Abstract:

A critical factor in any state of charge (SOC) estimator is the quality of the information provided by the EV sensory system; e.g., battery current and voltage measurements. This article presents a new methodology for the state of charge estimation (SOC) in electric vehicles without the use of a battery current sensor, relying on a virtual sensor, based on other available vehicle measurements, such as speed, battery voltage and acceleration pedal position. Support vector regressions and principal component analysis have been employed to build the virtual sensors. The estimator was derived from experimental data, employing support vector regression (SVR), principal component analysis (PCA) and a dual polarization (DP) battery model (BM). The methods herein proposed are scalable and can integrate knowledge from other sensors, such as temperature and torque, and can be combined with other machine learning methodologies.