A Virtual Sensor for Electric Vehicles’ State of Charge Estimation

Giambattista Gruosso,Diego Patino,Giancarlo Storti Gajani,Juan Diego Valladolid,Fredy Ruiz

doi:10.3390/electronics9020278

Giambattista Gruosso, Diego Patino + Show 3 more

Open Access

https://doi.org/10.3390/electronics9020278

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Abstract

The estimation of the state of charge is a critical function in the operation of electric vehicles. The battery management system must provide accurate information about the battery state, even in the presence of failures in the vehicle sensors. 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. The estimator was derived from experimental data, employing support vector regression (SVR), principal component analysis (PCA) and a dual polarization (DP) battery model (BM). It is shown that the obtained model is able to predict the state of charge of the battery with acceptable precision in the case of a failure of the current sensor.

Highlights

The increasing diffusion of electric vehicles (EV) is not accompanied by a corresponding solid tradition in terms of data collection
We propose methods for estimating the state of charge estimation (SOC) of batteries in an EV, starting from the dual polarization (DP) circuit equivalent battery model [10,11,12], relying on a virtual sensor for the battery current measurement, derived from experimental data, using principal component analysis (PCA) and Support Vector Regression (SVR)
The performance of the SOC estimation using the current virtual sensor based on the PCA + Gaussian kernel (GK) and PCA + PK2 methods and the DP battery model is evaluated

Summary

Introduction

The increasing diffusion of electric vehicles (EV) is not accompanied by a corresponding solid tradition in terms of data collection. Often the models for determining the states of charge of vehicles are obtained in the laboratory and do not take into account the variability of driving styles; the use of auxiliaries, such as air conditioning; and the environmental conditions in which vehicles can be found. This leads to incorrect estimates of the states of charge (SOCs) of the batteries in the vehicle and failure of perception by the drivers, which is defined in literature as “range anxiety” [1,2,3,4,5]. The models often depend on parameters that have to be calibrated manually with specific tests and are not appropriate for on-the-run analysis

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