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8b37c3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | """Leakage-safe current-cycle SOH labels and partial-discharge inputs.
The current cycle's full discharge capacity is used only to define the target.
Predictors are computed from the early 4.0 V to 3.6 V discharge segment and
from usage accumulated strictly before the current cycle.
"""
from __future__ import annotations
from dataclasses import dataclass
import numpy as np
import pandas as pd
from src.utils.config import FEATURE_COLS_V3, SEQUENCE_FEATURE_COLS_V3
@dataclass(frozen=True)
class PartialCycleConfig:
upper_voltage_v: float = 4.0
lower_voltage_v: float = 3.6
sequence_bins: int = 64
reference_cycles: int = 3
def add_soh_labels(
cycles: pd.DataFrame,
*,
battery_col: str = "battery_id",
capacity_col: str = "Capacity",
cycle_col: str = "cycle_number",
reference_cycles: int = 3,
) -> pd.DataFrame:
"""Add reference capacity and SOH without exposing them as predictors."""
required = {battery_col, capacity_col, cycle_col}
missing = required.difference(cycles.columns)
if missing:
raise KeyError(f"Missing columns for SOH labels: {sorted(missing)}")
out = cycles.sort_values([battery_col, cycle_col]).copy()
valid = out[np.isfinite(out[capacity_col]) & (out[capacity_col] > 0)]
refs = (
valid.groupby(battery_col, sort=False)[capacity_col]
.apply(lambda values: reference_capacity_from_characterization(
values, reference_cycles=reference_cycles
))
.rename("reference_capacity_ah")
)
out = out.join(refs, on=battery_col)
out["SoH"] = 100.0 * out[capacity_col] / out["reference_capacity_ah"]
return out.sort_index()
def reference_capacity_from_characterization(
capacities_ah: pd.Series | list[float] | np.ndarray,
*,
reference_cycles: int = 3,
characterization_fraction: float = 0.85,
) -> float:
"""Estimate Q_ref from the first three full-capacity characterizations.
NASA archives sometimes begin with intentionally partial diagnostic
discharges. Full-capacity candidates are therefore defined relative to the
battery's robust 95th-percentile capacity, and the earliest candidates are
used. This affects target calibration only; capacity is never a predictor.
"""
values = pd.to_numeric(pd.Series(capacities_ah), errors="coerce")
values = values[np.isfinite(values) & (values > 0)]
if len(values) < reference_cycles:
raise ValueError("Too few valid capacity characterizations")
threshold = characterization_fraction * float(values.quantile(0.95))
candidates = values[values >= threshold]
if len(candidates) < reference_cycles:
raise ValueError("Too few full-capacity characterization cycles")
return float(candidates.iloc[:reference_cycles].median())
def prior_equivalent_full_cycles(
capacities_ah: pd.Series,
rated_capacity_ah: float,
) -> pd.Series:
"""Cumulative discharged throughput strictly before each cycle."""
if not np.isfinite(rated_capacity_ah) or rated_capacity_ah <= 0:
raise ValueError("rated_capacity_ah must be positive")
cap = pd.to_numeric(capacities_ah, errors="coerce").fillna(0.0)
return cap.shift(1, fill_value=0.0).cumsum() / rated_capacity_ah
def select_partial_discharge(
cycle_df: pd.DataFrame,
*,
upper_voltage_v: float = 4.0,
lower_voltage_v: float = 3.6,
) -> pd.DataFrame:
"""Return the contiguous descending-voltage segment from 4.0 V to 3.6 V."""
if upper_voltage_v <= lower_voltage_v:
raise ValueError("upper_voltage_v must exceed lower_voltage_v")
if "Voltage_measured" not in cycle_df:
raise KeyError("Voltage_measured is required")
frame = cycle_df.reset_index(drop=True).copy()
voltage = pd.to_numeric(frame["Voltage_measured"], errors="coerce").to_numpy()
start_candidates = np.flatnonzero(voltage <= upper_voltage_v)
if start_candidates.size == 0:
raise ValueError("Cycle never reaches the upper partial-voltage boundary")
start = int(start_candidates[0])
end_candidates = np.flatnonzero(voltage[start:] <= lower_voltage_v)
if end_candidates.size == 0:
raise ValueError("Cycle never reaches the lower partial-voltage boundary")
end = start + int(end_candidates[0])
segment = frame.iloc[start : end + 1].copy()
if len(segment) < 3:
raise ValueError("Partial discharge segment contains fewer than three samples")
return segment.reset_index(drop=True)
def _linear_slope(time_s: np.ndarray, values: np.ndarray) -> float:
mask = np.isfinite(time_s) & np.isfinite(values)
if mask.sum() < 2 or np.ptp(time_s[mask]) == 0:
return 0.0
return float(np.polyfit(time_s[mask], values[mask], 1)[0])
def extract_partial_cycle_features(
cycle_df: pd.DataFrame,
*,
cycle_index: int,
prior_efc: float,
ambient_temperature: float,
rated_capacity_ah: float,
config: PartialCycleConfig = PartialCycleConfig(),
) -> dict[str, float]:
"""Extract the 18-feature v3 vector from an observable partial segment."""
