Data-Driven Insights: SandboxAQ’s analysis uses over 2 million hours of lab and simulated battery data.
Predictive Capability: Large Quantitative Models (LQMs) help forecast battery performance for U.S. Army conditions.
Operational Readiness: Refined shelf-life assessments may streamline inventory control and maintenance.
SandboxAQ announced progress in refining shelf-life predictions and predictive maintenance for U.S. Army batteries. Working with the U.S. Army Combat Capabilities Development Command (DEVCOM) and the Army’s Command, Control, Communications, Computers, Cyber, Intelligence, Surveillance and Reconnaissance (C5ISR) Center, the company compiled a dataset from more than 2 million hours of testing on 18650 cylinder cells. For example, the data simulates different temperatures, durations, and discharge rates to approximate real-world conditions.
Traditional shelf-life estimation relies on proxy tests that may not reflect actual performance after long periods of storage. Inaccurate estimates can lead to discarding usable batteries or deploying suboptimal units. SandboxAQ’s Large Quantitative Models (LQMs) intend to refine predictions, decreasing testing time and guiding adjustments in storage and deployment practices.
The LQMs use input from lab and simulated data to provide a more reliable understanding of battery behavior. In warehouse settings, new batteries can undergo quick assessments to confirm they meet shelf-life requirements. In operational scenarios, future chargers may deliver performance data to on-site personnel, helping them decide when to replace batteries.
"Most commercial battery applications do not have the same rigorous performance or shelf-life requirements as those intended for military use, so most cell manufacturers do not take shelf-life into consideration when designing advanced battery chemistries or sourcing materials. The comprehensive battery dataset we’ve compiled with C5ISR Center will add this new predictive capability to our Large Quantitative Models, enabling all of our customers and partners to benefit from these previously unavailable insights."
— Ang Xiao, Technical Lead, AI & Quantum Application, SandboxAQ
In October, SandboxAQ noted that its LQMs reduced lithium-ion battery end-of-life prediction times by a substantial margin compared to conventional methods. This advancement may also accelerate development cycles and reduce research costs for cell manufacturers. By applying these methods across multiple sectors, organizations could adopt improved battery technology faster.
SandboxAQ emerged as an independent company from Alphabet Inc. in 2022, backed by investors such as T. Rowe Price, Eric Schmidt, Breyer Capital, Guggenheim Partners, Marc Benioff, Thomas Tull, and Section32. The company’s LQMs address analytical needs across life sciences, financial services, navigation, cyber, and other areas.
