Standardized Computational Complexity Evaluation for Battery State-of-Charge Estimation Algorithms

To fairly assess the performance of battery state of charge estimation algorithms, a standardized evaluation method is required. This paper expands on a previously proposed evaluation method by including a measurement of computational complexity. Since traditional complexity metrics, such as computation time, may vary across devices and over time, this paper proposes a specialized metric to categorize computational complexity: approximate operational effort (AOE). AOE is determined by running the algorithm and a standard mathematical and memory operation on a device such as a PC and normalizing run time based on these operations. AOE is measured on three different devices for four algorithms, and is shown to decrease the variability between devices from a factor of ten to less than four, which is sufficient to differentiate algorithm performance across devices. The algorithms are also deployed to a microcontroller to compare with the PC based complexity measurement.

Authors

Institutions

Publication Details

Published
2025-06-18
DOI
https://doi.org/10.1109/itec63604.2025.11098050
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Standardized Computational Complexity Evaluation for Battery State-of-Charge Estimation Algorithms

M.C.F. Donkers, Phillip J. Kollmeyer, Atjen von Liebenstein
Advanced Battery Technologies Research
article

Standardized Computational Complexity Evaluation for Battery State-of-Charge Estimation Algorithms

M.C.F. Donkers, Phillip J. Kollmeyer, Atjen von Liebenstein
article en

Abstract

To fairly assess the performance of battery state of charge estimation algorithms, a standardized evaluation method is required. This paper expands on a previously proposed evaluation method by including a measurement of computational complexity. Since traditional complexity metrics, such as computation time, may vary across devices and over time, this paper proposes a specialized metric to categorize computational complexity: approximate operational effort (AOE). AOE is determined by running the algorithm and a standard mathematical and memory operation on a device such as a PC and normalizing run time based on these operations. AOE is measured on three different devices for four algorithms, and is shown to decrease the variability between devices from a factor of ten to less than four, which is sufficient to differentiate algorithm performance across devices. The algorithms are also deployed to a microcontroller to compare with the PC based complexity measurement.

Eindhoven University of Technology (NL), McMaster University (CA)
Affordable and clean energy
Openalex Percentile: Top 80%
Advanced Battery Technologies Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Standardized Computational Complexity Evaluation for Battery State-of-Charge Estimation Algorithms — M.C.F. Donkers, Phillip J. Kollmeyer, et al. · (2025) | TGRS Research Map | TGRS