A Neural Network Approach to Absolute State-of-Health Estimation in Electric Vehicles Battery Degradation Study Based on Fleet Data

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dc.contributor.authorJohansson, Herman
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Physics (Chalmers)en
dc.date.accessioned2019-07-03T14:49:17Z
dc.date.available2019-07-03T14:49:17Z
dc.date.issued2018
dc.description.abstractElectrification is a trend within the automotive industry. Many car manufacturers are launching electric vehicles, which are believed to be more sustainable and environmentally friendly. A major component in these cars is the battery, and its performance is crucial to the success of the electric vehicle. Therefor, the degradation of battery properties is interesting, especially the capacity decline. To understand and counter this degradation it must be measured with high precision in the cars, and be connected to car use. This project approaches this challenge by: using real fleet data, the aggregation of the data into events, and a neural network to estimate the state of the battery. The result is a proof of concept that gives an improved measure of the battery state and how different usage affects the capacity degradation. The result is, however, not validated at this point, shows unexplained properties, and should be further developed.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/255593
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectEnergi
dc.subjectGrundläggande vetenskaper
dc.subjectHållbar utveckling
dc.subjectInnovation och entreprenörskap (nyttiggörande)
dc.subjectAnnan teknik
dc.subjectEnergy
dc.subjectBasic Sciences
dc.subjectSustainable Development
dc.subjectInnovation & Entrepreneurship
dc.subjectOther Engineering and Technologies
dc.titleA Neural Network Approach to Absolute State-of-Health Estimation in Electric Vehicles Battery Degradation Study Based on Fleet Data
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeApplied physics (MPAPP), MSc
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