Edoardo Lelli
SOH prediction model through OCV based on AI for high voltage battery.
Rel. Daniela Anna Misul, Giovanni Belingardi, Alessia Musa. Politecnico di Torino, Master of science program in Automotive Engineering, 2022
Abstract
Lithium-ion batteries are representing nowadays the reference technology for the state-of-the-art energy storage system in hybrid and electric vehicles. Countless are the parameters to take into account for an optimal battery operation, from physical and chemical point of view, in addition to all the charging and discharging phases concerning the battery life and how it can be managed. A proper and accurate State of Health (SOH) prediction is needed to take right countermeasures and precautions defining a control strategy aimed to correctly exploit HV-Batteries along its whole life and extending it as well. From the customer point of view, having the proper idea of whenever the battery would be replaced is really important advantage that comes out from a proper SOH prediction.
The more reliable the SOH is, the more accurate the DTE will be, meaning for the customer no undesired surprises in the cluster after 50.000km, 100.000km..
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