Cambridge EnerTech主催

Battery Intelligence
( 電池インテリジェンス )

ビッグデータ・機械学習・人工知能による電池性能の最適化

2023年3月22 - 23日



電池市場が急成長するにつれて、その長期的な性能を最適化する必要性も高まっています。OEMや電池メーカー、電動車フリート管理者、電気自動車 (EV) にとって、電池の寿命を延ばす秘訣はデータにあります。機械学習 (ML) やデータ解析を活用して電池データを分析すれば、電池の寿命を的確に測定・予測・延長することができます。電池技術の領域で人工知能 (AI) がより破壊的な影響を及ぼす中で、電池の効率性や運用信頼性を高めるためには、予測的インテリジェンスやデータ解析が主な役割を担います。電池インテリジェンスの部会では、産業界・学術界の思想的リーダーが参集し、各企業が電池インテリジェンスをどう活用して、電池寿命を大幅かつ継続的に改善できるかについて議論します。

3月22日(水)

PLENARY KEYNOTE PROGRAM
全体基調講演

2:45 pm

Organizer's Remarks

Craig Wohlers, Executive Director, Conferences, Cambridge EnerTech

2:50 pmShep Wolsky Battery Innovator of the Year Award Presentation
3:00 pm KEYNOTE PRESENTATION:

If a Lithium-ion Cell Can Operate for More Than 6 Months at 85°C How Long Can It Last at Ambient Temperature?

Jeff Dahn, FRSC, PhD, Professor of Physics and Atmospheric Science, NSERC/Tesla Canada Industrial Research Chair, Canada Research Chair, Dalhousie University

In a few of our recent papers, we have presented Li-ion cell designs with liquid electrolytes that give astounding lifetime at temperatures as high as 85°C. In fact, we have been testing these cells now at 100°C and they are operating well for more than one month so far. I will discuss what is required to make such awesome cells and then consider what their lifetime at ambient temperature might be. I will show that the energy density of these cells is very reasonable and that Co-free moderate-nickel designs also work equally well.

3:30 pm KEYNOTE PRESENTATION:

Next-Generation Batteries - An Update on Li Metal Battery and All Solid-State Battery 

Shirley Meng, PhD, Professor, University of Chicago; Chief Scientist, Argonne Collaborative Center for Energy Storage Science, Argonne National Laboratory

With the recent success in deploying lithium-ion batteries for light-duty passenger cars, it is time for researchers and scientists to work on a road map of next-generation batteries beyond lithium-ion. In this talk, I will give an update on the current status of research efforts in enabling lithium metal batteries and all solid-state batteries. A few cutting-edge scientific tools will be introduced, including X-ray CT, Cryo-EM, Titration GC, and more, all aimed at quantitative understanding of the failure mechanisms of next-gen batteries.

Refreshment Break in the Exhibit Hall with Poster Viewing4:00 pm

MACHINE LEARNING FOR MATERIALS
材料関連の機械学習 (ML)

4:30 pmOrganizer's Remarks

Victoria Mosolgo, Conference Producer, Cambridge EnerTech

4:35 pm

Chairperson's Remarks

Tal Sholklapper, CEO & Co-Founder, Voltaiq, Inc.

4:40 pm

Machine Learning for Materials

Shijing Sun, PhD, Research Scientist, Energy & Materials, Toyota Research Institute

This talk will focus on autonomous materials research. We will discuss the recent progress and future challenges in machine learning for materials.

5:10 pm

Medicine to Materials: Adapting the Drug Discovery Model to the Battery Industry

Austin Sendek, PhD, Founder/CEO, Aionics, Inc.; Adjunct Professor, Stanford University

Over the last decade, pharmaceutical co-innovation partnerships have gotten new life-saving products to market faster. Given the importance decreasing time-to-market for battery innovations, we discuss how the playbook for accelerated drug discovery is being adapted to the battery industry. We compare and contrast these two industries from both business and technical perspectives, discuss how co-innovation partnerships are being successfully structured for battery R&D, and showcase several case studies from Aionics partners.

5:40 pm Talk Title to be Announced

Speaker to be Announced

Close of Day6:10 pm

3月23日(木)

Registration and Morning Coffee & Pastries7:25 am

BATTERY INTELLIGENCE FOR MODELING AND PRODUCTION
モデリング・生産用の電池インテリジェンス

7:55 am

Chairperson's Remarks

Tal Sholklapper, CEO & Co-Founder, Voltaiq, Inc.

Breakfast Opener - Sponsorship Opportunity Available8:00 am

8:30 am

Modeling of Solid-State Battery Materials with Machine Learning

Artrith Nongnuch, Assistant Professor, Materials Chemistry and Catalysis, Utrecht University

Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

9:00 am

Battery Production Yield Ramp & Quality with Enterprise Battery Intelligence

Tal Sholklapper, CEO & Co-Founder, Voltaiq, Inc.

