Machine Learning Enhanced Acoustic Inspection to Improve Battery Manufacturing
Feasible, Inc.
Recipient
Emeryville, CA
Recipient Location
7th
Senate District
18th
Assembly District
$1,000,000
Amount Spent
Active
Project Status
Project Update
This project completed all technical tasks in 2024. Through this project, Liminal (formerly Feasible) was able to demonstrate EchoStat’s ability to detect lithium-ion manufacturing issues earlier and with more sensitivity than standard methods at cell prototyping and pilot production lines. The project scaled EchoStat technology and products for deployment in Li-ion battery prototyping and pilot production lines; defined system requirements, business case, and roadmap for commercial adoption; and established a manufacturing operations plan and initial supply chain to enter low-rate production. Liminal will wrap up remaining tasks in 2025, including submission of the final report.
The Issue
With California facing the threats of both wildfires and power outages, lower-cost and more reliable batteries are a critical part of the solution to both transportation and power grid problems, as they enable widespread adoption of electric vehicles (EV) and energy storage systems (ESS). Additionally, as battery cells have grown in size and energy density, standard inspection methods are less sensitive to physical variations that affect quality. Manufacturing defects can impact battery performance and safety if they are not caught during the manufacturing process. Innovations for process inspection of batteries has the potential to reduce the cost, and improve efficiency, reliability, and safety of battery manufacturing.
Project Innovation
This project supports the development of a machine learning-driven battery inspection platform, called EchoStat, that uses ultrasound and data analytics to detect manufacturing issues earlier and with more sensitivity than standard inspection technologies. Currently available standard electrical methods for battery inspection are limited in their ability to detect small inconsistencies, which affect performance quality and increase cost and inefficiency in battery manufacturing. In addition, this project aims to reduce battery cell cost and to reduce the likelihood of safety incidents from unexpected battery failures.
Project Goals
Project Benefits
At scale, EchoStat will be able to reduce battery cell cost by $14/kWh. Aligned with SB 350, the proposed project will enable lower cost EV and ESS batteries as well as reduced scale-up time for next generation battery materials. Additionally, efficiently storing excess daytime energy generation through Vehicle-Grid Integration decreases the need for expensive peaker generation.
Affordability
At scale, EchoStat will be able to reduce battery cell cost by $14/kWh. Aligned with SB 350, the proposed project will enable lower cost EV and ESS batteries as well as reduced scale-up time for next generation battery materials.
Reliability
Pairing high-quality, high-performance batteries with renewable energy sources will lead to greater reliability by mitigating unexpected intermittencies.
Safety
This technology detects manufacturing and inherent physical defects earlier and more robustly than standard electrical methods. This dramatically reduces the likelihood of safety incidents from unexpected battery failures.
Subrecipients
James Brahney
Aerotek
Match Partners
Feasible, Inc.
