E3S Web Conf.
Volume 184, 20202nd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED 2020)
|Number of page(s)||7|
|Published online||19 August 2020|
ANN based Battery Health Monitoring - A Comprehensive Review
1 Dept. of Electrical & Electronics Engineering, GRIET, Hyderabad, Telangana., India
2 Dept. of Electrical & Electronics Engineering, GRIET, Hyderabad, Telangana., India
3 Dept. of Electrical & Electronics Engineering, GRIET, Hyderabad, Telangana., India
4 Dept. of Electrical & Electronics Engineering, GRIET, Hyderabad, Telangana., India
5 PES- Hardware, Valeo India Private Limited, Chennai, Tamilnadu, India
The development of electric vehicles has bought a great revolution in the field of battery management as it deals with the health of the battery and also the protection of the battery. State of Charge (SoC) and State of Health (SoH) are the important parameters in determining the battery’s health. Advancements in Artificial Neural Networks and Machine Learning, a growing field in recent years has bought many changes in estimating these parameters. Access to huge battery data has become very advantageous to these methods. This manuscript presents an overview of different Artificial Neural Network techniques like Feedforward Neural Network (FNN), Extreme Learning Machine (ELM), and the Long Short Term Memory (LSTM). These techniques are trained with already existing data samples consisting of different values of voltages, currents at different temperatures with different charging cycles and epochs. The errors in each technique are different from the other as the constraints in one method are rectified using the other method to get the least error percentage and get the nearest estimate of the SoC and SOH. Each method needs to be trained for several epochs. This manuscript also presents a comparison of different methods with input parameters and error percentages.
© The Authors, published by EDP Sciences, 2020
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