首页 > 资讯 > 2025年

2025年

泰然健康网
2025-05-20 19:14

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。

显示全部说明内容、图表列表常见问题解答价格Related Content

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 MetricsDetailsForecast Period2025 - 2035Forecast UnitsGlobal capacity (GWh), Market value (US$ millions)Regions CoveredWorldwideSegments CoveredMaterials informatics for batteries, AI-assisted cell testing, smart battery manufacturing, cloud-based diagnostics, on-edge diagnostics, second-life assessment

从 IDTechEx 访问分析师

所有报告购买订单均包括与一名专家分析师进行 30 分钟的电话交谈，专家分析师将帮助您将报告中的重要发现与您正在处理的业务问题联系起来。这需要在购买报告后的三个月内使用。

更多信息

1.EXECUTIVE SUMMARY1.1.The scope of this report1.2.Who should read this report?1.3.Research methodology1.4.Clarifying terms: machine learning vs artificial intelligence1.5.Inefficiencies of overuse1.6.Under- and over-fitting1.7.Challenges facing the rechargeable battery industry1.8.How AI can be applied throughout the battery lifecycle1.9.AI disruptions to the battery supply chain1.10.Use-case benchmarking1.11.Use-case maturity comparison1.12.AI in batteries for EVs1.13.AI in batteries for BESS1.14.Interest by region1.15.Scope of forecasts1.16.Methodologies1.17.Diagnostics by capacity served1.18.Diagnostics by market value1.19.On-edge AI: diagnostics1.20.On-edge AI: performance enhancement1.21.Cell testing by market value1.22.Second-life assessment by market value1.23.AI will see significant usage throughout the battery industry2.MACHINE LEARNING APPROACHES: AN OVERVIEW2.1.An introduction to AI - shifting goalposts2.2.Machine learning as a subset of artificial intelligence2.3.Machine learning approaches2.4.The importance of data - quality and dimensionality2.5.Standardizing data structures2.6.Supervised learning2.7.Unsupervised learning2.8.Problem classes in supervised and unsupervised learning2.9.Reinforcement learning2.10.Semi-supervised and active learning2.11.The ɛ parameter: exploitation vs. exploration2.12.Neural networks - an introduction2.13.An artificial neuron in the training process2.14.Types of neural network2.15.Support vector machines2.16.Decision tree methods2.17.k-nearest neighbor (kNN)2.18.k-means clustering2.19.Principal component analysis3.MATERIAL DISCOVERY3.1.Overview3.1.1.Material discovery in batteries - the attraction of AI3.1.2.Traditional material discovery and DFT3.1.3.An introduction to Materials Informatics3.1.4.Property prediction and material grouping3.1.5.Datasets and descriptors3.1.6.The golden grail - inverting the process3.1.7.Informed selection vs. novel material formulation3.1.8.Virtual screening3.1.9.De novo design3.1.10.Integration of LLM interface3.1.11.Electrodes3.1.12.Electrolytes3.1.13.Problem and algorithm classes3.2.Players in materials informatics for batteries3.2.1.BIG-MAP3.2.2.Microsoft Quantum - Azure Open AI3.2.3.Umicore3.2.4.Wildcat Discovery Technologies3.2.5.Schrödinger - an overview3.2.6.Schrödinger technical details3.2.7.Eonix Energy3.2.8.Citrine Informatics3.2.9.Morrow Batteries3.2.10.Chemix3.2.11.Aionics3.2.12.SES AI3.2.13.SES AI batteries3.3.Business analysis for AI in battery material discovery3.3.1.Business models/partnerships3.3.2.Existing client-supplier relationships3.3.3.Differentiation3.3.4.Challenges3.3.5.Materials informatics will see increasing use in the battery industry over the next decade4.CELL TESTING AND MODELLING4.1.Overview4.1.1.Traditional cell testing - shortcomings and challenges4.1.2.AI for high-throughput automated testing4.1.3.Data forms for cell modelling4.1.4.AI for design of experiment (DoE) and anomalous data identification4.1.5.AI for lifetime modelling4.1.6.AI for degradation modelling4.1.7.AI for temperature and pressure simulation4.1.8.Data driven cell architecture optimization4.1.9.Algorithmic approaches for different testing modes4.2.Players in AI for cell testing4.2.1.Stanford, MIT and Toyota Research Institute4.2.2.StoreDot - a data-first approach4.2.3.StoreDot's batteries4.2.4.Safion4.2.5.TWAICE4.2.6.Oorja Energy4.2.7.Addionics4.2.8.Monolith AI4.2.9.Speedgoat4.2.10.DNV Energy Systems via Veracity4.2.11.NOVONIX and SandboxAQ4.2.12.Cell testing players summary4.3.Business analysis for AI in cell testing4.3.1.Typical business models4.3.2.Differentiation4.3.3.Challenges4.3.4.AI is well-placed to revolutionize the cell testing process for battery development, but it will take time5.CELL ASSEMBLY