Publication Type : Conference Paper
Publisher : Springer Nature Singapore
Source : Lecture Notes in Electrical Engineering
Url : https://doi.org/10.1007/978-981-97-9037-1_16
Campus : Bengaluru
School : School of Engineering
Department : Electrical and Electronics
Year : 2025
Abstract : In this present age, Electric Vehicles are highly dependent on Lithium-ion batteries as they have a vital role in the storage of energy. For maximum efficiency and long-term durability Battery Management Systems (BMS) are important. In the context of Battery Management System RUL has a significant role in maintaining the safe, reliable, and effective operation of the batteries. The three Machine learning techniques used for the predicting the Remaining Useful Life of the batteries are Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) And Random Forest (RF). This paper discusses different ML techniques, generation of synthetic data, comparison of different parameters in ML techniques and prediction of RUL.
Cite this Research Publication : S. Tresa Sangeetha, Ayush Tiwari, C. R. Amrutha Varshini, Yeddula Poojitha, K. Deepa, Leveraging Synthetic Data and LSTM Networks for Reliable RUl Estimation of EV battery, Lecture Notes in Electrical Engineering, Springer Nature Singapore, 2025, https://doi.org/10.1007/978-981-97-9037-1_16