BatteryOCV: A Paired Dataset and Benchmark for High-to-Low Rate Profile Reconstruction

Battery measurements involve a trade-off between speed and diagnostic value. Higher currents shorten measurement time, but resistance, reaction kinetics, and ion-transport limits shift the measured voltage away from the low-rate reference used to study degradation. Lower currents reduce these effects and provide a more consistent diagnostic reference, but a complete low-rate charge-discharge measurement can take more than 40 hours. We ask how well this reference can be reconstructed from faster, lower-fidelity measurements of the same cell. BatteryOCV contains 182,519 high-rate cycle records and 1,894 repeated low-rate references from 404 industrial NCM/graphite cells, paired on the same physical cells as they degrade. The benchmark task predicts a measured low-rate voltage profile and its charge and discharge capacities from neighboring high-rate voltage cycles, using cell-disjoint splits. We compare signal-processing and classical controls with autoencoder and generative baselines. On unseen cells of the training design, copying, averaging, or smoothing the high-rate curves leaves the discrepancy from the reference largely unchanged, whereas learned mappings reduce it substantially, and a linear ridge model is competitive with the neural networks. Capacity and charge-aligned errors capture what profile error misses, and differential-voltage peak counts provide a coarse check of local features. On held-out product designs, errors increase markedly for both linear and neural models. BatteryOCV provides paired data and a fixed protocol for measuring how much of a slow diagnostic reference can be recovered from fast measurements.