AND MANUFACTURING5.1.Overview5.1.1.Overview of traditional manufacturing process5.1.2.Data quality challenges5.1.3.Data acquisition challenges in industrial settings5.1.4.AI for defect detection and quality control5.1.5.AI for manufacturing process efficiency5.1.6.Algorithmic approaches in manufacturing and cell assembly5.1.7.Digital twins5.1.8.FAT/SAT5.2.Smart battery manufacturing players5.2.1.CATL - smart factories5.2.2.CATL - manufacturing process optimization5.2.3.Siemens Xcelerator5.2.4.Samsung Robotic Laboratory: ASTRAL5.2.5.Voltaiq5.2.6.BMW Group and University of Zagreb5.2.7.EthonAI5.2.8.Elisa IndustrIQ5.2.9.Smart battery manufacturing players summary5.3.Business analysis for smart battery manufacturing5.3.1.Types of smart battery manufacturing players5.3.2.Challenges5.3.3.Smart factories could become standard for larger players, but startups will struggle to adopt6.BATTERY MANAGEMENT SYSTEM ANALYTICS6.1.Overview6.1.1.Battery management in mobility and ESS - the need for accurate diagnostics6.1.2.Management of multi-cell battery packs - a basic example6.1.3.The purpose of a BMS6.1.4.The data pipeline - from BMS to AI6.1.5.Data structures and forms for diagnostics6.1.6.Fault detection and analysis6.1.7.SoH and SoC determination for lifetime optimization6.1.8.The genesis of 'prescriptive' AI6.1.9.Algorithmic approaches to battery system management6.1.10.The Battery Passport6.2.Players in AI for battery diagnostics and management6.2.1.ACCURE Battery Intelligence6.2.2.TWAICE6.2.3.BattGenie6.2.4.volytica diagnostics6.2.5.On-edge AI6.2.6.Samsung: Battery AI in S256.2.7.Eatron and Syntient6.2.8.LG Energy Solution and Qualcomm6.2.9.Tesla BMS: optimization over a journey6.2.10.Cell diagnostics players summary6.3.Business analysis for AI-assisted battery diagnostics and management6.3.1.Business models6.3.2.Differentiation6.3.3.Challenges6.3.4.Data-focused battery analytics will take off in Europe and see growth in the wider mobility industry7.SECOND LIFE ASSESSMENT7.1.Overview7.1.1.Second-life batteries: an overview7.1.2.Determining the second-life stream7.1.3.Safety concerns and regulations7.1.4.The battery passport7.1.5.The use of AI7.1.6.Algorithmic approaches and data inputs/outputs7.2.Players in AI for second-life battery assessment7.2.1.ReJoule7.2.2.volytica diagnostics and Cling Systems7.2.3.NOVUM7.2.4.DellCon7.2.5.Second-life assessment player summary7.3.Business analysis for AI-assisted second-life assessment7.3.1.Revenue streams - somewhat ambiguous7.3.2.Types of players7.3.3.Differentiation7.3.4.Challenges7.3.5.AI for second-life assessment in batteries will become the norm in Europe8.FORECASTS8.1.Diagnostics by capacity served8.2.Diagnostics by market value8.3.Cell testing by market value8.4.Second-life assessment by market value9.COMPANY PROFILES9.1.ACCURE9.2.Addionics9.3.Aionics Inc.9.4.BattGenie Inc.9.5.Chemix9.6.Eatron Technologies9.7.Elisa IndustrIQ9.8.Eonix Energy9.9.EthonAI9.10.Monolith AI9.11.Oorja Energy9.12.ReJoule9.13.Safion GmbH9.14.Schrödinger Update9.15.SES AI9.16.Silver Power Systems9.17.StoreDot9.18.TWAICE9.19.Voltaiq9.20.volytica diagnostics9.21.Wildcat Discovery Technologies10.APPENDIX A: DATA CENTRES DRIVING BATTERY DEMAND10.1.A note on battery demand

About IDTechEx reports

What are the qualifications of the people conducting IDTechEx research?

Content produced by IDTechEx is researched and written by our technical analysts, each with a PhD or master's degree in their specialist field, and all of whom are employees. All our analysts are well-connected in their fields, intensively covering their sectors, revealing hard-to-find information you can trust.

How does IDTechEx gather data for its reports?

By directly interviewing and profiling companies across the supply chain. IDTechEx analysts interview companies by engaging directly with senior management and technology development executives across the supply chain, leading to revealing insights that may otherwise be inaccessible.

Further, as a global team, we travel extensively to industry events and companies to conduct in-depth, face-to-face interviews. We also engage with industry associations and follow public company filings as secondary sources. We conduct patent analysis and track regulatory changes and incentives. We consistently build on our decades-long research of emerging technologies.

