Title: Advanced battery management system design for SOC/SOH estimation for e-bikes applications

Authors: Carlo Taborelli; Simona Onori; Sebastien Maes; Peter Sveum; Said Al-Hallaj; Naz Al-Khayat

Addresses: Department of Automotive Engineering, Clemson University, 4 Research Drive, Greenville, SC 29607, USA ' Department of Automotive Engineering, Clemson University, 4 Research Drive, Greenville, SC 29607, USA ' AllCell Technologies, 2321 W. 41st St., Chicago, IL 60609, USA ' AllCell Technologies, 2321 W. 41st St., Chicago, IL 60609, USA ' AllCell Technologies, 2321 W. 41st St., Chicago, IL 60609, USA ' NRG Renew LLC, 4900 N. Scottsdale Road, Suite 5000 Scottsdale, AZ 85251; AllCell Technologies, 2321 W. 41st St., Chicago, IL 60609, USA

Abstract: In this work, state of charge (SOC) and state of health (SOH) estimation algorithms for battery management system are proposed and compared. These algorithms are developed on a battery pack designed specifically for light electric vehicle (electric scooter or bicycles) applications. The advanced battery management system is designed in order to evaluate the instantaneous charge available in the battery and at the same time to monitor the slowly varying battery aging parameters. Two SOC estimation algorithms are proposed: an extended Kalman filter (EKF) and an adaptive extended Kalman filter (AEKF). With the adaptive version of Kalman filter a proper value of the model noise covariance is adaptively set using the information coming from the online innovation analysis. In the second part of this paper, a new estimation algorithm based on least squares is proposed to estimate the battery SOH. A general framework for a combined evaluation of SOC/SOH is discussed.

Keywords: extended Kalman filter; adaptive EKF; AEKF; e-bikes; e-bicycles; battery management systems; BMS; state-of-charge; battery SOC; state-of-health; battery SOH; SOC estimation; SOH estimation; electric powertrains; light electric vehicles; electric scooters; electric bikes; electric bicycles; e-scooters; model noise covariance; least squares.

DOI: 10.1504/IJPT.2016.081795

International Journal of Powertrains, 2016 Vol.5 No.4, pp.325 - 357

Received: 23 Feb 2015
Accepted: 16 May 2015
Published online: 26 Jan 2017 *

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