prikaz prve stranice dokumenta Estimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila
  Download
PDF 10.48 MB
doctoral thesis
Estimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:548487
Maletić, Filip
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electric Machines, Drives and Automation
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Department of Robotics and Production System Automation
Chair of Engineering Automation

Institutional repository: FER Repository

Cite this document

Maletić, F. (2023). Estimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:548487

Maletić, Filip. "Estimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing; University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:168:548487

Maletić, Filip. "Estimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing; University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:168:548487

Maletić, F. (2023). 'Estimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing; University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 28 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:548487

Maletić F. Estimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2023 [cited 2024 April 28] Available at: https://urn.nsk.hr/urn:nbn:hr:168:548487

F. Maletić, "Estimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb; University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:548487

Metadata
TitleEstimacija varijabli stanja i podacima vođeno modeliranje starenja litij-ionske baterije električnoga vozila
Title (english)State variable estimation and data-driven modelling of electric vehicle lithium-ion battery aging
AuthorFilip Maletić
MBZ: 354674
MentorIgor Erceg (mentor)
MentorJoško Deur (komentor)
Committee memberDamir Sumina (predsjednik povjerenstva)
Committee memberMario Vražić (član povjerenstva)
Committee memberMario Hrgetić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Electric Machines, Drives and Automation)
Zagreb
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
(Department of Robotics and Production System Automation)
(Chair of Engineering Automation)
Zagreb
Defense date and country2023-05-09, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Universal decimal classification
(UDC)		621.3 - Electrical engineering
Abstract
Moderna električna cestovna vozila opremljena su litij-ionskim baterijskim paketima koje odlikuje visoka gustoća energije i isporučive snage. S druge strane, eksploatacija baterije treba biti u dopuštenim okvirima koji ne narušavaju njen životni vijek i sigurnost putnika, pa su stoga one opremljene sustavima upravljanja baterijom (BMS), koji osiguravaju optimalno upravljanje baterijskim paketom kako bi se osigurala visoka izlazna snaga s minimalnim utjecajem na životni vijek baterije. Temelji se na nizu algoritama estimacije relevantnih, ali teško mjerljivih varijabli stanja i parametara baterije, poput stanja napunjenosti (SoC), preostalog kapaciteta i unutarnjeg otpora, kao i preostalog životnog vijeka baterije predviđanjem starenja. U radu se predlaže sveobuhvatan pristup estimaciji stanja i parametara temeljen na numerički efikasnom modelu nadomjesnog električnog kruga (ECM), kao i metoda modeliranja starenja temeljena na snimljenim podacima stvarne vožnje električnog vozila i simulacijom fizičkog starenja baterije. U radu se prvo daje opći opis litij-ionskih baterija, uključujući i bazični postupak projektiranja ćelije. Potom se opisuju temeljni pristupi modeliranju baterija: fizikalni model te ECM modeli, gdje su potonji izdvojeni kao kompromis između kompleksnosti i točnosti, te su odabrani kao temelj razvoja predloženih estimacijskih algoritama. Prvo se postavlja estimator SoC-a i impedancijskih parametara ECM-a koji se temelji na dualnom proširenom Kalmanovom filtru (DEKF), te se provodi analiza osmotrivosti impedancijskih parametara. Potom se predlaže proširenje opisanog DEKF-a estimacijom karakteristike napona otvorenog kruga. Zbog izražene nelinearnosti i spore dinamike modela proračuna nabojskog kapaciteta, estimator kapaciteta temelji se na Kalmanovom filtru zasnovanom na sigma točkama (SPKF) te se odvaja u zaseban estimator i izvodi na dva reda veličine sporijoj vremenskoj skali. Pritom se poseban naglasak daje na uspostavljanje i analitičku parametrizaciju adaptivne sprege DEKF i SPKF estimatora kako bi se osigurala točnost i brza konvergencija sporog estimatora. Predloženi estimatori varijabli stanja i impedancijskih parametara ispituju se eksperimentalno, koristeći pritom snimljene podatke gradskih vožnji električnog mopeda opremljenog baterijom s C-NMC ćelijama. Konačno, predlaže se metoda podacima vođenog modeliranja starenja baterija, koja se temelji na sintezi