State of Charge estimation is very important to deliver essential information about battery charge and aging level of Li-ion batteries in Electric Vehicles. This paper applies the Deep Leaning and Machine Learning approaches comparing decision tree and Long Short-Term Memory for estimating the State of Charge. The datasets for the training and the evaluation have been generated with a Digital Twin model applying driving cycles at different ambient temperature. The proposed Digital Twin model includes non-linear phenomena.
Machine Learning for SOC Estimation in Li-Ion Batteries
Di Dio R.;Di Rienzo R.;Saletti R.
2024-01-01
Abstract
State of Charge estimation is very important to deliver essential information about battery charge and aging level of Li-ion batteries in Electric Vehicles. This paper applies the Deep Leaning and Machine Learning approaches comparing decision tree and Long Short-Term Memory for estimating the State of Charge. The datasets for the training and the evaluation have been generated with a Digital Twin model applying driving cycles at different ambient temperature. The proposed Digital Twin model includes non-linear phenomena.File in questo prodotto:
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