Accurate State of Charge (SoC) estimation is a critical function of modern Battery Management Systems (BMS) to ensure operational safety, optimize cell performance, and extend the lifespan of Lithium-ion battery packs.
This repository contains numerical implementations and algorithm evaluation tools for estimating battery SoC under dynamic load conditions. It provides a foundation for testing open-loop methods (such as Coulomb Counting/Ampere-hour Integration) and model-based estimation techniques (such as Extended Kalman Filter / Equivalent Circuit Models).
- SoC Estimation Algorithms: Implementation of core SoC algorithms for real-time tracking of cell charge state.
- Equivalent Circuit Modeling (ECM): Battery cell representation incorporating Open Circuit Voltage (OCV) curves, internal resistance, and RC pair dynamics.
- Dynamic Load Profile Simulation: Evaluation of estimator accuracy against dynamic discharge/charge current profiles.
- Error & Drift Analysis: Performance metrics comparing estimated SoC against ground truth values.
Ensure you have Python installed along with standard scientific computing packages:
pip install numpy matplotlib scipy pandas