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Reliability assessment and lifetime prediction of Li-ion batteries for electric vehicles

handle: 20.500.14243/458924 , 11570/3240454 , 20.500.11769/647961
Environmental climate change has encouraged countries across the world to develop policies aimed to the reduction in energy consumption and greenhouse gas emissions. The introduction of Zero-Emission Vehicles based on electrical powertrains, could reduce the emission of environmental pollutants, the noise levels and could increase the liveability of urban areas. Although in recent years research on batteries has brought several benefits to electric vehicle performance, key barriers to their adoption are still high cost, reduced autonomy, long charging times and the leak of a suitable network of charging stations. Substantial improvements in electric vehicles performance are expected with the development of new Li-ion batteries, thanks to some notable advantages over other types of batteries, such as: high energy density, high power density, long cycle life and long calendar life. This paper is aimed to present a reliability assessment procedure based on an ageing model able to estimate from datasheet information the lifetime of Lithium-ion batteries for electric vehicles, the residual capacity and reliability margins under different driving cycles, taking also into account the battery calendar ageing.
- University of Messina Italy
- National Research Council Italy
- University of Messina Italy
- Institute for Advanced Energy Technologies Italy
- University of Catania Italy
Electric vehicles, Lifespan prediction, Electric vehicles, Li-ion batteries, Lifespan prediction, Li-ion batteries, [object Object], [object Object
Electric vehicles, Lifespan prediction, Electric vehicles, Li-ion batteries, Lifespan prediction, Li-ion batteries, [object Object], [object Object
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).30 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
