Mingyuan Zhang
Comparison of state of charge estimation methods based on Artificial Intelligence algorithms for lithium-ion batteries used in automotive applications.
Rel. Angelo Bonfitto, Sara Luciani. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2022
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Abstract
In recent years, laws and regulations on vehicle emissions have become increasingly stringent in various countries, leading to the gradual replacement of internal combustion engine vehicles by electric vehicles (EVs) and hybrid electric vehicles (HEVs) as the mainstream. To ensure optimal management and safe operation of the battery, a battery management system (BMS) is introduced to estimate the state of the battery through two basic parameters: State of Charge (SOC) and State of Health (SOH). Due to the complex chemical reaction inside the battery and the aging phenomenon caused by frequent use, it is difficult to clearly observe the status of the battery, so estimating SOC and SOH in real time becomes a challenge.
This thesis presents a novel methodology based on Artificial Intelligence
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