velikog broja opteretnih ciklusa temeljem snimljenih podataka s električnog mopeda, te korištenju tako kreiranih ciklusa za simulaciju visokopreciznih fizikalnih modela starenja zbog rasta sloja na granici čvrste tvari i elektrolita (SEI) i zbog platiranja litija (LPL), koji su raspoloživi unutar GT-Suite/AutoLion simulacijskog okruženja. Predlaže se širok skup značajki opteretnih ciklusa te se temeljem simuliranih profila starenja i uz primjenu podacima vođenih metoda izdvajaju one najznačajnije, koje se potom koriste za tvorbu i parametriranje modela linearne regresije (LR) i neuronske mreže (NN). Predloženi model starenja u konačnom obliku sastoji se od LR modela rasta SEI sloja, NN klasifikatora za procjenu aktivacije LPL-a te LR modela pada kapaciteta zbog platiranja litija. Temeljni cilj rada je projektiranje algoritma estimacije ključnih stanja i parametara baterijskog paketa malog električnog vozila za rad u stvarnom vremenu, te tvorba modela starenja baterije na temelju procijenjenih pokazatelja starenja za potrebe predviđanja preostalog životnog vijeka baterije u stvarnom vremenu.
Abstract (english)
Lithium-ion batteries represent a central part of an electric vehicle, providing the electric energy storage in replacement for the fuel tank of conventional vehicles. Due to its design and manufacturing complexity, as well as complex physical processes involved, they are still expensive and may be prone to accelerated aging. Therefore, in order to achieve maximal utilisation of a vehicle battery pack, a great amount of effort has been made in the area of battery management systems (BMS) development. The BMS ensures an optimal management of the battery pack, so that it can provide high output power with minimum influence on the battery lifetime. It relies on a number of estimation algorithms that observe relevant and hard-to-measure battery state variables and parameters such as state-of-charge (SoC), remaining capacity and internal resistance, as well as the remaining useful life by predicting the battery aging. The thesis proposes a comprehensive approach to state and parameter estimation based on a numerically-efficient equivalent-circuit model (ECM), as well as a data-driven aging model based on naturalistic driving cycles and physically simulated battery aging. First, an estimator of the SoC and ECM impedance parameters, based on the dual extended Kalman filter (DEKF), is set up, followed by an observability analysis of its impedance parameters. The DEKF is then extended to estimate the open circuit voltage characteristic. Due to the pronounced nonlinearity and slow dynamics of the charge capacity calculation model, the capacity estimator is based on a separate, sigma-point Kalman filter (SPKF) that is executed on a two orders of magnitude slower time scale. The special emphasis is paid to the design and analytical parameterization of the adaptive coupling of DEKF and SPKF estimators, in order to ensure the accuracy and fast convergence of the slow estimator. The proposed estimators of state variables and impedance parameters are tested experimentally by using the recorded driving cycle data of an electric scooter equipped with a NMC battery. Finally, a method of data-driven battery aging modelling is proposed, which is based on simulated aging and load cycles synthesized from the recorded data. The aging simulation is carried out by using the high-precision physical models of solid-electrolyte interphase (SEI) growth and lithium plating (LPL), both available within the GT-Suite/AutoLion simulation environment. From the wide set of proposed load cycle features, the most statistically relevant ones are selected by using the LASSO regression method. The selected features are used to create and parameterize linear regression (LR) and neural network (NN) aging models. The proposed aging model in its final form consists of an LR model of SEI layer growth, a NN classifier for estimating LPL activation, and an LR model of capacity loss due to lithium plating. The main aim of the thesis is to design real-time estimators of the key battery states and parameters, and build up a data-driven aging model for predicting the remaining battery lifetime in real time.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:548487
Promotion2023
Study programmeTitle: Doctoral study programme "Electrical Engineering and Computing"
Study programme type: university
Study level: postgraduate
Academic / professional title: doktor/doktorica znanosti, po-dručje tehničkih znanosti (doktor/doktorica znanosti, po-dručje tehničkih znanosti)
Type of resourceText
Extent197 str. : graf. prikazi ; 31 cm.
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-09-19 12:00:42