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Energies 2018, 11(6), 1490; https://doi.org/10.3390/en11061490

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

1
Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
2
Nuvation Energy, 40 Bathurst Dr, Waterloo, ON N2V 1V6, Canada
*
Author to whom correspondence should be addressed.
Received: 18 April 2018 / Revised: 15 May 2018 / Accepted: 1 June 2018 / Published: 7 June 2018
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Abstract

Safe and efficient operation of a battery pack requires a battery management system (BMS) that can accurately predict the pack state-of-heath (SOH). Although there is no universal definition for battery SOH, it is often defined based on the increase in the battery’s internal resistance. Techniques such as extended Kalman filter (EKF) and recursive least squares (RLS) are two frequently used approaches for online estimation of this resistance. These two methods can, however, be computationally expensive, especially in the case of a battery pack composed of hundreds of cells. In addition, both methods require a battery model as well as chemistry specific parameters. Therefore, this paper investigates the performance of a direct resistance estimation (DRE) technique that requires minimal computational resources and can be implemented without any training data. This approach estimates the ohmic resistance only when the battery experiences sharp pulses in current. Comparison of results from the three algorithms shows that the DRE algorithm can accurately identify a degraded cell under various operating conditions while significantly reducing the required computational complexity. The findings will further advance diagnostic techniques for the identification of a weak cell in a large battery pack. View Full-Text
Keywords: lithium-ion battery; battery internal resistance; extended Kalman filter; recursive least squares; state-of-health lithium-ion battery; battery internal resistance; extended Kalman filter; recursive least squares; state-of-health
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Mathew, M.; Janhunen, S.; Rashid, M.; Long, F.; Fowler, M. Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems. Energies 2018, 11, 1490.

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