Reprint

Battery Management in Electric Vehicles: Current Status and Future Trends

Edited by
June 2024
248 pages
  • ISBN978-3-7258-1345-2 (Hardback)
  • ISBN978-3-7258-1346-9 (PDF)

This is a Reprint of the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends that was published in

Chemistry & Materials Science
Engineering
Physical Sciences
Summary

Lithium-ion batteries are an indispensable component of the global transition to zero-carbon energy and are instrumental in achieving COP26's objective of attaining global net-zero emissions by the mid-century. However, their rapid expansion comes with significant challenges. The continuous demand for lithium-ion batteries in electric vehicles (EVs) is expected to raise global environmental and supply chain concerns, given that the critical materials required for their production are finite and predominantly mined in limited regions worldwide. Consequently, significant battery waste management will eventually become necessary. By implementing appropriate and enhanced battery management techniques in electric vehicles, the performance of batteries can be improved, their lifespan extended, secondary uses enabled, and the recycling and reuse of EV batteries promoted, thereby mitigating global environmental and supply chain concerns. Therefore, this reprint was crafted to update the scientific community on recent advancements and future trajectories in battery management for electric vehicles. The content of this reprint spans a spectrum of EV battery advancements, ranging from fundamental battery studies to the utilization of neural network modeling and machine learning to optimize battery performance, enhance efficiency, and ensure prolonged lifespan.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
battery storage; battery management; electric vehicles; converter; controllers; optimizations; battery pack; design strategies; thermal management; lithium-ion battery; electric vehicles; battery; Li-ion; temperature; thermal map; parametric equation; battery; electric vehicle; topographical optimization; mechanical stresses; circular economy; adaptation; batteries; fuzzy; intelligent system; iterative; Kalman; lithium-ion; modeling; WLTP; electric vehicles; inductively coupled power transfer; EV battery charging; misalignment; coil design; compensation topology; electric vehicles; mobility; battery sharing; battery swapping; discrete-event simulation; orienteering problem; battery management; electric vehicle; reinforcement learning; simulation; artificial neural network; battery; direct oil cooling; electrical performance; electric vehicle; thermal performance; lithium-ion battery; state of health estimation; machine learning; transfer learning; n/a