Active Cell Balancing in Batteries Under Dynamic Driving Conditions

This repository provides an advanced simulation environment for active battery cell balancing using a reinforcement learning framework. It implements a custom Gymnasium environment that models the dynamics of a lithium-ion battery pack, focusing on energy transfer between adjacent cells to achieve optimal voltage and state-of-charge (SOC) balance.

Table of Contents

• Features
• Installation
• Usage
• Environment Overview
• Development
• License
• Citing
• References

Features

• Realistic Battery Modeling:
  Simulates individual cell properties including capacity, internal resistance, and voltage-SOC mapping based on experimental Li-NMC data.
• Driving Cycle Integration:
  Converts real-world driving cycle data into discharge current profiles, integrating vehicle dynamics and load conditions.
• Discrete Action Space:
  Offers a multi-dimensional discrete action space where each action controls energy transfer intensity and direction between adjacent cells.
• Custom Reward Function:
  Incentivizes effective cell balancing by rewarding the minimization of SOC imbalances.
• MDP Framework:
  The environment is modeled as a Markov Decision Process (MDP) with well-defined state and action spaces, transition dynamics, and long-term discounting.

Installation

1. Clone the Repository:

   git clone https://github.com/messlem99/Battery_Cell_Balancing.git
   cd Battery_Cell_Balancing

Usage

To test the simulation environment, follow these steps:

1. Initialize the Environment:

   import gymnasium as gym
   from Battery_Cell_Balancing import BatteryBalancingEnv
   env = BatteryBalancingEnv(driving_cycle_filepath="data.csv")
   observation, info = env.reset(seed=42)

2. Run a Simulation Episode:

   done = False
   while not done:
    action = env.action_space.sample()  # Sample random actions
    observation, reward, done, truncated, info = env.step(action)
    print(f"Reward: {reward}, Done: {done}")

Environment Overview

The environment is modeled as an MDP with the following components:

• State Space: Each state includes per-cell voltage, SOC values, and the instantaneous load current derived from the driving cycle.
• Action Space: A discrete multi-dimensional vector controlling energy transfers between adjacent cells.
• Transition Dynamics: Updates based on realistic battery dynamics including load current effects and energy balancing operations.
• Reward Function: Rewards effective balancing by minimizing SOC differences between adjacent cells.
• Termination Conditions: Episodes end when voltage or SOC imbalances exceed defined thresholds, or if all cells are fully discharged.

Development

Contributions and enhancements are welcome. To get started:

• Fork the repository and create your feature branch.
• Submit pull requests for review.

License

• This project is licensed under the MIT License. See the LICENSE file for details.

Citing

To cite this project in publications:

@misc{EVBatteryBalancing2025,
  author       = {Abdelkader Messlem and Youcef Messlem and Ahmed Safa},
  title        = {A Simulation Environment for Active Battery Cell Balancing Using Gymnasium},
  year         = {2025},
  howpublished = {\url{https://github.com/messlem99/Battery_Cell_Balancing}},
}

References

• F. Baronti, W. Zamboni, N. Femia, R. Roncella, and R. Saletti, “Experimental analysis of open-circuit voltage hysteresis in lithiumiron-phosphate batteries,” in IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society. Vienna, Austria: IEEE, Nov. 2013, pp. 6728–6733. [Online]. Available: http://ieeexplore.ieee. org/document/6700246/
• P. Ramadass, B. Haran, R. White, and B. N. Popov, “Mathematical modeling of the capacity fade of Li-ion cells,” Journal of Power Sources, vol. 123, no. 2, pp. 230–240, Sep. 2003. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0378775303005317
• G. Ning, B. Haran, and B. N. Popov, “Capacity fade study of lithium-ion batteries cycled at high discharge rates,” Journal of Power Sources, vol. 117, no. 1-2, pp. 160–169, May 2003. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0378775303000296