Estimation of Degradation Modes for Lithium-ion Batteries Estimation of degradation modes of an aged battery using Open-Circuit Voltage curves

dc.contributor.authorBalabhadra, Mukundh
dc.contributor.authorMadha, Yogith
dc.contributor.departmentChalmers tekniska högskola / Institutionen för elektrotekniksv
dc.contributor.examinerWik, Torsten
dc.contributor.supervisorFridholm, Björn
dc.date.accessioned2024-12-13T10:09:06Z
dc.date.available2024-12-13T10:09:06Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractLithium-ion batteries (LIB) have become essential in the automotive industry due to their favorable properties of high volumetric and weight-based energy density, lowself-discharge rates, and relatively affordable costs. Due to their widespread use, comprehending the life cycle dynamics of LIB is paramount. Over time, the internal chemistry of these batteries undergoes changes, leading to variations in power and capacity output, commonly referred to as power and capacity fade. Accurate prediction of end-of-life (EoL) is pivotal as it allows for the mitigation of accelerated degradation risks, thereby Improving operational lifespan. The present study endeavors to analyze and develop a non-destructive methodology for estimating the degradation modes of LIBs, utilizing both pristine and aged Open-Circuit Voltage (OCV) curves. The primary objective is to devise methodologies that can estimate the degradation mode to high accuracy given the batteries’ OCV. The ultimate goal is to establish techniques for estimating degradation modes within LIBs on an online platform, thus facilitating a better understanding of battery degradation processes. By quantifying the extent of aging within the battery, this approach aims to empower Battery Management Systems (BMS) to proactively adapt and optimize operational strategies, consequently prolonging the lifespan of battery packs deployed in vehicles. This dissertation presents findings that compare experimental results with simulations conducted using PyBaMM. The experiments are conducted under diverse circumstances mirroring real-world scenarios, encompassing considerations such as down-sampled data points, sensor noise, and data segmentation. Through this comprehensive investigation, this research contributes to advancing the understanding of LIB degradation dynamics and lays a foundation for the development of robust predictive maintenance strategies for traction batteries in automotive applications.
dc.identifier.coursecodeEENX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/309028
dc.language.isoeng
dc.relation.ispartofseries00000
dc.setspec.uppsokTechnology
dc.subjectlithium-ion
dc.subjectOpen-Circuit Voltage (OCV) curve
dc.subjectdegradation modes
dc.subjectopen-circuit potential
dc.subjectoptimization
dc.titleEstimation of Degradation Modes for Lithium-ion Batteries Estimation of degradation modes of an aged battery using Open-Circuit Voltage curves
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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