This thesis looks to provide the first ever Julia coded PBM optimisation toolbox utilising a PSO method and DFN model, this is a revolutionary piece of open-source software which enables a user to input measured cell data for an LGM50 and determine the physical and chemical parameters of a DFN model. At a larger scale this program could look to inform SOC and SOH prediction for the LGM50 by potentially using emulated higher fidelity terminal voltage responses and provide a better approximation of the physical and chemical parameters of a battery model than expensive experimental techniques. The thesis firstly outlines a 6-stage methodology including literature reviews and software exploration. Then targets the 6 most sensitive parameters and confirms by performing an OAT analysis that the thickness of the cathode is the most sensitive parameter for terminal voltage behaviour for a HPPC and GITT test cycle within the virtual PBM environment of an LGM50 cell. The thesis secondly builds and deploys an artificially informed optimisation to this virtual PBM environment and can improve the fitment of terminal voltage for a WLTP by 38% from an experimentally populated dataset for the LGM50. Which achieves a minimised voltage RMSE of 9.7mV with complete simulation time of ~1.5 hours averaging 8 seconds per WLTP drive cycle.forme In conclusion, the thesis provides an open-source toolbox which is unique and powerful at fitting terminal voltage response for an LGM50.
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Muir, Nick
Rights Holders: Muir, Nick Supervisors: Planden, Brady; Lukow, Katie
School of Engineering, Computing and MathematicsFaculty of Technology, Design and Environment
MSc Automotive Engineering with Electric Vehicles
2022
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