SUPR
Optimization of battery manufacturing parameters using aging curve by machine learning
Dnr:

NAISS 2025/22-468

Type:

NAISS Small Compute

Principal Investigator:

Qingbo Zhu

Affiliation:

Chalmers tekniska högskola

Start Date:

2025-03-24

End Date:

2026-04-01

Primary Classification:

20202: Control Engineering

Webpage:

Allocation

Abstract

The optimization of the electrode manufacturing process is critical to ensure high-quality lithium-ion battery (LIB) cells, particularly for automotive applications. LIB electrode manufacturing is a complex process involving multiple steps and parameters. In this work, we aim to achieve an innovative computational approach able to optimize the battery lifespan simultaneously and evaluate the process parameters to be adopted to manufacture them. we simulate the aging curves of 200 different batteries based on their manufacturing parameters and electrode properties using PyBaMM (a Python-based modeling toolbox). Then, based on the aging curves, we optimize the corresponding battery manufacturing parameters.