Version 1
: Received: 25 December 2023 / Approved: 25 December 2023 / Online: 26 December 2023 (10:05:23 CET)
How to cite:
Mousaei, A.; Naderi, Y. Machine Learning-Based Regression Models for State of Charge Estimation in Hybrid Electric Vehicles: A Review. Preprints2023, 2023121938. https://doi.org/10.20944/preprints202312.1938.v1
Mousaei, A.; Naderi, Y. Machine Learning-Based Regression Models for State of Charge Estimation in Hybrid Electric Vehicles: A Review. Preprints 2023, 2023121938. https://doi.org/10.20944/preprints202312.1938.v1
Mousaei, A.; Naderi, Y. Machine Learning-Based Regression Models for State of Charge Estimation in Hybrid Electric Vehicles: A Review. Preprints2023, 2023121938. https://doi.org/10.20944/preprints202312.1938.v1
APA Style
Mousaei, A., & Naderi, Y. (2023). Machine Learning-Based Regression Models for State of Charge Estimation in Hybrid Electric Vehicles: A Review. Preprints. https://doi.org/10.20944/preprints202312.1938.v1
Chicago/Turabian Style
Mousaei, A. and Yahya Naderi. 2023 "Machine Learning-Based Regression Models for State of Charge Estimation in Hybrid Electric Vehicles: A Review" Preprints. https://doi.org/10.20944/preprints202312.1938.v1
Abstract
This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.
Keywords
Hybrid Electric Vehicles (HEVs); State of Charge (SOC); MACHINE LEARNING; Support Vector Regression (SVR); Neural Network Regression (NNR)
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
