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2025年

來源：泰然健康網(wǎng) 時(shí)間：2025年05月20日 19:14

在電池生命周期內(nèi)，未來十年里人工智能將在五大應(yīng)用領(lǐng)域發(fā)揮重要作用，包括技術(shù)基準(zhǔn)設(shè)定以及數(shù)據(jù)驅(qū)動(dòng)的市場預(yù)測。超過20家公司簡介。

顯示全部說明內(nèi)容、圖表列表常見問題解答價(jià)格Related Content

本報(bào)告剖析了人工智能在電池行業(yè)的五大應(yīng)用領(lǐng)域，涵蓋技術(shù)革新、供應(yīng)鏈中斷及參與者創(chuàng)新等多個(gè)方面。同時(shí)，報(bào)告提供了未來十年的市場預(yù)測定量與定性分析。這是迄今為止對電池行業(yè)中機(jī)器學(xué)習(xí)應(yīng)用的最全面概述，揭示了其在電池開發(fā)、制造及使用過程中的巨大顛覆力與加速潛力。

本報(bào)告提供了人工智能（AI）在電池行業(yè)中應(yīng)用的市場分析和技術(shù)評估，涵蓋五個(gè)不同的應(yīng)用領(lǐng)域。內(nèi)容包括：

不同應(yīng)用領(lǐng)域中使用的技術(shù)和方法回顧：

機(jī)器學(xué)習(xí)和人工智能概述對現(xiàn)有技術(shù)的評估及其缺點(diǎn)討論如何通過使用AI創(chuàng)造價(jià)值A(chǔ)I應(yīng)用案例的基準(zhǔn)測試

各應(yīng)用領(lǐng)域的市場評估：

對每個(gè)應(yīng)用領(lǐng)域（材料發(fā)現(xiàn)、電池測試、制造、生命周期診斷和二次壽命評估）市場的定量和定性分析回顧電池行業(yè)面臨的問題，包括能量密度挑戰(zhàn)和實(shí)現(xiàn)凈零排放的需求對比現(xiàn)有技術(shù)，分析AI在理論和實(shí)際中的價(jià)值主張討論電池行業(yè)中不同參與者的商業(yè)模式和收入來源

全面的市場與參與者分析：

對參與者的技術(shù)和商業(yè)模式進(jìn)行回顧分析市場增長驅(qū)動(dòng)因素，特別是在歐洲、北美和東亞地區(qū)對三個(gè)領(lǐng)域進(jìn)行市場預(yù)測，并對其他領(lǐng)域進(jìn)行定性預(yù)測，同時(shí)討論每個(gè)領(lǐng)域的方法論和范圍執(zhí)行摘要機(jī)器學(xué)習(xí)方法概述材料發(fā)現(xiàn)電池測試與建模電池組裝與制造電池管理系統(tǒng)分析二次壽命評估預(yù)測公司簡介附錄 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

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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 ? 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.Schr?dinger - an overview3.2.6.Schr?dinger 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.Schr?dinger 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

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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.

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2025年-2035年人工智能驅(qū)動(dòng)電池技術(shù)：技術(shù)、創(chuàng)新和機(jī)遇

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預(yù)計(jì)在未來十年內(nèi)，基于云的人工智能電池診斷市場將以23.4%的復(fù)合年增長率實(shí)現(xiàn)增長

2025年-2035年人工智能驅(qū)動(dòng)電池技術(shù)：技術(shù)、創(chuàng)新和機(jī)遇

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