Sheikh, Abdul Muiz Ahmad (Eindhoven University of Technology), Donkers, M.C.F. (Tijs) (Eindhoven University of Technology), Bergveld, Henk Jan (Eindhoven University of Technology)

PyBatteryID - a Python Package for Data-Driven Battery Model Identification Using LPV Framework

Scheduled for presentation during the Regular Session "Battery modelling, identification and simulation" (WeA1), Wednesday, June 18, 2025, 11:00−11:20, Kapel

AAC 2025 11th IFAC International Symposium on Advances in Automotive Control, June 15-18, 2025, Eindhoven, Netherlands

This information is tentative and subject to change. Compiled on June 1, 2025

Abstract

This paper introduces an open-source Python package named PyBatteryID for data-driven battery model identification using the linear parameter-varying (LPV) framework. The package provides a convenient way to render the model parameters dependent on state-of-charge (SOC), current magnitude, current direction and temperature using a variety of possible basis functions. Furthermore, the selection of significant model terms and the estimation of free model parameters can be carried out by specifying the desired optimization routines in a sequence. Moreover, the package allows the generation of suitable current and temperature profiles to obtain an informative dataset for the model identification procedure. In addition, the package enables extensive comparison of several battery models by enabling offline storage of the identified models and providing utilities for plotting the simulated voltage curves. The code, documentation and examples related to the package can be found at https://github.com/tue-battery/PyBatteryID.