Title :
State of charge estimation using extended Kalman filters for battery management system
Author :
Taborelli, Carlo ; Onori, Simona
Author_Institution :
Dept. of Automotive Eng., Clemson Univ., Greenville, SC, USA
Abstract :
In this work, the problem of battery state of charge estimation is investigated using a model based approach. An experimentally validated model of a battery developed by AllCell Technologies, specific for light electric vehicles (electric scooter or bicycles) is used. Two state of charge estimation algorithms are developed: an extended Kalman filter and an adaptive extended Kalman filter. The adaptive version of Kalman filter is designed in order to adaptively set a proper value of the model noise covariance, using the information coming from the on-line innovation analysis. A comparison between the two approaches is conducted that shows that the adaptive Kalman filter can deal with the problem of incorrect value of the model noise covariance matrix producing lower estimation error.
Keywords :
adaptive Kalman filters; battery management systems; battery powered vehicles; covariance matrices; estimation theory; innovation management; nonlinear filters; secondary cells; AllCell technology; adaptive extended Kalman filters; battery management system; battery state of charge estimation algorithm; bicycles; electric scooter; incorrect value problem; light electric vehicles; lithium-ion battery pack; lower estimation error; model based approach; model noise covariance matrix; on-line innovation analysis; Batteries; Estimation; Integrated circuit modeling; Kalman filters; Mathematical model; System-on-chip; Voltage measurement; Adaptive; Battery; Estimation; Kalman filter;
Conference_Titel :
Electric Vehicle Conference (IEVC), 2014 IEEE International
DOI :
10.1109/IEVC.2014.7056126
