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Energies 2017,10, 1503 6. Petzl, M.; Danzer, M.A. Advancements in OCV measurement and analysis for lithium-ion batteries. IEEETrans. EnergyConvers. 2013,28, 675–681. [CrossRef] 7. Alves, J.; Baptista, P.C.;Gonçalves,G.A.;Duarte,G.O. Indirectmethodologies to estimate energyuse in vehicles:Applicationtobatteryelectricvehicles.EnergyConvers.Manag. 2016,124, 116–129. [CrossRef] 8. Tsang,K.M.;Sun,L.;Chan,W.L. Identificationandmodellingof lithiumionbattery.EnergyConvers.Manag. 2010,51, 2857–2862. [CrossRef] 9. Lu,L.;Han,X.;Li, J.;Hua, J.;Ouyang,M.Areviewonthekey issues for lithium-ionbatterymanagement in electricvehicles. J.PowerSources2013,226, 272–288. [CrossRef] 10. Offer,G.J.;Yufit,V.;Howey,D.A.;Wu,B.;Brandon,N.P.Moduledesignandfaultdiagnosis inelectricvehicle batteries. J.PowerSources2012,206, 383–392. [CrossRef] 11. Iraola,U.;Aizpuru, I.;Gorrotxategi,L.;Segade, J.M.C.;Larrazabal,A.E.;Gil, I. Influenceofvoltagebalancing onthetemperaturedistributionofaLi-ionbatterymodule. IEEETrans. EnergyConvers. 2015,30, 507–514. [CrossRef] 12. Zhang,F.; Liu,G.; Fang,L.;Wang,H.Estimationofbattery stateof chargewithH_observer: Applied to arobot for inspectingpower transmission lines. IEEETrans. Ind. Electron. 2012,59, 1086–1095. [CrossRef] 13. Chaoui,H.;Golbon,N.;Hmouz, I.; Souissi,R.;Tahar,S.Lyapunov-basedadaptivestateofchargeandstate ofhealthestimationfor lithium-ionbatteries. IEEETrans. Ind. Electron. 2015,62, 1610–1618. [CrossRef] 14. Zou,C.;Manzie, C.; Nešic´, D.; Kallapur,A.G.Multi-time-scale observer design for state-of-charge and state-of-healthofa lithium-ionbattery. J.PowerSources2016,335, 121–130. [CrossRef] 15. Zheng,F.;Xing,Y.; Jiang, J.; Sun,B.;Kim, J.;Pecht,M. Influenceofdifferentopencircuitvoltage testsonstate ofchargeonlineestimationfor lithium-ionbatteries.Appl. Energy2016,183, 513–525. [CrossRef] 16. Li, I.H.; Wang,W.Y.; Su, S.F.; Lee, Y.S. Amerged fuzzy neural network and its applications in battery state-of-chargeestimation. IEEETrans. EnergyConvers. 2007,22, 697–708. [CrossRef] 17. Hansen,T.;Wang,C.-J. Supportvectorbasedbatterystateof chargeestimator. J.PowerSources2005,141, 351–358. [CrossRef] 18. Gandolfo,D.;Brandão,A.;Patiño,D.;Molina,M.Dynamicmodelof lithiumpolymerbattery—Loadresistor methodforelectricparameters identification. J.Energy Inst. 2015,88, 470–479. [CrossRef] 19. Jeong,Y.M.;Cho,Y.K.;Ahn, J.H.;Ryu,S.H.;Lee,B.K.Enhancedcoulombcountingmethodwithadaptivesoc reset timeforestimatingOCV. InProceedingsof the2014 IEEEEnergyConversionCongressandExposition (ECCE),Pittsburgh,PA,USA,14–18September2014;pp.1313–1318. 20. Stockley,T.;Thanapalan,K.;Bowkett,M.;Williams, J.Designandimplementationofanopencircuitvoltage predictionmechanismfor lithium-ionbatterysystems.Syst. Sci. ControlEng. 2014,2, 707–717. [CrossRef] 21. Hussein,A.A.H.; Batarseh, I. State-of-charge estimation for a single lithiumbattery cell using extended kalmanfilter. InProceedingsof the2011 IEEEPowerandEnergySocietyGeneralMeeting,Detroit,MI,USA, 24–29 July2011;pp.1–5. 22. Yu,Z.;Huai,R.;Xiao,L.State-of-chargeestimationfor lithium-ionbatteriesusingakalmanfilterbasedon local linearization.Energies2015,8, 7854–7873. [CrossRef] 23. Boeing787AircraftGroundedafterBatteryProbleminJapan.BBCNews.Availableonline:http://www.bbc. com/news/av/business-25740181/boeing-787-aircraft-grounded-after-battery-problem-in-japan(accessedon 25September2017). 24. Bohlen, O.; Buller, S.; Doncker, R.W.D.; Gelbke,M.; Naumann, R. Impedance based battery diagnosis for automotiveapplications. InProceedingsof the2004 IEEE35thAnnualPowerElectronicsSpecialists Conference (IEEECat.No. 04CH37551),Aachen,Germany,20–25 June2004;Volume2794,pp.2792–2797. 25. Swiatowska, J.;Barboux,P.Lithiumbattery technologies: Fromtheelectrodes to thebatteries. InLithium ProcessChemistry: Resources,Extraction,Batteries, andRecycling;Elsevier:Amsterdam,TheNetherlands,2015; Volume125. 26. Liu, X.J. Research and application of intelligent battery fault diagnosis system. Master’s Thesis, BeijingUniversityofPostsandTelecommunications,Beijing,China,2011. 27. Hannan,M.A.; Lipu,M.S.H.;Hussain,A.;Mohamed,A.Areviewof lithium-ionbattery stateof charge estimation andmanagement system in electric vehicle applications: Challenges and recommendations. Renew. Sustain. EnergyRev. 2017,78, 834–854. [CrossRef] 28. Cheng, K.W.E.; Divakar, B.; Wu, H.; Ding, K.; Ho, H.F. Battery-management system (BMS) and SOC development forelectricalvehicles. IEEETrans.Veh. Technol. 2011,60, 76–88. [CrossRef] 156
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Emerging Technologies for Electric and Hybrid Vehicles
Title
Emerging Technologies for Electric and Hybrid Vehicles
Editor
MDPI
Location
Basel
Date
2017
Language
English
License
CC BY-NC-ND 4.0
ISBN
978-3-03897-191-7
Size
17.0 x 24.4 cm
Pages
376
Keywords
electric vehicle, plug-in hybrid electric vehicle (PHEV), energy sources, energy management strategy, energy-storage system, charging technologies, control algorithms, battery, operating scenario, wireless power transfer (WPT)
Category
Technik
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Emerging Technologies for Electric and Hybrid Vehicles