Battery Intelligence for Automotive Applications
( 電池相關情報 )
How Smart Analysis of Your Battery Data Can Drive On-Time New Vehicle Launches, Optimal Performance, and Increased Margins
With the rapidly approaching electrification of the global vehicle fleet, automotive OEMs and key suppliers of battery packs, cells, and related components are under tremendous pressure to launch more new product lines in a tighter time frame than has ever been done before. Increasingly demanding consumer expectations around EV range and fast-charging ability, combined with a scarcity of qualified engineering talent, add to the challenges of shipping on-time and on-budget. Battery Intelligence – the intelligent analysis that unlocks the potential of data that these organizations are already collecting today – is the key to staying competitive in the electrified automotive landscape. The Battery Intelligence for Automotive Applications symposium will discuss how organizations can leverage a variety of data sources and contemporary analytical techniques (statistical analysis, machine learning, predictive modeling) to accelerate product development cycles, rapidly scale battery platforms across multiple vehicle lines, ensure supply chain integrity, eliminate guesswork to make fully data-driven design decisions, predict battery life, reduce pack overbuild and associated costs, and maximize the productivity of their engineering teams.
Day 1 | Day 2
12:30 pm Symposia Registration
INTRODUCTION TO BATTERY INTELLIGENCE SYSTEMS
1:30 Chairperson’s Opening Remarks
Matthew Murbach, PhD, CTO, Hive Battery Labs
1:35 KEYNOTE PRESENTATION: Introduction to Battery Intelligence Systems (BIS)
Tal Sholklapper, CEO & Co-Founder, Voltaiq
While the industry is familiar with the battery and its battery management system (BMS), very few are aware of the critical need for a missing third layer, the Battery Intelligence System (BIS). The BIS is needed to unlock the significant advances in battery yield, energy density, and lifetime that the industry is calling for. Historically, product OEMs have treated batteries like black boxes, building mechanical and electrical interfaces to keep them stable. As batteries now become the make-or-break component in low-cost EVs and long-lived consumer electronics, companies need the BIS to provide a new level of insight and ensure that batteries are performant, reliable, and safe.
INTELLIGENT BATTERY MATERIALS DEVELOPMENT
1:55 Developing Better Materials Intelligently to Improve Cell Safety and Performance
Paul Homburger, Vice President, Business Development, NOHMS Technologies, Inc.
This talk describes efforts to organize and synthesize results during our extensive testing of electrolyte materials designed to improve the safety and performance of next-gen batteries. I will discuss how an intelligent systems approach allows us to develop better materials more efficiently. This approach allows for improved cell safety coming from the electrolyte level, so they themselves are indeed ‘intelligent battery materials’.
2:15 ALD-Optimized NMC 811: Iterating Faster to Achieve Key Performance Metrics
Barbara Hughes, Director of Energy Storage, Forge Nano
At Forge Nano, the use of ALD to become an industry leader in materials optimization in the battery space is predicated on our ability to optimize coating solutions through an iterative process between coating the materials and electrochemical testing. In this work we have employed Voltaiq analysis software as a tool to efficiently explore ALD coatings as a means of stabilizing Ni-rich NMC surfaces, enabling increased capacity retention and high voltage utilization.
2:35 Separator Innovation Unlocking Next-Generation Lithium Batteries
Travis Baughman, Vice President, Materials Innovation, Sepion Technologies
Sepion Technologies is developing advanced membranes to overcome barriers in the path to wide-spread electrochemical energy use and storage. Known fade mechanisms in Li-ion batteries associated with transitional metal crossover currently limit cycle life, thereby, reducing the impact of these technologies in electric vehicles. Our proprietary membrane technology works in concert with current separator technology to effectively block transition metal crossover resulting in increased energy density and cycle life.
2:55 Developing Advanced Battery Materials for Low Cost, Long Range, and Fast Charge EV
Mei Cai, PhD, Technical Fellow and Lab Group Manager, Energy Storage Materials Group, General Motors Global Research and Development Center
3:15 Refreshment Break
INTELLIGENT BATTERY MANUFACTURING
3:35 Industry 4.0 Cell Manufacturing Factory Software Architectures Permit Improved Cell Yield
Bob Zollo, Solution Architect for Battery Testing, Keysight Technologies
The cell formation and grading section of the cell manufacturing line is the largest, most expensive, and most costly-to-operate portion of the manufacturing line. Today, some manufacturing lines are relying on 20 year old technology because it is safe and reliable. But advances in factory automation, cell forming electronics, measurements, data collection, and control systems offer the promise of improved productivity and efficiency, better flexibility and agility, and increased profitability. With the increased availability of process data and big data machine learning, it is further possible to feedback real-time insight and adapt the process on the fly to give the highest quality cell output.
3:55 Customizing Lithium-Ion Cells – From the First Materials Test to Series Production
Torge Thönnessen, CEO, CUSTOMCELLS®
Batteries are not a one-fit-all solution. CUSTOMCELLS develops tailor-made and optimized battery configurations that can meet very specific requirements such as high energy density and C-rates as well as installation space or temperature requirements. In order to offer the highest quality and thus a long cycle life and safety, we strive for high transparency and corresponding traceability during the development and production process.
