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Title: Development of new on-board battery diagnosis/prognosis tools for extending lifetime and mitigating failure
Author: Merla, Yu
ISNI:       0000 0004 7223 4586
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Lithium-ion batteries age over their lifetime of operation through various complex degradation mechanisms. In order to maximise battery performance and lifetime, the industrial standard method of measuring the state of health (SOH) by capacity and power fade is no longer good enough. In this thesis, Differential Thermal Voltammetry (DTV) is introduced as a new in-situ diagnosis/prognosis tool that is capable of a more sophisticated diagnosis of SOH, and is application ready with minimal installation costs. DTV uses only cell voltage and surface temperature measurements to infer detailed SOH information. The method was validated through various accelerated ageing experiments carried out on commercial lithium-ion pouch cells as well as through industrial collaborations. DTV was demonstrated for use as a diagnostic tool on a cell level for tracking battery degradation throughout its operation, for screening 2nd life batteries to estimate the degradation state/history, and for estimating the state-of-charge (SOC) of lithium iron phosphate cells during partial charge/discharges. In anticipation of an EV application, the technique was tested for its capabilities in a battery pack. DTV was able to correctly identify aged cells within a pack in a quantitative manner validating its capability embedded within a battery management system (BMS). Through an industrial collaboration, DTV demonstrated its use as a prognosis tool for forecasting cell failure in a commercial BMS mounted on a high-power battery pack used in a motorsport application. The concept of adaptive operation through DTV analysis was explored which resulted in a slower rate of degradation. Given the application opportunities validated through experiments, this research aims to provide an alternative tool for battery diagnosis/prognosis in a real-world application.
Supervisor: Offer, Gregory ; Wu, Billy ; Yufit, Vladimir ; Martinez-Botas, Ricardo Sponsor: European Institute of Innovation and Technology ; Innovate UK
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral