kth.sePublications
Planned maintenance
A system upgrade is planned for 10/12-2024, at 12:00-13:00. During this time DiVA will be unavailable.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Parameter extraction in lithium ion batteries using optimal experiments
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Vehicle design.
2021 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Parameterbestämning av litium-jonbatterier med hjälp av optimala experiment (Swedish)
Abstract [en]

Lithium-ion (Li-Ion) batteries are widely used in various applications and are viable for automotive applications. The effective management of Li-Ion batteries in battery electric vehicles (BEV) plays a crucial role in performance and range. One can achieve good performance and range by using efficient battery models in battery management systems (BMS). Hence, these battery models play an essential part in the development process of battery electric vehicles. Physics-based battery models are used for design purposes, control, or to predict battery behaviour, and these require much information about materials and reaction and mass transport properties. Model parameterization, i.e., obtaining model parameters from different experimental sets (by fitting the model to experimental data sets), can be challenging depending on model complexity and the type and quality of experimental data. Based on the idea of parameter sensitivity, certain current/voltage data sets could be chosen that theoretically has a more considerable sensitivity for a given model parameter that is of interest to extract. In this thesis work, different methods for extracting model parameters for a Nickel-Manganese-Cobalt (NMC) battery composite electrode are experimentally tested and compared. Specifically, model parameterization using \emph{optimal experiments} based on performed parameter sensitivity analysis has been benchmarked against a 1C discharge test and low rate pulse tests. The different parameter sets obtained have then been validated on a drive cycle and 2C pulse tests. Comparing the methods show some promising results for the optimal experiment design (OED) method, but consideration regarding the state of charge (SOC) dependencies, the number of parameters has to be further evaluated.

Abstract [sv]

Litiumjonbatterier (Li-jon) används i olika applikationer och är ett bra alternativ förfordonsapplikationer. Den effektiva hanteringen av litiumjonbatterier i elbilar har en viktigroll för fordonens prestanda och räckvidd. Man kan nå bra prestanda och räckviddgenom att använda bra batterimodeller i batteriets övervakningssystem (BMS). Därförspelar dessa batterimodeller en viktig roll i utvecklingen av elbilar. Fysikbaseradebatterimodeller används för design, reglering eller för att prediktera beteendet hos batteriet,vilket kräver mycket information om material samt dess reaktion och andra beskaffenheter.Modellparametrisering, dvs. att införskaffa modellparametrar från olika experiment (genom attanpassa modell till experimentella data) kan vara utmanande beroende på modellkomplexitetoch typen samt kvalitén på experimentell data. Baserat på idén om parametersensitivitet kan data om ström och spänning väljas så att de teoretiskt har mer sensitivitet för engiven modellparameter som är av intresse att extrahera. I detta examensarbete testas ochjämförs olika metoder för att extrahera modellparametrar för en Nickelmangankobolt (NMC)batterielektrod. Mer specifikt, modellparametrisering genom optimala experiment baseradepå genomförd parametersesitivitetsanalys jämförts med 1C urladdningstest och låg nivåpulstest. Jämförande av metoderna visar goda resultat för OED metoden men flera parametrarmåste fortsatt utvärderas gällande laddningstatusberoenden (SOC).

Place, publisher, year, edition, pages
2021.
Series
TRITA-SCI-GRU ; 2021:288
Keywords [en]
Lithium-ion battery, State of Charge, Physics-based battery models, Model parameterization, Optimal experiments, Sensitivity analysis, Scanning electron microscopy, Brunauer–Emmett–Teller
Keywords [sv]
Litiumjonbatteri, fysikbaserade batterimodeller, modellparameterisering, optimala experiment, känslighetsanalys, skanningelektronmikroskopi, Brunauer – Emmett – Teller, batteri elfordon
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-302768OAI: oai:DiVA.org:kth-302768DiVA, id: diva2:1599555
External cooperation
SCANIA CV AB
Subject / course
Vehicle Engineering
Educational program
Master of Science - Vehicle Engineering
Supervisors
Examiners
Available from: 2021-10-01 Created: 2021-10-01 Last updated: 2022-06-25Bibliographically approved

Open Access in DiVA

fulltext(9551 kB)1476 downloads
File information
File name FULLTEXT01.pdfFile size 9551 kBChecksum SHA-512
5e72fb373c7b1ff2beac99fd7b95add6fd25174c1a2bed3192e8b6e5654325ee24ac2727031f61884ead4a7ec4df72023e52f5e481074346f28b0589d1764569
Type fulltextMimetype application/pdf

By organisation
Vehicle design
Vehicle Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 1477 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 1219 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf