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State and Parametric Estimation of Li-Ion Batteries in Electrified Vehicles
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The increasing demand for electric vehicles (EVs) has led to technological advancementsin the field of battery technology. State of charge (SOC) estimation is a vital function ofthe battery management system - the heart of EVs, and Kalman filtering is a commonmethod for SOC estimation. Due to the non uniformities in tuning and testing scenarios,quantifying performance of SOC estimation algorithms is difficult. Gathering data fordifferent operational scenarios is also cumbersome. In this thesis, SOC estimation algorithmsare developed and tested for a variety of scenarios like varying sensor noise andbias properties, varying state and parameter initializations as well as different initial celltemperatures. A validated and open-source simulation plant model is used to enable easygathering of data for different operational scenarios.The simulation results show that unscented Kalman filter performs better than extendedKalman filter in presence of hard nonlinearities and high initial uncertainties. However,both filters gave similar performance under nominal conditions implying that the choiceof estimation algorithms must depend on operational scenarios. Observability analysisalso gave valuable information to aid in selection of algorithms. The simulation plantmodel facilitated easy data collection for initial development of algorithms, which werethen tested successfully using a real dataset. Further testing using real datasets is requiredto enhance validation.

Abstract [sv]

Den ¨okande efterfr°agan p°a elfordon har lett till teknologiska framsteg inom omr°adet batteriteknik.Estimering av batteriets laddningstillst°and ¨ar en essentiell funktion i batteristyrsystemet,hj¨artat i ett elfordon, och g¨ors ofta genom att till¨ampa metoden Kalmanfiltrering.P°a grund av varierande implementations och testmetodik i litteraturen ¨ar detsv°art att kvantifiera estimeringsalgoritmer. I denna avhandling utvecklas algoritmer f¨oratt estimera ett batteris laddningstillst°and. Algoritmerna testas f¨or olika former av sensorfeloch initialtillst°and, samt f¨or en rad olika temperaturer. En validerad datormodell avbatteri, sensorer och omgivning nyttjas f¨or att generera representativa data f¨or de olikaf¨orh°allandena.Simuleringsresultat visar att den s°a kallade doftl¨osa varianten av Kalmanfiltret (UKF)presterade b¨attre ¨an det utvidgade Kalmanfiltret (EKF) i fall d¨ar systembeteendet ¨ar mycketolinj¨art och d°a initialtillst°andet ¨ar os¨akert. Under normala f¨orh°allanden presterardock de b°ada algoritmerna likv¨ardigt, vilket antyder att valet av algoritm b¨or g¨oras medavseende anv¨andningsscenario. En observerbarhetsanalys av de olika filtervarianterna gavytterligare v¨ardefull information f¨or valet av algoritm. Efter utveckling av filtreringsalgoritmernai simuleringsmilj¨o utf¨ordes tester p°a faktiska m¨atdata med goda resultat. F¨or attfullst¨andig validering av algoritmerna kr¨avs emellertid mer utt¨ommande tester.

Place, publisher, year, edition, pages
2017. , 76 p.
Series
TRITA-EE, ISSN 1653-5146 ; 2017:156
Keyword [en]
Battery management system, Kalman filter, li-ion cells, observability analysis, state and parametric estimation, sensor bias, state of charge
Keyword [sv]
Batteristyrsystemet, Kalmanfiltret, li-jonceller, laddningstillst°and, stat och parametrisk estimering, sensorfel, observerbarhetsanalys
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-217124OAI: oai:DiVA.org:kth-217124DiVA: diva2:1154001
External cooperation
NEVS
Educational program
Master of Science - Systems, Control and Robotics
Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2017-11-01Bibliographically approved

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