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Battery Management System for Future Electric Vehicles

Battery Management System for Future Electric Vehicles

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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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Keywords

  • air-cooled BTMS
  • ANN
  • Arrhenius
  • AUKF
  • back propagation neural network (BPNN)
  • battery electric vehicles
  • battery energy storage system
  • battery management
  • battery management system
  • Butler–Volmer equation
  • capacity allocation
  • compact lithium ion battery module
  • Control
  • cooperative optimization
  • coulomb efficiency
  • dual extended Kalman filter (DEKF)
  • dynamic response
  • economic dispatching
  • electric vehicle
  • Electric vehicles
  • History of engineering & technology
  • hybrid energy storage
  • joint estimation
  • LiFePO4 batteries
  • lithium-ion battery
  • measurement statistic uncertainty
  • microgrid
  • model parameter optimization
  • n/a
  • particle swarm optimization
  • Peukert
  • Renewable energy sources
  • second-order RC model
  • small-signal modeling
  • SOC
  • Stability
  • state of charge (SOC)
  • state-of-charge
  • Technology, engineering, agriculture
  • Technology: general issues
  • torque and battery distribution
  • variational Bayesian approximation
  • wireless power

Links

DOI: 10.3390/books978-3-03943-351-3

Editions

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