Online Battery Cell State of Charge Estimation for use in Electric Vehicle Battery Management Systems

dc.contributor.advisorAnbarjafari, Gholamreza, supervisor
dc.contributor.advisorAvots, Egils, supervisor
dc.contributor.authorDreija, Kristaps
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Tehnoloogiainstituutet
dc.date.accessioned2018-06-14T10:30:16Z
dc.date.available2018-06-14T10:30:16Z
dc.date.issued2018
dc.description.abstractThe electric vehicle (EV) is a complex, safety-critical system, which must ensure the safety of the operator and the reliability and longevity of the device. The battery management system (BMS) of an EV is an embedded system, whose main responsibility is the protection and safety of the high-voltage battery pack. The BMS must ensure that the requirements for health, status and deliverable power are met by maintaining the battery pack within the defined operational conditions for the defined lifetime of the battery. The state of charge (SOC) of a cell describes the ratio of its current capacity (amount of charge stored) to the nominal capacity as defined by the manufacturer. SOC estimation is a crucial, but not trivial BMS task as it can not be measured directly, but must be estimated from known and measured parameters, such as the cell terminal voltage, current, temperature, and the static and dynamic behavior of the cell in different conditions. Many different SOC estimation methods exist, out of which (currently) the most practical methods for implementing on a real-time embedded system are adaptive methods, which adapt to different internal and external conditions. This thesis proposes the use of the sigma point Kalman filter (SPKF) for non-linear systems as an equivalentcircuit model-based state estimator to be used in one of the current series production EV projects developed by Rimac Automobili. The estimator has been implemented and validated to yield better results than the currently used SOC estimation method, and will be deployed on the BMS of a high-performance hybrid-electric vehicle.en
dc.identifier.urihttp://hdl.handle.net/10062/60714
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Estonia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/ee/*
dc.subjectState of charge estimatoren
dc.subjectCentral difference sigma point Kalman filteren
dc.subjectBattery management systemen
dc.subjectBattery cell modellingen
dc.subjectElectric powertrain vehicleen
dc.subjectLaengu eindajaet
dc.subjectKeskmine erinevus sigma-punkt Kalmani filteret
dc.subjectAku juhtimissüsteemet
dc.subjectAku modelleerimineet
dc.subjectElektrisõiduket
dc.subject.othermagistritöödet
dc.titleOnline Battery Cell State of Charge Estimation for use in Electric Vehicle Battery Management Systemsen
dc.title.alternativeMeetod elektrisõiduki aku laetuse taseme täpsemaks hindamisekset
dc.typeThesisen

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