Cambridge EnerTech’s

Battery Intelligence for Automotive Applications

How Smart Analysis of Your Battery Data Can Drive On-Time New Vehicle Launches, Optimal Performance, and Increased Margins

DECEMBER 7-8, 2021 | SAN DIEGO, CA & ONLINE (PST)

For OEMs, battery pack manufacturers, electric fleet managers, and Electric Vehicle (EV), the key to unlocking battery life lies in the data. Core potential of battery data when utilizing machine learning and data analytics methods can accurately determine, predict and improve battery life. To achieve high battery efficiency and operational reliability, predictive intelligence, and data analytics will play key roles as artificial intelligence becomes more disruptive in the battery technology space. The Battery Intelligence for Automotive Applications symposium will bring thought leaders from industry and academia to discuss how organizations, when applying battery domain knowledge and intelligent systems, can leverage descriptive, diagnostic, predictive, and prescriptive analytics to significantly and continuously improve battery life. Gain an edge against competition, ensure cost optimizations and zero downtime, acquire better ROI throughout the battery lifecycle, and make the most of the emerging opportunities in this space.

Tuesday, December 7

7:30 am Registration and Morning Coffee

INTRODUCTION TO ENTERPRISE BATTERY INTELLIGENCE (EBI)

8:50 am Organizer's Remarks

Mary Ann Brown, Executive Director, Conferences, Cambridge EnerTech

INDUSTRY PERSPECTIVES

8:55 am

Chairperson's Remarks

Austin Sendek, PhD, Founder/CEO, Aionics, Inc.; Adjunct Professor, Stanford University
9:00 am

Introduction to Enterprise Battery Intelligence (EBI)

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

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 Enterprise Battery Intelligence (EBI) system. The EBI 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 EBI to provide a new level of insight and ensure that batteries are performant, reliable, and safe.

9:20 am

"Knees" in Lithium-Ion Battery Lifetime

Peter Attia, PhD, Department of Materials Science, Stanford University

One of the most challenging aspects of improving lithium-ion battery lifetime is the presence of "knees", or the sudden loss of capacity, power, or energy with cycling. Here, I summarize the work of an international collaboration to classify observed and proposed knee mechanisms from the literature. We find that some mechanisms can be predicted from electrochemical signals, while others cannot. This work informs strategies for battery lifetime prediction.

9:40 am

A Holistic Systems Approach for Lithium Metal Battery Development

Richard Wang, CEO & Founder, Cuberg

Successful commercial innovation in battery technology is uniquely difficult because of interrelated and coupled challenges stemming from the atomic scale all the way through GWh-level manufacturing. Cuberg, as part of Northvolt, has pioneered a vertically integrated approach towards battery innovation that combines excellence in simulation, data science, high-throughput testing, cell design, process development, supply chain, and customer distribution to deliver the world's first lithium metal battery at commercial scale. 

10:00 am MODERATED Q&A:

Session Wrap-Up

Panel Moderator:
Austin Sendek, PhD, Founder/CEO, Aionics, Inc.; Adjunct Professor, Stanford University
Panelists:
Tal Sholklapper, CEO & Co-Founder, Voltaiq, Inc.
Peter Attia, PhD, Department of Materials Science, Stanford University
Richard Wang, CEO & Founder, Cuberg
10:15 am Coffee Break
10:35 am

Chairperson's Remarks

Tal Sholklapper, CEO & Co-Founder, Voltaiq, Inc.
10:40 am

Industry 4.0 Software Architecture and Data Collection During Cell Production

Bob Zollo, Solution Architect for Battery Testing, Keysight Technologies

This presentation covers software and data collection for cell formation, aging, and grading on production lines. A tuned solution for formation lines can be based on Industry 4.0 technologies, thus providing a system with flexibility and agility that securely manages processes, data collection, and storage. This data is an important battery intelligence data source to track cell provenance and history as from cell development through manufacturing and eventually into batteries deployed in the field.

