Title :
Embedded state of charge and state of health estimator based on Kalman filter for electric scooter battery management system
Author :
Yamin, Rami ; Rachid, Ahmed
Author_Institution :
Lab. des Technol. Innovantes, Univ. of Picardie Jules Verne, Amiens, France
Abstract :
This article is about a state of charge (SOC) and the state of health (SOH) estimator designed for a battery management system (BMS). It was applied on a 48V lead-acid battery pack of an electrical scooter. A software Kalman based SOC sensor has been developed using a PIC microcontroller, by using a relatively simple battery model, combined with Kalman filter algorithm. The SOC has been estimated online (in real time) and displayed consequently on a low consumption LCD screen.
Keywords :
Kalman filters; battery chargers; battery management systems; battery powered vehicles; microcontrollers; motorcycles; secondary cells; Kalman filter algorithm; LCD screen; PIC microcontroller; SOC estimator; SOC sensor; SOH estimator; battery management system; electric scooter BMS; lead-acid battery pack; simple battery model; state of charge estimator; state of health estimator; voltage 48 V; Batteries; Equations; Estimation; Kalman filters; Mathematical model; System-on-chip; Voltage measurement; State of charge (SOC); battery; battery management system (BMS); electrical vehicle; estimation; renewable energy; state of health (SOH);
Conference_Titel :
Consumer Electronics ??? Berlin (ICCE-Berlin), 2014 IEEE Fourth International Conference on
Conference_Location :
Berlin
DOI :
10.1109/ICCE-Berlin.2014.7034282
