S
Sarvar Hussain Nengroo
Publications - 24
Citations - 520
Sarvar Hussain Nengroo is an academic researcher. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 9, co-authored 11 publications receiving 254 citations.
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Journal ArticleDOI
Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation
Muhammad Umair Ali,Amad Zafar,Sarvar Hussain Nengroo,Sadam Hussain,Muhammad Junaid Alvi,Hee-Je Kim +5 more
TL;DR: In this paper, Li-ion batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost, and a smart battery management system is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life.
Journal ArticleDOI
A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control
Muhammad Umair Ali,Sarvar Hussain Nengroo,Muhamad Adil Khan,Kamran Zeb,Muhammad Ahmad Kamran,Hee-Je Kim +5 more
TL;DR: The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current–constant-voltage (CC/CV) method, which assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.
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A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles
Sadam Hussain,Muhammad Umair Ali,Gwan-Soo Park,Sarvar Hussain Nengroo,Muhammad Khan,Hee-Je Kim +5 more
TL;DR: A real-time EMS is proposed, which is comprised of a fuzzy logic controller-based low-pass filter and an adaptive proportional integrator-based charge controller that intelligently distributes the required power from the battery and a supercapacitor during acceleration.
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Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features
TL;DR: Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS and can be utilized for online RUL estimation in electric vehicles.
Journal ArticleDOI
Dual Battery Storage System: An Optimized Strategy for the Utilization of Renewable Photovoltaic Energy in the United Kingdom
Sarvar Hussain Nengroo,Muhammad Kamran,Muhammad Umair Ali,Dohyun Kim,Min-Soo Kim,Altaf Hussain,Hee Jee Kim +6 more
TL;DR: In this paper, a dual battery storage system is coupled with a solar PV system and a low voltage grid, benefitting from the feed-in-tariff (FIT) policy.