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Context: the project archive (KXKM_Batterie_Parallelator-main) had no git history locally; a fresh repository is needed to host it on git.saillant.cc (electron/KXKM_Batterie_Parallelator). Approach: initialize a new repo on branch main, stage the archive content, and harden .gitignore before the first commit. Changes: - Import the full project tree: firmware/, firmware-idf/, firmware-rs/, iosApp/, kxkm-bmu-app/, kxkm-api/, hardware/, docs/, specs/, scripts/, models/, tests/ - Keep project dotfiles tracked despite the trailing '.*' ignore rule: .github/, .claude/, .superpowers/, .gitattributes, .markdownlint.json - Extend .gitignore: firmware/src/credentials.h (local secrets, template kept), kxkm-bmu-app/**/build/ (66 MB compiled iOS framework), .remember/ (session data) Impact: the project can now be maintained on the self-hosted Gitea forge with a clean, secret-free initial history.
235 lines
9.6 KiB
Python
235 lines
9.6 KiB
Python
#!/usr/bin/env python3
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"""
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scenario_templates.py — Parametric battery context generators for 8 diagnostic scenarios.
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Each scenario type produces a BatteryContext with realistic ranges for LiFePO4/Li-ion
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24-30V batteries in the KXKM BMU fleet (2-23 batteries per unit).
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"""
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from __future__ import annotations
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import random
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from dataclasses import dataclass, field, asdict
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from typing import Callable
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@dataclass
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class BatteryContext:
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"""Battery context JSON structure for LLM prompt input."""
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battery_id: int
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fleet_size: int
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soh_pct: float # 0-100
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rul_days: float # estimated remaining useful life
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anomaly_score: float # 0.0-1.0
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r_ohmic_mohm: float # mOhm
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r_total_mohm: float # mOhm
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r_int_trend_mohm_per_day: float # mOhm/day over 7 days
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v_avg_mv: float # mV
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i_avg_a: float # A
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cycle_count: int
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fleet_health_pct: float # 0-100
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soh_confidence: int # 0-100
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chemistry: str # "LiFePO4" or "Li-ion"
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scenario: str = "" # scenario label (not included in prompt)
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def to_dict(self) -> dict:
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"""Return dict for JSON serialization (excludes scenario label)."""
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d = asdict(self)
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d.pop("scenario", None)
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return d
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# ---------------------------------------------------------------------------
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# Parametric generators per scenario
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# ---------------------------------------------------------------------------
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def _healthy() -> BatteryContext:
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"""Healthy battery, normal operation."""
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return BatteryContext(
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battery_id=random.randint(0, 22),
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fleet_size=random.randint(4, 23),
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soh_pct=round(random.uniform(85, 100), 1),
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rul_days=round(random.uniform(300, 800), 0),
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anomaly_score=round(random.uniform(0.0, 0.1), 2),
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r_ohmic_mohm=round(random.uniform(8.0, 18.0), 1),
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r_total_mohm=round(random.uniform(12.0, 25.0), 1),
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r_int_trend_mohm_per_day=round(random.uniform(-0.02, 0.05), 3),
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v_avg_mv=round(random.uniform(26000, 28500), 0),
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i_avg_a=round(random.uniform(0.5, 8.0), 1),
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cycle_count=random.randint(10, 300),
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fleet_health_pct=round(random.uniform(85, 98), 1),
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soh_confidence=random.randint(75, 100),
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chemistry=random.choice(["LiFePO4", "Li-ion"]),
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scenario="healthy",
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)
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def _early_degradation() -> BatteryContext:
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"""Early degradation: R_int rising slowly, SOH 70-85%."""
