Latest revision as of 16:12, 12 February 2021

Abstract

This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system.

Document type: Article

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

• http://dx.doi.org/10.3390/en13102548 under the license https://creativecommons.org/licenses/by

• http://hdl.handle.net/11583/2826732 under the license http://creativecommons.org/licenses/by/3.0/

• https://www.mdpi.com/1996-1073/13/10/2548/pdf

• https://www.mdpi.com/1996-1073/13/10/2548,

https://doaj.org/toc/1996-1073 under the license cc-by

• https://www.mdpi.com/1996-1073/13/10/2548,

https://ideas.repec.org/a/gam/jeners/v13y2020i10p2548-d359485.html,

https://academic.microsoft.com/#/detail/3025182772

• https://www.mdpi.com/1996-1073/13/10/2548/pdf,

http://dx.doi.org/10.3390/en13102548

under the license https://creativecommons.org/licenses/by/4.0/