| 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 | 139 | 2021 |
| Battery state-of-health modelling by multiple linear regression SB Vilsen, DI Stroe Journal of Cleaner Production 290, 125700, 2021 | 40 | 2021 |
| 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 | 33 | 2018 |
| 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 | 18 | 2022 |
| 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 | 17 | 2017 |
| Smart Battery Technology for Lifetime Improvement R Teodorescu, X Sui, SB Vilsen, P Bharadwaj, A Kulkarni, DI Stroe Batteries 8 (10), 169, 2022 | 9 | 2022 |
| 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 | 9 | 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 | 9 | 2018 |
| 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 | 7 | 2015 |
| 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 | 6 | 2023 |
| The development of machine learning-based remaining useful life prediction for lithium-ion batteries X Li, D Yu, SB Vilsen, DI Store Journal of Energy Chemistry, 2023 | 6 | 2023 |
| Transfer learning for adapting battery state-of-health estimation from laboratory to field operation SB Vilsen, DI Stroe IEEE Access 10, 26514-26528, 2022 | 4 | 2022 |
| 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 | 3 | 2019 |
| 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 | 2 | 2023 |
| Statistical modelling of Massively Parallel Sequencing data in forensic genetics SB Vilsen Aalborg Universitetsforlag, 2018 | 2 | 2018 |
| An auto-regressive model for battery voltage prediction SB Vilsen, DI Stroe 2021 IEEE Applied Power Electronics Conference and Exposition (APEC), 2673-2680, 2021 | 1 | 2021 |
| DNA mixture deconvolution using an evolutionary algorithm with multiple populations, hill-climbing, and guided mutation SB Vilsen, T Tvedebrink, PS Eriksen arXiv preprint arXiv:2012.00513, 2020 | 1 | 2020 |
| 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 | | 2023 |
| 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 | | 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 | | 2023 |
