AI & ML
Public Battery Field Data
arXiv eess.SY0 views
arXiv:2610.05811v2 Announce Type: replace Abstract: Battery systems and algorithms are developed and evaluated primarily on laboratory cycling data, but fielded battery systems are subjected to duty cycles, temperatures, and measurement conditions that cannot be reproduced in a lab. Field data from batteries in service has begun to appear in public releases attached to individual papers, but each release has its own format, units, labels, and other gaps that make it difficult to compare against others. We present a curated collection of 23 public field-data releases from 17 research groups, covering applications that span passenger EVs, buses, light electric mobility, grid-scale storage, distributed storage, consumer electronics and industrial robotics. Where the releases are complete enough to measure, this heterogeneous collection contains 226 GB of data from at least 131,096 lithium-ion cells, 3,025 battery systems, 44.4 MWh of energy storage capacity, and 2,918 released unit-years of observation. It also contains 784 labeled or reported faults, which provide greater visibility into what real battery performance and safety issues look like outside the lab. We release a public repository of metadata and code for each of the field datasets. For each dataset it provides a registry entry with sources, license and access links, checksums to confirm a download is complete, a metadata table, and code for how to load it. We also report where released datasets differ from their papers. We hope the collection can help serve researchers (human or otherwise) as a useful platform for improving battery field diagnostics, fault detection and health estimation. Better field evidence can also feed back to improve manufacturing quality, inform second-life screening and residual-value estimates, and broadly accelerate electrification.
Source:https://arxiv.org/abs/2610.05811PreviousA Dynamic Generalized Kalman Consensus Filter for Switching Sensor NetworksNextAnytime-Feasible First-Order Optimization via Safe Sequential QCQP
Related posts
Qodo builds context into AI code review with Codex
0OpenAI Blog

Ford promises not to sell your driving habits to data brokers
0The Verge
Snap settles lawsuit accusing platform of fueling teen depression and eating disorders
0TechCrunch
Scrape LinkedIn job postings without logging in (Python + Google Sheets)
0dev.to
Same model, same defences: 7 document attacks down to 0, chat attacks barely moved
0dev.to
Skill to Create Product Videos
0Hacker News