A5068383518- Advancements in Battery Materials
- Advanced Battery Technologies Research
- Advanced Battery Materials and Technologies
- Machine Learning and ELM
- Graphene research and applications
- Advanced Algorithms and Applications
- Extraction and Separation Processes
- Smart Grid and Power Systems
- Electric Vehicles and Infrastructure
- Semiconductor materials and interfaces
- Semiconductor materials and devices
- Transition Metal Oxide Nanomaterials
- Graphite, nuclear technology, radiation studies
Technische Universität Berlin
2024-2025
Leipzig University
2008
University of Wollongong
2003-2006
As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into systems. Among other critical functionalities, BMSs provide information about key states of under including state charge (SOC) health (SOH). This paper proposes data-driven approach joint online estimation SOC SOH utilizing multi-task learning (MTL) approaches,...
High-resolution magnetoresistance data in highly oriented pyrolytic graphite thin samples manifest nonhomogenous superconductivity with critical temperature ${T}_{c}\ensuremath{\sim}25\text{ }\text{K}$ and higher temperature. Our claim is based mainly the observation of anomalous hysteresis loops resistance versus magnetic field that cannot be assigned to irreversibility but indicates existence Josephson-coupled superconducting grains. In addition we observe quantum resonances can Andreev...
In this work we investigated correlations between the internal microstructure and sample size (lateral as well thickness) of mesoscopic, tens nanometer thick graphite (multigraphene) samples temperature $(T)$ field $(B)$ dependence their electrical resistivity $\rho(T,B)$. Low energy transmission electron microscopy reveals that original highly oriented pyrolytic material -- from which multigraphene were obtained by exfoliation is composed a stack $\sim 50 $nm micrometer long crystalline...
The paper presents two approaches to generating load cycles for electrical energy storage systems. A cycle is described as the operation of an system. can include different metrics depending on application. Load analysis using rainflow counting method employed understand and validate generated. Current generation involve clustering methods, random microtrip machine learning techniques. study includes a that utilises Random Pulse Method (RPM) enhances it develop improved version called...
Lithium-ion batteries (LIBs) are widely used in diverse applications, ranging from portable ones to stationary ones. The appropriate handling of the immense amount spent has, therefore, become significant. Whether recycled or repurposed for second-life knowing their chemistry type can lead higher efficiency. In this paper, we propose a novel machine learning-based approach accurate identification electrode materials LIBs based on temperature dynamics under constant current cycling using...
The paper introduces an approach to extract information from measurements and generate new load cycles for electrical energy storage systems. Load cycle analysis is performed using rainflow counting, which helps evaluate data identify stress factors. generation can involve clustering methods, random micro-trip machine learning techniques. study utilises the Random Pulse Method (RPM) presents improved version called Gradient (gradRPM) that allows control over factors such as gradient of State...