PulseBMS Enhanced - Adaptive Battery Management System

Overview

PulseBMS Enhanced is an advanced battery management system designed specifically for second-life EV batteries (SLBs). It combines reinforcement learning, physics-based digital twins, and real-time telemetry to optimize charge/discharge operations across heterogeneous battery fleets while ensuring safety and extending battery life by up to 25%.

Key Features

• Adaptive RL-Powered BMS: Reinforcement learning algorithms for intelligent charge/discharge optimization
• Digital Twin Integration: PyBaMM-based physics models for accurate battery forecasting
• Real-Time Telemetry: MQTT-based streaming for low-latency, reliable communication
• Edge Computing: Local SoC/SoH estimation and safety interlocks on edge devices
• Fleet Coordination: Site-level power allocation across mixed SLB fleets
• Safety-First Design: Degradation-aware control with built-in safety constraints
• Live Dashboard: Real-time monitoring, health predictions, and actionable insights

Architecture

The system consists of five main components:

1. SLB Battery Packs: Varying chemistries and health states from retired EV batteries
2. Edge Devices: RTOS-powered local monitoring, SoC/SoH estimation, and RL policy execution
3. Digital Twin Service: Cloud-hosted PyBaMM models for short-horizon forecasting
4. Coordinator Service: Site-level power demand allocation with safety constraints
5. Dashboard & Analytics: Live telemetry visualization and predictive insights

Quick Start

Prerequisites

• Python 3.8+
• MongoDB (for telemetry storage)
• Redis (for caching and coordination)
• MQTT broker (optional, for telemetry streaming)

Installation

# Clone the repository
git clone https://github.com/lblommesteyn/PulseBMS.git
cd PulseBMS-Enhanced

# Install Python dependencies
pip install -r requirements.txt

# Start MongoDB and Redis services
# (Instructions vary by OS - see docs/SETUP.md)

5. Run integration tests

cd testing && python integration_tests.py

### Testing the System

```bash
# Run the comprehensive integration test suite
python testing/integration_tests.py

# Run hardware-in-the-loop tests
python testing/hil_testing_framework.py

Project Structure

PulseBMS-Enhanced/
├── docs/                   # Design documentation
├── backend/               # FastAPI backend services
├── edge/                  # Edge device code (RTOS simulation)
├── digital-twin/          # PyBaMM integration and physics-based models
├── optimization/          # MPC allocator and RL policy algorithms
├── coordination/          # Fleet coordination service integrating all optimization
├── testing/               # Integration tests and HIL testing framework
├── routers/               # API routing and telemetry handlers
├── backend/               # FastAPI backend services
├── dashboard/             # React-based live dashboard (coming soon)
├── docs/                  # Comprehensive design documentation
└── requirements.txt       # Python dependencies

Development Status

• ✅ Project initialization and architecture design
• ✅ MQTT telemetry streaming implementation
• ✅ Edge device SoC/SoH estimators and simulation
• ✅ Digital twin service with PyBaMM integration
• ✅ MPC baseline allocator with safety constraints
• ✅ Safe RL policy training and deployment
• ✅ Coordinator service integrating MPC, RL, and Digital Twin
• ✅ Hardware-in-the-loop (HIL) testing framework
• ✅ Comprehensive integration test suite
• 📋 React-based live dashboard (in progress)

License

MIT License - see LICENSE for details.

Contributing

See CONTRIBUTING.md for contribution guidelines.