Narayana Darapaneni, Ashish K
The rapid growth in the adoption of electric vehicles (EVs) has underscored the importance of efficient battery management systems (BMS) that can accurately predict charging voltage. This study aims to explore the challenges faced in battery performance evaluation and the latest approaches for assessing battery states, while highlighting advancements in BMS technology. By leveraging machine learning techniques, we propose a novel methodology for predicting battery charging voltage, addressing the limitations of traditional rule-based algorithms and simplistic models that fail to accurately capture complex interactions between battery parameters. Through a rigorous analysis, we demonstrate the effectiveness of our proposed model in optimizing the charging process, preventing overcharging or undercharging, and ultimately enhancing the longevity and efficiency of EV batteries. The results indicate that applying machine learning methodologies can significantly improve the accuracy of voltage predictions, thereby facilitating the development of more reliable and advanced BMS solutions for electric vehicles.
@article{113a1466-40ee-4e42-8677-2cbc21d17dce,
title={Forecasting Electric Vehicle Battery Output Voltage: A Predictive Modeling Approach},
author={Narayana Darapaneni and Ashish K},
year={2021},
language={English}
}TY - JOUR TI - Forecasting Electric Vehicle Battery Output Voltage: A Predictive Modeling Approach AU - Narayana Darapaneni AU - Ashish K PY - 2021 LA - English ER -
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