Søren B. Vilsen
Søren B. Vilsen
Assistant professor, Aalborg University
Verified email at - Homepage
Cited by
Cited by
A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery
X Sui, S He, SB Vilsen, J Meng, R Teodorescu, DI Stroe
Applied Energy 300, 117346, 2021
Battery state-of-health modelling by multiple linear regression
SB Vilsen, DI Stroe
Journal of cleaner production 290, 125700, 2021
Review of “grey box” lifetime modeling for lithium-ion battery: Combining physics and data-driven methods
W Guo, Z Sun, SB Vilsen, J Meng, DI Stroe
Journal of Energy Storage 56, 105992, 2022
The development of machine learning-based remaining useful life prediction for lithium-ion batteries
X Li, D Yu, VS Byg, SD Ioan
Journal of Energy Chemistry 82, 103-121, 2023
Stutter analysis of complex STR MPS data
SB Vilsen, T Tvedebrink, PS Eriksen, C Bøsting, C Hussing, ...
Forensic Science International: Genetics 35, 107-112, 2018
Smart battery technology for lifetime improvement
R Teodorescu, X Sui, SB Vilsen, P Bharadwaj, A Kulkarni, DI Stroe
Batteries 8 (10), 169, 2022
Battery health prognostic with sensor-free differential temperature voltammetry reconstruction and capacity estimation based on multi-domain adaptation
Y Che, SB Vilsen, J Meng, X Sui, R Teodorescu
Etransportation 17, 100245, 2023
Statistical modelling of Ion PGM HID STR 10-plex MPS data
SB Vilsen, T Tvedebrink, HS Mogensen, N Morling
Forensic Science International: Genetics 28, 82-89, 2017
Log-linear model for predicting the lithium-ion battery age based on resistance extraction from dynamic aging profiles
SB Vilsen, SK Kær, DI Stroe
IEEE Transactions on Industry Applications 56 (6), 6937-6948, 2020
Modelling allelic drop-outs in STR sequencing data generated by MPS
SB Vilsen, T Tvedebrink, PS Eriksen, C Hussing, C Børsting, N Morling
Forensic Science International: Genetics 37, 6-12, 2018
Transfer learning for adapting battery state-of-health estimation from laboratory to field operation
SB Vilsen, DI Stroe
IEEE Access 10, 26514-26528, 2022
Modelling noise in second generation sequencing forensic genetics STR data using a one-inflated (zero-truncated) negative binomial model
SB Vilsen, T Tvedebrink, HS Mogensen, N Morling
Forensic Science International: Genetics Supplement Series 5, e416-e417, 2015
Identification of mechanism consistency for LFP/C batteries during accelerated aging tests based on statistical distributions
W Guo, Z Sun, SB Vilsen, F Blaabjerg, DI Stroe
e-Prime-Advances in Electrical Engineering, Electronics and Energy 4, 100142, 2023
A digital twin to quantitatively understand aging mechanisms coupled effects of NMC battery using dynamic aging profiles
W Guo, Y Li, Z Sun, SB Vilsen, DI Stroe
Energy Storage Materials 63, 102965, 2023
Degradation behaviour analysis and end-of-life prediction of lithium titanate oxide batteries
M Soltani, SB Vilsen, AI Stroe, V Knap, DI Stroe
Journal of Energy Storage 68, 107745, 2023
Predicting Lithium-ion battery resistance degradation using a log-linear model
SB Vilsen, SK Kaer, D Stroe
2019 IEEE Energy Conversion Congress and Exposition (ECCE), 1136-1143, 2019
Accuracy Comparison of State-of-Health Estimation for Lithium-ion Battery Based on Forklift Aging Profile
X Li, D Yu, SB Vilsen, DI Store
2023 IEEE 14th International Symposium on Power Electronics for Distributed …, 2023
Hyperparameter optimization in bagging-based ELM algorithm for lithium-ion battery state of health estimation
X Sui, S He, SØB Vilsen, R Teodorescu, DI Stroe
2023 IEEE Applied Power Electronics Conference and Exposition (APEC), 1797-1801, 2023
Statistical modelling of Massively Parallel Sequencing data in forensic genetics
SB Vilsen
Aalborg Universitetsforlag, 2018
Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering
X Li, D Yu, SB Vilsen, DI Stroe
Journal of Energy Chemistry 92, 591-604, 2024
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