| { |
| "test_id": "CLO_B4C2_CYCLING", |
| "cell_id": "CLO_B4C2", |
| "test_type": "cycle_aging", |
| "temperature_C_min": 30.0, |
| "temperature_C_max": 30.0, |
| "soc_range_min": null, |
| "soc_range_max": null, |
| "soc_step": null, |
| "c_rate_charge": null, |
| "c_rate_discharge": 4.0, |
| "protocol_description": "Closed-loop Bayesian-optimized fast-charge aging at 30 degC. Charge policy (BO-selected): 3.6-6-5.6-4.755. 4C CC-CV discharge. Cycled toward 80% capacity retention (EOL); some cells do not reach EOL within the campaign window.", |
| "num_cycles": 743, |
| "soh_pct": null, |
| "soh_method": null, |
| "cycle_count_at_test": 0, |
| "test_year": 2018, |
| "n_samples": 600601, |
| "duration_s": 1897484.4767000019, |
| "voltage_observed_min_V": 1.9959044, |
| "voltage_observed_max_V": 3.6020846, |
| "current_observed_min_A": -4.010972090909091, |
| "current_observed_max_A": 6.0993333636363625, |
| "temperature_observed_min_C": 30.465247463787488, |
| "temperature_observed_max_C": 44.709492, |
| "sample_dt_min_s": 0.0, |
| "sample_dt_median_s": 5.001599999843165, |
| "sample_dt_max_s": 1805072.4484, |
| "source_doi": "10.1038/s41586-020-1994-5", |
| "source_url": "https://data.matr.io/1/projects/5d80e633f405260001c0b60a", |
| "source_citation": "Attia, P. M., Grover, A., Jin, N., et al. (2020). Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578, 397-402. https://doi.org/10.1038/s41586-020-1994-5", |
| "source_license": "CC-BY-4.0", |
| "source_license_url": "https://creativecommons.org/licenses/by/4.0/" |
| } |