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return BatteryContext(
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battery_id=random.randint(0, 22),
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fleet_size=random.randint(4, 23),
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soh_pct=round(random.uniform(70, 85), 1),
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rul_days=round(random.uniform(90, 300), 0),
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anomaly_score=round(random.uniform(0.1, 0.3), 2),
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r_ohmic_mohm=round(random.uniform(18.0, 30.0), 1),
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r_total_mohm=round(random.uniform(25.0, 42.0), 1),
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r_int_trend_mohm_per_day=round(random.uniform(0.05, 0.15), 3),
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v_avg_mv=round(random.uniform(25500, 27500), 0),
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i_avg_a=round(random.uniform(0.5, 8.0), 1),
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cycle_count=random.randint(300, 800),
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fleet_health_pct=round(random.uniform(75, 90), 1),
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soh_confidence=random.randint(60, 90),
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chemistry=random.choice(["LiFePO4", "Li-ion"]),
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scenario="early_degradation",
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)
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def _accelerated_degradation() -> BatteryContext:
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"""Accelerated degradation: R_int knee point, SOH dropping fast."""
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return BatteryContext(
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battery_id=random.randint(0, 22),
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fleet_size=random.randint(4, 23),
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soh_pct=round(random.uniform(55, 75), 1),
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rul_days=round(random.uniform(20, 90), 0),
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anomaly_score=round(random.uniform(0.3, 0.6), 2),
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r_ohmic_mohm=round(random.uniform(28.0, 50.0), 1),
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r_total_mohm=round(random.uniform(40.0, 70.0), 1),
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r_int_trend_mohm_per_day=round(random.uniform(0.15, 0.5), 3),
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v_avg_mv=round(random.uniform(24500, 26500), 0),
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i_avg_a=round(random.uniform(0.5, 6.0), 1),
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cycle_count=random.randint(600, 1500),
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fleet_health_pct=round(random.uniform(65, 85), 1),
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soh_confidence=random.randint(50, 80),
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chemistry=random.choice(["LiFePO4", "Li-ion"]),
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scenario="accelerated_degradation",
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)
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def _connection_issue() -> BatteryContext:
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"""Connection issue: sudden R_int jump, low confidence."""
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return BatteryContext(
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battery_id=random.randint(0, 22),
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fleet_size=random.randint(4, 23),
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soh_pct=round(random.uniform(60, 90), 1),
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rul_days=round(random.uniform(50, 200), 0),
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anomaly_score=round(random.uniform(0.5, 0.9), 2),
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r_ohmic_mohm=round(random.uniform(40.0, 120.0), 1),
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r_total_mohm=round(random.uniform(60.0, 180.0), 1),
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r_int_trend_mohm_per_day=round(random.uniform(0.5, 5.0), 3),
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v_avg_mv=round(random.uniform(25000, 28000), 0),
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i_avg_a=round(random.uniform(0.2, 5.0), 1),
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cycle_count=random.randint(50, 600),
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fleet_health_pct=round(random.uniform(70, 90), 1),
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soh_confidence=random.randint(10, 40),
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chemistry=random.choice(["LiFePO4", "Li-ion"]),
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scenario="connection_issue",
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)
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def _fleet_outlier() -> BatteryContext:
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"""Fleet outlier: GNN detected anomaly relative to peers."""
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return BatteryContext(
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battery_id=random.randint(0, 22),
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fleet_size=random.randint(6, 23),
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soh_pct=round(random.uniform(60, 80), 1),
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rul_days=round(random.uniform(40, 150), 0),
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anomaly_score=round(random.uniform(0.6, 0.95), 2),
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r_ohmic_mohm=round(random.uniform(25.0, 55.0), 1),
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r_total_mohm=round(random.uniform(35.0, 75.0), 1),
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r_int_trend_mohm_per_day=round(random.uniform(0.1, 0.4), 3),
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v_avg_mv=round(random.uniform(24500, 27000), 0),
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i_avg_a=round(random.uniform(0.5, 7.0), 1),
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cycle_count=random.randint(200, 1000),
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fleet_health_pct=round(random.uniform(80, 95), 1),
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soh_confidence=random.randint(55, 85),
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chemistry=random.choice(["LiFePO4", "Li-ion"]),
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scenario="fleet_outlier",
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)
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def _end_of_life() -> BatteryContext:
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"""End of life: SOH < 60%, RUL < 30 days."""