seg = select_partial_discharge(
cycle_df,
upper_voltage_v=config.upper_voltage_v,
lower_voltage_v=config.lower_voltage_v,
)
time_s = pd.to_numeric(seg.get("Time"), errors="coerce").to_numpy(dtype=float)
time_s = time_s - time_s[0]
voltage = pd.to_numeric(seg["Voltage_measured"], errors="coerce").to_numpy(dtype=float)
current = pd.to_numeric(seg.get("Current_measured"), errors="coerce").to_numpy(dtype=float)
current_c = np.abs(current) / rated_capacity_ah
observed_temp = "Temperature_measured" in seg and seg["Temperature_measured"].notna().any()
if observed_temp:
temperature = pd.to_numeric(seg["Temperature_measured"], errors="coerce").interpolate(limit_direction="both").to_numpy(dtype=float)
else:
temperature = np.full(len(seg), float(ambient_temperature))
dt = np.diff(time_s, prepend=time_s[0])
energy_wh = float(np.sum(np.abs(current) * voltage * np.maximum(dt, 0.0)) / 3600.0)
v_slope = _linear_slope(time_s, voltage)
if len(voltage) >= 3 and np.ptp(time_s) > 0:
x = time_s / np.ptp(time_s)
curvature = float(np.polyfit(x, voltage, 2)[0])
else:
curvature = 0.0
features = {
"cycle_index": float(cycle_index),
"prior_equivalent_full_cycles": float(prior_efc),
"ambient_temperature": float(ambient_temperature),
"segment_duration_s": float(time_s[-1]),
"segment_energy_wh": energy_wh,
"voltage_start_v": float(voltage[0]),
"voltage_end_v": float(voltage[-1]),
"voltage_mean_v": float(np.nanmean(voltage)),
"voltage_std_v": float(np.nanstd(voltage)),
"voltage_linear_slope_v_per_s": v_slope,
"voltage_curvature": curvature,
"current_mean_c_rate": float(np.nanmean(current_c)),
"current_std_c_rate": float(np.nanstd(current_c)),
"temperature_mean_c": float(np.nanmean(temperature)),
"temperature_std_c": float(np.nanstd(temperature)),
"temperature_rise_c": float(np.nanmax(temperature) - np.nanmin(temperature)),
"temperature_slope_c_per_s": _linear_slope(time_s, temperature),
"temperature_observed": float(observed_temp),
}
assert list(features) == FEATURE_COLS_V3
return features
def make_partial_cycle_sequence(
cycle_df: pd.DataFrame,
*,
ambient_temperature: float,
rated_capacity_ah: float,
config: PartialCycleConfig = PartialCycleConfig(),
) -> pd.DataFrame:
"""Interpolate the partial segment to a fixed 64 x 5 sequence."""
seg = select_partial_discharge(
cycle_df,
upper_voltage_v=config.upper_voltage_v,
lower_voltage_v=config.lower_voltage_v,
)
time_s = pd.to_numeric(seg["Time"], errors="coerce").to_numpy(dtype=float)
time_s = time_s - time_s[0]
if time_s[-1] <= 0:
raise ValueError("Partial segment has non-positive duration")
x = time_s / time_s[-1]
grid = np.linspace(0.0, 1.0, config.sequence_bins)
voltage = pd.to_numeric(seg["Voltage_measured"], errors="coerce").interpolate(limit_direction="both").to_numpy(dtype=float)
current = pd.to_numeric(seg["Current_measured"], errors="coerce").interpolate(limit_direction="both").to_numpy(dtype=float)
observed_temp = "Temperature_measured" in seg and seg["Temperature_measured"].notna().any()
if observed_temp:
temperature = pd.to_numeric(seg["Temperature_measured"], errors="coerce").interpolate(limit_direction="both").to_numpy(dtype=float)
else:
temperature = np.full(len(seg), float(ambient_temperature))
out = pd.DataFrame({
"normalized_time": grid,
"voltage_v": np.interp(grid, x, voltage),
"current_c_rate": np.abs(np.interp(grid, x, current)) / rated_capacity_ah,
"temperature_c": np.interp(grid, x, temperature),
"temperature_observed": float(observed_temp),
})
return out[SEQUENCE_FEATURE_COLS_V3]
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