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Optimizing Electric Vehicle Battery Management Systems for Enhanced Performance and Efficiency in the Automotive Sector
Subject area: Management and Commerce · Area of research: Battery Management Systems
Abstract
In the context of global efforts to decarbonize transportation and reduce dependency on fossil fuels, electric vehicles (EVs) have emerged as a vital solution. However, their effectiveness hinges on the performance of one critical subsystem: the Battery Management System (BMS). This research explores how advancements in BMS design and integration, specifically through the adoption of artificial intelligence (AI), real-time analytics, and cloud computing, can significantly enhance the operational efficiency, safety, and user acceptance of electric vehicles. The study further investigates how behavioral factors affect EV adoption, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Value-Belief-Norm (VBN) model, with data gathered from diverse cultural contexts including India, Spain, and Nigeria. The study proposes a modular, cloud-based Automotive Plant Management System (APMS) integrated with BMS data, aiming to digitize internal operations in manufacturing plants. This paper offers comprehensive recommendations for both automotive managers and policymakers to accelerate EV adoption and improve production efficiencies.
How to cite this paper
@article{1709089,
author = {Aman Sharma},
title = {Optimizing Electric Vehicle Battery Management Systems for Enhanced Performance and Efficiency in the Automotive Sector},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {331-333},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709089.pdf},
abstract = {In the context of global efforts to decarbonize transportation and reduce dependency on fossil fuels, electric vehicles (EVs) have emerged as a vital solution. However, their effectiveness hinges on the performance of one critical subsystem: the Battery Management System (BMS). This research explores how advancements in BMS design and integration, specifically through the adoption of artificial intelligence (AI), real-time analytics, and cloud computing, can significantly enhance the operational efficiency, safety, and user acceptance of electric vehicles. The study further investigates how behavioral factors affect EV adoption, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Value-Belief-Norm (VBN) model, with data gathered from diverse cultural contexts including India, Spain, and Nigeria. The study proposes a modular, cloud-based Automotive Plant Management System (APMS) integrated with BMS data, aiming to digitize internal operations in manufacturing plants. This paper offers comprehensive recommendations for both automotive managers and policymakers to accelerate EV adoption and improve production efficiencies.},
month = {June},
}