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Modularized Battery Management Systems for Lithium-Ion Battery Packs in EVs
KTH, School of Electrical Engineering (EES).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The (Battery management system)BMS has the task of ensuring that for the individual bat-tery cell parameters such as the allowed operating voltage window or the allowable temperature range are not violated. Since the battery itself is a highly distinct nonlinear electrochemical de-vice it is hard to detect its internal characteristics directly. The requirement of predicting battery packs’ present operating condition will become one of the most important task for the BMS. Therefore, special algorithms for battery monitoring are required.In this thesis, a model based battery state estimation technique using an adaptive filter tech-nology is investigated. Different battery models are studied in terms of complexity and accuracy. Following up with the introduction of different adaptive filter technology, the implementation of these methods into battery management system is decribed. Evaluations on different estimation methods are implemented from the point of view of the dynamic performance, the requirement on the computing power and the accuracy of the estimation. Real test drive data will be used as a reference to compare the result with the estimation value. Characteristics of different moni-toring methods and models are reported in this work. Finally, the trade-offs between different monitor’s performance and their computational complexity are analyzed.

Abstract [sv]

BMS (eng. battery management system) har till uppgift att se till att viktiga parametrar såsom tillspännings- och temperaturintervall upprätthålls för varje individuell battericell. Då en battericells beteende är ickelinjärt är det svårt att bestämma cellens interna karakteristika direkt. Att kunna förutsäga dessa karakteristika för ett komplett batteripack kommer att en mycket viktig funktion hos framtida BMS.

I detta examensarbete har en modellbaserad tillståndsestimeringsmetod med användande av adaptiv filtrering undersökts. Olika batterimodeller har studerats med avseende på komplexitet och noggrannhet. Efter introduktionen av olika metoder för adaptiv filtrering har dessa metoder implementerats i en BMS modell. Utvärdering av de olika metoderna för att åstadkomma tillståndsestimering har sedan utförts med avseende på dynamisk prestanda, krav på beräkningskraft och noggrannhet hos de resulterande estimaten. Data från uppmätta kördata från ett fordon har använts som referens för att jämföra de olika estimaten. Slutligen presenteras en jämförelse mellan de olika tillståndsestimeringsmetodernas prestanda när de appliceras på de olika batterimodellerna.

Place, publisher, year, edition, pages
2016. , 69 p.
Series
EES Examensarbete / Master Thesis, TRITA-EE 2016:136
Keyword [en]
battery management system, electric vehicle, Kalman Filter, Li-ion battery cell model, state estimation
Keyword [sv]
BMS, elbil, Kalmanfiltrering, litiumjonbatterimodell, tillståndsestimering
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-194316OAI: oai:DiVA.org:kth-194316DiVA: diva2:1039141
External cooperation
National Electric Vehicle Sweden
Examiners
Available from: 2016-10-21 Created: 2016-10-21 Last updated: 2016-11-15Bibliographically approved

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