Battery Management Systems
Our research on novel Battery Management Systems (BMS) for electrification of transport. We are interested in the optimal battery operation, failure mitigation and lifetime extension. Our approach to look at novel methods of battery state estimation for BMS operation. Specifically, we look at using magnetic field and current density measurements of batteries to estimate state parameters of cells. These internal parameters include a battery’s internal temperature distribution, internal resistance and capacity loss. In our research, we employ a combination of a modelling and experimental approach to validate our state estimation methodology.
Battery Modelling – We have built a MATLAB-based custom full order physics model ( based on the Doyle-Fuller-Newman model) of a Lithium ion battery. Our model works for multiple different electrode chemistries and electrolyte chemistries and is validated against common physics-based battery simulators like COMSOL and DUALFOIL. Our battery models are designed to account for common degradation phenomena considering the solid electrolyte interphase (SEI) layer growth and Lithium plating.
Modelling of Magnetic field distribution in LiBs – We are interested in developing a novel physics model of the magnetic field intensity across the thickness of a Lithium ion battery, and investigate how a MFI distribution can be used to determine the internal state parameters in the battery model. We use our custom experimental rig for measuring the MFI distribution to validate our modelling work and develop a custom BMS platform for real-time SoX (SoC, SoH, SoF, SoP, and SoS) estimation.
Analysis of current density in the electrode and electrolyte of Lithium-ion cells for ageing estimation applications
Simulation Assisted Current Density Monitoring for Lithium-ion Batteries in Electric Vehicles
A Novel Online State of Health Estimation Method for Electric
Vehicle Pouch Cells Using Magnetic Field Imaging and Convolution Neural Networks