Energy Storage Systems for Electric Vehicles

doi:10.3390/books978-3-03936-963-8

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Energy Storage Systems for Electric Vehicles

Edited by

September 2020

550 pages

ISBN978-3-03936-962-1 (Hardback)
ISBN978-3-03936-963-8 (PDF)

https://doi.org/10.3390/books978-3-03936-963-8

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This book is a reprint of the Special Issue Energy Storage Systems for Electric Vehicles that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Physical Sciences

Summary

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.

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Keywords

lithium-ion batteries; non-aqueous electrolyte; nitrile-based solvents; butyronitrile; SEI forming additives; fast charging; power batteries; improved second-order RC equivalent circuit; fuzzy unscented Kalman filtering algorithm; joint estimation; electric bus; battery; energy efficiency; environmental conditions; hybrid electric vehicles (HEVs); battery life; multi-objective energy management; adaptive equivalent consumption minimization strategy (A-ECMS); pontryagin’s minimum principle (PMP); particle swarm optimization (PSO); recurrent-neural-network (RNN); fuel cell hybrid electric vehicle; least squares support vector machines (LSSVM); driving conditions identification; power distribution; electric vehicle; lithium-ion battery; estimation; Kalman filter; state-of-charge; state-of-health; resistance; open-circuit voltage; battery capacity; battery modelling and simulation; battery testing cycler; battery thermal model; lithium-ion polymer battery; SLI battery; electric vehicle; dual-motor energy recovery; regenerative braking system; CVT speed ratio control; motor minimum loss; energy consumption and efficiency characteristics; braking force distribution; oil–electric–hydraulic hybrid system; lowest instantaneous energy costs; energy management; global optimization; retired batteries; energy storage applications; layered bidirectional equalization; equalization algorithm; state of charge; available capacity; adaptive model-based algorithm; square root cubature Kalman filter; joint estimation; li-ion battery; performance degradation modelling; electrified propulsion; battery sizing; powertrain optimization; optimal energy management; heat and mass transfer; thermal analysis; Lithium-ion battery; micro-channel cooling plate; battery thermal management; MeshWorks; CFD; diffusion induced stress; hydrostatic stress influence on diffusion; electrode particle model; battery mechanical aging; li-ion battery; coulomb counting; lithium-ion battery; open circuit voltage; state of charge; state of health; temperature; new energy vehicle; power battery; battery reusing; echelon utilization; battery recycling; electric vehicles; electro-hydraulic braking; braking intention; mode switching; torque coordinated control; Electric Truck Simulator; Electric Vehicle (EV); Vehicle Routing Problem (VRP); Traveling Salesman Problem (TSP); least-energy routing algorithm; energy efficiency; EV batteries; metric evaluation; AC–AC converters; battery chargers; electric vehicles; power conversion harmonics; wireless power transmission; electrochemical–thermal model; lithium-ion battery; fast charging; battery; state of charge; state of health; artificial intelligence; artificial neural networks; hybrid vehicles; electric vehicles; estimation; state-of-charge estimation (SOC); linear quadratic estimator; lithium ion battery; iron phosphate; cell expansion; force; lithium-ion cobalt battery; state of charge; state of energy; adaptive EKF SOC estimation; linear observer SOC estimation; MATLAB; Simscape; electric buses; thermal energy storage; latent heat storage; metallic phase change material; cabin heating; fuel cell; automated guided vehicle; hybrid energy storage system; model-based design; waveforms modeling; autoregressive models of nonstationary signals