Article citationsMore>>

Jin, S., Sui, X., Huang, X., Wang, S., Teodorescu, R. and Stroe, D. (2021) Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction. Electronics, 10, Article 3126.
https://doi.org/10.3390/electronics10243126

has been cited by the following article:

• TITLE: Edge Impulse Based ML-Tensor Flow Method for Precise Prediction of Remaining Useful Life (RUL) of EV Batteries

  AUTHORS: Tarik Hawsawi, Mohamed Zohdy

  KEYWORDS: Electric Vehicle, EV Charging, Machine Learning, Lifetime, Edge Computing

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.12 No.6, June 26, 2024

  ABSTRACT: Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful to avoid the complete discharging of the battery. The complete discharge of the battery degrades its lifetime and requires a longer charging duration. In the present work, a novel approach leverages the Edge Impulse platform for live prediction of the battery status and early alert signal to avoid complete discharging. The proposed method predicts the actual remaining useful life of batteries. A powerful edge computing platform utilizes Tensor Flow-based machine learning models to predict EV charging needs accurately. The proposed method improves the overall lifetime of the battery by the efficient utilization and precise prediction of the battery status. The EON-Tuner and DSP processing blocks are used for efficient results. The performance of the proposed method is analyzed in terms of accuracy, mean square error and other performance parameters.