11:00 am

AI/ML Life Prediction in Use-Cases Having Complex Roles and Variable EOL Definitions

Susan Babinec, Program Lead, Stationary Storage, Argonne Collaborative Center for Energy Storage Science (ACCESS), Argonne National Laboratory

AI/ML cycle life prediction is emerging as the broadly transformational capability; initial focus has been on urgent transportation needs.   Argonne’s expanded scope includes diverse stationary markets, where useful life extends beyond 80% capacity and use-protocols are complex and changeable (e.g., stacking or role changes during asset life) and requires alternative research strategies. This talk compares results with various ML approaches and with the use of both experimental and synthetic data.

Paul van Wijk, Vice President, Sales, PhotonFirst

Growing numbers of electric propulsion for advanced performance (auto)motive applications drive the need for battery condition and health monitoring of temperature, voltage and current in and outside the pack as well as active cell balancing. PhotonFirst develops fiber optic sensing systems based on photonic integrated circuits (PICs) enabling low cost, robust, low weight and scalable solutions. Paul van Wijk will share how the company unlocks this technology for your application.

11:40 am MODERATED Q&A:

Session Wrap-Up

Panel Moderator:
Tal Sholklapper, CEO & Co-Founder, Voltaiq, Inc.
Panelists:
Bob Zollo, Solution Architect for Battery Testing, Keysight Technologies
Susan Babinec, Program Lead, Stationary Storage, Argonne Collaborative Center for Energy Storage Science (ACCESS), Argonne National Laboratory
Paul van Wijk, Vice President, Sales, PhotonFirst
11:55 am Networking Lunch

START-UP PERSPECTIVES

12:55 pm

Chairperson's Remarks

Eli Leland, PhD, CTO and Co-Founder, Voltaiq, Inc.
1:00 pm

Introduction to Battery Machine Learning

Christianna N. Lininger, PhD, Application Engineer, Voltaiq, Inc.

The field of battery development and manufacturing is full of opportunities for the application of machine learning. Machine learning techniques have accelerated materials discovery at the fundamental atomic scale, and have also impacted the commercial and manufacturing scale, accelerating failure predictions. In this talk, we will be covering some case studies of impactful machine learning applications in the battery field, spanning these time, length, and cost dimensions.

1:20 pm

Predictive Battery Analytics: A Major Driver for Unlocking Value in the Battery Lifecycle – Examples from the Electric Bus & Automotive Industry

Stephan Rohr, Founder & Co-CEO, TWAICE

Battery Analytics gives customers control over batteries along the lifecycle: development, for optimized battery design; in-life, for optimized fleet management including maintenance, and in second life scenarios: reselling or repurposing in new use cases. Drawing on customer success stories, we will present arguments for deploying battery analytics in electric vehicle fleets. This holistic approach to lifecycle management enables lower costs, improved quality and a sustainable contribution to renewable energy.

1:40 pm

Accelerating Battery Materials Discovery with Physics-Based Machine Learning

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

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. 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.

2:00 pm MODERATED Q&A:

Session Wrap-Up

Panel Moderator:
Eli Leland, PhD, CTO and Co-Founder, Voltaiq, Inc.
Panelists:
Christianna N. Lininger, PhD, Application Engineer, Voltaiq, Inc.
Stephan Rohr, Founder & Co-CEO, TWAICE
Austin Sendek, PhD, Founder/CEO, Aionics, Inc.; Adjunct Professor, Stanford University
2:15 pm Refreshment Break
2:35 pm

Chairperson's Remarks

Christianna N. Lininger, PhD, Application Engineer, Voltaiq, Inc.
2:40 pm

Charging Algorithms – Powered by Machine Learning – Can Ultra-Fast Charge Today’s Lithium-Ion Batteries with Minimal Degradation

Kostyantyn Khomutov, Co-Founder and Chief Executive Officer, GBatteries

GBatteries will present an innovative and compelling way to charge unaltered Li-ion batteries, powered by machine learning. One which reduces irreversible chemical reactions and allows for ultra-fast charging. To increase the adoption of EVs globally and drastically reduce greenhouse gas emissions, GBatteries has developed a charging algorithm that can ultra-fast charge lithium-ion batteries without compromising battery lifespan. No need to change the manufacturing process or improve the chemistry. GBatteries’ technology works with off-the-shelf batteries that have been produced by any of the leading manufacturers.

