Abstract
Lithium-ion batteries are the current most promising device for electric vehicle applications. They have been widely used because of their advantageous features, such as high energy density, many cycles, and low self-discharge. One of the critical factors for the correct operation of an electric vehicle is the estimation of the battery charge state. In this sense, this work presents a comparison of the state of charge estimation (SoC), tested in four different conduction profiles in different temperatures, which was performed using the Multiple Linear Regression without (MLR) and with spline interpolation (SPL-MLR) and the Generalized Linear Model (GLM). The models were calibrated by three different bio-inspired optimization techniques: Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The computational results showed that the MLR-PSO is the most suitable for SoC prediction, overcoming all other models and important proposals from the literature.
Funder
Coordenação de Aperfeicoamento de Pessoal de Nível Superior
National Council for Scientific and Technological Development
Fundação Araucária
São Paulo Research Foundation
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Cited by
16 articles.
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