Narayana Darapaneni, Ashish K
The battery management system plays a vital role in ensuring the safety and dependability of electric and hybrid vehicles. It is responsible for various functions, including state evaluation, monitoring, charge control, and cell balancing, all integrated within the BMS. Nonetheless, due to the uncertainties surrounding battery performance, implementing these functionalities poses significant challenges. In this study, we explore the latest approaches for assessing battery states, highlight notable advancements in battery management systems (BMS), address existing issues with current BMS technology, and put forth possible solutions for predicting battery charging voltage. By leveraging machine learning techniques to predict charging voltage in EVs, we aim to improve the accuracy of voltage predictions, leading to optimized charging processes, prevention of overcharging or undercharging, and enhancement of overall battery longevity. Our findings indicate that integrating machine learning into BMS can significantly enhance performance, making electric vehicles more efficient and sustainable.
@article{01c5bc26-576a-4c2f-b582-b772ad2bfe14,
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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