Experimental Study on Lithium-Ion Batteries Remaining Useful Life Prediction by Developing a Feedforward and a Long-Short-Time-Memory (LSTM) Neural Network for Electric Vehicles Application
| International Journal of Computer Science and Engineering |
| © 2023 by SSRG - IJCSE Journal |
| Volume 10 Issue 6 |
| Year of Publication : 2023 |
| Authors : Nguyen Huy Hoang, Nguyen Thuy Tien, Le Dinh Lam, Vo Thanh Ha |
10.14445/23488387/IJCSE-V10I6P103
How to Cite?
Nguyen Huy Hoang, Nguyen Thuy Tien, Le Dinh Lam, Vo Thanh Ha, "Experimental Study on Lithium-Ion Batteries Remaining Useful Life Prediction by Developing a Feedforward and a Long-Short-Time-Memory (LSTM) Neural Network for Electric Vehicles Application," SSRG International Journal of Computer Science and Engineering , vol. 10, no. 6, pp. 15-21, 2023. Crossref, https://doi.org/10.14445/23488387/IJCSE-V10I6P103
Abstract:
This paper proposes a model to predict Lithium-ion battery life for electric cars based on a supervised machinelearning linear regression algorithm. The capacity prediction of Lithium-ion batteries is based on voltage-dependent per-cell modeling. When sufficient test data is available, a linear regression learning algorithm will train this model to give a promising battery capacity prediction result. The paper's results are based on the voltage value for battery Lithium-ion to measure a battery's voltage. Thus, the battery's remaining life is predicted. Expected results are proven by an experiment table system with NVIDIA Jetson Nano 4GB Developer Kit B01, battery, and voltage sensor. This result allows rapid identification of battery manufacturing processes and will enable users to decide to replace defective batteries when deterioration in battery performance and lifespan are identified.
Keywords:
Lithium-ion, Machine Learning, Electric car, Linear regression, Electric vehicle battery.
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