程中的巨大颠覆力与加速潜力。
本报告提供了人工智能（AI）在电池行业中应用的市场分析和技术评估，涵盖五个不同的应用领域。内容包括：
不同应用领域中使用的技术和方法回顾：
- 机器学习和人工智能概述
- 对现有技术的评估及其缺点
- 讨论如何通过使用AI创造价值
- AI应用案例的基准测试
各应用领域的市场评估：
- 对每个应用领域（材料发现、电池测试、制造、生命周期诊断和二次寿命评估）市场的定量和定性分析
- 回顾电池行业面临的问题，包括能量密度挑战和实现净零排放的需求
- 对比现有技术，分析AI在理论和实际中的价值主张
- 讨论电池行业中不同参与者的商业模式和收入来源
全面的市场与参与者分析：
- 对参与者的技术和商业模式进行回顾
- 分析市场增长驱动因素，特别是在欧洲、北美和东亚地区
- 对三个领域进行市场预测，并对其他领域进行定性预测，同时讨论每个领域的方法论和范围
- 执行摘要
- 机器学习方法概述
- 材料发现
- 电池测试与建模
- 电池组装与制造
- 电池管理系统分析
- 二次寿命评估
- 预测
- 公司简介
- 附录 A
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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| 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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预计在未来十年内，基于云的人工智能电池诊断市场将以23.4%的复合年增长率实现增长
报告统计信息
| 幻灯片 | 190 |
|---|---|
| Companies | 21 |
| 预测 | 2035 |
| 已发表 | Nov 2024 |
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