Search
6 articles for “State-of-Health (SoH)”
-
Review of Battery Management System and SOC
Abstract: The perception of electric cars (EVs) as a strong alternative to for internal combustion engine automobiles is growing. For electric vehicle (EV) technologies to advance, they must develop quickly, especially in the area of battery technology. light weight, increased energy capacity, quick charging times, lithium-ion (Li-ion) batteries are generally used for electric vehicles (EVs). The efficiency of the battery management system (BMS) and battery performance are critical elements influencing electric …
Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 1, 2026 · pp. 08–16 Read article
-
Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems
Abstract: Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging. This work introduces a streamlined machine learning model based on the ability to use multi-dimensional sensor measurements in terms of voltage, current, temperature, and cycle number to forecast battery conditions with high accuracy. Decent preprocessing, such as noise elimination, feature scaling, and calculated features, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 11–23 Read article
-
Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article
-
Performance Evaluation of Lithium-Ion Batteries Considering State of Charge and State of Health for Electric Vehicle Applications
Abstract: This study aims to compare temperature-regulated and uncontrolled charging methodologies to determine the most effective approach for optimizing battery performance while minimizing charging duration. The study assesses both constant current (CC) and temperature-controlled pulse charging (TRPC) methods. The battery temperature increases in direct proportion to the applied current during CC charging and cannot be controlled in the absence of external cooling systems. Key performance measures, such as the effect on …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 15, Issue 3, 2025 · pp. 32–47 Read article
-
IoT-Based Battery Health Monitoring for Electric Vehicles Using Machine Learning
Abstract: With increasing utilization of the Electric Vehicles (EV)s in global scale, battery health management becomes a critical factor which has great impact on vehicle performance, safety and longevity. Battery materials, such as NMC LFP lithium-ion batteries and lithium-ion batteries, degrade over time from charging behaviour, heat stress, discharging voltage profiles and environmental limits. Conventional BMS only offer threshold based health diagnostics and cannot perform accurate degradation prediction. This work presents …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 Read article
-
A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis
Abstract: Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 1–12 Read article