Li-ion Battery Models using PyBaMM Framework

Lithium-ion (Li-ion) batteries are currently the mainstream technology being used in vehicle electrification as well as in stationary storage systems. As adoption of these technologies increase worldwide, it is becoming more important to design better batteries and manage battery systems well. This requires having good battery models that can describe the dynamic behavior of batteries, determine their state and predict their life. Accurate modeling and simulation of battery degradation are critical for optimizing the performance and lifespan of lithium-ion batteries, particularly NMC (Nickel Manganese Cobalt) cells.

PyBaMM is a Python based open-source battery modeling and simulation package, that provides users with different battery models to choose from to simulate battery behavior. PyBaMM offers a flexible and open-source environment for simulating electrochemical processes within batteries, enabling detailed analysis of degradation mechanisms such as SEI layer growth, lithium plating, and particle cracking.

This project aims to implement and simulate various degradation models within the PyBaMM framework to generate cell-level data for NMC cells under different cycling conditions. The generated data include key metrics such as capacity fade, internal resistance increase, and cycle life. By systematically comparing these models, we aim to identify the most accurate and computationally efficient approaches for predicting long-term battery performance.

Required steps include the following:

• Studying lithium-ion batteries and battery models
• Simulating selected models in PyBaMM framework under different usage conditions and comparing results

This project requires studying electrochemical basics of batteries, simulating battery models in PyBaMM framework, working with differential equations, as well as working in Python. Junior and senior year students will be considered.