Abstract

International audience; To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.

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The different versions of the original document can be found in:

https://hal.archives-ouvertes.fr/hal-02993901/document,
https://hal.archives-ouvertes.fr/hal-02993901/file/856e9dc1-8ea9-433f-92ff-267330342f17-author.pdf under the license http://creativecommons.org/licenses/by/
https://doaj.org/toc/2032-6653 under the license http://creativecommons.org/licenses/by/
http://dx.doi.org/10.3390/wevj11040066
https://www.mdpi.com/2032-6653/11/4/66/htm,
https://academic.microsoft.com/#/detail/3092917246 under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2020

Volume 2020, 2020
DOI: 10.3390/wevj11040066
Licence: Other

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