CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file. Format follows Keep a Changelog and Semantic Versioning.

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[2.0.0] — 2026-02-25 — Current Release

Major Features

• Intra-battery chronological split methodology — Fixes critical data leakage in v1's cross-battery split. Per-battery 80/20 temporal split enables valid within-battery RUL prognostics for deployed systems.
• 99.3% SOH accuracy achieved — Weighted ensemble of ExtraTrees, SVR, and GradientBoosting achieves R²=0.975, MAE=0.84%, exceeding 95% accuracy gate.
• Artifact versioning system — Isolated v1 and v2 models, scalers, results, and figures in artifacts/v1/ and artifacts/v2/ with version-aware loading.
• API versioning — /api/v1/* (legacy, cross-battery) and /api/v2/* (current, intra-battery) endpoints run in parallel for backward compatibility.
• Comprehensive IEEE-style research documentation — Full research paper (8 sections, 290+ lines) with methodology, results, ablation studies, and deployment architecture.
• Production-ready deployment — Single Docker container on Hugging Face Spaces with health checks, model registry, and versioned endpoints.

What's New (v1 → v2)

• Fixed avg_temp corruption bug: v1 API silently modified input temperature when near ambient—removed in v2
• Fixed recommendation engine: v1 returned 0 cycles for all recommendations—v2 uses physics-based degradation rates
• 5 classical ML models exceeding 95% accuracy: ExtraTrees (99.3%), SVR (99.3%), GradientBoosting (98.5%), RandomForest (96.7%), LightGBM (96.0%)
• Model performance comparison:
  • v1 (group-battery split, buggy): 5/12 passing, 94.2% best accuracy, high False Positives
  • v2 (intra-battery chrono split, fixed): 5/8 passing, 99.3% best accuracy, 0% False Positives

Technical Improvements

• ExtraTrees & GradientBoosting added — Identified through Optuna HPO as top performers on chronological split
• SHAP feature importance — cycle_number and delta_capacity dominate; electrical impedance (Rct) secondary
• Ensemble voting strategy — Weighted combination (ExtraTrees 0.40, SVR 0.30, GB 0.20) balances precision and inference speed
• Deep learning analysis — 10 architectures (LSTM, Transformer, TFT, VAE-LSTM) tested; underperform by 10-20% due to 2.7K sample insufficiency; classical ML preferred
• Per-battery accuracy analysis — Uniform >95% accuracy across all 30 batteries; no dataset bias detected
• Feature scaling strategy — Tree models use raw features; linear/kernel models use StandardScaler (fit on train only)

Infrastructure & Deployment

• Docker container: Single aibattery:v2 image deployable to any Kubernetes/cloud platform
• Versioned artifact management: Enables rigorous A/B testing and rollback capability
• Reproducibility guardrails: Fixed random_state=42, locked requirements.txt, frozen Docker base image
• Monitoring endpoints: /health, /api/v2/models, /docs (Swagger) for ops visibility

Code Quality & Documentation

• 13 dead Python scripts removed from root directory (development artifacts)
• Research paper embedded in frontend — Markdown rendering with MathJax for equations
• Technical research notes — 11 sections covering architecture, data pipeline, bug fixes, ensemble strategy
• Jupyter notebook (NB03) — 14 cells, fully executed, covers data loading → model training → evaluation → visualization

Known Limitations

• XGBoost underperformance — Despite Optuna HPO (100 trials), achieves only 90% within-5%; fundamentally incompatible with intra-battery split geometry—ensemble preferred
• Deep learning sample insufficiency — 2,678 cycles / 30 batteries ≈ 89 per battery; insufficient for stable LSTM/Transformer learning
• Linear models hard limit — Ridge/Lasso capped at 32-33% despite hyperparameter tuning; linear decision boundaries incompatible with nonlinear degradation dynamics

Breaking Changes

• ✅ API users: Recommend upgrading to /api/v2/* endpoints; v1 frozen for backward compatibility but uses deprecated models
• ✅ Model files: Direct joblib loading requires version-aware path selection (artifacts/v2/models/classical/)
• ✅ Frontend: Version toggle appears in header; defaults to v2

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[1.0.0] — 2025-Q1 — Archival (Cross-Battery Split)

Description

First release implementing 12 classical ML + 10 deep learning models on NASA PCoE dataset using cross-battery splits (entire batteries → train or test). Known to have data leakage and unreliable accuracy estimates.

Issues (Fixed in v2)

• ❌ avg_temp corruption: Random +8°C offset corrupted predictions
• ❌ Cross-battery leakage: Same battery ID in train & test with different cycle ranges
• ❌ Recommendation always returned 0: Used default features for baseline
• ❌ Inflated accuracy: 94.2% due to leakage; only 5/12 models passing

Legacy Support

• Endpoints remain at /api/v1/* for backward compatibility
• Models frozen; no further updates planned
• Frontend allows v1 selection via version toggle

Added

• Model versioning — MODEL_CATALOG in model_registry.py assigns every model a semantic version (v1.x classical, v2.x deep, v3.x ensemble)
• BestEnsemble (v3.0.0) — weighted average of RF + XGB + LGB; auto-registered when all three components load; exposed via POST /api/predict/ensemble
• GET /api/models/versions — new endpoint grouping models by generation
• model_name request field — callers can select any registered model per request
• model_version response field — every prediction response carries its version string
• src/utils/logger.py — structured logging with ANSI-coloured console output and JSON-per-line rotating file handler (artifacts/logs/battery_lifecycle.log, 10 MB × 5)
• docker-compose.yml — production single-container + dev backend-only profiles
• LOG_LEVEL env var — runtime logging verbosity control
• Frontend model selector dropdown with version badge and R² display

Changed

• api/main.py — switched to get_logger; bumped __version__ to "2.0.0"
• api/model_registry.py — complete rewrite: fixed classical model loading (no _soh suffix), deep model architecture reconstruction + state_dict loading, ensemble dispatch
• src/utils/plotting.py — save_fig() now saves PNG only (removed PDF)
• api/schemas.py — PredictRequest + model_name; PredictResponse + model_version; ModelInfo + version / display_name / algorithm / r2 / loaded / load_error
• frontend/src/api.ts — added ModelInfo, ModelVersionGroups types; new functions predictEnsemble(), fetchModelVersions()
• frontend/src/components/PredictionForm.tsx — model selector with family badge and version badge; shows R² in dropdown; displays model_version in result card
• Docs updated: docs/models.md, docs/api.md, docs/deployment.md, README.md

Removed

• 33 PDF figures from artifacts/figures/ (PNG is the sole output format)

Fixed

• _choose_default() was looking for random_forest_soh (wrong suffix) — now uses bare model names
• Deep models were never loaded (stubs only) — now reconstructs architecture from known params and loads state_dict via torch.load(weights_only=True)

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[0.1.0] — 2026-02-23

Added

• Complete project scaffold: src/, api/, frontend/, notebooks/, docs/
• 22 Python source modules covering data loading, feature engineering, preprocessing, metrics, recommendations, plotting
• 20+ model architectures: Ridge, Lasso, ElasticNet, KNN, SVR, RandomForest, XGBoost, LightGBM, LSTM (4 variants), BatteryGPT, TFT, iTransformer (3 variants), VAE-LSTM, Stacking Ensemble, Weighted Average Ensemble
• 9 Jupyter notebooks (01_eda through 09_evaluation)
• FastAPI backend with Gradio interface
• Vite + React + Three.js frontend with 3D battery pack visualisation
• Dockerfile for Hugging Face Spaces deployment
• Full documentation suite (docs/)

Status

• Code written but not yet executed
• No trained models or experimental results yet