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Battery Management System for Future Electric Vehicles
| Battery Management System for Future Electric Vehicles |
| Autore | Söffker Dirk |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (154 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
air-cooled BTMS
ANN Arrhenius AUKF back propagation neural network (BPNN) battery electric vehicles battery energy storage system battery management battery management system Butler-Volmer equation capacity allocation compact lithium ion battery module control cooperative optimization coulomb efficiency dual extended Kalman filter (DEKF) dynamic response economic dispatching electric vehicle electric vehicles hybrid energy storage joint estimation LiFePO4 batteries lithium-ion battery measurement statistic uncertainty microgrid model parameter optimization n/a particle swarm optimization Peukert renewable energy sources second-order RC model small-signal modeling SOC stability state of charge (SoC) state of charge (SOC) state-of-charge torque and battery distribution variational Bayesian approximation wireless power |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557625603321 |
Söffker Dirk
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Wind Power Integration into Power Systems: Stability and Control Aspects
| Wind Power Integration into Power Systems: Stability and Control Aspects |
| Autore | Meegahapola Lasantha |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (264 p.) |
| Soggetto topico |
Energy industries & utilities
Technology: general issues |
| Soggetto non controllato |
3D aerodynamic model
active power output artificial neural network (ANN) capacity allocation capacity configuration of SCES collaborative capacity planning control parameters correction modules cumulant-based method (CBM) DFIG distributed wind power (DWP) doubly fed induction generator doubly fed induction generator (DFIG) eigenvalue analysis electromechanical dynamics electromechanical loop correlation ratio (ELCR) energy storage system (ESS) error following forget gate-based long short-term memory ES FCWG dynamic correlation ratio (FDCR) FCWG dynamics FORTRAN frequency control frequency response metrics full-converter wind fuzzy clustering fuzzy logic controller hybrid prediction model impedance modeling inertial response kinetic energy linear sensitivity-based method (LSM) load frequency control (LFC) long short-term memory low inertia low voltage ride through (LVRT) model-based operational planning multi-model predictive control n/a optimization particle swarm optimization permanent magnet synchronous generator (PMSG) PSS/E quasi- electromechanical loop correlation ratio (QELCR) regional RoCoF renewable energy sources (RESs) Reynolds-averaged Navier-Stokes method rotor overspeed control scenario analysis strong interaction sub-synchronous resonance subsynchronous oscillation supercapacitor energy storage (SCES) the center of inertia turbulence model ultra-short-term prediction variable-structure copula virtual inertia control virtual synchronous generator wavelet decomposition weak grids wind farm wind generation wind integration wind power wind power generation wind turbine wake model |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Wind Power Integration into Power Systems |
| Record Nr. | UNINA-9910557761903321 |
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Meegahapola Lasantha
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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