3:00 pm

Early Warning Prognostics and Prevention of Thermal Runaway

Niles Fleischer, CEO, ALGOLiON Ltd.

Lithium battery fires are becoming more common. ALGOLiON developed a software solution for the EV and other markets that provides the earliest detection of lithium battery fire hazards, warning of explosions days in advance instead of the seconds available now. AlgoShield provides a better ROI on your battery, and protects your products, the people who use them and your bottom line by lowering warranty costs and exposure to liabilities.

Thyag Sadasiwan, Marketing, KULR Technology

KULR will showcase its latest technology, CellCheck. CellCheck senses and analyzes a battery’s full life history, and adverse incidents including electrical, physical, and environmental events which impact battery health.  CellCheck is able to accurately predict battery health and safety throughout its lifecycle, delivering immediate assessments regarding hazards and risks, degradation of reliability and safety resulting from daily real-world use or abuse of energy dense batteries.

3:40 pm MODERATED Q&A:

Session Wrap-Up

Panel Moderator:
Christianna N. Lininger, PhD, Application Engineer, Voltaiq, Inc.
Panelists:
Kostyantyn Khomutov, Co-Founder and Chief Executive Officer, GBatteries
Niles Fleischer, CEO, ALGOLiON Ltd.
Thyag Sadasiwan, Marketing, KULR Technology

ACADEMIC PERSPECTIVES

3:55 pm

Chairperson's Remarks

Christianna N. Lininger, PhD, Application Engineer, Voltaiq, Inc.
4:00 pm

Multi-Task Learning for One-Shot Prediction of Battery Capacity and Power Degradation

Weihan Li, Independent Junior Research Group Leader, RWTH Aachen University

We introduce a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately with as little as 100 cycles. The stable prediction ability of the model facing capacity and resistance estimation errors further demonstrate the model’s robustness and generalizability. Compared with single-task learning models for forecasting the capacity and power degradation, the model shows a significant prediction accuracy improvement and reduces 50% of the total computational cost.

4:20 pm

Learn Inter-Cycle Features for Battery Life Prognostics and Planning

Anna G. Stefanopoulou, PhD, William Clay Ford Professor of Technology, Professor Mechanical Engineering, Professor of Electrical and Computer Engineering, University of Michigan

Accurate predictions of degradation and lifetime of lithium-ion batteries are essential for reliability, safety, and key to repurposing. Cycle life is a key performance metric for a battery management system since it can dynamically adjust operation limits based on their impact on lifetime 10-20 years ahead. A health-conscious power derating or a temporary stretch of power capability will be continuously adjusted depending on the short-term economics and long-term durability predictions. We show an adaptive inter-cycle extrapolation algorithm that allows us to simulate the entire lifetime of the battery in seconds for a real-time decision. The accelerated simulation allows us also to iteratively tune (learn) degradation parameters to match experimental observations of capacity fade, loss of lithium inventory, and individual electrode capacities (features) from both cycling and calendar aging.


4:40 pm

Machine Learning and Robotic Experimentation to Accelerate Battery Materials Innovation

Venkat Viswanathan, Assistant Professor, Mechanical Engineering, Carnegie Mellon University

We unveil our new robot, Clio, for optimizing nonaqueous battery electrolytes. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics.

