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Widanalage Dhammika Widanage
Researcher at University of Warwick
Publications - 65
Citations - 1784
Widanalage Dhammika Widanage is an academic researcher from University of Warwick. The author has contributed to research in topics: Battery (electricity) & Computer science. The author has an hindex of 14, co-authored 52 publications receiving 973 citations. Previous affiliations of Widanalage Dhammika Widanage include VU University Amsterdam & Vrije Universiteit Brussel.
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A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery
TL;DR: The combined LSTM+GPR model outperforms other counterparts and is capable of achieving accurate results for both 1-step and multistep ahead capacity predictions and predicting the RUL at the early battery cycle stage.
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Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries
TL;DR: This is the first-known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis and shows good generalization ability and accurate prediction results for calendar aging under various storage conditions.
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On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system
Kotub Uddin,Timothy J. Jackson,Widanalage Dhammika Widanage,Gael Henri Chouchelamane,Paul Jennings,James Marco +5 more
TL;DR: In this article, the authors developed a comprehensive battery degradation model based on long-term ageing data collected from more than fifty longterm degradation experiments on commercial C6/LiNiCoAlO2 batteries.
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Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters
TL;DR: This work presents the framework for an ageing diagnostic tool based on identifying and then tracking the evolution of model parameters of a fundamental electrochemistry-based battery model from non-invasive voltage/current cycling tests, and optimisation algorithm developed allows for rapid parametrisation of the pseudo-two dimensional, Doyle-Fuller-Newman, battery model.
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A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells.
TL;DR: It is shown that the resistance estimated from any technique can be identified – to a high level of confidence – from EIS by matching their timescales, given that EIS is a perturbative characterisation technique, employing a spectrum of perturbation frequencies.