Energy Storage Systems for Electric Vehicles

• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (550 p.)
• Disciplina: 621.3124240284
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: AC-AC converters; adaptive EKF SOC estimation; adaptive equivalent consumption minimization strategy (A-ECMS); adaptive model-based algorithm; artificial intelligence; artificial neural networks; automated guided vehicle; autoregressive models of nonstationary signals; available capacity; battery; battery capacity; battery chargers; battery life; battery mechanical aging; battery modelling and simulation; battery recycling; battery reusing; battery sizing; battery testing cycler; battery thermal management; battery thermal model; braking force distribution; braking intention; butyronitrile; cabin heating; cell expansion; CFD; coulomb counting; CVT speed ratio control; diffusion induced stress; driving conditions identification; dual-motor energy recovery; echelon utilization; electric bus; electric buses; Electric Truck Simulator; electric vehicle; Electric Vehicle (EV); electric vehicles; electrified propulsion; electro-hydraulic braking; electrochemical-thermal model; electrode particle model; energy consumption and efficiency characteristics; energy efficiency; energy management; energy storage applications; environmental conditions; equalization algorithm; estimation; EV batteries; fast charging; force; fuel cell; fuel cell hybrid electric vehicle; fuzzy unscented Kalman filtering algorithm; global optimization; heat and mass transfer; hybrid electric vehicles (HEVs); hybrid energy storage system; hybrid vehicles; hydrostatic stress influence on diffusion; improved second-order RC equivalent circuit; iron phosphate; joint estimation; Kalman filter; latent heat storage; layered bidirectional equalization; least squares support vector machines (LSSVM); least-energy routing algorithm; li-ion battery; linear observer SOC estimation; linear quadratic estimator; lithium ion battery; lithium-ion batteries; lithium-ion battery; Lithium-ion battery; lithium-ion cobalt battery; lithium-ion polymer battery; lowest instantaneous energy costs; MATLAB; MeshWorks; metallic phase change material; metric evaluation; micro-channel cooling plate; mode switching; model-based design; motor minimum loss; multi-objective energy management; new energy vehicle; nitrile-based solvents; non-aqueous electrolyte; oil-electric-hydraulic hybrid system; open circuit voltage; open-circuit voltage; optimal energy management; particle swarm optimization (PSO); performance degradation modelling; pontryagin's minimum principle (PMP); power batteries; power battery; power conversion harmonics; power distribution; powertrain optimization; recurrent-neural-network (RNN); regenerative braking system; resistance; retired batteries; SEI forming additives; Simscape; SLI battery; square root cubature Kalman filter; state of charge; state of energy; state of health; state-of-charge; state-of-charge estimation (SOC); state-of-health; temperature; thermal analysis; thermal energy storage; torque coordinated control; Traveling Salesman Problem (TSP); Vehicle Routing Problem (VRP); waveforms modeling; wireless power transmission
• Sommario/riassunto: The global electric car fleet exceeded 7 million battery electric vehicles and plug-in hybrid electric vehicles in 2019, and will continue to increase in the future, as electrification is an important means of decreasing the greenhouse gas emissions of the transportation sector. The energy storage system is a very central component of the electric vehicle. The storage system needs to be cost-competitive, light, efficient, safe, and reliable, and to occupy little space and last for a long time. It should also be produced and disposed of in an environmentally friendly manner. This leaves many research challenges, and the purpose of this book is therefore to provide a platform for sharing the latest findings on energy storage systems for electric vehicles (electric cars, buses, aircraft, ships, etc.) Research in energy storage systems requires several sciences working together, and this book therefore include contributions from many different disciplines; this covers a wide range of topics, e.g. battery-management systems, state-of-charge and state-of-health estimation, thermal-battery-management systems, power electronics for energy storage devices, battery aging modelling, battery reuse and recycling, etc.
• Titolo autorizzato: Energy Storage Systems for Electric Vehicles
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557375603321
• Lo trovi qui: Univ. Federico II