Real-world study for the optimal charging of electric vehicles
TLDR
In this article, the authors quantitatively analyzes the energy losses that take place during the charging of a battery electric vehicle (BEV), focusing especially in the previously unexplored 80% -100% State of Charge (SoC) area.About:
This article is published in Energy Reports.The article was published on 2020-11-01 and is currently open access. It has received 98 citations till now. The article focuses on the topics: State of charge & Battery electric vehicle.read more
Citations
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Optimized Ni‐Rich NCMA Cathode for Electric Vehicle Batteries
Testing energy efficiency and driving range of electric vehicles in relation to gear selection
TL;DR: In this paper, the authors investigated the effect of gear changes on energy consumption and driving range efficiency on an EV-converted Ford Focus using a chassis dynamometer in a controlled test environment in accordance with international standards.
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Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all European countries
Andrea Mangipinto,Francesco Lombardi,Francesco Davide Sanvito,Matija Pavičević,Sylvain Quoilin,Emanuela Colombo +5 more
TL;DR: In this paper , the authors developed and validated an open-source model that simulates such patterns at high (1-min) temporal resolution, based on easy-to-gather, openly accessible data and applied the model to 28 European countries, showing for the first time the existence of marked differences in mobility and charging patterns across those, due to a combination of weather and socioeconomic factors.
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Policy support measures for widespread expansion of fast charging infrastructure for electric vehicles
Felix Baumgarte,Felix Baumgarte,Matthias Kaiser,Matthias Kaiser,Robert M. Keller,Robert M. Keller +5 more
TL;DR: In this paper, the authors investigated the impact of different support measures on the profitability of three different charging power categories at three different located charging sides along the German freeway and showed that the profitability depends on the location's surrounding charging facilities and population characteristics and decreases with increasing charging power.
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A review on energy efficient technologies for electric vehicle applications
TL;DR: In this article , the authors presented the technological advancements of the electric vehicles (EVs) all over the world and focused on the utilization of the SiC devices for the EV applications.
References
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A review on the key issues for lithium-ion battery management in electric vehicles
TL;DR: In this article, a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, is given.
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Degradation diagnostics for lithium ion cells
TL;DR: In this paper, the authors present experimental evidence supporting the widely reported degradation modes by means of tests conducted on coin cells, engineered to include different, known amounts of lithium inventory and active electrode material.
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Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030
Gert Jan Berckmans,Maarten Messagie,Jelle Smekens,Noshin Omar,Lieselot Vanhaverbeke,Joeri Van Mierlo +5 more
TL;DR: In this paper, the authors predict the cost of a battery pack in 2030 when considering two aspects: firstly a decade of research will ensure an improvement in material sciences altering a battery's chemical composition.
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Studies on the cycle life of commercial lithium ion batteries during rapid charge–discharge cycling
TL;DR: In this paper, the Nyquist plots of commercial Li-ion batteries are shown to have an inductive tail at high frequency followed by two semicircles at medium and low frequencies.
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Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model
TL;DR: In this paper, trinal proportional-integral (PI) observers with a reduced physics-based EM are proposed to simultaneously estimate state of charge (SOC), capacity and resistance for lithium-ion batteries.