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Production Feature Engineering for Prediction
When predicting SOH, we don't have SOH yet - need to:
1. Predict SOH iteratively (for rolling features)
2. Or use only features that don't require SOH
3. Or bootstrap initial SOH estimate
This version handles production prediction scenarios.
"""
import pandas as pd
import numpy as np
from typing import Optional, Dict
import warnings
warnings.filterwarnings('ignore')
from feature_engineering_causal import calculate_energy_throughput
def engineer_features_production(
df: pd.DataFrame,
initial_capacity: float,
predicted_soh_series: Optional[pd.Series] = None,
use_fixed_bounds: bool = True
):
"""
Engineer causal features for production prediction.
When SOH is not available (production prediction):
- Uses capacity to estimate SOH: soh_estimate = capacity / initial_capacity
- Creates rolling features based on estimated SOH
- Or uses predicted_soh_series if available (iterative prediction)
Parameters:
-----------
df : DataFrame
BMS data (may not have 'soh' column)
initial_capacity : float
Initial battery capacity
predicted_soh_series : Series, optional
Predicted SOH values from previous iterations (for iterative prediction)
use_fixed_bounds : bool
Use fixed bounds for normalization
Returns:
--------
DataFrame: Features ready for model prediction
"""
df_feat = df.copy()
if 'cycle_index' not in df_feat.columns:
raise ValueError("cycle_index column required")
# Sort by cycle_index
df_feat = df_feat.sort_values('cycle_index').reset_index(drop=True)
cycle_groups = df_feat.groupby('cycle_index')
features = pd.DataFrame()
features['cycle_index'] = cycle_groups['cycle_index'].first()
# ========================================================================
# 1. RAW MEASUREMENTS (causal - current cycle only)
# ========================================================================
if 'voltage' in df_feat.columns:
features['voltage_mean'] = cycle_groups['voltage'].mean()
if use_fixed_bounds:
features['voltage_norm'] = (features['voltage_mean'] - 0) / (5.0 - 0)
else:
features['voltage_norm'] = features['voltage_mean'].expanding().apply(
lambda x: (x.iloc[-1] - x.min()) / (x.max() - x.min()) if x.max() > x.min() else 0.5
)
if 'current' in df_feat.columns:
features['current_mean'] = cycle_groups['current'].mean()
if use_fixed_bounds:
features['current_norm'] = (features['current_mean'] + 10) / 20
else:
features['current_norm'] = features['current_mean'].expanding().apply(
lambda x: (x.iloc[-1] - x.min()) / (x.max() - x.min()) if x.max() > x.min() else 0.5
)
features['is_charge'] = (features['current_mean'] > 0).astype(int)
features['is_discharge'] = (features['current_mean'] < 0).astype(int)
if 'temperature' in df_feat.columns:
features['temperature_mean'] = cycle_groups['temperature'].mean()
if use_fixed_bounds:
features['temperature_norm'] = (features['temperature_mean'] - 0) / (50.0 - 0)
else:
features['temperature_norm'] = features['temperature_mean'].expanding().apply(
lambda x: (x.iloc[-1] - x.min()) / (x.max() - x.min()) if x.max() > x.min() else 0.5
)
if 'capacity' in df_feat.columns:
features['capacity'] = cycle_groups['capacity'].mean()
features['capacity_norm'] = features['capacity'] / initial_capacity if initial_capacity > 0 else features['capacity']
# ========================================================================
# 2. ESTIMATE OR USE SOH
# ========================================================================
# If SOH not in data, estimate from capacity
if 'soh' not in df_feat.columns:
if predicted_soh_series is not None:
# Use predicted SOH from previous iteration
features['soh'] = features['cycle_index'].map(predicted_soh_series).fillna(features['capacity_norm'])
else:
# Estimate SOH from capacity (simple approach for production)
features['soh'] = features['capacity_norm'].clip(0, 1)
else:
features['soh'] = cycle_groups['soh'].mean()
# ========================================================================
# 3. STATISTICAL FEATURES (causal - current cycle only)
# ========================================================================
for col in ['voltage', 'current', 'temperature']:
if col in df_feat.columns:
features[f'{col}_mean'] = cycle_groups[col].mean()
features[f'{col}_std'] = cycle_groups[col].std().fillna(0)
features[f'{col}_min'] = cycle_groups[col].min()
features[f'{col}_max'] = cycle_groups[col].max()
features[f'{col}_range'] = features[f'{col}_max'] - features[f'{col}_min']
features[f'{col}_median'] = cycle_groups[col].median()
if 'current' in df_feat.columns and 'capacity' in df_feat.columns:
charge_capacity = df_feat[df_feat['current'] > 0].groupby('cycle_index')['capacity'].mean()
discharge_capacity = df_feat[df_feat['current'] < 0].groupby('cycle_index')['capacity'].mean()
features['charge_discharge_diff'] = (charge_capacity - discharge_capacity).fillna(0)
if 'voltage' in df_feat.columns and 'current' in df_feat.columns:
try:
energy = calculate_energy_throughput(df_feat)
