2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· 0 citations
Abstract
This research paper introduces an AI-driven Battery Management System (BMS) to estimate
State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries in real-time to be
used in electric vehicles. The suggested framework will combine embedded sensing with
ESP32 microcontroller, hybrid estimation with Coulomb counting and Extended Kalman
Filter (EKF), and machine learning models such as Random Forest, XGBoost, and LSTM.
Filtering, normalization, and feature engineering are used to preprocess real-time battery
data which includes voltage, current, and temperature to improve the performance of
models. The experimentally verified hardware prototype of the system is a multi-cell
lithium-ion battery pack, sensing modules, relay-based protection, and cloud-based
monitoring based on MQTT. The outcomes indicate that the estimation is very accurate, and
the quality of R2 is over 0.98, and the error in SOC is 1-2%. The system can be operated
with good performance in different temperature conditions and allows good monitoring and
fault detection and protection, which makes it appropriate in the practical management of
battery in electric vehicles.
This paper presents an integrated hardware–software framework for real-time SOC estimation and intelligent power control using a machine learning approach and demonstrates high prediction accuracy with a mean absolute error of 0.0062% and R2 of 99.9997%, outperforming conventional and deep learning-based approaches.
P. David, S. Vani, S. Kavitha et al.· Engineering Research Express· 0 citations
Electric-vehicle battery packs operate under nonlinear, temperature-dependent and aging-sensitive conditions, making state estimation and safety control difficult for conventional rule-based battery management systems (BMSs). This methodology paper develops a reproducible artificial-intelligence-assisted BMS framework...
T P Saravanan· International Journal of Adv...· 0 citations
Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and life cycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy st...
Abdel-Hamid Mahamat Ali, Luc Vivien Assiene Mouodo, Paune Félix et al.· Applied Sciences· 0 citations
For the purpose of assuring the safety, dependability, and prolonged lifespan of energy storage systems in electric vehicles, it is vital to have an accurate estimation of the level of charge of the lithium-ion battery. Attempts to capture the complicated and non-linear behaviors of batteries are frequently unsuccessfu...
S.Kanthalakshmi G.R.Radhika· Lex Localis-journal of Local...· 0 citations
The increasing number of electric vehicles (EVs) being adopted has made it necessary for battery management systems to go beyond simple protection and state-of-charge estimation and instead focus on continuous state-of-health (SOH) assessment, degradation forecasting, and prediction of remaining useful life (RUL). The...
S. Warkad, Dhanashri Pohokar, S. Takale et al.· International Journal For Mu...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
