BatteryTaskSim is a Python framework focused on task allocation and thermal simulation for batteries based on Pybamm. It supports multi-battery task allocation, simulation, data visualization, and result export.
- simulation_data/
- Stores all simulation results exported as CSV files for offline analysis.
- assign_tasks.py
- Implements task allocation logic by dynamically assigning tasks based on priority, battery health, and temperature conditions.
- extract_plot.py
- Extracts variables (e.g., SOC, SOH, temperature) from simulation results and supports data visualization and CSV export.
- is_task_feasible.py
- Checks whether a task is suitable for a specific battery based on available capacity, health status, and temperature limits.
- main.py
- Main entry point. Executes task allocation, simulation runs, and result output (including Gantt charts and simulation curves).
- plot_gantt_chart.py
- Generates Gantt charts to visually display task allocation and timing across batteries.
- plot_results.py
- Plots simulation results and saves them as CSV files for offline analysis.
- preprocess.py
- Provides data preprocessing tools to align simulation variables with time and handle multidimensional data.
- print_assignment_results.py
- Outputs task allocation results, detailing task assignment to batteries by time, power, and priority.
- run_simulation.py
- Runs battery simulations using PyBaMM models, supporting multi-tasking and dynamic thermal models.
- scores.py
- Provides battery scoring functions for dynamic decision-making in task allocation.
- setup_model.py
- Configures battery models and parameters, supporting thermal model upgrades from "Lumped" to "x-lumped".
- simulate_battery.py
- Runs multi-task simulations for individual batteries, returning key metrics like SOC, SOH, and temperature.
- sort_tasks.py
- Sorts tasks by priority, ensuring high-priority tasks are allocated first.
- update_battery_health.py
- Dynamically updates battery states, including SOC, SOH, and temperature, based on task results.
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Task Allocation
- Dynamically assigns tasks to multiple batteries.
- Considers battery health (SOC, SOH, temperature) and task requirements (power, duration, priority).
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Thermal Model Simulation
- Supports thermal model upgrades from Lumped to x-lumped, enabling precise thermal behavior simulations.
- Allows dynamic parameter configuration based on battery dimensions and geometry.
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Result Visualization and Export
- Automatically generates Gantt charts to display task allocation.
- Extracts and plots SOC, SOH, temperature trends over time.
- Exports all simulation data as CSV files for offline analysis.
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Clone the repository:
git clone https://github.com/yourusername/BatteryTaskSim.git cd BatteryTaskSim -
Install dependencies:
pip install -r requirements.txt
or
pip install matplotlib numpy pybamm pandas
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Run the main program:
python main.py
- Gantt Chart: Displays task allocation and battery usage over time.
- Simulation Data: Exports detailed time-series data (SOC, SOH, temperature, etc.) as CSV files.
- Simulation Curves: Plots trends of battery states over time.
- Electric Vehicle (EV) Task Scheduling: Optimizes charging and discharging strategies to extend battery lifespan.
- Energy Storage System (ESS) Analysis: Task scheduling and battery thermal behavior simulation.
- Research and Teaching: Supports battery modeling and thermal simulation in academic environments.
- Add support for more battery chemistries, such as solid-state or lithium iron phosphate batteries.
- Integrate advanced scheduling algorithms, such as real-time load balancing.
- Expand environmental simulation, including cooling and temperature gradient modeling.
We welcome contributions and feedback! If you encounter any issues or have feature requests, feel free to submit an Issue on GitHub.
This project is licensed under the MIT License. Feel free to use, modify, and distribute it.