Battery Life Improvement Using Battery Management
Keywords:
Electric Vehicle (EV), Lithium-Ion (Li-Ion), State Of Charge (SOC), Battery Management System (BMS), Long Short-Term Memory (LSTM), Decision Tree (DT), KNearest Neighbors (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM).Abstract
One of the greatest challenges faced by
Electric Vehicle (EV) manufactures is insufficient
charging stations. Estimating the aging of the battery in
the electric vehicle helps the driver to predict the
driving range of the vehicle. This paper proposes a
battery management system that is developed to predict
remaining battery charge of the Electric Vehicle. The
aging of the lithium-ion (Li-Ion) battery present in the
electric vehicle is predicted using different machine
learning and deep learning algorithms. The parameters
such as voltage, current and temperature are taken
from the sensors connected to the LPC2148 ARM board
and the values are given as dataset to the Long Short-
Term Memory (LSTM), Decision Tree (DT), K-Nearest
Neighbors (KNN), Naïve Bayes (NB) and Support
Vector Machine (SVM) Algorithms. The experimental
results indicate that for real-time data Naïve Bayes
algorithm gave the best results in terms of metrics such
as Accuracy, Precision, Recall and F1-score. Naïve
Bayes produced results with the accuracy rate of 88%
and used to calculate the Remaining Battery Capacity
which helps predicting the aging of the lithium- ion
battery.
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