4:15 In situ Electronics and Sensors for Intelligence Energy Storage
Joe Fleming, PhD, Assistant Professor, Engineering, Coventry University
This work illustrates turning standard cells into intelligent cells, through the integration of in situ sensors and wireless communication systems during manufacturing, thus enabling significant advancements in performance mapping and cell monitoring technology. Furthermore, the technology demonstrated can be transferred to many cell chemistry and form factors.
4:35 Sponsored Presentation (Opportunity Available)
5:20 Grand Opening Reception in the Exhibit Hall with Poster Viewing
6:20 Close of Day
Day 1 | Day 2
8:30 am Morning Coffee
DATA STRATEGY, SECURITY, AND TRACEABILITY
9:00 Chairperson’s Remarks
Eli Leland, PhD, Co-Founder and Chief Product Officer, Voltaiq
This session is reserved for discussing data sources and computational intelligence techniques that can be leveraged to launch a data strategy and security plan. Additionally, blockchain technology will be discussed as an important role in providing long-term sustainability, higher operational efficiency, as well as enhancing data security, privacy, traceability, accountability, and authentication. Visit www.advancedautobat.com/aabc-us/battery-intelligence.html to keep up with agenda developments and see who will be presenting.
BATTERY INTELLIGENCE IN TRANSPORTATION
11:00 Sponsored Presentation (Opportunity Available)
11:20 Data-Driven Machine Learning Methods for Battery Modelling and State Estimation
Pawel Malysz, PhD, PEng, SMIEEE, Senior Technical Specialist, Electrified Powertrain Systems Engineering, FCA USA LLC
The increased amount of battery testing data and growth of machine learning based tools has made it easier to apply such tools to model battery cells and to perform battery state estimation. The first part of the presentation will show methods into modelling battery cells using machine learning approached based on feed forward neural network (FNN) and recurrent neural networks such as Gated Recurrent Unit (GRU) and Long-term Short-term Memory (LTSM). Practical details such as design, collection and processing of the data for effective training/testing of neural networks shall be discussed. The second part of the presentation shall focus on neural network based approaches for battery state-of-charge (SOC) estimation. Pragmatic approaches designed to train for robustness based on augmented data generation, drive cycle profile design, and repeated random seeding shall be discussed.
11:40 Data-Driven Safety Envelope of Lithium-Ion Batteries for Electric Vehicles
Juner Zhu, PhD, Postdoctoral Associate, Mechanical Engineering and Chemical Engineering, MIT; Co-director, MIT Industrial Battery Consortium
We demonstrated the use of the powerful machine learning tool to develop the “safety envelope” of lithium-ion batteries for electric vehicles that provides the range of mechanical loading conditions ensuring safe operation. The daunting challenge of obtaining a large databank of battery tests was overcome by utilizing a high-accuracy finite element model of a pouch cell to generate over 2500 numerical simulations. The safety envelope will serve as important guidelines to the design of EV and batteries.
12:00 pm Safety Testing Lithium-Ion Batteries for Aviation Applications
Thomas Bloxham, PhD, CRE, Battery Technology Lead, Uber
12:40 Networking Lunch
1:35 Dessert Break in the Exhibit Hall with Poster Viewing
MACHINE LEARNING FOR BATTERIES
2:35 Chairperson’s Remarks
Tal Sholklapper, CEO & Co-Founder, Voltaiq
2:40 Machine Learning for Accelerated EV Battery Development
Christianna Lininger, PhD, Application Engineer, Voltaiq
Developing an EV battery pack is a lengthy process comprising multiple interconnected stages that span from choosing the right cell to integrating the full pack into the vehicle. In this talk we’ll introduce the topic of machine learning and highlight key applications to accelerate the complex process of developing and launching an EV.
3:00 Accelerating Battery Materials Discovery with Physics-Based Machine Learning
Austin Sendek, PhD, Founder and CEO, AIONICS
Discovering promising new materials is central to our ability to design better batteries, but research progress can be limited by an incomplete understanding of structure/property relationships, slow testing cycles, and overwhelmingly large numbers of candidate materials. New machine learning (ML) approaches offer a route to accelerated materials discovery by training predictive models on existing experimental data and then using these models to screen databases of candidate materials. Our research at Stanford University suggests that careful ML modeling can provide a significant acceleration in the rate of new materials discovery, even when trained on small amounts of data. In this talk, we present our research in using ML to accelerate electrode and electrolyte discovery, discuss best practices for the application of ML to materials design, and highlight the Aionics materials design software platform.
3:20 Prediction and Optimization of Battery Lifetime Using Machine Learning
Peter Attia, Senior Data Analyst, Tesla, Inc.
Battery lifetime testing is a major bottleneck in battery development due to both the number and the duration of required experiments. In this talk, I present work from my time at Stanford on both early prediction, which reduces the time per experiment by predicting the final cycle life using data from early cycles, and Bayesian optimization, which reduces the number of experiments by balancing exploration and exploitation to efficiently learn the parameter space.
3:40 Sponsored Presentation (Opportunity Available)
4:20 Networking Reception in the Exhibit Hall with Poster Viewing
5:25 Close of Symposium