A hurdle to achieving profitability for new battery manufacturing plants is ramping battery production to meet rising market demand and competition. EBI accelerates production yield ramp and quality optimization by automating analysis, pinpointing root causes of defects, and generating insight by combining performance, traceability, and process & equipment data. We also discuss how EBI enables scaling of these analyses to handle the large volumes of data generated from battery production.

9:30 am Talk Title to be Announced

Speaker to be Announced

Coffee Break in the Exhibit Hall with Poster Viewing10:00 am

MODELING DEGRADATION WITH AI
人工知能 (AI) による劣化のモデリング

10:45 am

Lithium-ion Battery Degradation: How to Model It

Billy Wu, PhD, Senior Lecturer Electrochemical Engineering, Faculty of Engineering, Imperial College London

In this paper, the first published attempt to directly couple more than two degradation mechanisms in the negative electrode is reported. The results are used to map different pathways through the complicated path dependent and non-linear degradation space.

11:15 am

Integrating Physics-Based Modeling and Machine Learning for Degradation Diagnostics of Lithium-ion Batteries

Chao Hu, PhD, Associate Professor, Mechanical Engineering, Iowa State University

This talk will review past and ongoing research studies on battery capacity forecasting and early life prediction and discuss the long-term testing and methodology development efforts led by a team of researchers at the University of Connecticut and Iowa State University.

Sponsored Presentation (Opportunity Available)11:45 am

Enjoy Lunch on Your Own12:15 pm

Dessert Break in the Exhibit Hall - Last Chance for Poster Viewing1:05 pm

NEXT-GENERATION INTELLIGENT MANAGEMENT SYSTEMS
次世代型のインテリジェント管理システム

1:30 pm

Chairperson's Remarks

Weihan Li, Young Research Group Leader, RWTH Aachen University

1:35 pm

Integrating Physics and Machine Learning for Battery Management in the Cloud

Weihan Li, Young Research Group Leader, RWTH Aachen University

Here, we present a degradation diagnosis framework for lithium-ion batteries by integrating field data, impedance-based modeling, and artificial intelligence, revolutionizing the degradation identification with accurate and robust estimation of both capacity and power fade together with degradation mode analysis.

2:05 pm

Next-Generation Intelligent Battery Management System with Enhanced Safety for Transportation Electrification

Nikolaus Keuth, PhD, Senior Group Product Manager, IODP XI Data Analytics Solutions, AVL List GmbH

With AVL Battery Life Cycle Management, a holistic approach of optimizing the battery lifecycle from material mining, battery design, development & testing, through the whole in-use-phase to second-life utilization and recycling is addressed. It will be shown how intelligent modelling a cloud based environment, based on federated learning enables highly accurate prediction of battery degradation for single vehicles and complete fleets. This allows to improve cell and battery design and also in time maintenance scheduling and second-life usage.

2:35 pm

Industrial AI for EV Battery Analytics

Jay Lee, PhD, Vice Chairman, Foxconn Technology Group

Industrial AI, Big Data Analytics, Machine Learning, and Cyber-Physical Systems are changing the way we design product, manufacturing, and service systems. It is clear that as more sensors and smart analytics software are integrated in the EV systems, predictive technologies can further learn and autonomously optimize mobility and performance. This presentation will address the trends of Industrial AI for smart battery realization. First, Industrial AI systematic approach will be introduced. Case studies on advanced predictive analytics technologies for smart battery health management and mobility optimization will be discussed. In addition, issues battery data quality in future predictive mobility will be discussed.

3:05 pm

Needle Penetration Studies on Automotive Lithium-ion Battery Cells

Hyojeong Kim, Graduate Student, Battery Safety, BMW AG

Thermal runaway can be enabled when the heat dissipated from internal short circuit (ISC) leads to a failure of separator. In prismatic cells, a crucial type of ISC is located between the cell can on cathode potential and the outermost anode layer. In this study, we investigate this critical short circuit introduced by needle penetration and assess the influence of safety device on thermal runaway.

INTELLIGENT CHARGING
インテリジェント充電

3:35 pm

Ultra Power Dynamic Charging System for EV

Takamitsu Tajima, Chief Engineer, EV Development, Honda R&D Co. Ltd.

This presentation details a dynamic charging system, achieving an unlimited EV cruising range by charging the EV at high power during cruising. This system would help make it possible to finish battery charging in a short time by contact with the EV while cruising and enable drivers to freely cruise their intended routes after charging.

Presentation to be Announced4:05 pm

Close of Conference4:35 pm

* 不測の事態により、事前の予告なしにプログラムが変更される場合があります。