A5026389193- Advanced Battery Technologies Research
- Advancements in Battery Materials
- Electric Vehicles and Infrastructure
- Musculoskeletal pain and rehabilitation
- IoT Networks and Protocols
- Age of Information Optimization
- Reliability and Maintenance Optimization
- Ergonomics and Musculoskeletal Disorders
- Fuel Cells and Related Materials
- Advanced Battery Materials and Technologies
- Gas Sensing Nanomaterials and Sensors
- EEG and Brain-Computer Interfaces
- Advanced battery technologies research
- Occupational health in dentistry
- Assistive Technology in Communication and Mobility
University of Michigan–Dearborn
2019-2022
Obafemi Awolowo University
2021
Federal Medical Centre
2019
For the safe and reliable operation of battery-driven machines, accurate state-of-charge (SOC) estimations are necessary. Unfortunately, existing methods often fail to identify patterns relevant long-term SOC estimation due complex battery cell characteristics such as aging. In this paper, we propose Uncorrelated Sparse Autoencoder with Long Short-Term Memory (USAL). USAL is a novel neural network that addresses challenging task given limited initial history cell's charge-discharge behavior....
Enhanced single-particle models (eSPMs) have been extensively studied in the development of advanced battery management systems for their accuracy and capability tracking physical quantities, as well reduced computational load. This article proposes an optimal discretization approach to model reduction eSPM using a particle swarm optimization algorithm. The diffusion dynamics were solved different finite difference approaches, that is, even (baseline model) uneven (optimized model). Because...
Objective: Musculoskeletal disorders (MSDs) are one of the major complaints in work place. This study investigated prevalence and pattern work-related MSDs, risk factors strategies management among nurses working various specialty areas a tertiary health institution Nigeria. Methods: cross-sectional survey recruited 150 government own South-west, Data were obtained on demographic characteristics, occupational profile, musculoskeletal symptoms, perceptions job strategies. presented using...
This paper proposes a hybrid LSTM network for robust state-of-charge estimation of Li-ion batteries. The proposed model improves the accuracy typical by using SOC estimations other trained machine learning (ML) models in addition to original measurable battery cell parameters train LSTM. intrinsically learns timely activate proper ML complex dependencies between and parameters. is shown achieve around 25% improvement MAE last twenty cycles (near end-of-life) estimation.
This paper proposes the Sparse Autoencoded Long Short-Term Memory network (SAEL) for long-term State-of-Charge (SOC) estimations. SAEL addresses challenge of estimating SOC near end-of-life after only running a few charge-discharge cycles. transforms inputs (e.g., voltage) into space informative features then feeds transformed an LSTM to identify temporal trends that support estimation. In our experiments, outperformed benchmark models by over 63% when evaluated on three battery cells....
Purpose: To evaluate disability profile and accessibility limitations among Persons Living with Disabilities (PLWDs) in Nigeria. Methods: 61 PLWDs (44 men, 17 women) consented for this study. World Health Organization Disability Assessment Schedule 2.0, Facilitators Barriers Survey People Mobility Limitations version 2, Barthel Index, Medical Expenditure Panel Questionnaires were used to obtain data on physical profile, level of access barriers, activities daily living quality health care...
This paper studies the state estimation of a solid-state battery. Partial differential equations based on nonporous insertion model are presented to solid Two assumptions simplifying battery under-lie study estimation: that Li-ion concentration in electrolyte is uniform and charge transfer coefficient at positive electrode 0.5. The made resolve issue very weak observability: diffusion dynamics connected via Butler-Volmer equation contribution overpotential found be insignificant. For...
State-of-health (SOH) prediction is one of the key tasks Battery Management System (BMS) to ensure improved efficiency and safe operations Lithium-ion (Li-ion) Batteries (LiBs). However, most existing SOH methods are either constrained by high model complexity or insufficient information about historical degradation patterns battery cell. This paper proposes a Polynomial Regression Model with Bayesian Inference (PRMBI) for robust Li-ion batteries. The proposed PRMBI architecture leverages...