Search arXiv⌕ Search

arXiv · 2610.05811

Public Battery Field Data

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 22 public field-data releases from 16 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 223 GB of data from at least 131,096 lithium-ion cells, 1,998 battery systems, 44.1 MWh of energy storage capacity, and 1,253 released unit-years of observation. It also contains 293 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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert Masse. 2026-10-05. Public Battery Field Data. https://arxiv.org/abs/2610.05811

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Residual Bias Compensation Dual Extended Kalman Filter for Physics-Based SOC Estimation in Lithium Iron Phosphate Batteries

This paper addresses state of charge (SOC) estimation for lithium iron phosphate (LFP) batteries, where the relatively flat open-circuit voltage (OCV-SOC) characteristic reduces observability. A residual bias compensation dual extended Kalman filter (RBC-DEKF) is developed. Unlike conventional bias compensation methods that treat the bias as an augmented state within a single filter, the proposed dual-filter structure decouples residual bias estimation from electrochemical state estimation. One EKF estimates the system states of a control-oriented parameter-grouped single particle model with thermal effects, while the other EKF estimates a residual bias that continuously corrects the voltage observation equation, thereby refining the model-predicted voltage in real time. Unlike bias-augmented single-filter schemes that enlarge the covariance coupling, the decoupled bias estimator refines the voltage observation without perturbing electrochemical state dynamics. Validation is conducted on an LFP cell from a public dataset under three representative operating conditions: US06 at 0 degC, DST at 25 degC, and FUDS at 50 degC. Compared with a conventional EKF using the same model and identical state filter settings, the proposed method reduces the average SOC RMSE from 3.75% to 0.20% and the voltage RMSE between the filtered model voltage and the measured voltage from 32.8 mV to 0.8 mV. The improvement is most evident in the mid-SOC range where the OCV-SOC curve is flat, confirming that residual bias compensation significantly enhances accuracy for model-based SOC estimation of LFP batteries across a wide temperature range.

eess.SY↗

Distributed Coordination Algorithms with Efficient Communication for Open Multi-Agent Systems with Dynamic Communication Links and Processing Delays: Extended Version

In this paper we focus on the distributed quantized average consensus problem in open multi-agent systems consisting of dynamic directed communication links among active nodes. We propose three communication-efficient distributed algorithms designed for different scenarios. Our first algorithm solves the quantized averaging problem over the currently active node set under finite network openness (i.e., when the active set eventually stabilizes). Our second algorithm extends the aforementioned approach for the case where nodes suffer from arbitrary bounded processing delays. Our third algorithm operates over indefinitely open multi-agent networks with dynamic communication links (i.e., with continuous node arrivals and departures), computing the average that incorporates both active and historically active nodes. We analyze our algorithms' operation, establish their correctness, and present novel necessary and sufficient topological conditions ensuring their finite-time convergence. Numerical simulations on distributed sensor fusion for environmental monitoring demonstrate fast finite-time convergence and robustness across varying network sizes, departure/arrival rates, and processing delays. Finally, it is shown that our proposed algorithms compare favorably to algorithms in the existing literature.

eess.SY↗

Toward Single-Step MPPI via Differentiable Predictive Control

Model predictive path integral (MPPI) is a sampling-based method for solving complex model predictive control (MPC) problems, but its real-time implementation is challenged by computational and sample requirements that grow with the prediction horizon, as well as sensitivity to manually tuned sampling parameters. To address these issues, we propose Step-MPPI, a framework that learns a sampling distribution and MPPI parameters for efficient single-step lookahead MPPI. Specifically, a neural network parameterizes the MPPI sampling mean and covariance at each time step, while the single-step cost weights and temperature are jointly learned in a self-supervised manner over long horizons using the MPC cost, constraint penalties, and maximum-entropy regularization. By embedding long-horizon objectives into the learned cost and sampling policy, Step-MPPI achieves the foresight of multi-step optimization with the millisecond-level latency of single-step lookahead. We demonstrate its efficiency across challenging tasks involving high-dimensional systems and/or long control horizons.

eess.SY↗