The research posters presented at conferences and events take a huge amount of effort to create. Hence we thought it would be great to turn these into posts that more people could read and engage with.
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- Lithium-metal batteries (LMB) hold promise as successors to lithium-ion batteries (LIB) due to its high energy-density.
- LMB have metallic lithium anodes which introduce complications to modelling their long-term behaviour. Particularly, a dead-lithium layer grows over time, and this must be described in any battery-management-system (BMS) model to enable accurate estimates of state-of-charge, state-of-health, and power limits.
Aloisio Kawakita de Souza is a PhD student in the Department of Electrical and Computer Engineering, University of Colorado Colorado Springs. This poster was produced for the 22nd Advanced Automotive Battery Conference– San Diego, CA – December 5-8, 2022.
Empirical Wavelet Transform and Deep-Learning Neural Network for State of Health Estimation of Lithium-ion Batteries
This work presents a state of health (SOH) estimation of lithium-ion batteries based on advanced signal processing technique called empirical wavelet transform (EWT) and deep-learning neural network. The proposed approach first performs features extraction using WT for health degradation, and then the extracted features are used to train a deep neural network to estimate the state of health in the battery. The proposed approach is validated using NASA and Stanford University experimental datasets.
Ma’d El-Dalahmeh, School of Computing Engineering and Digital Technologies, Teesside University
- Accurate prediction of the remaining useful life (RUL) in Lithium‐ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs.
- Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. The author introduces a new online RUL prediction for LiB using smooth particle filter (SPF)‐ based likelihood approximation method incorporating physics-based modelling into a physics based approach. An experimentally validated high-fidelity model is employed to generate big data training data for a comprehensive operating condition matrix. This data is used to train a SPF to predict the RUL of lithium ion batteries
- The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimisation problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy.
Mo’Ath El-Dalahmeh, Teesside University, Department of Instrumentation and Control Engineering
- The capacity and resistance differences of cells amplify the inhomogeneity at a system level and results in accelerated aging and degradation.
- For the module design, where many cells are in parallel, the BMS typically does not have access to individual cell currents and temperatures.
- We aim to predict current, state of health and temperature of each cell in the module (or pack) via modelling the interaction between cell and busbar and weld quality.
Yaxing is a Faraday Institution Research Fellow at the University of Warwick, working on the Multi Scale Modelling project. His research interests are on mathematical modelling and control system design with a particular focus on sustainable energy and electric vehicles.
- The synthesis and optimisation of Ni-rich cathode materials are of interest as they have a higher energy density and limit the cobalt content However, higher Ni content is detrimental to the bulk and surface stability of material, resulting in structural breakdown and rapid capacity fade.
- Previous studies indicate use of additives can act to stabilise the 003 lattice space, thus improving battery performance.
- Optimised manufacturing methods can reduce the economic and environmental impact of this process.
Ethan Williams is a PhD researcher at the University of Birmingham investigating the bulk and surface stabilization of high voltage cathode materials for Li ion batteries, with particular interest in Ni rich materials . He is working on WP3 of the CATMAT project, focusing on materials synthesis and electrode manufacture. He obtained an MChem from Durham University in Chemistry with an industrial placement at CPI working on electrode slurry optimization for Li ion cathodes.
- Emerging battery technology – promising cost, safety, sustainability, and performance advantages over current commercialised lithium-ion batteries1,2.
- widely available
- inexpensive raw materials
- rapidly scalable technology
- meeting global demand for carbon-neutral energy storage solutions3,4.
- Adding metals would increase the overall energy density, but results in volumetric changes leading to failure.
Giar Alsofi is a PhD researcher at The University of Birmingham. Interested in anodes of Sodium-ion Batteries, aspiring to understand the volumetric expansions and capacity fading of anodes.
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