TY - GEN
T1 - State of charge estimation for an electric wheelchair using a fuel gauge model
AU - Miller, Christopher
AU - Wolkowicz, Kelilah
AU - Safi, Jariullah
AU - Brennan, Sean N.
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. DGE1255832. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
Copyright © 2016 by ASME.
PY - 2016
Y1 - 2016
N2 - Electric wheelchair users depend on a reliable power system in order to regain mobility in their daily lives. If a wheelchair's battery power depletes without the user being aware, the individual may become stranded, further limiting their freedom of mobility and potentially placing the user in a harmful situation. This research seeks to develop a State-of-Charge (SOC) estimator for the batteries of an electric wheelchair. A second-order equivalent circuit battery model is developed and parameterized for a wheelchair's lead-acid battery pack. To simplify the SOC estimation, this algorithm models a vehicle's fuel gauge. A coulomb accumulator is incorporated to estimate energy usage in the non-linear region of the OCV-SOC curve, while a Kalman filter is used to estimate SOC in the linear region of the curve. The estimator is verified using experimentally collected data on-board a robotic wheelchair. The implementation of these algorithms with powered wheelchairs can significantly improve the estimation of wheelchair battery power and can ultimately be coupled with warning systems to alert users of depleting battery life, as well as enable low-power modes to increase wheelchair user safety.
AB - Electric wheelchair users depend on a reliable power system in order to regain mobility in their daily lives. If a wheelchair's battery power depletes without the user being aware, the individual may become stranded, further limiting their freedom of mobility and potentially placing the user in a harmful situation. This research seeks to develop a State-of-Charge (SOC) estimator for the batteries of an electric wheelchair. A second-order equivalent circuit battery model is developed and parameterized for a wheelchair's lead-acid battery pack. To simplify the SOC estimation, this algorithm models a vehicle's fuel gauge. A coulomb accumulator is incorporated to estimate energy usage in the non-linear region of the OCV-SOC curve, while a Kalman filter is used to estimate SOC in the linear region of the curve. The estimator is verified using experimentally collected data on-board a robotic wheelchair. The implementation of these algorithms with powered wheelchairs can significantly improve the estimation of wheelchair battery power and can ultimately be coupled with warning systems to alert users of depleting battery life, as well as enable low-power modes to increase wheelchair user safety.
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U2 - 10.1115/DSCC2016-9802
DO - 10.1115/DSCC2016-9802
M3 - Conference contribution
AN - SCOPUS:85015842458
T3 - ASME 2016 Dynamic Systems and Control Conference, DSCC 2016
BT - Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
PB - American Society of Mechanical Engineers
T2 - ASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Y2 - 12 October 2016 through 14 October 2016
ER -