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return BatteryContext(
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battery_id=random.randint(0, 22),
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fleet_size=random.randint(4, 23),
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soh_pct=round(random.uniform(30, 60), 1),
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rul_days=round(random.uniform(0, 30), 0),
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anomaly_score=round(random.uniform(0.7, 1.0), 2),
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r_ohmic_mohm=round(random.uniform(45.0, 100.0), 1),
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r_total_mohm=round(random.uniform(65.0, 150.0), 1),
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r_int_trend_mohm_per_day=round(random.uniform(0.3, 2.0), 3),
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v_avg_mv=round(random.uniform(23000, 25500), 0),
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i_avg_a=round(random.uniform(0.1, 3.0), 1),
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cycle_count=random.randint(1000, 3000),
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fleet_health_pct=round(random.uniform(55, 80), 1),
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soh_confidence=random.randint(40, 75),
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chemistry=random.choice(["LiFePO4", "Li-ion"]),
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scenario="end_of_life",
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)
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def _post_replacement() -> BatteryContext:
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"""Post replacement: new battery, low cycle count, high SOH."""
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return BatteryContext(
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battery_id=random.randint(0, 22),
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fleet_size=random.randint(4, 23),
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soh_pct=round(random.uniform(95, 100), 1),
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rul_days=round(random.uniform(600, 1200), 0),
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anomaly_score=round(random.uniform(0.0, 0.15), 2),
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r_ohmic_mohm=round(random.uniform(6.0, 12.0), 1),
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r_total_mohm=round(random.uniform(9.0, 18.0), 1),
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r_int_trend_mohm_per_day=round(random.uniform(-0.01, 0.02), 3),
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v_avg_mv=round(random.uniform(27000, 29000), 0),
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i_avg_a=round(random.uniform(0.5, 8.0), 1),
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cycle_count=random.randint(0, 20),
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fleet_health_pct=round(random.uniform(80, 95), 1),
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soh_confidence=random.randint(30, 60),
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chemistry=random.choice(["LiFePO4", "Li-ion"]),
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scenario="post_replacement",
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)
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def _fleet_imbalance() -> BatteryContext:
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"""Fleet imbalance: one weak battery dragging others, low fleet health."""
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return BatteryContext(
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battery_id=random.randint(0, 22),
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fleet_size=random.randint(6, 23),
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soh_pct=round(random.uniform(50, 72), 1),
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rul_days=round(random.uniform(30, 120), 0),
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anomaly_score=round(random.uniform(0.4, 0.8), 2),
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r_ohmic_mohm=round(random.uniform(30.0, 65.0), 1),
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r_total_mohm=round(random.uniform(45.0, 90.0), 1),
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r_int_trend_mohm_per_day=round(random.uniform(0.1, 0.4), 3),
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v_avg_mv=round(random.uniform(24000, 26500), 0),
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i_avg_a=round(random.uniform(0.3, 5.0), 1),
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cycle_count=random.randint(400, 1200),
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fleet_health_pct=round(random.uniform(45, 70), 1),
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soh_confidence=random.randint(50, 80),
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chemistry=random.choice(["LiFePO4", "Li-ion"]),
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scenario="fleet_imbalance",
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)
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# ---------------------------------------------------------------------------
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# Registry
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# ---------------------------------------------------------------------------
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SCENARIO_GENERATORS: dict[str, Callable[[], BatteryContext]] = {
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"healthy": _healthy,
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"early_degradation": _early_degradation,
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"accelerated_degradation": _accelerated_degradation,
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"connection_issue": _connection_issue,
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"fleet_outlier": _fleet_outlier,
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"end_of_life": _end_of_life,
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"post_replacement": _post_replacement,
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"fleet_imbalance": _fleet_imbalance,
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}
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def generate_context_for_scenario(scenario: str) -> BatteryContext:
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"""Generate a random battery context for a given scenario type."""
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if scenario not in SCENARIO_GENERATORS:
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raise ValueError(f"Unknown scenario: {scenario}. Valid: {list(SCENARIO_GENERATORS.keys())}")
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return SCENARIO_GENERATORS[scenario]()
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