Zhang, D.; Zhong, C.; Xu, P.; Tian, Y. Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review. Machines2022, 10, 912.
Zhang, D.; Zhong, C.; Xu, P.; Tian, Y. Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review. Machines 2022, 10, 912.
Zhang, D.; Zhong, C.; Xu, P.; Tian, Y. Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review. Machines2022, 10, 912.
Zhang, D.; Zhong, C.; Xu, P.; Tian, Y. Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review. Machines 2022, 10, 912.
Abstract
As one of the critical state parameters of the battery management system, lithium battery state of charge (SOC) can provide an essential reference for battery safety management, charge/discharge control, and energy management of electric vehicles. To analyze the application of deep learning in electric vehicle power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, structural characteristics, advantages and disadvantages of lithium battery SOC estimation in deep learning method. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Secondly, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of four types of deep learning methods, were concluded using the structure of neural network used for training as the classification criterion. Finally, the challenges and future development directions of lithium battery SOC estimation in deep learning method were explained.
Keywords
Electric Vehicles; Review; SOC Estimation; Deep Learning; Lithium-ion Battery
Subject
Engineering, Energy and Fuel Technology
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.
