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FPGA based Battery Management System for battery powered Electric cars

Dennis Babu, Anirudh Kumar, Joydeb Roy Chowdhury

Abstract


The continuously increasing oil energy prices and call for green and sustainable mobility have made electric vehicles a prime choice for transportation. The batteries have a great impact in the performance of electric vehicles as it determines the driving range, peak power and maximum acceleration attributes of the vehicle. A battery management system is thus necessary for successful deployment of electric vehicles. In this paper the authors present the development of a predictive battery management system (BMS) for an electric vehicle. The main parts of the BMS includes estimation of state of charge(SOC), instantaneous peak power together with communication with external modules and data logging using IEEE 802.15.4 Zigbee protocol. An Extended Kalman Filter (EKF) which uses a Thevenins battery model coupled with coulomb counting is used to estimate the remaining charge of the Li-ion battery pack. Battery model based continuous peak power is also estimated for safety and range optimization. The entire model was implemented in a SPARTAN 6 LX-45 FPGA board and is tested in a laboratory model of differentially driven single geared electric car thus making the system parallel and scalable. The IEEE 805.14 Zigbee module is used for wireless data transfer from the Electric vehicle BMS to the remote computer for data storage and analysis. The results shows that the SOC estimation and peak power estimation is accurate enough for online implementation.

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References


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DOI: http://dx.doi.org/10.21535%2FProICIUS.2013.v9.238

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