| { |
| "test_id": "CLO_B4C28_CYCLING", |
| "cell_id": "CLO_B4C28", |
| "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): 6-5.6-4.4-3.834. 4C CC-CV discharge. Cycled toward 80% capacity retention (EOL); some cells do not reach EOL within the campaign window.", |
| "num_cycles": 829, |
| "soh_pct": null, |
| "soh_method": null, |
| "cycle_count_at_test": 0, |
| "test_year": 2018, |
| "n_samples": 702476, |
| "duration_s": 2409475.0075000003, |
| "voltage_observed_min_V": 1.9965405, |
| "voltage_observed_max_V": 3.6018128, |
| "current_observed_min_A": -4.001615272727273, |
| "current_observed_max_A": 6.013960363636363, |
| "temperature_observed_min_C": 30.328297, |
| "temperature_observed_max_C": 35.360762573516205, |
| "sample_dt_min_s": 0.0, |
| "sample_dt_median_s": 5.003000000026077, |
| "sample_dt_max_s": 2408179.6373, |
| "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/" |
| } |
