Advanced Energy Storage Technologies and Their Applications (AESA)

© 2019 by the authors; CC BY license

lithium ion battery; distributed parameter equivalent circuit model; internal non-uniformity; electrochemical process analysis; SOC-OCV modeling; SOC estimation; lithium-ion batteries; lithium-ion battery; operating scenario; equivalent circuit modeling; parameter estimation; hybrid electric vehicles (HEVs); energy management strategy (EMS); particle swarm optimization (PSO); multiple driving cycles; driving pattern recognition; lithium-ion battery; internal temperature estimation; impedance; phase shift; electric vehicles (EVs); pump-turbine; pressure fluctuation; S-shaped region; vortex rope; rotating stall; large-scale electrochemical storage; energy and power intensive; ancillary services; superconducting magnetic energy storage (SMSE); load frequency control; generalized predictive control (GPC); energy internet; battery equalizers; battery management systems; switched-capacitor (SC) converters; zero-voltage gap (ZVG); modularization; electric vehicles (EVs); thermal energy storage; shell-and-tube; phase change material (PCM); circular fins; silicon carbide (SiC) MOSFET; silicon (Si) IGBTs; permanent magnet synchronous motor (PMSM); switching characteristics; dynamic performance; power estimation; parameter identification; ratio vector; cell difference; recursive least squares; plug-in hybrid electric vehicles; energy management strategy; road grade; state of charge; mode transition; torque coordination; data-driven predictive control (DDPC); hybrid electric vehicle (HEV); DC/DC converters; DC/AC inverters; silicon carbide (SiC); electric vehicles (EV); powertrain system; battery; lithium-ion battery; PTC self-heating method; self-heating experiment; thermal modeling; borehole thermal energy storage; seasonal thermal energy storage; BTES; ground source heat pump (GSHP) transient system simulation tool (TRNSYS); EnergyPlus; diurnal storage; solar thermal; solar-coupled GSHP; system modeling; component modeling; electric bus; hybrid energy storage system; energy management; convex optimization; LiFePO₄ battery degradation; thermal runaway; battery systems; big data platform; National Service and Management Center for Electric Vehicles; compressed air energy storage (CAES); adiabatic CAES; high temperature electrolysis; hydrogen storage; thermodynamics; lithium ion battery; low temperature preheating; temperature-rise model; heating time; power consumption; ultrahigh-head pump-turbine; multiobjective optimization; blade loading; blade lean; lithium-ion battery; electric vehicle; energy storage