Eventos Anais de eventos
COBEM 2023
27th International Congress of Mechanical Engineering
Experimental data reduction using the statistical computing for analysis of the battery interconnection tubes circuit of an electric vehicle.
Submission Author:
Guilherme Plácido , SP
Co-Authors:
Guilherme Plácido, Bruno Ferreira Rossanês, ALUISIO PANTALEAO, Leandro Salviano
Presenter: Guilherme Plácido
doi://10.26678/ABCM.COBEM2023.COB2023-0524
Abstract
One of the main challenges of current and future vehicle projects is finding alternative propulsion to conventional energy sources, such as oil and its derivatives, in order to mitigate the environmental impact, as well as ensuring energy security and urban mobility due to the depletion of its fossil sources of extraction. Thus, the development of electric vehicles emerges as an attractive alternative considering environmental, financial and technological aspects. However, these vehicles are often designed in their European which have different climate conditions from those registered in Brazil and Latin America. Therefore, data verification and validation processes are essential to meet specific operational needs mainly related to higher ambient temperature and solar radiation under risk of performance drop. In this context, the present study develops methodologies for data reduction using descriptive and bivariate statistical methods applied to the battery interconnection ducts to the thermal management system of an electric bus under high-temperature climate conditions. Thereby, for the analysis of experimental data of the vehicle tests, the R language was used to reduce the data from road tests which are generated in huge quantities. Initial estimates indicate that an increase of only 0.4°C in the temperature of coolant will result in an increase of approximately 2.5% in the energy demand of the thermal management system. Furthermore, from the analysis of the data referring to the dynamics of the flow and heat transfer process, it was possible to provide boundary conditions for numerical simulations by Computational Fluid Dynamics (CFD), as well as identify parameters that influence the life of batteries by Pearson correlation method. Although they can act in isolation, their interaction can affect battery performance in different ways. Finally, the coherence of the implemented strategy will allow the methodology to be replicated for similar vehicles applications, which enables the reduction of testing costs and the introduction of this approach for more assertive decision-making, thus contributing to the adoption of electric vehicles in the national territory, the mitigation of environmental impacts, the improvement of urban mobility and the development of virtualization tools for increasingly challenging problems.
Keywords
electric vehicle, Data reduction, Thermal management, R language, Pearson correlation
