Real-Time State of Charge Estimation of Light Electric Vehicles Based on Active Power Consumption

Abstract

Battery state of charge estimation is not new, and the literature is rich in examples. Still, in most cases, state of charge algorithms are embedded in battery chargers and do not present the charging stage with a standard communication form, thus limiting the possibility of applying advanced smart charging algorithms. To overcome this limitation, this study proposes a method for the estimation of the state of charge of onboard batteries installed on light electric vehicles and the identification of their charging stage, if charged by the constant hyp current/voltage method, without the need for direct current measurements or communication from the light electric vehicle battery management system. The method is based on a preliminary off hyp line characterization of the full charge of a given battery/charger set by implementing a learning algorithm. The parameters learned during the off hyp line test are then used in on hyp line applications to estimate the light electric vehicle's state of charge and identify its charging stage by only using the active power measured at the light electric vehicle charging socket. The method was tested on two different instances of the same battery/charger set family installed on two different light electric vehicles, providing a state of charge estimation with an overall accuracy of about 1.5%, computed as root mean square error over the whole state of charge range. The results showed that the proposed method could be successfully applied to estimate the real hyp time state of charge of light electric vehicles by only using low hyp cost electronic devices.