Temperature-Dependent State of Charge Estimation for Electric Vehicles Based on a Machine Learning Approach

Lithium-ion batteries (LiBs) are extensively used in numerous applications, with electric vehicles being one of the most important. Consequently, significant research efforts have been directed towards developing battery models that can predict battery behavior, increase efficiency, enhance safety, and reduce degradation. One of the key state parameters for this purpose is the state of charge (SOC). Many estimation methods in the literature rely on understanding the open circuit voltage (OCV)-SOC relationship, which is affected by battery temperature and can be modeled using different approaches, such as table-based, analytical, physical-based, and machine learning (ML). ML approaches are gaining increasing popularity and interest, although they require extensive experimental data and the identification of the most informative features. Specifically, given the nature of the problem, we proposed an ML algorithm based on deep neural networks capable of estimating the SOC of an LiB for electric vehicles using only a single measurement of the actual OCV and battery temperature. Finally, the proposed algorithm was validated through an extensive experimental campaign.