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

2:40 pmBechtel Break Sponsor Intro
2:45 pm

Organizer's Remarks

Craig Wohlers, Executive Director, Conferences, Cambridge EnerTech

2:50 pmBest of Show Poster Award Presentation Sponsored by Granutools
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.

Best of Show Exhibitor Award Ceremony & 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, PhD, 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 Process & Production Innovation: How to Protect Innovation in the Rapidly Evolving Battery Patent Space

Hyun Jin (HJ) In, Principal, Legal, Fish & Richardson

Daniel Tishman, Principal, Fish & Richardson

The worldwide transition to electric vehicles has resulted in a major increase in the development of intellectual property for battery technologies, leading to a notable increase in patent filings at the United States Patent and Trademark Office. As more companies enter the marketplace and seek patent protection, the IP space becomes increasingly complex and disputes among competitors are heating up.  This talk will address winning IP strategies, both defensive and offensive.

Close of Day6:10 pm

3月23日(木)

Registration Open7:30 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

7:45 amCoffee & Pastries Hosted by Honeywell
7:55 amBrenntag Break Sponsor Intro
8:00 am

Chairperson's Remarks

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

8:05 am Optimizing Battery Production through AI/ML-Enabled Production Insight

Shanita Woodard, Product Marketing Leader, Honeywell Connected Industrial, Honeywell

Battery performance and longevity are a function of multiple variables from battery composition and design to production, packaging and storage. As the first critical stage of battery lifespan, the manufacturing process has significant influence on the performance of batteries. Here we will look at how analytics and insights gained through AI/ML intelligence from the production process can enable optimization across the supply chain for maximum battery performance.

8:20 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.

8:50 am

Battery Production Yield Ramp & Quality with Enterprise Battery Intelligence

Tal Sholklapper, PhD, 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.

9:20 am Talk Title to be Announced

Tina Angerer, Head of Battery Concept, Akkodis

Sascha Harm, Dr., Senior Expert Cell Chemistries, Akkodis

A challenge with greater electrification through battery-powered devices and vehicles is access to an intelligent, modular battery system.  Our talk will address this challenge by presenting initial findings from an internal R&D project for modular batteries which can be used to power larger electronic devices and be transported from one device to another.  We will demonstrate how this module-based battery ecosystem contributes to the development of a sustainable society.

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

Multi-Scale Modelling of Lithium-ion Battery Degradation

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

This talk will explore how multi-scale models can capture coupled thermal-electrochemical-mechanical battery degradation processes across scales. This includes exploring the interplay between particle cracking, lithium-plating, and solid-electrolyte interphase growth, highlighting the heterogeneous behaviour across scales, from single particles to entire battery packs, path dependency, and positive/negative feedback effects.

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.

11:45 am Dynamic Fast Charging Protocols to Minimize Battery Cell Degradation: Case Study of Apple, Samsung & Xiaomi Smartphones

Ali Khazaeli, Dr., Subject Matter Expert, TechInsights

Join us to learn how fast charging techniques used by Apple, Samsung, and Xiaomi are impacting the future state.  We will share our research & observations on how these phones benefit from adaptive charging algorithms to suppress battery degradation, which would generally result from the high applied current.   As industries and research embrace concepts like AI and big data, we anticipate seeing more advance charging profiles benefiting from online parameter estimations.

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

By collecting battery data from the field and building up the battery digital twin in the cloud, the degradation and safety of batteries can be monitored online and the information regarding the degradation modes can be extracted from the data. Physics-based models are gaining more and more success in describing cell behavior and early-stage capacity fade, while the emergence of machine learning models further generates rapid predictions of future health based on indicators learned purely from data. Blending the physics-based model and machine learning is challenging. Here, we will present our work in last years to show the methodology and benefits of integrating physics-based models and machine learning based data-driven methods for battery management.

2:05 pm

Advanced Model-Based Battery Lifetime Prediction for Vehicle Fleets

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.

4:05 pm

Accelerating Battery Materials Development with Materials Informatic

Jake Mohin, PhD, Senior Data Solutions Engineer, Citrine Informatics

A challenge to bring novel battery materials to market is the incredible complexity of materials design and selection required for a high-performance battery. Materials Informatics (MI) is a flourishing field which utilizes advanced data management and machine learning to enhance materials research. Battery development is a compelling use-case for MI due to the high-dimensionality of materials selection and the often outsized effect that these materials have on downstream cycle life performance. This talk will outline some of these use-cases of MI in battery materials development and demonstrate in particular how electrolyte formulation can be accelerated to yield novel materials combinations with high predicted performance.

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。