Battery Charging Simulation

A comprehensive ODE-based simulation framework for analyzing and optimizing lithium-ion battery charging policies.

Report | Website

Overview

Simulates the dynamic behavior of lithium-ion batteries during charging, accounting for:

• Thermal dynamics (Joule heating, environmental cooling)
• State of charge dynamics (charge accumulation, open circuit voltage)
• Transient voltage response (RC circuit polarization model)
• SEI layer growth (temperature and current-dependent degradation)

Quick Start

Installation

pip install -r requirements.txt

Run Simulation

Single-trajectory interaction simulations can be found at Battery Simulation Website.

To produce figures and comparison results, run the follow scripts in this order to store generated figures in log/ directory.

# Run parameter sweep (tests multiple current/voltage combinations)
python sweep_policies.py

# Generate comparison plot with grouped bars
python compare_policies.py

File Structure

Core Simulation:

• main.py - Main simulation runner
• config.py - Centralized configuration
• charging_policy.py - Charging policy implementations
• update_state.py - ODE system and state evolution
• utils.py - Utility functions

Analysis & Visualization:

• sweep_policies.py - Parameter sweep framework
• compare_policies.py - Policy comparison with grouped bar charts

Output:

• log/ - Simulation results (CSV logs, metrics, plots)
• requirements.txt - Python dependencies

Charging Policies

• CC (Constant Current): Maintains constant current
• CV (Constant Voltage): Maintains constant voltage
• CCCV (CC-CV Two-stage): Constant current followed by constant voltage
• CCCVPulse (Three-stage): CC → CV → Pulse charging

Output Metrics

Each simulation generates:

• Charging Time (hours): Total time to reach 100% SoC
• Peak Temperature (K): Maximum temperature during charge
• SEI Growth: Final SEI layer thickness (degradation indicator)

Comparison Visualization

The comparison plot shows:

• Grouped bars by policy type (CC, CV, CCCV, CCCVPulse)
• Different bar heights representing different parameter values
• Color shading to distinguish parameter intensity
• Parameter labels below each bar (current in Amps or voltage in Volts)
• Four panels:
  1. Charging time comparison
  2. Peak temperature (thermal stress)
  3. Voltage and current vs State of Charge
  4. SEI growth (degradation)

Numerical Method

Uses Runge-Kutta 4th Order (RK4) integration for solving coupled ODEs with high accuracy and stability.