New York Scientific Data Summit 2022: Data-Driven Discovery in Science and Industry

Session

Energy Storage

19 Oct 2022, 11:00

This summit will be held as a virtual event.

Conveners

Energy Storage

• Feng Wang (Argonne National Laboratory)

1. Opening and Welcome

Kerstin Kleese van Dam (Brookhaven National Laboratory)

19/10/2022, 11:00

2. Analytics and Tools for Grid-Connected Energy Storage Applications

Dexin Wang (PNNL)

19/10/2022, 11:10

Abstract: The increasing penetration of renewable generation imposes challenges on power system operation due to its natural uncertainty and variability. Energy storage can save energy from electricity at a point in time for later use and respond instantaneously to unpredictable variations in demand and generation. Various energy storage technologies, such as batteries, pumped hydro,...

3. Apply Data Science for the R&D of Next Generation Battery

Chen Ling (Toyota Research Institute of North America)

19/10/2022, 11:40

Abstract: Batteries dominate the power source of portable electronic devices. Yet in large scale applications the requirement remains to improve the battery performances and reduce the cost. It urges the research of new battery chemistries as well as seeking better strategies for the deployment and recycling. The traditional simulation and experiment methods in battery research usually...

4. Digital Twin-based optimization of battery manufacturing processes

Alejandro A. Franco (Université de Picardie Jules Verne and Institut Universitaire de France)

19/10/2022, 12:10

Abstract: The needed significant deployment of rechargeable lithium ion batteries (LIBs), in particular to satisfy the demand from the Electric Vehicle (EV) sector, triggers the massive planification and deployment of giga-factories to reduce the cost of production. This is accompanied by efforts at the lab or pilot line scales to further optimize LIBs in terms of performance, durability,...

5. Data-driven early battery life prediction for a diverse experimental dataset

Noah Paulson (Argonne National Laboratory, Data Science and Learning and Applied Materials Divisions )

19/10/2022, 12:40

Abstract: Lithium-ion battery life prediction is a critical component of the clean energy transition as it enables accelerated development of new battery designs and materials and reduces deployment risk in transportation and grid storage. This is challenging due to diverse failure modes and rates that are a function of battery chemistry, design, and usage scenario. In this work, we...

6. Real time data and multiscale process control in manufacturing of battery materials and roll-to-roll manufacturing

Santenu Chaudhuri (Argonne National Laboratory)

19/10/2022, 13:10

Abstract: Artificial intelligence is revolutionizing science and engineering. As we transition from traditional energy sources to an electrified and decarbonized economy, the need for better data systems ready for application of AI in manufacturing and operations of energy systems is critically important. In order to accelerate materials scale-up and manufacturing. we have created a new...