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210512 ||| eng |
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|a 9783039433506
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|a books978-3-03943-351-3
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|a 9783039433513
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| 100 |
1 |
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|a Söffker, Dirk
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| 245 |
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|a Battery Management System for Future Electric Vehicles
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (154 p.)
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| 653 |
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|a particle swarm optimization
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| 653 |
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|a renewable energy sources
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| 653 |
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|a microgrid
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| 653 |
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|a AUKF
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| 653 |
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|a dynamic response
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| 653 |
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|a coulomb efficiency
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| 653 |
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|a n/a
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| 653 |
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|a ANN
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| 653 |
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|a model parameter optimization
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| 653 |
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|a second-order RC model
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| 653 |
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|a small-signal modeling
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| 653 |
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|a cooperative optimization
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| 653 |
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|a hybrid energy storage
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a Butler-Volmer equation
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| 653 |
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|a air-cooled BTMS
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| 653 |
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|a battery management system
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| 653 |
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|a joint estimation
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| 653 |
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|a LiFePO4 batteries
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| 653 |
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|a state of charge (SOC)
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| 653 |
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|a electric vehicle
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| 653 |
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|a electric vehicles
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| 653 |
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|a state of charge (SoC)
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| 653 |
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|a measurement statistic uncertainty
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| 653 |
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|a economic dispatching
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| 653 |
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|a torque and battery distribution
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| 653 |
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|a state-of-charge
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| 653 |
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|a compact lithium ion battery module
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| 653 |
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|a battery management
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| 653 |
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|a variational Bayesian approximation
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| 653 |
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|a control
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| 653 |
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|a wireless power
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| 653 |
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|a stability
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| 653 |
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|a battery electric vehicles
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| 653 |
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|a Arrhenius
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| 653 |
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|a SOC
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| 653 |
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|a battery energy storage system
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| 653 |
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|a back propagation neural network (BPNN)
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| 653 |
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|a dual extended Kalman filter (DEKF)
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| 653 |
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|a capacity allocation
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| 653 |
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|a Peukert
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| 653 |
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|a lithium-ion battery
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| 700 |
1 |
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|a Moulik, Bedatri
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| 700 |
1 |
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|a Söffker, Dirk
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| 700 |
1 |
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|a Moulik, Bedatri
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
8 |
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|a 10.3390/books978-3-03943-351-3
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| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/3061
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/69272
|z DOAB: description of the publication
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|a 900
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|a 333
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|a 620
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|a 330
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|a The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components.
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