5:00 pm MODERATED Q&A:

Session Wrap-Up

Panel Moderator:
Christianna N. Lininger, PhD, Application Engineer, Voltaiq, Inc.
Panelists:
Weihan Li, Independent Junior Research Group Leader, RWTH Aachen University
Anna G. Stefanopoulou, PhD, William Clay Ford Professor of Technology, Professor Mechanical Engineering, Professor of Electrical and Computer Engineering, University of Michigan
Venkat Viswanathan, Assistant Professor, Mechanical Engineering, Carnegie Mellon University
5:15 pm Grand Opening Networking Reception in the Exhibit Hall with Poster Viewing
6:30 pm Evening Tutorials*

Seven tutorials will take place at AABC across Tuesday and Thursday. The tutorials are designed to be instructional, interactive and provide in-depth information on a specific topic. Tutorial themes include introductions for those new to the field as well as explanations on more technical aspects than time allows during our partnering forum, symposia and main conference programs. Instructors are drawn from industry and academia alike, many of whom are recognized in their fields or have teaching experience.

*Tutorials included in All Access Pricing or separate registration required. See Tutorial page for details.

8:00 pm Close of Day

Wednesday, December 8

8:30 am Registration and Morning Coffee

ACADEMIC PERSPECTIVES

8:55 am

Chairperson's Remarks

Tal Sholklapper, CEO & Co-Founder, Voltaiq, Inc.
9:00 am

Model-Based Battery Management Systems for Lithium-Ion Batteries

Venkat R. Subramanian, PhD, Ernest Dashiell Cockrell Chair Engineering & Professor, Mechanical & Materials Science Engineering, University of Texas Austin

Using model-based control strategies, we have developed optimal charging protocols to minimize the capacity fade due to SEI-layer formation, lithium-plating and intercalation-induced stresses, while controlling internal temperatures inside the batteries. In collaboration with NREL, we have shown increase in cycle-life by more than 100%. We will present further results for faster charging and improved battery life on 2.2kWh battery modules.

9:20 am

Big Data for Li-Ion Battery Diagnosis and Prognosis

Matthieu Dubarry, PhD, Assistant Researcher, Battery Testing & Evaluation & Modeling, University of Hawaii

Battery diagnosis and prognosis algorithms are critical to increase penetration of electrochemical storage systems. Current data-driven models are often limited by the non-representativity of the training data available. This work will showcase how synthetic big data training datasets can be used for transfer learning to solve this issue. This approach offers the benefits of the broad applicability to various cell chemistries, designs, and operating modes, as well as high fidelity.

9:40 am

Lithium-Ion Battery Degradation: What You Need to Know and How to Model and Diagnose It 

Yatish Patel, PhD, Research Associate Mechanics of Materials, Mechanical Engineering, Imperial College, London
Gregory J. Offer, PhD, Professor in Electrochemical Engineering, Imperial College London

Professor Offer will present some of the latest work from the Electrochemical Science and Engineering Group at Imperial College London. Greg will present groundbreaking research on understanding and modeling battery degradation. This includes advancing our understanding of lithium plating, and the first model of positive electrode (cathode) decomposition implemented in a continuum scale cell model. Building on this, the majority of the presentation will be on developing novel diagnostic and state estimation techniques which uniquely take advantage of the temperature of the cell and not just voltage and current. The latest work uses knowledge of the entropic behavior of the individual electrodes, (in an analogous way to open-circuit voltage fitting used by the dQ/dV or ICA techniques), to extract loss of lithium inventory (LLI), loss of active material (LAM) or stoichiometric drift, under realistic real-world operating conditions.

10:00 am MODERATED Q&A:

Session Wrap-Up

Panel Moderator:
Tal Sholklapper, CEO & Co-Founder, Voltaiq, Inc.
Panelists:
Venkat R. Subramanian, PhD, Ernest Dashiell Cockrell Chair Engineering & Professor, Mechanical & Materials Science Engineering, University of Texas Austin
Matthieu Dubarry, PhD, Assistant Researcher, Battery Testing & Evaluation & Modeling, University of Hawaii
Yatish Patel, PhD, Research Associate Mechanics of Materials, Mechanical Engineering, Imperial College, London
Gregory J. Offer, PhD, Professor in Electrochemical Engineering, Imperial College London
10:15 am Coffee Break in the Exhibit Hall with Poster Viewing
10:45 am Close of Symposium





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