features['energy_throughput'] = features['cycle_index'].map(energy).fillna(0)
except:
features['power_mean'] = features['voltage_mean'] * features['current_mean']
features['energy_throughput'] = features['power_mean'].abs()
# ========================================================================
# 4. TEMPORAL FEATURES (causal)
# ========================================================================
features['cycles_from_start'] = features['cycle_index'] - features['cycle_index'].min()
if 'time' in df_feat.columns:
features['cumulative_time'] = cycle_groups['time'].sum()
features['time_per_cycle'] = cycle_groups['time'].mean()
else:
features['cumulative_time'] = features['cycles_from_start']
features['time_per_cycle'] = 1.0
features['days_since_start'] = features['cycles_from_start'] * 0.5
# ========================================================================
# 5. DEGRADATION FEATURES (causal - backward-looking only)
# ========================================================================
# These use SOH, which we've estimated above
features['soh_fade_rate'] = features['soh'].diff().fillna(0)
features['soh_fade_rate_abs'] = features['soh_fade_rate'].abs()
features['soh_rolling_mean_5'] = features['soh'].rolling(window=5, min_periods=1, center=False).mean()
features['soh_rolling_std_5'] = features['soh'].rolling(window=5, min_periods=1, center=False).std().fillna(0)
features['soh_ema'] = features['soh'].ewm(span=5, adjust=False).mean()
features['soh_second_derivative'] = features['soh_fade_rate'].diff().fillna(0)
features['cumulative_capacity_loss'] = (1 - features['soh']) * initial_capacity if initial_capacity > 0 else (1 - features['soh'])
if 'capacity' in features.columns:
features['capacity_fade_rate'] = features['capacity'].diff().fillna(0)
features['capacity_fade_rate_pct'] = features['capacity'].pct_change().fillna(0)
# ========================================================================
# 6. PHYSICS-INFORMED FEATURES (causal)
# ========================================================================
if 'current' in df_feat.columns:
charge_by_cycle = df_feat[df_feat['current'] > 0].groupby('cycle_index')['current'].sum()
discharge_by_cycle = df_feat[df_feat['current'] < 0].groupby('cycle_index')['current'].sum().abs()
features['charge_discharge_asymmetry'] = (charge_by_cycle / discharge_by_cycle).fillna(1.0)
features['charge_discharge_asymmetry'] = features['cycle_index'].map(features['charge_discharge_asymmetry']).fillna(1.0)
if 'temperature' in df_feat.columns:
temp_stress = cycle_groups['temperature'].apply(lambda x: (x > 40).sum())
features['temperature_stress'] = temp_stress / cycle_groups['temperature'].count()
features['max_temperature'] = cycle_groups['temperature'].max()
features['min_temperature'] = cycle_groups['temperature'].min()
features['temperature_range'] = features['max_temperature'] - features['min_temperature']
if 'voltage' in df_feat.columns and 'current' in df_feat.columns:
discharge_voltage_var = df_feat[df_feat['current'] < 0].groupby('cycle_index')['voltage'].var().fillna(0)
features['voltage_variance_discharge'] = features['cycle_index'].map(discharge_voltage_var).fillna(0)
if 'voltage_mean' in features.columns and 'current_mean' in features.columns:
if 'power_mean' not in features.columns:
features['power_mean'] = features['voltage_mean'] * features['current_mean']
features['power_abs'] = features['power_mean'].abs()
# Fill any remaining NaN values
features = features.fillna(0)
return features
def engineer_features_iterative(
df: pd.DataFrame,
initial_capacity: float,
model,
feature_cols: list,
max_iterations: int = 3
):
"""
Iterative feature engineering for production prediction.
Approach:
1. Estimate SOH from capacity initially
2. Engineer features with estimated SOH
3. Predict SOH
4. Use predicted SOH to re-engineer features
5. Re-predict SOH (iterative refinement)
Parameters:
-----------
df : DataFrame
BMS data (no SOH column)
initial_capacity : float
Initial capacity
model : RandomForestRegressor
Trained model
feature_cols : list
Feature columns expected by model
max_iterations : int
Maximum iterations for refinement
Returns:
--------
DataFrame: Features with refined SOH predictions
"""
predicted_soh = None
for iteration in range(max_iterations):
# Engineer features with current SOH estimate
features = engineer_features_production(
df,
initial_capacity,
predicted_soh_series=predicted_soh,
use_fixed_bounds=True
)
# Predict SOH
X = features[feature_cols].values
new_predicted_soh = model.predict(X)
predicted_soh = pd.Series(new_predicted_soh, index=features['cycle_index'])
# Check convergence
if iteration > 0:
change = np.abs(new_predicted_soh - old_predicted_soh).max()
if change < 0.001: # Converged
break
old_predicted_soh = new_predicted_soh.copy()
# Final feature engineering with converged SOH
features = engineer_features_production(
df,
initial_capacity,
predicted_soh_series=predicted_soh,
use_fixed_bounds=True
)
return features
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