Energy Aware Battery Powered Electric Vehicles:A Predictive Model Driven Approach

DOI

10.2991/cse.2013.49How to use a DOI?

Keywords

battery management system; FPGA; gradient descent algorithm; Extended Kalman Filter; EffBMS

Abstract

Active energy management for power optimization is essential for successful commercialization of battery powered electric vehicles. In this paper we propose and implement an energy aware electric vehicle using an efficient battery management system, EffBMS which estimates the remaining charge of the Li-ion battery online, learns the rate of change of SOC and accordingly modulates the velocity and acceleration of the vehicle for optimal energy usage. An Extended Kalman Filter (EKF) was used to estimate the remaining charge of the Li-ion battery pack. An online dual time frame gradient descent algorithm with higher time frame for updating dataset and lower time frame for parameter tuning learns the SOC curve and employs a set point controller for energy management in motion. The entire model was developed in a SPARTAN 6 LX-45 reconfigurable FPGA board and is tested in a laboratory model of differentially driven single geared electric car. Case study of an upward slope hill motion is performed and results shows 10-12% energy savings.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dennis Babu
AU  - Anirudh Kumar
AU  - Joydeb Roychowdhury
PY  - 2013/07
DA  - 2013/07
TI  - Energy Aware Battery Powered Electric Vehicles:A Predictive Model Driven Approach
BT  - Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013)
PB  - Atlantis Press
SP  - 215
EP  - 220
SN  - 1951-6851
UR  - https://doi.org/10.2991/cse.2013.49
DO  - 10.2991/cse.2013.49
ID  - Babu2013/07
ER  -