We assess emerging technologies against existing solutions, evaluate market demand and provide data-driven forecasts based on our models. This provides a clear, unbiased outlook on the future of each technology or industry that we cover.

What is your forecast methodology?

We take into account the following information and data points where relevant to create our forecasts:

Historic data, based on our own databases of products, companies' sales data, information from associations, company reports and validation of our prior market figures with companies in the industry.Current and announced manufacturing capacitiesCompany production targetsDirect input from companies as we interview them as to their growth expectations, moderated by our analystsPlanned or active government incentives and regulationsAssessment of the capabilities and price of the technology based on our benchmarking over the forecast period, versus that of competitive solutionsTeardown data (e.g. to assess volume of materials used)From a top-down view: the total addressable marketForecasts can be based on an s-curve methodology where appropriate, taking into account the above factorsKey assumptions and discussion of what can impact the forecast are covered in the report.

How can I be confident about the quality of work in IDTechEx reports?

Based on our technical analysts and their research methodology, for over 25 years our work has regularly received superb feedback from our global clients. Our research business has grown year-on-year.

Recent customer feedback includes:

"It's my first go-to platform"

- Dr. Didi Xu, Head of Foresight - Future Technologies, Freudenberg Technology Innovation

"Their expertise allows us to make data-driven, strategic decisions and ensures we remain aligned with the latest trends and opportunities in the market."

- Ralf Hug, Global Head of Product Management & Marketing, Marquardt

What differentiates IDTechEx reports?

Our team of in-house technical analysts immerse themselves in industries over many years, building deep expertise and engaging directly with key industry players to uncover hard-to-find insights. We appraise technologies in the landscape of competitive solutions and then assess their market demand based on voice-of-the-customer feedback, all from an impartial point of view. This approach delivers exceptional value to our customers—providing high-quality independent content while saving customers time, resources, and money.

Why should we pick IDTechEx research over AI research?

A crucial value of IDTechEx research is that it provides information, assessments and forecasts based on interviews with key people in the industry, assessed by technical experts. AI is trained only on content publicly available on the web, which may not be reliable, in depth, nor contain the latest insights based on the experience of those actively involved in a technology or industry, despite the confident prose.

How can I justify the ROI of this report?

Consider the cost of the IDTechEx report versus the time and resources required to gather the same quality of insights yourself. IDTechEx analysts have built up an extensive contact network over many years; we invest in attending key events and interviewing companies around the world; and our analysts are trained in appraising technologies and markets.

Each report provides an independent, expert-led technical and market appraisal, giving you access to actionable information immediately, rather than you having to spend months or years on your own market research.

Can I speak to analysts about the report content?

All report purchases include up to 30 minutes of telephone time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

What is the difference between a report and subscription?

A subscription from IDTechEx can include more reports, access to an online information platform with continuously updated information from our analysts, and access to analysts directly.

Before purchasing, I have some questions about the report, can I speak to someone?

About IDTechEx

Who are IDTechEx's customers?

IDTechEx has served over 35,000 customers globally. These range from large corporations to ambitious start-ups, and from Governments to research centers. Our customers use our work to make informed decisions and save time and resources.

Where is IDTechEx established?

IDTechEx was established in 1999, and is headquartered in Cambridge, UK. Since then, the company has significantly expanded and operates globally, having served customers in over 80 countries. Subsidiary companies are based in the USA, Germany and Japan.

Questions about purchasing a report

How do I pay?

In most locations reports can be purchased by credit card, or else by direct bank payment.

How and when do I receive access to IDTechEx reports?

When paying successfully by credit card, reports can be accessed immediately. For new customers, when paying by bank transfer, reports will usually be released when the payment is received. Report access will be notified by email.

How do I assign additional users to the report?

Users can be assigned in the report ordering process, or at a later time by email.

Can I speak to someone about purchasing a report?

Ordering Information

2025年-2035年人工智能驱动电池技术：技术、创新和机遇

£€$元¥₩

电子版及 1 份硬拷贝文件（1-5 名用户）

元58,000.00

电子版及 1 份硬拷贝文件（6-10 名用户）

元80,000.00

Click here to enquire about additional licenses.

If you are a reseller/distributor please contact us before ordering.

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

2025年-2035年人工智能驱动电池技术：技术、创新和机遇

报告统计信息

幻灯片 190 Companies 21 预测 2035 已发表 Nov 2024

预览内容

Customer Testimonial

"The resources provided by IDTechEx, such as their insightful reports and analysis, engaging webinars, and knowledgeable analysts, serve as valuable tools and information sources... Their expertise allows us to make data-driven, strategic decisions and ensures we remain aligned with the latest trends and opportunities in the market."

Global Head of Product Management and Marketing
Marquardt GmbH

ISBN: 9781835700761