- Google scholar: Link.
- VBN: Link.
- 2024:
- Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering. Xingjun Li, Dan Yu, Søren Byg Vilsen, and Daniel-Ioan Stroe, Journal of Energy Chemistry (2024), doi:10.1016/j.jechem.2024.01.037.
- Dataset of Lithium-ion battery degradation based on a forklift mission profile for state-of-health estimation and lifetime prediction. Søren Byg Vilsen and Daniel-Ioan Stroe, Data in Brief (2024), doi:10.1016/j.dib.2023.109861.
- Diagnosing NMC Battery Aging Modes Using Digital Twin. Wendi Gou, Yaqi Li, Zhongchao Sun, Søren Byg Vilsen, Changfu Zou, and Daniel-Ioan Stroe, ECS Meeting Abstracts (2024), doi:10.1149/MA2024-012508mtgabs.
- Digital Twin-Assisted Degradation Diagnosis and Quantification of NMC Battery Aging Effects During Fast Charging. Wendi Gou, Zhongchao Sun, Jia Gou, Yaqi Li, Søren Byg Vilsen, and Daniel-Ioan Stroe, Advanced Energy Materials (2024), doi:10.1002/aenm.202401644.
- Health status estimation for lithium-ion batteries with partial charging information using mixed inputs LSTM. Wendi Gou, Zhongchao Sun, Yaqi Li, Siyu Jin, Søren Byg Vilsen, and Daniel-Ioan Stroe, 2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia), doi:10.1109/IPEMC-ECCEAsia60879.2024.10567562.
- On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health. Søren Byg Vilsen and Daniel-Ioan Stroe, Batteries (2024), doi:10.3390/batteries10060193.
- 2023:
- Accuracy Comparison of State-of-Health Estimation for Lithium-ion Battery Based on Forklift Aging Profile. Xingjun Li, Dan Yu, Søren Byg Vilsen, and Daniel-Ioan Stroe, 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), doi:10.1109/PEDG56097.2023.10215152.
- A digital twin to quantitatively understand aging mechanisms coupled effects of NMC battery using dynamic aging profiles. Wendi Gou, Yaqi Li, Zhongchao Sun, Søren Byg Vilsen, and Daniel-Ioan Stroe, Journal of Energy Storage Materials, doi:10.1016/j.ensm.2023.102965.
- Battery Aging Behavior Evaluation under Variable and Constant Temperatures with Real Loading Profiles. Yunhong Che, Xin Sui, Daniel-Ioan Stroe, Søren Byg Vilsen, Xiaosong Hu, and Remus Teodorescu, 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), doi:10.1109/APEC43580.2023.10131534.
- Battery health prognostic with sensor-free differential temperature voltammetry reconstruction and capacity estimation based on multi-domain adaptation. Yunhong Che, Søren Byg Vilsen, Jinhao Meng, Xin Sui, and Remus Teodorescu, eTransportation (2023), doi:10.1016/j.etran.2023.100245.
- Degradation behaviour analysis and end-of-life prediction of lithium titanate oxide batteries. Mahdi Soltani, Søren Byg Vilsen, and Daniel-Ioan Stroe, Journal of Energy Storage (2023), doi:10.1016/j.est.2023.107745.
- How to identify mechanism consistency for LFP/C batteries during accelerated calendar and cycling aging using the lognormal distribution. Wendi Guo, Zhongchao Sun, Yaqi Li, Søren Byg Vilsen, and Daniel-Ioan Stroe, 2023 IEEE Applied Power Electronics Conference and Exposition (2023), doi:10.1109/APEC43580.2023.10131387.
- Hyperparameter Optimization in Bagging-Based ELM Algorithm for Lithium-Ion Battery State of Health Estimation. Xin Sui, Shan He, Søren Byg Vilsen, Remus Teodorescu, and Daniel-Ioan Stroe, 2023 IEEE Applied Power Electronics Conference and Exposition (2023), doi:10.1109/APEC43580.2023.10131387.
- Identification of mechanism consistency for LFP/C batteries during accelerated aging tests based on statistical distributions. Wendi Guo, Zhongchao Sun, Søren Byg Vilsen, Frede Blaabjerg, and Daniel-Ioan Stroe, e-Prime (2023), doi:10.1016/j.prime.2023.100142.
- Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data. Xingjun Li, Dan Yu, Søren Byg Vilsen, and Daniel-Ioan Stroe, 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), doi:10.23919/EPE23ECCEEurope58414.2023.10264581.
- Solid electrolyte interface layer growth - crack formation coupled model for Lithium-ion battery capacity fade prediction. Wendi Guo, Yaqi Li, Zhongchao Sun, Søren Byg Vilsen, and Daniel-Ioan Stroe, 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), doi:10.23919/EPE23ECCEEurope58414.2023.10264261.
