PyBaMM, Pyprobe,scikitlearn, Li-ion Grid Model
Budget: ₹1,500 – ₹12,500 INR
I am building a detailed lithium-ion battery model in PyBaMM from laboratory experimental data, aimed specifically at grid-scale energy storage. The priority is to capture capacity fade and overall lifespan accurately across realistic stationary-storage duty cycles for application specific.
Here is what I need from you:
Pyprobe to read given .csv file, extract data and plot data
• Configure the appropriate PyBaMM electrochemical/degradation model and tune it for large-format Li-ion cells.
• Import or create a complete parameter set (dimensions, kinetics, thermal data) that matches contemporary grid-storage cells; I can supply partial data, but expect some literature mining.
• Script charge-discharge profiles at multiple C-rates and temperatures typical of grid applications, then run and post-process simulations to show capacity retention over thousands of cycles.
• Validate or benchmark the results against the reference data I provide and highlight any discrepancies.
• Deliver clean, well-commented Python code (Jupyter notebook or .py), a brief walkthrough of the modelling approach, and plots/tables summarising predicted lifespan and capacity trends.
If you already have experience extending PyBaMM, fitting degradation parameters, or coupling it with optimisation libraries, using pyprobe or similar to read and extract data, statistical and ml using scikitlearn, let me know—that will help us iterate faster.
Here is what I need from you:
Pyprobe to read given .csv file, extract data and plot data
• Configure the appropriate PyBaMM electrochemical/degradation model and tune it for large-format Li-ion cells.
• Import or create a complete parameter set (dimensions, kinetics, thermal data) that matches contemporary grid-storage cells; I can supply partial data, but expect some literature mining.
• Script charge-discharge profiles at multiple C-rates and temperatures typical of grid applications, then run and post-process simulations to show capacity retention over thousands of cycles.
• Validate or benchmark the results against the reference data I provide and highlight any discrepancies.
• Deliver clean, well-commented Python code (Jupyter notebook or .py), a brief walkthrough of the modelling approach, and plots/tables summarising predicted lifespan and capacity trends.
If you already have experience extending PyBaMM, fitting degradation parameters, or coupling it with optimisation libraries, using pyprobe or similar to read and extract data, statistical and ml using scikitlearn, let me know—that will help us iterate faster.