E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||8|
|Published online||07 October 2021|
An Extensive Study on Machine Learning based Battery Health Estimation
1 Dept. of Electrical & Electronics Engineering, GRIET, Hyderabad, Telangana., India.
2 Architect, Wipro Limited, Bangalore, India.
This manuscript is a comparative study on various machine learning Regression methods like Decision Tree and Random Forest and SVM and other improvised methods along with unsupervised methods like Reinforcement learning, ANN methods like DNN are also discussed along with advanced methods like GRU, CNN, LSTM for estimating the battery health in order to estimate its life which is used in the modern-day technology of Battery Management System. The evolution of the present day BMS bought a great opportunity to study more about adaptive learning systems as it provides greater efficiency and tunes itself basing on environmental changes for battery health estimation studying on various methods on the subsets of artificial intelligence can be helpful to build more accurate correlation between the input and output. Adaptive learning even having a self-adjusting feature the computational limitations and the data being used is also important in producing correct result with a promising accuracy, so multiple algorithms, architectures and models are studied for better understanding in order to come to conclusions for selecting the apt model for satisfying results. Compared to other conventional methods Artificial Intelligence and their subsets learn from the error and adopt which outperforms other models in accuracy.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.