Abstract
Current study results on Machine Learn ing have been published. According to news reporting from Chennai, India, by New sRx journalists, research stated, "The LiFePO4 battery temperature in electric b ikes significantly affects their lifespan and performance. This study employed f our machine learning models (Random Forest (RF), Long short-term memory (LSTM), Multilayer perceptron (MLP), and support vector machine (SVM)) to predict temper ature at different speeds, with and without passengers, and during day/night cha rging." The news correspondents obtained a quote from the research from the Department o f Automobile Engineering, "Random Forest outperformed the others, boasting the h ighest R-squared (0.9228) and lowest root mean squared error (RMSE) of 0.9023, s ignifying exceptional accuracy. The result shows that the temperature increases with passenger load and daytime charging, whereas the front side consistently sh owed higher temperatures due to external factors. These findings pave the way fo r optimized electric bike battery management and improved efficiency."
NETL
NSTL