Energy Storage and Management for Electric Vehicles

Marco, James; Truong, Dinh Quang; Longo, Stefano

doi:10.3390/books978-3-03921-863-9

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https://www.mdpi.com/books/pdfview/book/1948

Auteur

Marco, James

Truong, Dinh Quang

Longo, Stefano

Language

English

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Résumé

This Special Edition of Energies on “Energy Storage and Management for Electric Vehicles” draws together a collection of research papers that critically evaluates key areas of innovation and novelty when designing and managing the high-voltage battery system within an electrified powertrain. The addressed topics include design optimisation, mathematical modelling, control engineering, thermal management, and component sizing.

URI

https://directory.doabooks.org/handle/20.500.12854/46388

Keywords

battery charging; lithium ion battery; adaptive forgetting factor; operating expenses; cell sorting; linear programming; Simulink; equivalent circuit model; genetic algorithm; multi-parameters sorting; hybrid power system; electric vehicle; energy storage ageing and degradation; parameter estimation; Simscape; supercapacitors; state-of-health (SOH); battery energy storage system; ECE15; efficiency; residential battery storage; timetable optimization; self-discharge; dynamic programming approach; state of charge estimation; regenerative energy; fuel cell; energy storage system; nonlinear battery model; charging scheme; Li-Sulfur batteries; Matlab; dynamic flow rate optimization; rule-based optimal strategy; second-life energy storage applications; Identification; hybrid vehicle; recursive least square; thermal modelling; zinc–nickel single-flow battery; Luenberger observer; HPPC; vehicle-to-building; ?-constraint method; lithium-ion battery; life cycle assessment; parameter identification; Lipschitz nonlinear system; lithium titanate oxide batteries; batteries; battery degradation; improved artificial bee colony; optimal control; thermal behaviour; supercapacitor models; battery cycle-life extension; cycle-life; self-organizing maps clustering; principal component analysis