Battery state of charge estimation using an Artificial Neural Network
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The automotive industry is currently experiencing a paradigm shift from conventional, diesel and gasoline-propelled vehicles into the second generation hybrid and electric vehicles. Since the battery pack represents the most important and expensive component in the electric vehicle powertrain, extensive monitoring and control is required. Therefore, extensive research is being conducted in the field of electric vehicle battery condition monitoring and control. In this paper, an Artificial Neural Network (ANN) is used for Lithium-Ion (Li-Ion) battery state-of-charge (SOC) estimation. When properly trained using the random current profile described in this paper, a single-layered Neural Network is capable of capturing the non-linear characteristics of a battery. The ANN is able to estimate a non-measurable parameter such as battery SOC level based on battery measurable parameters such as voltage and current. The ANN in this paper is trained using experimental data generated from an experimental battery using a R-RC model with SOC/OCV relationship. The SOC/OCV relationship was derived from a commercial 3.6V 3.4Ah Li-Ion battery cell. The network is trained using current, and voltage as inputs and SOC as the output. The trained network is tested using benchmark driving cycles to be capable of estimating the battery SOC with a relatively high degree of accuracy.
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