@inproceedings{zhugeOverviewMachineLearningEnabled2023,
 abstract = {To ensure safe usage and robust performance of energy storage batteries, accurate state-of-charge (SOC) and state-of-health (SOH) estimations are required. Due to recent breakthroughs in machine learning and artificial intelligence methods, data-driven methods have attracted increased attention. This paper reports state-of-the-art research progress in machine learning-enabled methods for SOC and SOH estimations. Comprehensive comparisons are made in terms of the dataset, estimation accuracy, and battery type to provide a clear picture for SOC and SOH estimation. Moreover, the challenges and research opportunities on future SOC and SOH estimation are disclosed.},
 author = {Zhuge, Yingjian and Yang, Hengzhao and Wang, Haoyu},
 booktitle = {2023 IEEE Applied Power Electronics Conference and Exposition (APEC)},
 doi = {10.1109/APEC43580.2023.10131605},
 file = {C\:\\Users\\MyPC\\Zotero\\storage\\475CPVHK\\Zhuge et al_2023_Overview of Machine Learning-Enabled Battery State Estimation Methods.pdf;C\:\\Users\\MyPC\\Zotero\\storage\\SM9WRBQP\\10131605.html},
 issn = {2470-6647},
 keywords = {Batteries,deep learning,Electric potential,Machine learning,Power electronics,State estimation,State of charge,state of charge (SOC),state of health (SOH)},
 langid = {english},
 month = {March},
 pages = {3028--3035},
 title = {Overview of Machine Learning-Enabled Battery State Estimation Methods},
 urldate = {2024-06-12},
 year = {2023}
}