DOI: 10.5593/SGEM2015/B41/S16.005


B. Kumar, N.Khare, Pk Chaturvedi
Friday 25 September 2015 by Libadmin2015

References: 15th International Multidisciplinary Scientific GeoConference SGEM 2015, www.sgem.org, SGEM2015 Conference Proceedings, ISBN 978-619-7105-38-4 / ISSN 1314-2704, June 18-24, 2015, Book4, 37-44 pp

Batteries are widely used as the most common electrical energy storage device in electric vehicles (EVs) as a replacement of traditional fuel. The run time monitoring of battery state for any critical hazarder conditions becomes necessary for safety to the EVs as well as to passengers. Indications of State of Charge (SoC) and State of Health (SoH) of the battery system provide protection for any malfunctioning or aging in the system. This research paper attempts development and implementation of advanced Battery Management System (BMS) using VHDL. Advanced BMS design includes Neural Network Controller (NNC), Fuzzy Logic Controller (FLC) & Statistical Model. The Neuro-Fuzzy approach is used to model the electrochemical behavior of the Lead-acid battery then used to estimate the SoC. The Statistical model is used to address battery’s aging and real time consumption of the battery that mapped into SoH. For easy migration, a Matlab to FPGA design methodology is preferred for advanced BMS chip implementation. All individual functional blocks of advanced BMS & the entire register transfer level (RTL) model of advanced BMS are simulated & implemented in FPGA using ISE Design Suite 14.1. The advanced BMS simulation results are validated by experimental results and found very satisfactory. VHDL model of advanced BMS provides more than 98% accuracy in SoC and reasonably accurate SoH.

Keywords: SoC, SoH, BMS, VHDL, FPGA.