LEADER 03707nam  22006015  450 
001 9910741167403321
005 20230818073946.0
010   $a981-9953-44-8
024 7 $a10.1007/978-981-99-5344-8
035   $a(MiAaPQ)EBC30711939
035   $a(Au-PeEL)EBL30711939
035   $a(DE-He213)978-981-99-5344-8
035   $a(PPN)272274941
035   $a(EXLCZ)9928004895600041
100   $a20230818d2023      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aLong-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs$b[electronic resource] /$fby Qi Huang, Shunli Wang, Zonghai Chen, Ran Xiong, Carlos Fernandez, Daniel-I. Stroe
205   $a1st ed. 2023.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023.
215   $a1 online resource (101 pages)
311 08$aPrint version: Huang, Qi Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs Singapore : Springer,c2023 9789819953431 
327   $aChapter 1 Introduction -- Chapter 2 Electrochemical modeling of energy storage lithium battery -- Chapter 3 Extraction of multidimensional health indicators based on lithium-ion batteries -- Chapter 4 Research on health state estimation method of the lithium-ion battery pack -- Chapter 5 Experimental verification and analysis of health state estimation for lithium-ion battery pack.
330   $aThis book investigates in detail long-term health state estimation technology of energy storage systems, assessing its potential use to replace common filtering methods that constructs by equivalent circuit model with a data-driven method combined with electrochemical modeling, which can reflect the battery internal characteristics, the battery degradation modes, and the battery pack health state. Studies on long-term health state estimation have attracted engineers and scientists from various disciplines, such as electrical engineering, materials, automation, energy, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of extraction for health indicators and the significant influence of electrochemical modeling and data-driven issues in the design and optimization of health state estimation in energy storage systems. The book is intended for undergraduate and graduate students who are interested in new energy measurement and control technology, researchers investigating energy storage systems, and structure/circuit design engineers working on energy storage cell and pack.
606   $aEnergy storage
606   $aElectronics$xMaterials
606   $aMathematical models
606   $aMechanical and Thermal Energy Storage
606   $aElectronic Materials
606   $aMathematical Modeling and Industrial Mathematics
615  0$aEnergy storage.
615  0$aElectronics$xMaterials.
615  0$aMathematical models.
615 14$aMechanical and Thermal Energy Storage.
615 24$aElectronic Materials.
615 24$aMathematical Modeling and Industrial Mathematics.
676   $a621.3126
700   $aHuang$b Qi$01424657
701   $aWang$b Shunli$01424658
701   $aChen$b Zonghai$01424659
701   $aXiong$b Ran$01424660
701   $aFernandez$b Carlos$0609295
701   $aStroe$b Daniel-I$01424661
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910741167403321
996   $aLong-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs$93554056
997   $aUNINA