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Battery diagnosis, prognosis and health management models play a critical role in the integration of battery systems in energy and mobility fields. However, large-scale deployment of these models is hindered by a myriad of challenges…
Lithium-ion batteries are pivotal to technological advancements in transportation, electronics, and clean energy storage. The optimal operation and safety of these batteries require proper and reliable estimation of battery capacities to…
The Electric Vehicle (EV) Industry has seen extraordinary growth in the last few years. This is primarily due to an ever increasing awareness of the detrimental environmental effects of fossil fuel powered vehicles and availability of…
Multimodel ensembling has been widely used to improve climate model predictions, and the improvement strongly depends on the ensembling scheme. In this work, we propose a Bayesian neural network (BNN) ensembling method, which combines…
Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complete and valid quantification of model uncertainty. We…
This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for control of safety-critical systems. Decision making for safety-critical systems is challenging due to performance requirements with significant consequences in the…
In recent years, the use of lithium-ion batteries has greatly expanded into products from many industrial sectors, e.g. cars, power tools or medical devices. An early prediction and robust understanding of battery faults could therefore…
Lithium-ion battery health management has become increasingly important as the application of batteries expands. Precise forecasting of capacity degradation is critical for ensuring the healthy usage of batteries. In this paper, we…
Accurate prediction of the Remaining Useful Life (RUL) is essential for enabling timely maintenance of lithium-ion batteries, impacting the operational efficiency of electric applications that rely on them. This paper proposes a RUL…
This paper presents a comprehensive review of AI-driven prognostics for State of Health (SoH) prediction in lithium-ion batteries. We compare the effectiveness of various AI algorithms, including FFNN, LSTM, and BiLSTM, across multiple…
Health monitoring of remote critical infrastructure is a complex and expensive activity due to the limited infrastructure accessibility. Inspection drones are ubiquitous assets that enhance the reliability of critical infrastructures…
Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power. The installed capacity of battery energy storage system (BESS), mainly the lithium ion batteries are…
Accurate parking availability prediction is critical for intelligent transportation systems, but real-world deployments often face data sparsity, noise, and unpredictable changes. Addressing these challenges requires models that are not…
For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding…
The surging demand for batteries requires advanced battery management systems, where battery capacity modelling is a key functionality. In this paper, we aim to achieve accurate battery capacity prediction by learning from historical…
To further improve Lithium-ion batteries (LiBs), a profound understanding of complex battery processes is crucial. Physical models offer understanding but are difficult to validate and parameterize. Therefore, automated machine-learning…
In high renewables-integrated power systems, irrespective to their sizes, energy storage is commonly included and utilized to mitigate fluctuations from both the load and renewable power generation, ensuring system reliability, among which…
Electric Vehicles (EVs) are emerging as battery energy storage systems (BESSs) of increasing importance for different power grid services. However, the unique characteristics of EVs makes them more difficult to operate than dedicated BESSs.…
One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable to build a reliable decision rules for feature space classification in the presence of many…
Electrochemical hybrid battery models have major potential to enable advanced physics-based control, diagnostic, and prognostic features for next-generation lithium-ion battery management systems. This is due to the physical significance of…