- The development of machine learning-based remaining useful life prediction for lithium-ion batteries. Xingjun Li, Dan Yu, Søren Byg Vilsen, and Daniel-Ioan Stroe, Journal of Energy Chemistry (2023), doi:10.1016/j.jechem.2023.03.026.
- 2022:
- Review of “grey box” lifetime modeling for lithium-ion battery: Combining physics and data-driven methods. Wendi Guo, Zhongchao Sun, Søren Byg Vilsen, Jinhao Meng, and Daniel-Ioan Stroe, Journal of Energy Storage (2022), doi:10.1016/j.est.2022.105992.
- Smart Battery Technology for Lifetime Improvement. Remus Teodorescu, Xin Sui, Søren Byg Vilsen, Pallavi Bharadwaj, Abhijit Kulkarni, and Daniel-Ioan Stroe, Batteries (2022), doi:10.3390/batteries8100169.
- Transfer Learning for Adapting Battery State-of-Health Estimation from Laboratory to Field Operation. Søren Byg Vilsen, and Daniel-Ioan Stroe, IEEE Access (2022), doi:10.1109/ACCESS.2022.3156657.
- 2021:
- An auto-regressive model for battery voltage prediction. Søren Byg Vilsen, and Daniel-Ioan Stroe, 2021 IEEE Applied Power Electronics Conference and Exposition (2021), doi:10.1109/APEC42165.2021.9487060.
- A Review of Non-probabilistic Machine Learning-based State of Health Estimation Techniques for Li-ion Battery. Xin Sui, Shan He, Søren Byg Vilsen, Jinhao Meng, Remus Teodorescu, and Daniel-Ioan Stroe, Applied Energy (2021), doi:10.1016/j.apenergy.2021.117346.
- Battery state-of-health modelling by multiple linear regression. Søren Byg Vilsen, and Daniel-Ioan Stroe, Journal of Cleaner Productions (2021), doi:10.1016/j.jclepro.2020.125700.
- Fast and Robust Estimation of Lithium-ion Batteries State of Health Using Ensemble Learning Xin Sui, Shan He, Søren Byg Vilsen, Remus Teodorescu, Daniel-Ioan Stroe, 2021 IEEE Energy Conversion Congress and Exposition (2021) doi:10.1109/ECCE47101.2021.9595113.
- 2020:
- DNA mixture deconvolution using an evolutionary algorithm with multiple populations, and guided mutation and hill-climbing Søren Byg Vilsen, Torben Tvedebrink, and Poul Svante Eriksen, arXiv:2012.00513.
- A Time-Varying Log-linear Model for Predicting the Resistance of Lithium-ion Batteries. Søren Byg Vilsen, Xin Sui, and Daniel-Ioan Stroe, Energy Conversion Congress and Exposition Asia (2020), doi:10.1109/IPEMC-ECCEAsia48364.2020.9367839.
- Log-Linear Model for Predicting the Lithium-ion Battery Age based on Resistance Extraction from Dynamic Aging Profiles Søren Byg Vilsen, Søren Knudsen Kaer, and Daniel-loan Stroe, IEEE Transactions on Industry Applications (2020), doi:10.1109/TIA.2020.3020529.
- 2019:
- Predicting Lithium-ion Battery Resistance Degradation using a
Log-Linear Model Søren Byg Vilsen, Søren Knudsen Kaer, and Daniel-loan Stroe, Energy Conversion Congress and Exposition (2019), doi:10.1109/ECCE.2019.8912770.
- 2018:
- Stutter analysis of complex STR MPS data Søren Byg Vilsen, Torben Tvedebrink, Poul Svante Eriksen, Christian Hussing, Claus Børsting, Helle Smidt Mogensen, and Niels Morling, FSI:GEN (2018), DOI: doi:10.1016/j.fsigen.2018.04.003.
- Modelling allelic drop-outs in STR sequencing data generated by MPS Søren Byg Vilsen, Torben Tvedebrink, Poul Svante Eriksen, Christian Hussing, Claus Børsting, and Niels Morling, FSI:GEN (2018), DOI: doi:10.1016/j.fsigen.2018.07.017.
- 2017:
- Statistical modelling of Ion PGM HID STR 10-plex MPS data, Søren Byg Vilsen, Torben Tvedebrink, Helle Smidt Mogensen, and Niels Morling, FSI:GEN (2017), DOI: doi:10.1016/j.fsigen.2017.01.017
- 2015:
- Modelling noise in second generation sequencing forensic genetics STR data using a one-inflated (zero-truncated) negative binomial model, Søren Byg Vilsen, Torben Tvedebrink, Helle Smidt Mogensen, and Niels Morling, FSI:GSS (2015), DOI: doi:10.1016/j.fsigss.2015.09.165
