This paper presents a new methodology for Stateof- Charge (SoC) estimation based on Support Vector Regression (SVR), Principal component Analysis (PCA) and the Dual Polarization (DP) battery model. The proposed methodology considers several factors that are significant for the prediction of battery current in Electric Vehicles (EVs) such as: speed, acceleration, voltage, pedal position; this information is then used as input to the DP battery model. Battery parameters were estimated using the Nonlinear Least Square (NLS) algorithm.

State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model

Gruosso G.;Storti Gajani G.;ruiz palacios fredy orlando
2019-01-01

Abstract

This paper presents a new methodology for Stateof- Charge (SoC) estimation based on Support Vector Regression (SVR), Principal component Analysis (PCA) and the Dual Polarization (DP) battery model. The proposed methodology considers several factors that are significant for the prediction of battery current in Electric Vehicles (EVs) such as: speed, acceleration, voltage, pedal position; this information is then used as input to the DP battery model. Battery parameters were estimated using the Nonlinear Least Square (NLS) algorithm.
2019
2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings
978-1-7281-1249-7
Electric Vehicle (EV)
LiFePO4 Battery
Modeling
Principal Component Analysis (PCA)
State of Charge (SoC)
Support Vector Regression (SVR)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1158189
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