Background and activities
Asanthi is working on modeling the lithium-ion battery production process, with a focus on benchmarking the energy and material usages in connection to life cycle assessment studies under the supervision of professors Odne Stokke Burheim and Anders Hammer Strømman. She received her Ph.D. in process, energy, and automation from the University of South-Eastern Norway in 2019, where she worked on dynamic modeling and model-based real-time estimation of the return flow rate of oil drilling fluids. Her research interests include state and parameter estimation and modeling of dynamic systems.
Scientific, academic and artistic work
Displaying a selection of activities. See all publications in the database
- (2021) Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles. Energies. vol. 14 (11).
- (2021) A Flexible Model for Benchmarking the Energy Usage of Automotive Lithium-Ion Battery Cell Manufacturing. Batteries. vol. 7 (1).
- (2021) The Importance of Optical Fibres for Internal Temperature Sensing in Lithium-ion Batteries during Operation. Energies. vol. 14 (12).
- (2020) Model Based Early Kick/Loss Detection and Attenuation with Topside Sensing in Managed Pressure Drilling. Linköping Electronic Conference Proceedings. vol. 176.
- (2020) Adaptive Moving Horizon Estimator for Return Flow Rate Estimation using Fluid Levels of a Venturi Channel. Modeling, Identification and Control. vol. 41 (2).
- (2020) Estimation of Mud Losses during the Removal of Drill Cuttings in Oil Drilling. SPE Journal. vol. 25 (5).
- (2020) Non-Newtonian Fluid Flow Measurement in Open Venturi Channel Using Shallow Neural Network Time Series and Non-contact Level Measurement Radar Sensors. SPE Norway Subsurface Conference . SPE; Bergen. 2020-09-14 - 2020-09-14.
- (2020) Study of an Industrial Electrode Dryer of a Lithium-Ion Battery Manufacturing Plant: Dynamic Modelling. Linköping Electronic Conference Proceedings. vol. 176.
- (2019) Models and Estimators for Flow of Topside Drilling Fluid. 2019. ISBN 9788272065293.
- (2019) Improved Real-Time Estimation of Return Flow Rate of Drilling Fluids by Model Adaptation for Friction Parameter. IEEE Sensors Journal. vol. 19 (20).
- (2019) Semi-kidd: Possibilities for kick/loss detection with measured return flow rate. Telemark Offshorekonferansen 2019 . Telemark Offshore; Skien. 2019-10-31 - 2019-10-31.
- (2018) Modeling and Analysis of Fluid Flow through A Non-Prismatic Open Channel with Application to Drilling. Modeling, Identification and Control. vol. 39 (4).
- (2018) Model based Real – Time Flow Rate Estimation in Open Channels with Application to Conventional Drilling. International Conference on Control, Automation and Systems.
- (2018) A dynamic model for drain back to active mud pit combined with a well model during drilling. Journal of Petroleum Science and Engineering. vol. 167.
- (2017) Use of Orthogonal Collocation Method for a Dynamic Model of the Flow in a Prismatic Open Channel: For Estimation Purposes. Linköping Electronic Conference Proceedings.
- (2017) Model based flow measurement using venturi flumes for return flow during drilling. Modeling, Identification and Control. vol. 38 (3).
- (2016) Dynamic Model of an Ammonia Synthesis Reactor Based on Open Information. 2016 9th EUROSIM Congress on Modelling and Simulation Oulu, Finland 12-16 September 2016.