Battery state of charge estimation using adaptive extended kalman filter for electric vehicle application
Document Type
Conference Item
Publication Date
1-1-2020
Abstract
To build up a proficient battery management system, it is required to accurately estimate the state of charge (SOC) of the electric vehicle (EV) battery. Generally, the accuracy of the conventional extended Kalman Filter (CEKF) algorithm is exceptionally affected by the method used to update the noise covariance matrices under running conditions. In this work, the new adaptive extended Kalman filter (AEKF) algorithm is designed for the SOC estimation. Methods such as forgetting factor method and moving window are used for estimation of measurement noise and sensor noise covariance matrix respectively. Pulse discharge and customized dynamic stress tests are conducted to check the robustness of the proposed algorithm. Experimental results indicated that proposed AEKF has superior performance than CEKF under dynamic load conditions.
Keywords
Lithium-ion battery, State of charge, Kalman filter, Battery management System, Electric vehicle
Divisions
fsktm,sch_ecs
Funders
undamental Research Grant Scheme under UM grant (FRGS/1/2018/TK07/UM/02/4),undamental Research Grant Scheme under UM grant (FP095-2018A)
Publisher
IEEE
Publisher Location
345 E 47TH ST, NEW YORK, NY 10017 USA
Event Title
2020 IEEE 9th International Power Electronics and Motion Control Conference IPEMC2020-ECCE ASIA)
Event Location
Nanjing, China
Event Dates
29 November - 02 December 2020
Event Type
conference
Additional Information
IEEE 9th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), Nanjing, Peoples R China, Nov 29-Dec 02, 2020